Patent Publication Number: US-11665051-B2

Title: Port configuration for cloud migration readiness

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is a continuation of U.S. Nonprovisional patent application Ser. No. 17/183,336, filed Feb. 23, 2021, entitled “PORT CONFIGURATION FOR CLOUD MIGRATION READINESS,” which is a continuation in part of U.S. Nonprovisional patent application Ser. No. 16/794,174, filed Feb. 18, 2020, entitled “SYSTEMS AND METHODS FOR CLOUD MIGRATION READINESS,” now U.S. Pat. No. 11,102,283, which claims the benefit of U.S. Provisional Patent Application No. 62/806,696, filed Feb. 15, 2019, entitled “CLOUD MIGRATION READINESS (CMR): METHOD TO DE-RISK LARGE SCALE MIGRATIONS OF WORKLOADS,” all of which are incorporated by reference herein. 
    
    
     FIELD OF THE INVENTION(S) 
     Embodiments of the present invention(s) generally relate to cloud migration readiness for enterprise systems and, in particular, assessing and reporting challenges associated with transitioning and running all or part of enterprise systems to cloud systems. 
     BACKGROUND 
     The complexity of enterprise networks has increased to a point where even information technology (IT) administrators may not have a clear picture of the network utilization of the enterprise network. Enterprise networks are increasingly moving towards a combination of on-premise and cloud-based infrastructure or making the ability to determine computing and storage resources associated with the business-related application more difficult. 
     Migration of some or all parts of an enterprise network onto a cloud-based infrastructure usually requires IT administrators to rewrite or customize the code for enterprise network applications. Example enterprise network applications include, but are not limited to, email service, web service, database, customer relationship management (CRM), data file, virtual desktop infrastructure (VDI), and the like. Often customized code links enterprise network applications to resources offered by a cloud provider. Such approaches can turn into unproductive and costly endeavors due to time-consuming post re-configurations, failure to achieve the original workload performance, or unnecessary overprovisioning of resources. 
     There are several steps involved in the migration of enterprise network applications. One of the many challenges is to cause as little disruption to the enterprise network as possible. A source of disruption can be a breakdown in communication between programs or components of the enterprise network. For example, one source of disruption may be caused by a misconfigured firewall or router that inadvertently blocks communication between an application in the cloud and another program that has not been migrated to the cloud-based infrastructure. 
     The firewall or router may monitor network traffic coming into and out of entities of the enterprise network and may determine whether to allow or block traffic based on a defined set of rules. Rules specify the type of data traffic or transport protocol and the required service port(s). For example, web service may use the Hypertext Transfer Protocol (HTTP), which uses the Transmission Control Protocol (TCP) and port 80. 
     It is important to keep track of the service ports being used by each entity of the enterprise network and the service as they are moved from on-premises to cloud-based infrastructure. One method of obtaining service port and data traffic information (e.g., the type of data traffic used by the service port) is to utilize ss commands or netstat commands. The ss command may be used to dump socket statistics and display information in a manner that is faster than netstat. Not all systems have such commands. Further, using such commands to track communication over time is complex, difficult, and may be computationally inefficient. 
     Another method of obtaining service port and data traffic information involves installing agent software on an entity of the enterprise network. The agent software may capture communication statistics to a predefined service or respond to poll requests. Many companies and users prefer to avoid installing extra software such as agent software because the agent software adds potential security risks, increases the complexity of the system, and further slows computation efficiency. 
     As discussed herein, the need to lift all or some of the computational needs to the cloud is a problem that has been created by computers and networks. As such, solutions to determining computational readiness and identifying agent and server software solve problems that have been brought about by computers and networks. 
     In other instances, an enterprise may take the “lift and shift” approach, in which the enterprise makes minimal or zero changes to the code for enterprise network applications. In such instances, the IT administrator may lift the storage requirements of the on-premises infrastructure and shift or replicate the storage requirements in the cloud-based infrastructure. This may result in an over-estimation of required cloud resources and would run contrary to an overall goal of an enterprise of profitability. Every corporation must strike a balance between the cost of additional storage versus performance, reliability, and redundancy benefits of the additional storage resources. 
     Another approach is to choose the right on-premise configurations before migration. Although this approach is an improvement over replicating the existing on-premise configuration, this approach does not lead directly to a right-sized cloud configuration. The CSPs use high scalable and heavily customized hypervisors. CPUs, memory, and IOPs performance will vary significantly between on-premise and CSPs hypervisors. The outcome of current approaches results in migration projects that take longer and cost more than expected and, in many cases, leads to costly failures. 
     SUMMARY 
     An example system comprising at least one process and memory including executable instructions that are executable by the at least one processor to: discover workload attributes and identify dependencies among compute, network, and storage elements of an enterprise system, the enterprise system including any number of networks, applications on servers, and storage devices which are on-premise, receive utilization performance measurements including memory utilization measurements of at least a subset of workloads, each workload including the workload attributes and identified dependencies, group workloads based on the workload attributes, the dependencies, and the utilization performance measurements into affinity groups, determine at least one representative synthetic workload for each affinity group, each representative synthetic workload including a time slice of a predetermined period of time when there are maximum performance values for any number of utilization performance measurements among virtual machines of that particular affinity group, determine at least one cloud service provider (CSP)&#39;s cloud services based on performance of the representative synthetic workloads, and generate a report for at least one of the representative synthetic workloads, the report identifying the at least one of the representative synthetic workloads and the at least one CSP&#39;s cloud services including cloud workload cost, the cloud workload cost being based on a periodic cost associated with executing the at least one of the representative synthetic workload on the at least one CSP&#39;s cloud services. 
     In various embodiments, the utilization performance measurements include CPU utilization measurements. In some embodiments, the group workloads based on the workload attributes, the dependencies, and the utilization performance measurements into the affinity groups comprising utilizing k-means clustering to partition at least the utilization performance measurements into a number of sets so as to minimize a within-cluster sum of squares. In some embodiments, the group workloads based on the workload attributes, the dependencies, and the utilization performance measurements into the affinity groups comprising utilizing k-means clustering to partition at least the utilization performance measurements and the CPU utilization measurements into a number of sets so as to minimize a within-cluster sum of squares. In one embodiment, the discover workload attributes and identify dependencies among compute, network, and storage elements of an enterprise system include receiving on-premise network data from hardware probes within the enterprise system and determining source and destination of the on-premise network data. In various embodiments, the workload characteristics include attributes of the entity associated with the workload. In some embodiments, the wherein the executable instructions that are executable by at least one processor to further: determine cloud virtual machines instance needs for each of the representative synthetic workloads, identify cloud storage disk types for each of the representative synthetic workloads based on the storage devices associated with the respective representative synthetic workload, for each representative synthetic workload, determine periodic cost associated with the cloud virtual machine instances and the cloud storage disk types, wherein the cloud workload cost is based, at least in part, on the periodic cost. In one embodiment, one or more of the representative synthetic workloads is performed in at least one CSP&#39;s cloud services to assist in determining cloud virtual machine instance needs. In various embodiments, the k-means clustering includes given a set of utilization metrics measurements (x 1 , x 2  . . . . , x n ), where each utilization metrics measurement is a d-dimensional real vector, utilizing k-means clustering to partition n utilization metrics measurement into k (≤n) sets S={S 1 , S 2 , . . . , S k } so as to minimize a within-cluster sum of squares (WCSS). 
     An example computer readable medium including instructions, the instructions being executable by a processor to perform a method, the method comprising: discovering workload attributes and identify dependencies among compute, network, and storage elements of an enterprise system, the enterprise system including any number of networks, applications on servers, and storage devices which are on-premise, receiving utilization performance measurements including memory utilization measurements of at least a subset of workloads, each workload including the workload attributes and identified dependencies, grouping workloads based on the workload attributes, the dependencies, and the utilization performance measurements into affinity groups, determining at least one representative synthetic workload for each affinity group, each representative synthetic workload including a time slice of a predetermined period of time when there are maximum performance values for any number of utilization performance measurements among virtual machines of that particular affinity group, determining at least one cloud service provider (CSP)&#39;s cloud services based on performance of the representative synthetic workloads, and generating a report for at least one of the representative synthetic workloads, the report identifying the at least one of the representative synthetic workloads and the at least one CSP&#39;s cloud services including cloud workload cost, the cloud workload cost being based on a periodic cost associated with executing the at least one of the representative synthetic workload on the at least one CSP&#39;s cloud services. 
     A method comprising: discovering workload attributes and identify dependencies among compute, network, and storage elements of an enterprise system, the enterprise system including any number of networks, applications on servers, and storage devices which are on-premise, receiving utilization performance measurements including memory utilization measurements of at least a subset of workloads, each workload including the workload attributes and identified dependencies, grouping workloads based on the workload attributes, the dependencies, and the utilization performance measurements into affinity groups, determining at least one representative synthetic workload for each affinity group, each representative synthetic workload including a time slice of a predetermined period of time when there are maximum performance values for any number of utilization performance measurements among virtual machines of that particular affinity group, determining at least one cloud service provider (CSP)&#39;s cloud services based on performance of the representative synthetic workloads, and generating a report for at least one of the representative synthetic workloads, the report identifying the at least one of the representative synthetic workloads and the at least one CSP&#39;s cloud services including cloud workload cost, the cloud workload cost being based on a periodic cost associated with executing the at least one of the representative synthetic workload on the at least one CSP&#39;s cloud services. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    depicts a block diagram of a cloud migration readiness (CMR) service system which discovers, profiles, and performs playback for migration of one or more entities of the enterprise network onto a cloud service provider (CSP). 
         FIG.  2    depicts a block diagram of an example CMR service system according to some embodiments. 
         FIG.  3    depicts a flowchart of a cloud migration process of an enterprise system according to some embodiments. 
         FIG.  4    depicts in further detail the discovery phase of the cloud migration process according to some embodiments. 
         FIG.  5    depicts in further detail of an application discovery step of the discovery phase according to some embodiments. 
         FIG.  6    depicts in further detail of a flow source discovery of the discovery phase according to some embodiments. 
         FIG.  7 A  is a chart depicting traffic dependency of internal and external network traffic according to some embodiments. 
         FIG.  7 B  is a diagram depicting the traffic dependency of internal and external network traffic according to some embodiments. 
         FIG.  8    depicts an example output of virtual machine and external entity traffic according to some embodiments. 
         FIG.  9    is a diagram depicting an example application, external sources, and the amount of network traffic between the two according to some embodiments. 
         FIG.  10 A  depicts three examples of “what if” workload movements scenario according to some embodiments. 
         FIG.  10 B  is a chart depicting the bandwidth and cost associated with each of the “what if” workload movement scenarios of  FIG.  10 A . 
         FIG.  11 A  depicts an initial communication map between internal and external virtual machines according to some embodiments. 
         FIG.  11 B  depicts a second communication map between internal and external virtual machines according to some embodiments. 
         FIG.  11 C  depicts a third communication map between internal and external virtual machines according to some embodiments. 
         FIG.  12 A  depicts an example of an on-premises application&#39;s external and internal dependencies before migration according to some embodiments. 
         FIG.  12 B  depicts an example of an application&#39;s external and internal dependencies after migration according to some embodiments. 
         FIG.  13    depicts in further detail the profiling phase of the cloud migration process according to some embodiments. 
         FIG.  14    depicts affinity groups and representative synthetic workloads of the enterprise network according to some embodiments. 
         FIG.  15    is a table comprising temporal data capturing utilization levels of an example workload according to some embodiments. 
         FIG.  16    depicts graphical charts of network traffic metrics of two example representative synthetic workloads according to some embodiments. 
         FIG.  17    is a chart depicting cloud configuration and the cost of example applications of the enterprise network according to some embodiments. 
         FIG.  18    depicts in further detail the playback phase of the cloud migration process according to some embodiments. 
         FIG.  19    depicts an example output of an estimated monthly cloud cost for two cloud service providers according to some embodiments. 
         FIG.  20    depicts an example output of the CMR playback phase according to some embodiments. 
         FIG.  21    depicts in further detail the monitor phase of the cloud migration process according to some embodiments. 
         FIG.  22    depicts a block diagram illustrating entities of an example machine according to some embodiments. 
         FIG.  23 A  depicts an example of a virtual machine sending a request to an email server illustrating a transport server and a transport client according to some embodiments. 
         FIG.  23 B  depicts an example of the email server sending data to multiple entities of the enterprise network according to some embodiments. 
         FIG.  24    depicts a block diagram of another example CMR service system according to some embodiments. 
         FIG.  25    depicts an example port frequency distribution table for an entity of the enterprise network according to some embodiments. 
         FIG.  26    depicts another example of the discovery phase of the cloud migration process according to some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Various embodiments leverage performance analytics, workload utilization profiling, and cloud simulation to determine which workloads of the enterprise network can be migrated to a cloud infrastructure as a service (IasS) and their associated cost. As a result, these systems may provide insight into the performance and suitability of applications of the enterprise network in different cloud service providers (CSP)s. For example, systems discussed herein may reduce the risk of large-scale migrations of workloads or applications to a public and/or private cloud before migrating the actual workloads to the cloud. 
     The “end to end” migration process to an Infrastructure as a Service (IaaS) cloud is a challenging undertaking consisting of many critical phases and spanning IT infrastructure and application organizations. “Lift and Shift” or “Re-hosting” migrations to the cloud are inherently risky due to many unknown answers to fundamental yet, critical questions. In “lift and shift” migrations, the enterprise may utilize applications in the cloud that are effectively a clone of the existing data center implementation. In the “re-hosting” migration, the application may be similar to the existing data center implementation, but individual services may be replaced with cloud-native services. In some embodiments, there may be a combination of the two where some applications are a clone of the existing data center implementation while others are replaced with cloud-native services. 
     A common approach to cloud migration is to replicate the existing on-premise configuration in the cloud. Such approaches can turn into unproductive and costly endeavors due to time-consuming post re-configurations, failure to achieve the original workload performance or unnecessary overprovisioning of resources. In many cases, the outcome may be repatriation back to the on-premise data center. 
     Another approach is to right-size the on-premise configurations before migration. Although this approach is an improvement over replicating the existing on-premise configuration, it still does not lead directly to a right-sized cloud configuration. The CSPs use high scalable and heavily customized hypervisors. CPUs, memory, and IOPs performance will vary significantly between on-premise and CSPs hypervisors. 
     In the absence of workload simulation, workload performance can only be verified by moving the actual application to the cloud and there is no guarantee that the CSP will support the resource demands of the workloads and deliver acceptable performance. 
     Even if cloud configuration adjustments are made to the original workloads after the migration, the operating cost may be significantly higher than expected, which could lead to repatriation due to an unsustainable and unjustifiable investment. 
     The outcome of current approaches results in projects that take longer and cost more than expected and, in many cases, leading to very costly failures. 
     Some embodiments described herein include a unique consultative iterative approach to the discovery and understanding of the workload&#39;s or application&#39;s dependencies. Some embodiments blend a consultative approach that leverages an agile data discovery, data manipulation, and analysis implemented by experienced data scientists together with automation. In one example, the approach may be designed to handle the initial surge of raw and unstructured data collected during the first iteration. During the following iterations, the focus may be gradually tuned to the priorities and data characteristics revealed during each iteration. By the third iteration, the dependency mapping may be stable and available to create the “move groups” and “move events.” After the final iteration, customers may be offered the option to access a SaaS platform to continue the dependency mapping data manipulation. 
     In one example, a system may identify and determine workload characteristics, baseline on-premises service level agreements (SLAs), and dependencies between compute, networking, and storage elements of the enterprise network. The system may then identify workloads of the enterprise network and categorize them into affinity groups, which are used to create synthetic workloads. A synthetic workload on CSP profiles may be used to simulate the synthetic workload on different CSPs to estimate the performance and cost of migrating the workload onto different CSPs. In some embodiments, after the customer chooses a particular CSP, the system may monitor actual workloads after the cloud migration to identify unforeseen performance or capacity issues. 
     A cloud migration readiness (CMR) service system may be used to give IT administrators an awareness of the dependencies of the various elements of the enterprise system. For example, the CMR service system may utilize an application discovery system and flow source discovery system to analyze the performance of applications of the enterprise network and profile workload utilization to determine workloads that may be migrated to a CSP. 
     The application discovery system may give IT administrators and operators awareness of applications and related network infrastructure. The system may obtain real-time views of the infrastructure that is relevant to the network and usage of the network. In addition, the application discovery system may determine entities of the infrastructure where important applications are located, monitor application behavior, and assess/monitor their effect on infrastructure resources. The CMR service system may utilize the output of the application discovery system to provide real-time visibility into the performance, health, and utilization of applications across physical, virtual, and cloud computing environments. Entities are logical and intuitive groupings of system wide devices and workloads that may be based on function, correlation, and/or interdependency. Entities enable users to logically group system-wide resources, from physical devices to application workloads, in order to provide the authoritative insights required to understand how resources and applications are performing. IT teams may configure entities to show all of the resources supporting a specific application, business unit, or tier of service. 
     Flow source discovery system may collect traffic data in the form of data packets from a wide variety of routers and switches manufactured by different companies. The results of the data flow source discovery process may be used to detect network anomalies, control bandwidth utilization, optimize application performance, and/or troubleshoot problems. Examples of network anomalies include DDoS attacks, SPAM, BotNets, abnormal downloads/uploads, or the like. The data flow source discovery system may control bandwidth utilization by monitoring data traffic metrics and providing alarms or alerts for various data traffic metrics such as read response time, fabric transmission errors, link errors, link transmission errors, network usage rate, port utilization, etc. The CMR service system may utilize the output of the flow source discovery system to determine a traffic dependency of internal and external entities of the enterprise network. 
     A network device such as a router or firewall may monitor network traffic coming into and out of an enterprise network. The network device may determine whether to allow or block network traffic based on a defined set of network rules. Network rules may specify a type of network traffic and a port (e.g., of a router, switch, and/or firewall) that needs to be open in order to allow the type of network traffic to pass. In some embodiments, the port may correspond to a port of an entity of the enterprise network associated with the network rule. An entity may be or represent an abstract of an individual network device, individual network, group of networks, group of subnets, an application, server, and/or the like. 
     A communication port of an entity, or the “entity port,” may be a communication endpoint. The port may be identified for each transport protocol and a port number. Two examples of the transport protocol include TCP and the User Datagram Protocol (UDP). A port number associated with an entity of the enterprise network, such as a host, may be associated with an internet protocol (IP) address of the host. 
     Data traffic may be captured using network traffic analyzing software such as NetFlow (i.e., a network protocol developed by CISCO for collecting IP traffic information and monitoring network flow). Although NetFlow is described as network traffic analyzing software, it will be appreciated that any different network traffic analyzing software may be used. 
     In various embodiments, the network traffic analyzing software may identify internet protocol (IP) addresses and ports associated with a source and destination of the data traffic. In some embodiments, the network traffic analyzing software may provide attributes of the data traffic. For example, the network traffic analyzing software may provide attributes of the data in the form of a communication message or tuple in a format of (IP 1, Port 1, IP 2, Port 2), where IP 1 and IP 2 corresponding to the source IP address and destination IP address respectively, while Port 1 and Port 2 corresponds to a source port and destination port respectively. The communication tuple may be different depending on the directionality of the communication. 
     Systems and methods discussed herein may review this information over time to create an understanding regarding how different network components (e.g., hardware, applications, virtual machines, and the like) communicate over the network. The information may then be used to assess how communication may flow across the network, identify applications, identify servers, and/or identify ports to open and configure routers accordingly. All or some of the information and configuration(s) may be required for planning, simulating, and ultimately moving all or some of an enterprise network to cloud-based services. 
       FIG.  1    depicts a block diagram of an enterprise system  100 , including a CMR service system  185 , capable of simulating a synthetic workload on different CSPs to estimate the performance and cost of migrating one or more applications of the enterprise network onto different CSPs. In this example, the enterprise system  100  comprises an enterprise network  105 , a network traffic analyzing software platform  150 , and an infrastructure performance management (IPM) appliance  160 . The enterprise network  105  includes a storage device  110 , a server/host  120 , a switch fabric  130 , and traffic access points (TAP)  140 . The IPM appliance  160  includes a flow source discovery system  170 , an application discovery system  180 , and the CMR service system  185 . 
     Storage devices  110  of the enterprise system  100  includes any number of storage devices that store data. In one embodiment, the storage devices  110  include one or more disk arrays. For example, the storage devices  110  may include a storage array network (SAN). In various embodiments, the storage device is cloud storage. 
     Server/host  120  may be any digital device with an instance of an operating system. For example, any number of hosts  120  may be a physical computer managed by Microsoft Windows. Hosts  120  may include instances of UNIX, Red Hat, Linux and/or others. The hosts  120  may include one or more virtual machines. 
     The switch fabric  130  may provide communication between any two entities of the enterprise system  100 , such as the storage devices  110 , the server/host  120 , the TAP  140 , and the network traffic analyzing software platform  150 . The switch fabric  130  may use packet switching to receive, process, and forward data from a source device to a destination device. The switch fabric  130  may refer to switches (e.g., flow sources) that are used to direct and assist in the communication of information of the enterprise network  105 . 
     The TAP  140  may provide connectivity to links between storage ports of the storage device  110  and switches of the switch fabric  130 . In various embodiments, the TAP  140  may provide connectivity on both sides of fabric-based storage virtualizers. In one example, the TAP  140  is an optical splitter that provides a copy of data passing through a fiber optic channel of the enterprise network  105  without affecting the integrity of the data. In this example, the fiber optic channel may connect storage devices (of a SAN) with servers of the enterprise network. The copy of the data may be used for real-time performance monitoring of traffic traveling through the fiber optic channel and/or to assist with application discovery. 
     The network traffic analyzing software platform  150  may discover flow sources on the enterprise network  105 . The network traffic analyzing software platform  150  may be any third-party platform that is integrated into routers or switches by their respective manufacturers to aid users in monitoring the performance of traffic data entering and exiting that specific switching hardware. An example of a network traffic analyzing software platform  150  is NetFlow. Although the network traffic analyzing software platform  150  of a particular provider may perform some flow source detection, the network traffic analyzing software platform  150  may provide only limited information about the flow sources (e.g., limited metrics) and may not include other switches of other manufacturers (i.e., that is not a part of that particular providers network traffic analyzing software platform  150 ). 
     The application discovery system  180  may receive from the flow source discovery system  170  possible roles of network endpoints. These possible network endpoint roles may be used by the application discovery system  180  to discover applications through heuristic analysis. For example, data received from a known flow source (e.g., discovered by the flow source discovery system  170 ) may be assessed to determine what applications provided and/or received information from the data. Data received from a known flow source may be, in one example, intercepted or copied from a TAP that interfaces with communication paths of the enterprise network  105 . Based on that information, as well as the type of communication, the frequency of communication, and/or the like, the application discovery system  180  or the flow source discovery system  170  may label a network endpoint with one or more roles performed within the enterprise network  105 . The output of the application discovery system  180  may be a list of applications on the enterprise network and entities of the enterprise system associated with each of the applications. 
     The CMR service system  185  may utilize data from the network traffic analyzing software platform  150 , the flow source discovery system  170 , and the application discovery system  180  to identify applications and determine traffic dependency of internal and external entities of the enterprise network. The CMR service system  185  may receive the identified application and traffic dependencies of internal and external entities of the enterprise network and categorize workloads of the enterprise network into multiple affinity groups. The CMR service system  185  may determine representative synthetic workload for the multiple affinity groups. The CMR service system  185  may use simulated cloud service providers and the representative synthetic workload to simulate the representative synthetic workload on different CSPs to estimate the performance and cost of migrating the workload onto different CSPs. During each phase of the cloud migration readiness process, the CMR service system  185  may provide a user interface during each phase of the cloud migration readiness process. The user interface may provide a chart depicting traffic dependencies of internal and external network traffic, such as an example chart of  FIG.  7 A . The cloud migration readiness process may include four phases, a discovery phase, a profiling phase, a playback phase, and a monitor phase. More details of these four phases of the cloud migration readiness process will be discussed in  FIG.  3   . 
     The output of the CMR service system  185  may be an estimated performance and cost of moving one or more applications or portions of one or more applications. A customer may utilize this information to determine the applications or portions of the application to migrate to a CSP. After the migration is completed, the CMR service system  185  may monitor the workloads associated with the application to identify unforeseen performance or capacity issues. 
     It will be appreciated that some embodiments may provide a variety of different services beyond cloud migration readiness. For example, the CMR service system  185  may provide infrastructure performance assessment. In this example, the CMR service system  185  may enable mitigation of the risk of major infrastructure initiatives and optimize infrastructure performance through application discovery, identification of workgroups, categorization of workgroups into affinity groups, creation of representative synthetic workloads, and simulation of the representative synthetic workloads into different representative infrastructure initiatives. This process may provide definitive insight into current and planned workload and performance. Further, this process may be utilized in customer planning technology refreshes, migrations, consolidations, or new deployments. 
     Further, the CMR service system  185  may provide storage performance validation in some embodiments. For example, the CMR service system  185  may provide accurate workload modeling and guidance for storage infrastructure validation. Through application discovery, identification of workgroups, categorization of workgroups into affinity groups, creation of representative synthetic workloads, and simulation of the representative synthetic workloads into different representative infrastructure initiatives, the CMR service system  185  may enable accurate application workload validation models may be built to validate storage. This process may be used to enable the evaluation and testing of storage infrastructure. 
       FIG.  2    depicts a block diagram of an example CMR service system  185  according to some embodiments. The CMR service system  185  includes a communication module  202 , a migration module  204 , an input module  206 , an application discovery interface module  208 , a workload module  210 , an affinity group module  212 , a flow source discovery interface module  214 , a traffic analyzing module  216 , a scheduling module  218 , a workload datastore  220 , a cloud service provider profile datastore  222 , a reporting module  224 , and a flow source datastore  226 . 
     The communication module  202  may send and receive requests or data between any of the CMR service system  185 , the network traffic analyzing software platform  150 , the application discovery system  180 , and the flow source discovery system  170 . The communication module  202  may receive a request from the IT administrator of the enterprise network  105  to commence the cloud migration readiness process. In some embodiments, the communication module  202  may send a request to the application discovery system  180  for data regarding applications of the enterprise network  105  discovered by the application discovery system  180 . 
     The communication module  202  may receive from the network traffic analyzing software platform  150  any number of data packets. The communication module  202  may send a request to the flow source discovery system  170  for discovered flow source(s) and their associated attributes (e.g., in a table, chart, graph, or the like). In some embodiments, the communication module  202  may receive a request from the reporting module  224  to provide, in the form of charts or graphs, traffic dependencies of internal and external network traffic. 
     The migration module  204  may manage the cloud migration readiness process and/or may commence the cloud migration readiness process. The migration module  204  may commence the cloud migration readiness process when the migration module  204  determines that any number of trigger conditions is satisfied. For example, a trigger condition may include the scheduling module  218 , determining that a current time equals a scheduled cloud migration readiness process start time. In some embodiments, one of the trigger conditions includes receiving from the input module  206  a request to commence the cloud migration readiness process. Upon the reception of the request to commence the cloud migration readiness process from the scheduling module  218  or the input module  206 , the migration module  204  may commence the cloud migration readiness process. 
     The migration module  204  may suspend the flow source discovery process when the migration module  204  determines that a trigger condition is satisfied. For example, a trigger condition may include the scheduling module  218  determining that a current time equals the scheduled cloud migration readiness process end time. In this example, a scheduling module  218  may determine or be configured to identify a time when resources are being used by the enterprise (e.g., peak times or business hours), which may satisfy the trigger continue to suspend the flow source discovery process (e.g., thereby conserving resources for use by the enterprise. Similarly, there may be a time during the evenings or off-peak times identified by the scheduling module  218 , which may trigger a transition from suspend to re-engaging the flow source discovery process. In some embodiments, the migration module  204  may flag or bookmark one or more points (e.g., at suspension points or any point) in the flow source discovery process such that when the process is re-engaged after being suspended, the flow source discovery process may begin at or near the point in the flow source discovery process when the process was suspended. 
     In some embodiments, one of the trigger conditions includes receiving from the input module  206  a request to suspend the cloud migration readiness process. Upon the reception of the request to commence the flow source discovery from the scheduling module  218  or the input module  206 , the migration module  204  may suspend the cloud migration readiness process. 
     In some embodiments, the migration module  204  manages a commencement or suspension of any one of the multiple phases of the cloud migration readiness process. For example, the migration module  204  may commence the discovery phase of the cloud migration readiness process when the migration module  204  determines that any number of trigger conditions is satisfied. In one embodiment, the discovery phase of the cloud migration readiness process lasts two to four weeks. In various embodiments, the profiling phase lasts two weeks, the playback phase lasts two weeks, and the monitoring phase lasts four weeks. 
     The input module  206  may initiate the cloud migration readiness process or a phase of the cloud migration readiness process (e.g., based on receiving a request from the IT administrator of the enterprise network  105 ). In some embodiments, the input module  206  may receive a request to start an application discovery process. In various embodiments, the input module  206  may receive a request to start a flow source discovery process. In some embodiments, the input module  206  receives a schedule of the cloud migration readiness process. For example, the input module  206  may receive a start date and time of the cloud migration readiness process start time. In some embodiments, the input module  206  may receive, from the IT administrator of the enterprise network  105 , known workloads. 
     The application discovery interface module  208  interfaces with the application discovery system  180 . In some embodiments, the application discovery interface module  208  may send the output of the application discovery process to the input module  206 . The output of the application discovery process may include a list of application entries found in the enterprise network  105 . Each application entry may include attributes or characteristics (e.g., metrics) of the discovered application, such as a suggested name of the discovered application, as well as attributes associated with the entities which make up the discovered application. The application discovery process will be discussed further in  FIG.  5   . 
     During the discovery phase of the cloud migration readiness process, the workload module  210  may identify multiple workloads of the enterprise network  105 . In some embodiments, a workload represents the amount of work or processing accomplished by an entity of the enterprise network  105 . Entities of the enterprise network  105  may include compute devices, network elements, and storage elements. In one embodiment, entities of the enterprise network  105  include enterprise network applications. In some embodiments, entities of the enterprise network include virtual machines. 
     In various embodiments, a workload may comprise multiple workloads. For example, the workload represents the amount of work or processing accomplished by an application of the enterprise network  105 . Thereby, one workload representing the email service application of the enterprise network comprises multiple virtual machine workloads and server workloads, which function as part of the email service application. 
     In some embodiments, during the discovery phase of the cloud migration readiness process, the workload module  210  may determine the characteristics of the workload. Characteristics of the workload may include attributes of the entity associated with the workload. For example, a workload of a virtual machine may include attributes of the virtual machine such as applications of the enterprise network  105  associated with the virtual machine and utilization such as maximum read speed, maximum write speed, central processing unit (CPU) MHz, memory utilization percentage, storage demand, and input/output operations per second (IOPS). For example, a workload of a storage device may include attributes of the storage device such as read and write latency. 
     In some embodiments, the workload module  210  may send a list of workloads identified by the workload module  210  to the affinity group module  212 . The affinity group module  212  may receive the list of workloads and categorize or group some or all of the received workloads into one or more affinity groups. In some embodiments, each workload is categorized into one affinity group. The affinity group module  212  may group any number of workloads into synthetic workloads based, at least in part, on resource utilization (e.g., CPU utilization, memory utilization, storage utilization and/or the like). 
     The workload module  210  may determine workload interdependencies (e.g., how workloads depend on one another). Workload interdependencies help to determine the sequence of workloads, how services are migrated to the cloud, and what workloads and services should stay on-premise. 
     The affinity group module  212  may categorize workloads identified by the workload module  210  into affinity groups. As discussed herein, affinity groups (e.g., representative synthetic workloads) may consist of workloads with similar resource utilization levels and temporal or time-based characteristics. For example, workloads with similar read speed and write speeds may be grouped together into the same affinity group. In one example, workloads that have a CPU usage of greater than 90% during a particular time frame each day may be placed in the same affinity groups. In some embodiments, a workload may belong to more than one affinity group. In other embodiments, each workload may only belong to one affinity group. 
     In one method, the CMR service system  185  may utilize a machine learning algorithm to categorize or group workloads into affinity groups. In some embodiments, the workload module may apply a k-means clustering algorithm to the multiple workloads identified by the workload module  210 . The k-means clustering algorithm may have a feature set represented by average, a percentile of maximum values of utilization metrics. In some embodiments, utilization metrics include CPU utilization, memory utilization, input/out (IO) read utilization, IO write utilization, IO number of reads, IO number of writes, network receive rate, and network transmit rate. 
     In another method, the affinity group module  212  groups workloads by their maximum or percentile utilization metric fitness into available cloud instances for a specific CSP. This method may be utilized when a particular CSP to use has already been determined. 
     Once the affinity group module  212  determines the workloads that belong to each affinity group, the affinity group module  212  may determine the representative synthetic workload for each affinity group. The representative synthetic workload for each affinity group may be a sum of the multiple workloads which make up the affinity group. In various embodiments, the representative synthetic workload for each affinity group may be a weighted sum of the multiple workloads which make up the affinity group, with each of the multiple workloads given a weight based on attributes or characteristics of the workload. In some embodiments, the affinity group module  212  may send a request to the workload datastore  220  to store the representative synthetic workload associated with each affinity group. 
     The affinity group module  212  may generate a table or chart which comprises temporal data capturing utilization level for some or all of the utilization metrics. An example of this may be seen in  FIG.  15   . Table  1500  of  FIG.  15    contains temporal data capturing utilization levels of two example workloads of the enterprise network  105 . The columns of the example table  1500  are associated pairs of date/time of data and the name of the workload that is being monitored by the infrastructure performance management appliance  160  for a particular utilization metric. The number of column pairs is equal to the number of workloads being monitored by the workload module  210 . For example, column  1510  provides a timestamp for measuring the particular utilization metric, while column  1520  provides the measurement for the particular utilization metric associated with the timestamp in the same row. 
     The flow source discovery interface module  214  interfaces with the flow source discovery system  170 . In some embodiments, the flow source discovery interface module  214  may send the output of the flow source discovery process to the input module  206 . The output of the flow source discovery process may include a list of flow source entries. The list of flow source entries may include attributes associated with discovered flow sources. Attributes may include the type of flow source, total byte count, incoming/outgoing byte count, incoming/outgoing bit rate, total packet rate, and/or incoming/outgoing endpoint count. In some embodiments, attributes associated with flow source entries may include applications associated with the flow source. The flow source discovery process will be discussed further in  FIG.  6   . In some embodiments, the flow source discovery interface module  214  interfaces with the network traffic analyzing software platform  150 . In some embodiments, the flow source discovery interface module  214  may send a request to the flow source datastore  226  to store the flow source entries. 
     The traffic analyzing module  216  analyzes network traffic received from the flow source discovery system  170 . In some embodiments, the traffic analyzing module  216  receives network traffic from the network traffic analyzing software platform  150 . The traffic analyzing module  216  may analyze IP communications between entities of the enterprise network  105  as well as IP communications between entities of the enterprise network and entities that are external to the enterprise network  105 . The traffic analyzing module  216  may provide the output of traffic dependency on internal and external network traffic. An example of this can be seen in  FIG.  7 A . Example chart  700  depicts the amount and type of traffic that goes through a particular virtual machine. Row  710  depicts the amount of traffic between the particular virtual machine to external entities of the enterprise network  105 . Network traffic is separated by incoming and outgoing network traffic. Similarly, row  720  depicts the amount of traffic between the particular virtual machine to other entities, which are not virtual machines, of the enterprise network  105 . Row  730  depicts the amount of traffic between the particular virtual machine and other virtual machines of the enterprise network  105 . The traffic analyzing module  216  may output the traffic dependency in forms other than a table. An example of this can be seen in  FIG.  7 B . An example output interface  750  depicts the same information as the example chart  700  of  FIG.  7 A , but in the form of a state diagram. 
     In one embodiment, the traffic analyzing module  216  may provide a traffic dependency mapping of the various entities or workloads of the enterprise network  105 . The dependency mapping of entities or workloads of the enterprise network may be done iteratively. For example, an initial internal and external communication map may be produced and reviewed by a customer, or alternately, the external communication map may be reviewed by an analytical system. The initial communication map may provide an overall view of the enterprise network. The customer or analytical system may review the overall view of the enterprise network to identify and filter entities or workloads which are not of interest and tune priorities and data characteristics reveal during each iteration. An example of an initial communication map may be seen in  FIG.  11 A . The customer or analytical system may receive an example initial communication map  1100 . Subsequent to receiving the initial communication map  1100  of  FIG.  11 A , the customer or analytical system may refine the IP addresses or entities to focus on a particular set of entities to produce a second communication map  1110  of  FIG.  11 B . Subsequently, a third iteration of the communication map  1120  of  FIG.  11 C  may be produced. The initial communication map  1100 , the second communication map  1110 , and the third communication map  1120  may be used to determine or identify bully virtual machines and zombie virtual machines. A bully virtual machine is a virtual machine that utilizes too many resources and causes other virtual machines to be without resources. A bully virtual machine may cause cluster performance to degrade. Identifying bully virtual machines may help in analyzing whether one or more of these virtual machines need additional resources or need to be re-routed to other applications or workloads. 
     A zombie virtual machine is inactive or is used by very few or no applications or workloads. In one example, a virtual machine is considered a zombie when it is powered on but does fewer than 30 IOPS and receives or transfers fewer than 1000 bytes per day for the past 30 days. To prevent host resource wastage, zombie virtual machines need to be removed or re-routed to other applications or workloads. 
     In some embodiments, the traffic analyzing module  216  may receive network traffic data from the network traffic analyzing software platform  150  or the flow source discovery system  170  after the network traffic analyzing software platform  150  has performed flow source detection for a predetermined period of time. In some embodiments, the predetermined period of time is at least two weeks. 
     The scheduling module  218  may receive from the input module  206  the start time or end time of one or more of the phases of the cloud migration readiness process. In some embodiments, the scheduling module  218  may suspend the cloud migration readiness process or any phase of the cloud migration readiness process when any number of trigger conditions is satisfied. 
     The workload datastore  220  may be any structure and/or structures suitable for storing data entries or records (e.g., an active database, a relational database, a self-referential database, a table, a matrix, an array, a flat file, a documented-oriented storage system, a non-relational No-SQL system, an FTS-management system such as Lucene/Solar, and the like). The workload datastore  220  may store the representative synthetic workload associated with each affinity group. In some embodiments, the workload datastore  220  may create a workload entry for each workload identified by the migration module  204 . Each workload entry may include metrics or attributes associated with the workload. The attributes of the workload may depend on what the workload represents. For example, if the workload represents an application, the attributes of the workload may include attributes of the application, such as identifiers of the entities associated with the enterprise network  105 , tier, or criticality of the application. In another example, if the workload represents a virtual machine, attributes of the workload may include attributes of the virtual machine such as maximum read speed, maximum write speed, central processing unit (CPU) MHz, memory utilization percentage, storage demand, and IOPS. 
     The cloud service provider profile datastore  222  may be any structure and/or structures suitable for storing data entries or records (e.g., an active database, a relational database, a self-referential database, a table, a matrix, an array, a flat file, a documented-oriented storage system, a non-relational No-SQL system, an FTS-management system such as Lucene/Solar, and the like). The cloud service provider profile datastore  222  may store a CSP profile entry for various CSPs such as Microsoft Azure, IBM Cloud, and the like. Each CSP profile entry may include attributes of the CSP such as available memory storage, types of security levels available, cost per gigabyte, methods of retrieving data stored on the cloud, and types of deployment available to a consumer. The migration module  204  may utilize the cloud service provider profile datastore  222  to suggest one or more CSP to the customer. 
     The reporting module  224  may receive a request to provide an example output of the network traffic between a particular virtual machine and external entities, such as the example output  800  of  FIG.  8   . During the discovery phase of the cloud migration readiness process, the reporting module  224  may provide the example output  800 , which depicts a virtual machine  810 , given an identifier “ORDDB02.” The example output  800  also depicts identifiers of other virtual machines that are in communication with the “ORDDB02” virtual machine. Element  802  of  FIG.  8    depicts an entity external to the enterprise network or an external entity that is in communication with the “ORDDB02” virtual machine. The reporting module  224  may receive a request to provide example outputs from various phases of the cloud migration readiness process, such as the example workload utilization table  1500  of  FIG.  15   . 
     A module may be hardware or software. In some embodiments, the module may configure one or more processors to perform functions associated with the module. Although different modules are discussed herein, it will be appreciated that the CMR service system  185  may include any number of modules performing any or all functionality discussed herein. 
       FIG.  3    depicts a flowchart of a cloud migration readiness process  300  of an enterprise system according to some embodiments. The cloud migration readiness process  300  includes a discovery phase  302 , a profiling phase  304 , a playback phase  306 , and a monitor phase  308 . 
     During the discovery phase  302 , the CMR service system  185  may identify applications and data flow on the enterprise network to determine an initial internal and external communication map. In some embodiments, the customer may provide business objectives, known applications of the enterprise network, a targeted workload which may include mission-critical applications or virtual machines that have been causing problems, critical peak business time period, and desired completion dates. The CMR service system  185  may use some or all of this information during the discovery phase  302  and other phases of the cloud migration readiness process to generate a report identifying workloads and the cost of migrating the identified workload to different CSPs. Some example output of the discovery phase  302  may be targeted workload inventory, target workload dependency matrices, and a baseline assessment of the health, utilization, and performance aspects of the workloads&#39; on-premises infrastructure. In some embodiments, the discovery phase  302  may have a duration between 2 to 4 weeks. The customer may receive some or all of the output of the discovery phase  302  to determine workloads to profile in the profiling phase  304  of the cloud migration readiness process. The determination of the workloads may be done based on the customer&#39;s goals. For example, based on the characterization of the applications of the enterprise network, the customer may decide to migrate a database application to a cloud-based infrastructure—diagram  1200  of  FIG.  12 A  depicts an example of multiple application&#39;s external and internal dependencies before migration. As seen in diagram  1200 , the internal and external dependencies of three categories of applications of the enterprise network  105  can be seen. For example, the analysis application is dependent on an internal SMB application and an external HTTP application. 
     During the profiling phase  304 , the CMR service system  185  may receive, from the customer, workloads to profile and categorize the workloads into affinity groups. The number of workloads profiled in this phase of the cloud migration readiness process may range from hundreds to thousands. Workloads may be categorized into affinity groups based on utilization levels and temporal characteristics. The workload profiling includes CPU, memory, IO, and network utilization for all on-premises workloads that have been targeted for migration to the cloud. It will be appreciated that in some embodiments, the affinity groups may each be a representative synthetic workgroup. Alternately, in some embodiments, once affinity groups have been created, the CMR service system  185  creates representative synthetic workloads for each affinity group. 
     In various embodiments, the representative synthetic workload may be a sum of the multiple workloads. Based on the representative synthetic workloads, the CMR service system  185  may select initial cloud configuration choices and be presented to the customer. Some example output of the profiling phase may include targeted workload profile, workload affinity group definitions, synthetic workloads, candidate cloud configuration, and candidate cloud configuration costs. In some embodiments, the profiling phase  304  may have a duration of 2 weeks. The customer may receive some or all of the output of the profiling phase to determine workloads for the playback phase. 
     In some embodiments, during the discovery phase  302  and/or the profiling phase  304 , the existing network may be monitored using NetFlow or other network software. In some embodiments, the existing network may include any number of hardware probes that assess data passing through network cables (e.g., optical cables) to further learn about the existing network. Further, in some embodiments, the existing network may include appliances and network devices that may monitor data flows within the network. The hardware probes, appliances, network software, and/or network devices may provide the logged information, monitored information, and/or reports to the CMR service system  185 . The CMR service system  185  may collect and/or aggregate the information from any number of sources (e.g., the hardware probes, appliances, network software, and/or network devices). The CMR service system  185  may identify applications, ports, virtual applications, storage devices, storage networks, configurations, network devices, and the like to assist with playback discussed herein. Further, the CMR service system  185  may utilize the received information to identify ports (as discussed herein) to configure software, hardware, and/or virtual network devices to ensure needed ports are open (and, optionally, to close any unneeded ports or ports that may represent a security risk). 
     During the playback phase  306 , the CMR service system  185  may target workloads to target for the playback phase and plays back the workloads into a profile of different CSPs. In some embodiments, the CMR service system  185  may select one or more synthetic representative workloads as target workloads. In various embodiments, a user may select one or more synthetic representative workloads as target workloads. It will be appreciated that, in some embodiments, all of the synthetic representative workloads are target workloads. 
     The output of the playback may be a simulated performance of the workload(s) on a particular CSP (or a variety of CSPs). The on-premises performance level is compared to the simulated performance, and costs estimates are re-calculated. Some example output of the playback phase  306  includes a synthetic workload cloud performance report, an updated candidate cloud configuration, and an updated candidate cloud configuration costs. The output of the playback phase  306  may be one or more recommendations of which workloads of the enterprise network to migrate to a CSP and which CSP may be best suited for the customer&#39;s needs. In some embodiments, the playback phase  306  may have a duration of 2 weeks. The customer may receive some or all of the output of the playback phase to determine workloads or applications to migrate to a CSP and a particular CSP to use. Diagram  1250  of  FIG.  12 B  depicts an example of multiple application&#39;s external and internal dependencies after migration. As seen in diagram  1250 , the CMR service system  185  may provide a recommendation to all internal applications except the SMB application to a cloud-based infrastructure. 
     The optional monitor phase  308  takes place after the customer migrates the workload or application to a CSP. During the monitor phase  308 , the CMR service system  185  verifies that the applications&#39; performance remains within acceptable levels. Some example output of the monitor phase may include cloud workload performance report and cloud configuration report, and post-migration recommendations. In one example, the monitor phase  308  may have a duration of 4 weeks. The customer may receive some or all of the output of the monitor phase, determine if fewer or more resources from the cloud infrastructure are required. Further details of each of the phases of the cloud migration readiness process  300  are discussed in  FIGS.  4 ,  13 ,  18 , and  21   . 
       FIG.  4    depicts in further detail the discovery phase  302  of the cloud migration process according to some embodiments. In step  402 , the input module  206  may receive one or more cloud migration objectives. Cloud migration objectives may include a request that the playback phase is completed before a particular date so that the enterprise may make a decision regarding the cloud migration of one or more applications of the enterprise network. In some embodiments, cloud migration objectives may include one or more choices of CSP or a required level of security for some or all of the applications or workloads that need to be migrated to a cloud infrastructure, a required level of performance for some or all applications or workloads that need to be migrated to the cloud infrastructure, and/or the like. 
     In various embodiments, the migration module  204  may create an inventory of workloads of the enterprise network  105 . The migration module  204  may send a request to the reporting module  224  to provide an inventory table or inventory chart, documenting the workloads of the enterprise network&#39;s on-premises infrastructure. The inventory table may include the workloads&#39; on-premises configuration, such as the number of virtual central processing units (vCPUs), memory size, and configured storage. The information collected may serve as a reference to understand the on-premise resource demands and requirements of the targeted workloads. One or more phases of the cloud migration readiness process may require at least some of the cloud migration objectives obtained in step  402 . 
     In step  404 , the CMR service system  185  may send a request to the application discovery system  180  to discover applications of the enterprise network  105 . The application discovery system  180  may receive from the flow source discovery system  170  possible roles of network endpoints. These possible network endpoint roles may be used by the application discovery system  180  to discover applications through heuristic analysis. The output of the application discovery system  180  may be a list of applications on the enterprise network and entities of the enterprise system associated with each of the applications. The application discovery system  180  may send the list of applications to the application discovery interface module  208 . The migration module  204  may send a request to the workload datastore  220  to create a workload entry for one or more applications in the list of applications received from the application discovery system  180 . Further details of the application discovery process are discussed in  FIG.  5   . 
     In step  406 , the CMR service system  185  may send a request to the flow source discovery system  170  to determine one or more flows of data in the enterprise network. A flow in an enterprise network may represent communication between a source internet protocol (IP) address and a destination IP address. The output of the flow source discovery system  170  may include a list of flow source entries. The list of flow source entries may include attributes associated with discovered flow sources. Attributes may include the type of flow source, total byte count, incoming/outgoing byte count, incoming/outgoing bit rate, total packet rate, and/or incoming/outgoing endpoint count. In some embodiments, attributes associated with flow source entries may include applications associated with the flow source. The flow source discovery system  170  may send the list of flow source entries to the flow source discovery interface module  214 . The migration module  204  may store the list of flow source entries to the flow source datastore  226 . Further details of the flow source discovery process are discussed in  FIG.  6   . 
     Using data from the application discovery system  180  and the flow source discovery system  170 , the CMR service system  185  may be able to create an on-premise inventory of workloads of the enterprise network  105 . Workloads of the enterprise network  105  may be entities or applications of the enterprise network  105 . In some embodiments, the reporting module  224  may provide to the customer an inventory table or inventory chart, documenting the workloads of the enterprise network&#39;s on-premises infrastructure. The inventory table may include the workloads&#39; on-premises configuration, such as the number of virtual central processing units (vCPU), memory size, and configured storage. The information collected may serve as a reference to understand the on-premise resource demands and requirements of the targeted workloads. 
     In step  408 , the traffic analyzing module  216  analyzes network traffic received from the flow source discovery system  170 . The traffic analyzing module  216  may receive network traffic data from a “peak business” period. The peak business period may be a period of at least one week. The data received from the flow source discovery system may be stored in the flow source datastore  226 . The traffic analyzing module  216  may determine the amount of data traffic that originates or terminates at a particular virtual machine or another entity of the enterprise network  105  and output this information to the customer in the form of a table or a graphic. An example of this output may be seen in  FIGS.  7 A and  7 B . Example chart  700  of  FIG.  7 A  depicts the amount and type of traffic that goes through a particular virtual machine. Row  710  depicts the amount of traffic between the particular virtual machine to external entities of the enterprise network  105 —example output interface  750  of  FIG.  7 B  depicts the same information as the example chart  700  of  FIG.  7 A . Virtual machine  760  represents the particular virtual machine, while element  770  represents all entities external to the enterprise network  105 , which is in communication with the virtual machine  760 . Arrows  780  and  785  depicts the directional of the data traffic and the amount of data traffic. 
     In step  410 , the workload module  210  may identify any number of workloads of the enterprise network  105  and the interdependencies of those workloads. In some embodiments, the interdependencies may be utilized to assist in identifying workloads. 
     In some embodiments, the traffic analyzing module  216  may receive a request from the workload module  210  for network traffic between two or more workloads of the enterprise network  105 . The workload module may determine the amount of data traffic that originates from a particular application of the enterprise network  105 . Example application network traffic diagram  900  of  FIG.  9    depicts an example application, “Great_Plains_App,” and the external entities with which this application may communicate. Furthermore, the example application network traffic diagram  900  depicts an average amount of network traffic that originates or terminates at the “Great_Plains_App” application. 
     When the workload module  210  identifies a workload, the workload module may send a request to the workload datastore  220  to create a workload entry. Each workload entry may include metrics or attributes associated with the workload. The attributes of the workload may depend on what the workload represents. For example, if the workload represents a virtual machine, the attributes of the workload may include attributes of the virtual machine such as maximum read speed, maximum write speed, central processing unit (CPU) MHz, memory utilization percentage, storage demand, and IOPS. 
     In optional step  412 , the workload module  210  may generate “what if” workload movement scenarios, which exposes possible scenarios to aid in deciding to migration one or more workloads based on their bandwidth consumption, dependency on other workloads, and the potential cost associated with which scenario or cloud-on premises network configurations—for example,  FIG.  10 A  depicts three examples of “what if” workload movement scenarios. 
     In “what if” scenario  1000 , six workloads were chosen to be hypothetically moved to a cloud-based infrastructure such as Microsoft Azure. The workload module  210  may determine the bandwidth requirement for these six virtual machines. The workload module  210  may determine the sum of the amount of network traffic between the six virtual machines, the amount of incoming and outgoing network traffic between any one of the six virtual machines and other virtual machines or entities of the enterprise network  105 , and the amount of incoming and outgoing network traffic between any one of the six virtual machines and other entities outside the enterprise network. The sum of these may determine the bandwidth requirement of the six virtual machines. The workload module  210  may send a request to the cloud service provider profile datastore  222  for the Azure CSP profile entry. The workload module  210  may determine the memory storage requirement, the required type of security, and cost per gigabyte to estimate the cost of storing the six workloads into Azure. The reporting module  224  may receive a request from the workload module  210  to provide an estimated cost per month of storing the “what if” scenario  1000  in Azure. Row  1050  of chart  1040  of  FIG.  10 B  provides the estimated cost per month of storing the “what if” scenario  1000  in Azure, along with the bandwidth requirement of the six virtual machines. 
     In “what if” scenario  1020 , five workloads were chosen to be hypothetically moved to Microsoft Azure. The workload module  210  may determine the bandwidth requirement for these five virtual machines. The workload module  210  may determine the sum of the amount of network traffic between the five virtual machines, the amount of incoming and outgoing network traffic between any one of the five virtual machines and other virtual machines or entities of the enterprise network  105 , and the amount of incoming and outgoing network traffic between any one of the five virtual machines and other entities outside the enterprise network. The sum of these may determine the bandwidth requirement of the five virtual machines. The workload module  210  may send a request to the cloud service provider profile datastore  222  for the Azure CSP profile entry. The reporting module  224  may receive a request from the workload module  210  to provide an estimated cost per month of storing the “what if” scenario  1020  in Azure. Row  1060  of chart  1040  of  FIG.  10 B  provides the estimated cost per month of storing the “what if” scenario  1020  in Azure, along with the bandwidth requirement of the five virtual machines. 
     In “what if” scenario  1030 , four workloads were chosen to be hypothetically moved to Microsoft Azure. The workload module  210  may determine the bandwidth requirement for these four virtual machines. The workload module  210  may determine the sum of the amount of network traffic between the four virtual machines, the amount of incoming and outgoing network traffic between any one of the four virtual machines and other virtual machines or entities of the enterprise network  105 , and the amount of incoming and outgoing network traffic between any one of the four virtual machines and other entities outside the enterprise network. The sum of these may determine the bandwidth requirement of the four virtual machines. The workload module  210  may send a request to the cloud service provider profile datastore  222  for the Azure CSP profile entry. The reporting module  224  may receive a request from the workload module  210  to provide an estimated cost per month of storing the “what if” scenario  1030  in Azure. Row  1070  of chart  1040  of  FIG.  10 B  provides the estimated cost per month of storing the “what if” scenario  1030  in Azure, along with the bandwidth requirement of the four virtual machines. 
     In step  414 , the reporting module  224  may receive a request from the workload module  210  to provide one or more of the targeted workload inventory, target workload dependency matrices, and a baseline assessment of the health, utilization, and performance aspects of the workloads&#39; on-premises infrastructure. Examples of the output of the discovery phase  302  can be seen in  FIG.  7 A through  11 C . Based on the “what if” scenarios and other output of the discovery phase  302 , the IT administrator of the enterprise network  105  may identify and fine-tune applications or workloads to target for cloud migration. 
       FIG.  5    depicts in further detail an application discovery step of the discovery phase according to some embodiments. In step  502 , the application discovery system  180  may function to determine if one of a plurality of trigger conditions is satisfied. The application discovery system  180  may receive a notification or indication that one of the plurality of trigger conditions has been satisfied. The trigger conditions may include a request to commence the application discovery process, a current time equaling a start time of a scheduled application discovery process, a signal, or resource utilization of one or more entities if less than an entity utilization threshold. 
     In optional step  504 , the application discovery system  180  may receive from the IT administrator a list of applications running on the enterprise network  105  and entities of the enterprise network  105  associated with each of the plurality of applications. In some embodiments, the application discovery system  180  is configured to receive attributes of any number of the applications running on the enterprise network  105  and attributes of each of the entities associated with each of the plurality of applications. 
     In various embodiments, the application discovery system  180  may create and store any number of discovered application entries based on information received from the IT administrator. 
     In optional step  506 , the application discovery system  180  determines the infrastructure of the enterprise network  105 . In some embodiments, the application discovery system  180  discovers attributes of any number of entities of the enterprise network  105  and creates and stores entity entry. Building an infrastructure of the enterprise network  105  may aid users of the application discovery system  180  with a visual representation of the infrastructure in the context of key applications of the enterprise. 
     In step  508 , the application discovery system  180  may receive data regarding applications; the data received regarding the application may include attributes of the application, such as the name, the number of entities making up the application, the tier mapping of the application. The data received regarding the application may include attributes of each of the entities associated with the application. Attributes of the entities may include entity category, entity type, speed of the channel, and alarms. Some entity attributes may be dependent on the type of entity; for example, fiber channels may have an attribute describing the speed of the channel. 
     The application discovery system  180  may receive data from the IT management software platform subscribed to by the enterprise network  105 , such as ServiceNow. In various embodiments, the application discovery system  180  may receive data regarding discovered applications from other IT management software platforms. In various embodiments, the application discovery system  180  may create or update entity entries with data received from ServiceNow or other IT management software platforms. 
     In some embodiments, if the application discovery system  180  discovers that a particular application has not changed in the last number of iterations of the application discovery process. In such a case, the application discovery system  180  may choose to limit the application discovery process to avoid re-identification and/or re-discovery of a subset of applications periodically. In various embodiments, the number of iterations that triggers the periodic discovery of an application may change according to attributes of the particular application, such as tier or criticality of the particular application. 
     The application discovery system  180  may receive data from application performance software platforms subscribed to by the enterprise network  105 , such as AppDynamics. The application discovery system  180  may create or update entity entries with data received from AppDynamics or other application performance software platforms. 
     In some embodiments, if the application discovery system  180  discovers that a particular application has not changed in the last number of iterations of the application discovery process. In such a case, the application discovery system  180  may choose to identify or discover the application periodically. In various embodiments, the number of iterations that triggers the periodic discovery of an application may change according to attributes of the particular application, such as tier or criticality of the particular application. 
     In step  510 , the application discovery system  180  may receive a request from the flow source discovery system  170  to accept flow records between two IP addresses. The application discovery system  180  may receive flow records from switches that enable a network protocol that provides the ability to collect IP network traffic using software such as NetFlow. These flow records may be analyzed by the application discovery system  180  to determine possible network endpoints and possible applications on the enterprise network  105 . 
     In some embodiments, the application discovery system  180  retrieves flow records from NetFlow during scheduled retrieval periods. In various embodiments, the application discovery system  180  retrieves flow records when the application discovery system  180  determines that the utilization of switches enabled with the NetFlow software is below the entity utilization threshold. 
     In some embodiments, the application discovery system  180  receives flow data to and from storage devices  110 . Probe data may include IP address, probe type, network address, speed of the channel and status of the probe, number of CPUs, operating system (OS) version, and applications running on the host. 
     In step  512 , the application discovery system  180  may receive the network traffic data from the network traffic analyzing software platform  150 . In some embodiments, the application discovery system  180  suggests, based on heuristic implications, applications that could exist. The result of the implications may be a possible discovered application. For example, the application discovery system  180  may observe that a particular IP address is the flow source/destination to/from thousands of seemingly random IP addresses and determine that the particular IP address belongs to an edge device. 
     In another example, through SSH or WMI, the application discovery system  180  may determine that one of the host  120  of the enterprise network  105 , which is part of the SAP, is running out-of-date software, however, that particular host  120  may have been replaced with different software. The out-of-date software may not have been removed from one of the host  120 . The attributes of the SAP application, as well as attributes associated with the out-of-date software running on one of the host  120 , may be sent to the application discovery system  180 . 
     In various embodiments, the application discovery system  180  may perform a heuristic analysis of the attributes of any number of entities of the enterprise network  105 . Attributes may include the name of the entity when an entity was introduced to or became a part of the enterprise network  105 . Attributes may be used to determine if any number of entities is a part of an application. For example, entities with names containing a prefix or suffix that are the same may be a part of the same application. 
     In some embodiments, a time when entities of the enterprise network are introduced into the enterprise network may be analyzed and compared with an entity&#39;s introduction threshold that may be used to determine potential applications. For example, entities introduced to the enterprise network  105  within the entity&#39;s introduction threshold of 5 minutes may be part of the same application; in either case, the application discovery system  180  may create a discovered application entry for each of the potential applications. The entities&#39; introduction threshold may be set by the IT administrator or may be set by the application discovery system  180 . In various embodiments, the application discovery system  180  may suggest that two particular entities of the network may comprise an application if the two particular entities are in constant contact throughout the day, with the amount of IP traffic passing back and forth between the two particular entities is more than any other pair of entities of the network. 
     In step  514 , the application discovery system  180  outputs a list of discovered application entries and/or a plurality of entity entries. The application discovery system  180  may output any number of the discovered application entries in the form of a chart, with each discovered application entry as well as application attributes displayed in text form. In some embodiments, entities associated with each discovered application, as well as attributes associated with each entity, may be displayed in text form. 
     Attributes of the discovered application entry output by the application discovery system  180  may include the software platform responsible for the application associated with the discovered application entry. Other attributes include tier mapping of applications discovered by different networks, names of the application. Attributes of the entities may include entity category, entity type, speed of the channel, and/or alarms. Some entity attributes may be dependent on the type of entity; for example, fiber channels may have an attribute describing the speed of the channel. 
     In some embodiments, an application conflict may be provided by the application discovery system  180  to the IT administrator to be resolved. Consider an example, if AppDynamics identifies a first SAP application with eight entities, including three cloud-based storage devices and four locally-based storage devices. The application discovery system  180  implies a second SAP application with the same eight entities, including the three cloud-based storage devices and the four locally-based storage devices, as the first SAP application plus an additional cloud-based storage device, which is not shown in the first SAP application. The discovered application entry may be created for each of the SAP application, and the application conflict may be outputted by the application discovery system  180 . The application conflict may be resolved by the IT administrator. 
     In various embodiments, the application discovery system  180  may resolve the conflict without assistance from the IT administrator. In various embodiments, the application discovery system  180  may resolve the conflict by using the tier mapping of software platforms. For example, the IT administrator may choose to prioritize applications discovered by ServiceNow over applications discovered by AppDynamics, such that if there is an application conflict between applications discovered by ServiceNow and AppDynamics, the conflict will be automatically resolved by the application discovery system  180  by choosing the application discovered by ServiceNow as the correct one. 
     Once entities of the enterprise network  105  have been identified, data from the network software platforms and network probes may have been received and analyzed by the application discovery system  180 , and the application discovery process may proceed to step  516 . In step  516 , an iteration of the application discovery process is complete. The output of the application discovery system  180  may output to the CMR service system  185  a list of application entries on the enterprise network  105 , and entities of the enterprise system associated with each of the applications. 
     In some embodiments, the iteration application discovery process may be deemed as complete after the application discovery system  180  receives data from software platforms and network probes for a predetermined period of time. In some embodiments, the predetermined period of time is seven days. In various embodiments, the predetermined period of time is 24 hours. In one embodiment, the predetermined period of time is set by the IT administrator. In some embodiments, the iteration application discovery process may be deemed as complete if the application discovery system  180  determines that a duplicated application counter is greater than a duplicated application threshold. 
     Other details and features of the application discovery process may be to suspend the application discovery process and creating a bookmark of the last network data received from the network traffic monitoring platform, a request to suspend the application discovery process may be triggered when a suspension trigger condition is satisfied, for example, which may be combinable with those described here, can be found in U.S. patent application Ser. No. 16/234,353, filed on Dec. 27, 2018; the contents of which are hereby incorporated by reference in its entirety. 
       FIG.  6    depicts a flowchart of a flow source discovery process of an enterprise network according to some embodiments. In step  602 , the flow source discovery system  170  may execute the flow source discovery. In some embodiments, the flow source discovery system  170  may receive a request to start the flow source discovery process when any number of trigger conditions are satisfied. For example, if a current time equals a predetermined start time, a trigger condition may be satisfied. 
     In optional step  604 , the flow source discovery system  170  may determine any number of attributes and/or metrics of discovered flow sources to monitor and provide. The flow source discovery system  170  may provide a set of attributes of discovered flow sources. The set of attributes and/or metrics may include the type of flow source, total byte count, incoming/outgoing byte count, incoming/outgoing bit rate, total packet rate, and/or incoming/outgoing endpoint count. In the subsequent flow source discovery process, the flow source discovery system  170  may monitor and output a subset of attributes of discovered flow sources. 
     In step  606 , the flow source discovery system  170  may receive IP network traffic data from any number of network traffic analyzing software platform  150  and/or TAP  140 . The IP network traffic data may be in data packets. Data packets collected from different routers and switches with different network traffic analyzing software platforms may be in different formats. In some embodiments, the flow source discovery system  170  may store any number of the data packet entries associated with each of the data packets received from the network traffic analyzing software platform  150 . 
     In optional step  608 , the flow source discovery system  170  may determine any number of entities of the switch fabric to monitor. In an initial flow source discovery process, the flow source discovery system  170  may analyze flow records associated with all entities of the switch fabric. In response to the output of the initial flow source discovery process, the flow source discovery system  170  may receive from the IT administrator of the enterprise network  105  a list of entities of the switch fabric that the IT administrator would like to continue monitoring. The flow source discovery system  170  may store the list. In the subsequent flow source discovery process, the flow source discovery system may ignore or reject flow records from entities of the switch fabric not listed in the first list. 
     In step  610 , the flow source discovery system  170  may analyze any number of received data packets and determine a flow source of flow records. In some embodiments, the flow source discovery system  170  does not begin to analyze any number of data packets until the end of the time frame. In various embodiments, the flow source discovery system  170  analyzes any number of data packets as it is being received by the flow source discovery system  170 . 
     In step  612 , the flow source discovery system  170  may provide any number of flow source entries to an interface or report. For example, the flow source discovery system  170  may provide any number of flow source entries in the form of a chart, with each discovered flow source entry as well as attributes associated with discovered flow sources displayed in text form. In some embodiments, entities of the enterprise network  105  found along the data path associated with the discovered flow source entry, as well as attributes associated with each entity, may be displayed in text or in the form of an infrastructure topology view. 
     In step  614 , the flow source discovery system  170  may build or update the infrastructure of the enterprise network  105 . In some embodiments, as any number of flow source entries are created or updated, the flow source discovery system  170  may obtain more information regarding the connectivity of entities of the enterprise network  105 . 
     The flow source discovery system  170  may provide other information besides the connectivity of entities in the enterprise network  105 . For example, the representation of entities of the enterprise network  105  may include alarms or alerts associated with one or more entities. 
     In some embodiments, once initiated, the flow source discovery process may continue until it is completed. In step  616 , the flow source discovery system  170  may determine that the flow source discovery process is complete after retrieving traffic data from one or more network traffic analyzing software platform for a fixed interval of time. In various embodiments, the flow source discovery system  170  may determine that the flow source discovery process is complete when the flow source discovery time frame is over. 
     In step  618 , in response to displaying or report any number of flow source entries, the flow source discovery system  170  may receive information from the IT administrator. The received feedback may include a first list containing any number of entities of the switch fabric that the IT administrator wants to continue monitoring. In some embodiments, the received feedback includes a second list containing any number of attributes of the switch fabric which the IT administrator would like to continue monitoring. 
     The second list may be used in optional step  604  to determine any number of attributes or metrics of discovered flow sources to monitor and provide in subsequent flow source discovery processes. The first list may be used in step  608  to determine any number of entities of the switch fabric to monitor and provide in subsequent flow source discovery processes. 
     Other details and features of the flow source discovery process may be to suspend the flow source discovery process may further comprise identifying a flow packet as belonging to one of at least two flow package types based on a format, for example, which may be combinable with those described here, can be found in U.S. patent application Ser. No. 16/234,402, filed on Dec. 27, 2018; the contents of which are hereby incorporated by reference in its entirety. 
       FIG.  13    depicts in further detail the profiling phase  304  of the cloud migration process according to some embodiments. During the profiling phase  304 , analytics are applied to the workloads that were characterized during the discovery phase  302 . Depending on the organization&#39;s goals, the number of workloads under consideration may range from hundreds to thousands. In some embodiments, the first step in the profiling phase  304  may be to utilize the workload characterization data (e.g., attributes) to produce affinity groups. 
     The workload characterization or attribute data may include, for example, CPU, memory, IO, and network utilization for all or some on-premise workloads that have been targeted for migration to the cloud. 
     The affinity groups may each include workloads with similar resource utilization levels and temporal (time-based) characteristics. In some embodiments, the affinity group module  212  associate workloads into groups based on their utilization levels. It may be appreciated that the workloads may be associated with groups in many different ways. 
     In step  1302 , the migration module  204  may send a request to the workload datastore  220  for the workloads discovered in the discovery phase  302 . The discovery output may include workload characteristics and interdependencies among compute, network, and storage elements of an on-premises enterprise system. 
     In step  1304 , the affinity group module  212  may categorize workloads identified by the workload module  210  into affinity groups. In some embodiments, the affinity group module  212  may receive performance metrics. In some embodiments, performance metrics may include utilization metrics. It will be appreciated that the metrics may be independent of any cloud service provider. 
     Utilization metrics may be received from any number of different sources. In some embodiments, the utilization metrics and measurements may be reported from applications, virtual machines, hypervisors, operating systems, applications, servers, or third-party performance service providers (e.g., NetFlow or ServiceNow). In some embodiments, the utilization metrics may include any number of the following: 
     1. CPU Utilization 
     2. Memory Utilization 
     3. IO Read Utilization 
     4. IO Writes Utilization 
     5. IO Number of Reads 
     6. IO Number of Writes 
     7. Network Receive Rate 
     8. Network Transmit Rate 
     Affinity groups may consist of workloads with similar resource utilization levels and temporal or time-based characteristics. For example, workloads with similar CPU utilization, IO number of reads, and IO number of writes may be grouped into the same affinity group. In some embodiments, a workload may belong to more than one affinity group. In other embodiments, each workload may only belong to one affinity group. 
     In one method, the CMR service system  185  may utilize a machine learning algorithm to categorize or group workloads into affinity groups. In some embodiments, the workload module may apply a k-means clustering algorithm to the multiple workloads identified by the workload module  210 . The k-means clustering algorithm may have a feature seat represented by average, a percentile of maximum values of utilization metrics. In some embodiments, utilization metrics include CPU utilization, memory utilization, input/out (IO) read utilization, IO write utilization, IO number of reads, IO number of writes, network receive rate, and network transmit rate. 
     In some embodiments, the CMR service system  185  applies a k-means clustering algorithm with a feature set represented by average, the percentile of maximum values of the utilization metrics. This method may be independent of the cloud service provider and relies solely on the performance of the workloads. 
     K-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. K-means minimizes within-cluster variances such as squared Euclidean distances. 
     In one example, given a set of utilization metrics measurements (x 1 , x 2  . . . . , x n ), where each utilization metrics measurement is a d-dimensional real vector, the affinity group module  212  may utilize k-means clustering to partition the n utilization metrics measurement into k (≤n) sets S={S 1 , S 2  . . . . , S k } so as to minimize the within-cluster sum of squares (WCSS) (i.e., variance). The objective may be to find: 
                 arg   s     ⁢   min   ⁢       ∑     i   =   1     k         ∑     x   ∈     S   i                      x   -     μ   i            2           =       arg   s     ⁢   min   ⁢       ∑     i   =   1     k           ❘   &#34;\[LeftBracketingBar]&#34;       S   i       ❘   &#34;\[RightBracketingBar]&#34;       ⁢   VAR   ⁢           S   i                 
where μ i  is the mean of points in S i .
 
     In another method, the affinity group module  212  groups workloads by their maximum or percentile utilization metric fitness into available cloud instances for a specific CSP. This method may be utilized when a particular CSP to use has already been determined. There can be other methods achieving the same goal of grouping workloads in addition to the two described above. As a result of such grouping, selection of a cloud instance for a group means selection of the cloud instance for all members of the group and, thus, a process of selecting thousands of instances and verification of the performance is reduced to selection and verification of several dozens of instances. 
     Once the affinity group module  212  determines the workloads that belong to each affinity group, the affinity group module  212  may determine the representative synthetic workload for each affinity group. The representative synthetic workload may reduce playback time by capturing relevant utilization attributes of the workloads within an affinity group. 
     In one example, time slices of a predetermined time duration (e.g., between 30 min-60 min long) may be selected containing the maximum performance values for each of the metrics among all virtual machines within the affinity group. A time slice may have statistics on all metrics for a chosen virtual machine containing a maximum value of a specific metric. Next, the all selected time slices may be concatenated to build a synthetic representative workload for an affinity group. It may be constructed by concatenating temporal segments of various workloads to preserve relevant utilization levels for the group. The result is a high-fidelity synthetic representation of the hundreds or thousands of workloads. 
     It may be appreciated that armed with synthetic representative workloads, the number of workloads that require verification in the cloud is significantly reduced. The complexity and scale are condensed to a manageable set without losing the original and critical characteristics of the on-premise workloads. 
     The representative synthetic workload for each affinity group may be a sum of the multiple workloads which make up the affinity group. In various embodiments, the representative synthetic workload for each affinity group may be a weighted sum of the multiple workloads which make up the affinity group, with each of the multiple workloads given a weight based on attributes or characteristics of the workload. In some embodiments, the affinity group module  212  may send a request to the workload datastore  220  to store the representative synthetic workload associated with each affinity group. 
     The affinity group module  212  may generate a table or chart which comprises temporal data capturing utilization level for some or all of the utilization metrics during a temporal data capture period. An example of this may be seen in  FIG.  15   . Table  1500  of  FIG.  15    contains temporal data capturing utilization levels of two example workloads of the enterprise network  105 . The columns of the example table  1500  are associated pairs of date/time of data and the name of the workload that is being monitored by the infrastructure performance management appliance  160  for a particular utilization metric. The number of column pairs is equal to the number of workloads being monitored by the workload module  210 . For example, column  1510  provides a timestamp for measuring the particular utilization metric, while column  1520  provides the measurement for the particular utilization metric associated with the timestamp in the same row. 
     Once the affinity groups have been created, step  1306  may proceed. In step  1306 , the CMR service system  185  may utilize a machine learning algorithm to create a representative synthetic workload for each affinity group. The representative synthetic workload may reduce the time required to playback or simulate the workload on a CSP profile by capturing relevant utilization attributes of the workloads within an affinity group. 
     As discussed herein, the affinity group module  212  may select a workload time slice which contains the maximum performance values for each of the utilization metric among all virtual machines within the affinity group. In some embodiments, the workload time slice is predetermined by the CMR service system  185 . In one embodiment, the workload time slice is between 30 to 60 minutes long. A workload time slice has statistics on all metrics for a chosen virtual machine containing a maximum value of a specific metric. A particular affinity group may have workload time slices of different lengths taken at different time frames of the temporal data capture period. For example, for a particular affinity group, the workload time slice containing a maximum value for memory utilization may be a 30-minute time slice taken on Monday between 9:00 a.m. and 9:30 a.m. For the same affinity group, the workload time slice containing a maximum value for CPU utilization may be a 45-minute time slice taken on Tuesday between 4:30 p.m. and 5:15 p.m. In various embodiments, a particular affinity group may have workload time slices of the same length taken at the same time frame of the temporal data capture period. For example, for the same affinity group, the workload time slice containing a maximum value for all utilization metrics may be a 60-minute time slice taken on Friday between 11:00 a.m. and 12:00 p.m. All selected time slices are concatenated to build a representative synthetic workload for an affinity group. It is constructed by concatenating temporal segments of various workloads to preserve relevant utilization levels for the group. The result is a high-fidelity synthetic representation of the hundreds or thousands of workloads. Armed with representative synthetic workloads, the number of workloads that require verification in the cloud is significantly reduced. The complexity and scale are condensed to a manageable set without losing the original and critical characteristics of the on-premise workloads. 
     An example of affinity groups may be seen in  FIG.  14   , which depicts affinity groups and representative synthetic workloads of the enterprise network according to some embodiments. Area  1410  represents all virtual machines discovered by the CMR service system  185  in the enterprise network  105 . Through machine learning algorithms  1420 , the virtual machines are grouped into multiple affinity groups, such as affinity group 0  1430 , which includes 35 virtual machines and representative synthetic workload 0  1440 .  FIG.  16    depicts graphical charts of network traffic metrics of two example representative synthetic workloads according to some embodiments. For example, charts  1610  through  1640  depict utilization metrics of a 4-hour workload for one affinity group. The utilization metrics include memory usage, CPU utilization, read speed, and write speed. Charts  1650  through  1680  depict the same utilization metrics of the 4-hour workload for another affinity group. 
     In step  1308 , the reporting module  224  may receive a request from the workload module to provide one or more of a synthetic workload performance report, candidate cloud configuration, and candidate cloud configuration cost. Examples of the output of the profiling phase  304  can be seen in  FIG.  14  through  17   . 
     Based on the representative synthetic workloads and the on-premises inventory configurated determined during the discovery phase of the cloud migration readiness process, the workload module  210  may select an initial cloud configuration and estimate the cost of migrating the representative synthetic workload onto a CSP. The estimated cost of migrating the representative synthetic workload to the CSP can be seen in  FIG.  17   . Table  1700  of  FIG.  17    depicts a candidate CSP computer and the on-demand cost of multiple applications and their associated virtual machines. For example, row  1710  depicts the estimated monthly cost of migrating each of the two virtual machines of the “Analysis” application to the Azure CSP. The estimated cost is seen in table  1700  of  FIG.  17    may be a candidate cloud configuration cost. In some embodiments, the migration module  204  may suggest one or more workloads for the playback phase  306  and one or more CSPs best suited for the needs of the customer based on the representative synthetic workload, the one or more CSP profile entries, and the business objectives received from the customer in the discovery phase  302 . The customer may receive some or all of the output of the profiling phase to determine one or more workloads to playback on one or more CSP profiles in the playback phase. 
       FIG.  18    depicts in further detail the playback phase  306  of the cloud migration process according to some embodiments. In step  1802 , the input module  206  may receive from the customer one or more workloads to target for the playback phase  306 . The migration module  204  may send a request to the cloud service provider profile datastore  222  for the CSP profile entry of one or more CSPs. Each CSP profile entry may include attributes of the CSP such as available memory storage, types of security levels available, cost per gigabyte, methods of retrieving data stored on the cloud, and types of deployment available to a consumer. 
     In step  1804 , the migration module  204  may send a request to the workload datastore  220  for the one or more workload to simulate in the playback phase  306 . In some embodiments, the customer may select the one or more workloads to simulate in the playback phase  306 . In various embodiments, the migration module  204  may make a choice. Using the attributes of the CSP from the CSP profile entry, the migration module  204  may emulate the key dimensions of a workload with high fidelity. The dimensions include CPU MHz, memory utilization in gigabytes, read/write IOPs, read/write throughputs, and network received/transmit in bytes per second. 
     In step  1806 , migration module  204  executes or runs the representative synthetic workload created during the profiling phase  304  of the cloud migration readiness process. In some embodiments, a CMR playback module (not shown) may execute the representative synthetic workload on top of the operating system in the target cloud service provider environment and configuration. 
     In step  1808 , the performance of the representative synthetic workload on one or more CSP profiles as compared to the on-premises levels and cost estimates are re-calculated. In various embodiments, by performing the representative synthetic workload on a CSP cloud services platform, types of virtual machines on the CSP cloud services platform may be identified, and types of storage (e.g., disk type) on the CSP cloud services platform may be identified. Periodic (e.g., monthly or yearly) cost on the CSP cloud services platform may be determined based at least in part on the types of virtual machines and the types of storage. 
     The result of the calculation may be provided by the reporting module  224  in the form of a chart, such as a chart  1900  of  FIG.  19   . The chart  1900  depicts a monthly cloud cost estimate comparison of a representative synthetic workload of the enterprise network  105  for two CSPs, Amazon Web Services (AWS) and Microsoft Azure. In one embodiment, the result of the calculation done in step  1806  may be provided by the reporting module  224  in the form of a table, such as a table  2000  of  FIG.  20   . The table  2000  depicts a monthly cloud cost estimate of multiple applications or workloads of the enterprise network  105  on one CSP, such as Microsoft Azure. Note that in this example, the estimated monthly cost in table  2000  of  FIG.  20    is the same as the estimated monthly cost seen in table  1700  of  FIG.  17   . 
     The cloud migration readiness process  300  is iterative until a cloud configuration that meets the performance criteria while minimizing cost is found. The goal of the cloud migration readiness process  300  is to determine a cloud configuration that meets the original on-premises levels at the lowest cost. Optionally, enterprises may be interested in testing higher levels of utilization or performance beyond those of the on-premises baseline. In some examples, higher levels of utilization may be executed as multiple of two times CPU utilization or four times the memory utilization, etc. 
     The migration module  204  generates utilization levels matching, as much as possible, the on-premises utilization levels. The on-premise utilization levels of synthetic workloads are the reference that is compared to the representative synthetic workload utilization levels in the cloud. The CMR service system  185  applies adapted linear correlation and relative error to compare cloud utilization values against the reference on-premises utilization values. Some example output of the playback phase  306  includes a synthetic workload cloud performance report, an updated candidate cloud configuration, and an updated candidate cloud configuration costs. The output of the playback phase  306  may be one or more recommendations of which workloads of the enterprise network to migrate to a CSP, and which CSP may be best suited for the customer&#39;s needs. The customer may utilize this information to determine which CSP to migrate their on-premises infrastructure to and which CSP to choose. 
       FIG.  21    depicts in further detail the monitor phase  308  of the cloud migration process according to some embodiments. The monitor phase  308  takes place after the customer migrates the workload or application to a CSP. The CMR service system  185  may collect data from the application discovery system  180 , the flow source discovery system  170 , the network traffic analyzing software platform  150 , and native CSP monitoring software to verify that the applications migrated to the cloud infrastructure is performing within acceptable levels. 
     In step  2102 , the workload module  210  may receive utilization metrics from one or more of the network traffic analyzing software platform  150  or the flow source discovery system  170 . In step  2104 , the workload module  210  may compare received utilization metrics with predetermined utilization metric thresholds to determine if the applications with one or more entities migrated to the cloud infrastructure are performing at acceptable levels. The predetermined utilization metric thresholds may be set by the customer; in some embodiments, the predetermine utilization metric thresholds are set by the CMR service system  185 . 
     In step  2106 , the reporting module  224  may provide a utilization and performance report to the customer. The utilization and performance report findings include performance trend deviations and recommendations related to the remediation of issues and optimization opportunities. Based on the utilization and performance report, the customer may determine if additional cloud infrastructure resources are required. 
       FIG.  22    is a block diagram illustrating entities of an example machine able to read instructions from a machine-readable medium and execute those instructions in a processor to perform the machine processing tasks discussed herein, such as the engine operations discussed above. Specifically,  FIG.  22    shows a diagrammatic representation of a machine in the example form of a computer system  2200  within which instructions  2224  (e.g., software) for causing the machine to perform any one or more of the methodologies discussed herein may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines, for instance, via the Internet. In a networked deployment, the machine may operate in the capacity of a server machine or a client machine in a server-client network environment or as a peer machine in a peer-to-peer (or distributed) network environment. 
     The machine may be a server computer, a client computer, a desktop computer, a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a cellular telephone, a smartphone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions  2224  (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute instructions  2224  to perform any one or more of the methodologies discussed herein. 
     The example computer system  2200  includes a processor  2202  (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), one or more application-specific integrated circuits (ASICs), one or more radio-frequency integrated circuits (RFICs), or any combination of these), a main memory  2204 , and a static memory  2206 , which are configured to communicate with each other via a bus  2208 . The computer system  2200  may further include a graphics display unit  2210  (e.g., a plasma display panel (PDP), a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)). The computer system  2200  may also include an alphanumeric input device  2212  (e.g., a keyboard), a cursor control device  2214  (e.g., a mouse, a trackball, a joystick, a motion sensor, or other pointing instrument), a data store  2216 , a signal generation device  2218  (e.g., a speaker), an audio input device  2226  (e.g., a microphone) and a network interface device  2220 , which also are configured to communicate via the bus  2208 . 
     The data store  2216  includes a machine-readable medium  2222  on which is stored instructions  2224  (e.g., software) embodying any one or more of the methodologies or functions described herein. The instructions  2224  (e.g., software) may also reside, completely or at least partially, within the main memory  2204  or within the processor  2202  (e.g., within a processor&#39;s cache memory) during execution thereof by the computer system  2200 , the main memory  2204  and the processor  2202  also constituting machine-readable media. The instructions  2224  (e.g., software) may be transmitted or received over a network (not shown) via the network interface device  2220 . 
     While machine-readable medium  2222  is shown in an example embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions (e.g., instructions  2224 ). The term “machine-readable medium” shall also be taken to include any medium that is capable of storing instructions (e.g., instructions  2224 ) for execution by the machine and that cause the machine to perform any one or more of the methodologies disclosed herein. The term “machine-readable medium” includes, but should not be limited to, data repositories in the form of solid-state memories, optical media, and magnetic media. 
     As discussed herein, one or more modules of the CMR service system  185  (see  FIG.  2   ) may support tracking communication flows through one or more networks to identify ports that may be required to be controlled (e.g., opened on a firewall appliance, firewall software, switch, operating system and/or other network devices). In order to preserve security, many operating systems and networks may disable ports that would otherwise allow communication from, to, or within the network. An entity (e.g., IT support, company, authorized personnel, or the like) may track ports that are used for communication within their network. For example, many ports are widely used as a standard for certain programs or functions. Other ports may be understood as being necessary for critical software or critical functions of the particular network (e.g., certain mission-critical programs and their parameters are likely to be well understood). Due to the growth of components, software, operating systems, virtual machines, and the like within an enterprise network, it is difficult, if not impractical to know all the sources and destinations of all information flows and the ports that they use. As such, it may be important to discover or confirm ports that need to be open when considering cloud migration and simulation (e.g., playback) of workloads to confirm readiness and/or cost. 
     When considering a partial or full move to the cloud, the entity controlling the enterprise network may consider tracking the information within their network to identify ports, keep all ports open, or guess which ports to keep open or closed with the expectations that they can possibly make corrections when things cease to function. Keeping all ports open is insecure. Making guesses as to which ports should or should not be open renders any predictions about readiness for cloud migration speculative if not useless. 
       FIG.  24    depicts a block diagram of another example CMR service system  2485  according to some embodiments. The CMR service system  2485  may be similar to the CMR service system  185  of  FIG.  2   . The CMR service system  2485  includes a communication module  2402 , a migration module  2404 , an input module  2406 , an application discovery interface module  2408 , a workload module  2410 , an affinity group module  2412 , a flow source discovery interface module  2414 , a traffic analyzing module  2416 , a scheduling module  2418 , a workload datastore  2420 , a cloud service provider profile datastore  2422 , a reporting module  2424 , and a discovery datastore  2426 . In some embodiments, any number of functions described with respect to  FIGS.  24 - 26    may be performed within the modules depicted in  FIG.  2   . 
     The communication module  2402  may send and receive requests or data between any modules of the CMR service system  2485 , the network traffic analyzing software platform  150 , the application discovery system  180 , and/or the flow source discovery system  170 . The communication module  2402  may receive a request from the IT administrator of the enterprise network  105  to commence the cloud migration readiness process. In some embodiments, the communication module  2402  may send a request to the application discovery system  180  for data regarding applications of the enterprise network  105  discovered by the application discovery system  180 . 
     The communication module  2402  may request any amount of data (e.g., any number of data packets) from the network traffic analyzing software platform  150 . In some embodiments, the input module  2406  may receive data packets from the network traffic analyzing software platform  150 . The communication module  2402  may send a request to the flow source discovery system  170  for discovered flow source(s) and their associated attributes (e.g., in a table, chart, graph, or the like). In some embodiments, the communication module  2402  may receive a request from the reporting module  2424  to provide the information in the form of charts or tables. 
     The migration module  2404  may identify entities of the enterprise network based on information contained within the data received from the network traffic analyzing software platform  150 . For example, attributes of the data packets, such as an IP address, may be utilized to identify entities of the enterprise network. 
       FIG.  23 A  illustrates a transport server and a transport client according to some embodiments. For example,  FIG.  23 A  depicts an example communication  2300  of a virtual machine  2310  sending a request  2330  to an email server  2324 . A client may be the originator of the communication. Once the connection is established, messages are passed between the client and server, bidirectionally. In the example of communication  2300 , the client is the virtual machine  2310 , in communication with the email server  2324 . The communication message (e.g., tuple) captured by the network traffic analyzing software may include IP Address A, Port 49152, IP Address B, and port 587. 
       FIG.  23 B  depicts an example communication  2350  of the email server  2324  sending data to multiple clients of the enterprise network, virtual machines  2310 ,  2312 ,  2314 , and  2316  according to some embodiments. In this example, the communication between the email server  2324  is established by each clients (e.g., virtual machines  2312 - 2316 ) and the communication is bidirectional. The communication tuple captured by the network traffic analyzing software may include, for example, IP Address B, port 587, IP Address A, and Port 49152. It will be appreciated that the email server  2324  may enter into communications with any number of the virtual machines and communicate bidirectional through ports 49555, 49786, and/or 49881. Any number of communication tuples may capture information regarding such communication. 
     Specific port numbers may be reserved for specific transport protocols. The Internet Assigned Numbers Authority (IANA) is an organization that is responsible for maintaining the assignment of port numbers for specific uses. For example, ports 20 and 21 are assigned by the IANA to the File Transfer Protocol (FTP). Regardless of the assignment by the IANA, any port may be used for different, unassigned functions (i.e., there is no requirement that IANA assignments be followed). As such, even with the information regarding port registration for different tasks, an enterprise may respect some, none, or all of the port assignments of the IANA. For this reason, it may be important to assess network traffic and identify ports that are used from the monitored network traffic. 
     The migration module  2404  may build a port frequency distribution table (or any data structure) for the entities of the enterprise network. The port frequency distribution table, for example, may identify any or all of the following: an entity name, a port number, the transport protocol associated with the port number, and/or a count of the number of times data traffic traveled into or out of a particular port. An example port frequency distribution table  2500  can be seen in  FIG.  25   , which includes the port frequency distribution table for the enterprise network. Each row of the port frequency distribution table  2500  may represent a numbered port of the enterprise network and an access frequency of the particular port. In some embodiments, the access frequency of a particular port represents a number of times the particular port provides (e.g., sends and receives) data traffic (e.g., the number of times data is sent through the port). In yet another embodiment, the access frequency of the particular port represents a sum of the number of times the particular port sends and receives data traffic using a particular transport protocol. 
     In some embodiments, the migration module  2404  may determine a communication tuple based on the data packets received from the network traffic analyzing software platform  150 . The communication tuple may include, for example, a source IP address, a source port number, a destination IPO address, and/or a destination port number. The source IP address and the source port number may be associated with a source entity. The destination IP address and the destination port number may be associated with a destination entity. The source entity and the destination entity may be an entity of the enterprise network. Values may be utilized in the generation of the port frequency distribution table  2500 . 
     Based on any number of communication tuples, the migration module  2404  may designate source port(s) and/or destination port(s). For example, one of the ports may be designated as a server port, and another may be designated as a client port. The designation of the server port and the client port may be accomplished using a heuristic algorithm. For example, the algorithm may include: 1) for a communication tuple (source IP address, source port, destination IP address, destination port), the port with a higher port access frequency may be designated the server port; 2) if the source port and the destination port has the same access frequency, then the port that is registered with an organization such as IANA may be designated as the server port; and, optionally, 3) if neither the source port and the destination port have the same access frequency and if neither the source port and the destination port are registered, then the port with the lowest port number may be designated as the server port. 
     For example, the migration module  2404  may determine a communication tuple from the network traffic analyzing software platform  150 . The communication tuple in this example includes (1) port 161 of an entity VM-123, using the UDP protocol, and IP address IPC and (2) port 5353 of an entity HOST-45, using the UDP protocol. IP address IPF correspond to rows  2510  and  2520  respectively of the port frequency distribution table  2500  of  FIG.  25   . The communication tuple may include (IPC, 161, IPF, 5353). The migration module  2404  may compare the access frequency of port 161 of the entity VM-123 and port 5353 of the entity HOST-45 and determine that port 161 of the entity VM-123 is the server port while port 5353 of the entity HOST-45 is the client port. In some embodiments, the migration module  2404  may send a request to the discovery datastore  2426  to update attributes associated with port 161 of the entity VM-123 and port 5353 of an entity HOST-45 with the designation of the server port and the client port, respectively. 
     In another example, the migration module  2404  may determine a communication tuple from the network traffic analyzing software platform  150 . The communication tuple may include port 80 of an entity VM-123, using the TCP protocol, and IP address IPB and port 243 of an entity SERV-17, using the TCP protocol, and IP address IPD, which correspond to rows  2530  and  2540  respectively of the port frequency distribution table  2500  of  FIG.  25   . The communication tuple may include (IPB, 80, IPD, 243). The migration module  2404  may compare the access frequency of port 80 of the entity VM-123 and port 243 of the entity SERV-17. In this example, the migration module  2404  may determine that port 80 of the entity VM-123 and port 243 of the entity SERV-17 have the same access frequency. In that case, the migration module  2404  may determine or confirm port(s) (if any) that are registered with IANA for their respective transport protocol. Port 80 is registered with TCP for the HTTP application, so port 80 of the entity VM-123 is designated as the server port, while port 243 of the entity SERV-17 is designed as the client port. In some embodiments, the migration module  2404  may send a request to the discovery datastore  2426  to update attributes associated with port 80 of the entity VM-123 and port 243 of the entity SERV-17 with the designation of the server port and the client port, respectively. In some embodiments, the flow source discovery interface module  2414  determines the communication tuple from the network traffic analyzing software platform  150 . 
     In some embodiments, the migration module  2404  may receive data packets from the software platform  150 , such as a NetFlow. The software platform  150  may collect IP traffic data from routers or switches (e.g., those that support NetFlow). For example, the collected IP traffic data may be in the form of NetFlow conversation record, which includes a source IP address, a destination IP address, a source port for UDP or TCP, and a destination port for UDP or TCP. Each conversation record may define a unidirectional sequence of data packets. Based on the received conversation records, the migration module  2404  may build a port frequency distribution table, such as the example port frequency distribution table  2500  of  FIG.  25   . 
     The migration module  2404  may designate a source port and a destination port based on one or more conversation records to create a port frequency distribution table  2500 . In some embodiments, the migration module  2404  may use a port decision rule (e.g., based on a heuristic algorithm) to designate the source port and the destination port. 
     In one example of implementing the port decision rule discussed herein, the migration module  2404  may receive a conversation record, which includes a unidirectional sequence of data packets from an entity VM-123 with an IP address of IPC to an entity SERV-17 with an IP address IPE using the UDP protocol. Based on the conversation record, the migration module  2404  may seek increment the port access frequency count associated with these two entities in the port frequency distribution table  2500 . 
     If rows corresponding to one or more entities involved in the conversation record have not been created on the port frequency distribution table, the migration module  2404  may send a request to the discovery datastore  2426  to create one or more port frequency distribution entries. Each port frequency distribution entry may correspond to a row of the port frequency distribution table  2500  of  FIG.  25   . 
     In this example, the migration module  2404  may be controlled by the port decision rule to designate of one of the two ports of a conversation record as a source port and the other port as a destination port. The port decision rule may have any number of ordered “if/then” clauses. For example, the port decision rule may require that if only one of the ports identified by the conversation record is registered with IANA (the other port is not registered), then the registered port may is designated as the server port, and the unregistered port is designated as the destination port. The rule port decision rule in this example further requires that if both ports identified by the conversation record are registered or both are unregistered, then the migration module  2404  designates the port with a higher access frequency as the source port and the port with the lower access frequency as the destination port. The access frequency of a particular port represents the number of times the particular port provides (e.g., sends and receives) data traffic (e.g., the number of times data is sent through the port). In some embodiments, the access frequency of the particular port represents a sum of the number of times the particular ports send and receive data traffic using a particular transport protocol. Further, the port decision rule may further require that if both ports identified by the conversation record are both registered or unregistered and both ports have the same access frequency, then the migration module  2404  is required to designate the port with a lower port number as the source port. It may be appreciated that there may be any number of steps in any order. Further, there may be any number of different rules in any order that control the migration module. 
     The migration module  2404  may manage the cloud migration readiness process and/or may commence the cloud migration readiness process. The migration module  2404  may commence the cloud migration readiness process when the migration module  2404  determines that any number of trigger conditions are satisfied. For example, a trigger condition may include the scheduling module  2418  determining that a current time equals a scheduled cloud migration readiness process start time. In some embodiments, one of the trigger conditions includes receiving from the input module  2406  a request to commence the cloud migration readiness process. Upon the reception of the request to commence the cloud migration readiness process from the scheduling module  2418  or the input module  2406 , the migration module  2404  may commence the cloud migration readiness process. 
     The input module  2406  may initiate the cloud migration readiness process or a phase of the cloud migration readiness process (e.g., based on receiving a request from the IT administrator of the enterprise network  105 ). In some embodiments, the input module  206  may receive a request to start an application discovery process (e.g., as a part of the initiation of the cloud migration readiness phase), a request to start a flow source discovery process, and/or a schedule of the cloud migration readiness process. For example, the input module  2406  may receive a start date and time of the cloud migration readiness process start time. In some embodiments, the input module  2406  may receive, from the IT administrator of the enterprise network  105 , known workloads. 
     The application discovery interface module  2408  may interface with the application discovery system  180 . In some embodiments, the application discovery interface module  2408  may send an output of the application discovery process to the input module  2406 . The output of the application discovery process may include a list of application entries found in the enterprise network  105 . Each application entry may include attributes or characteristics (e.g., metrics) of the discovered application, such as a suggested name of the discovered application, as well as attributes associated with the entities which make up the discovered application. In some embodiments, the output may include discovered ports. The application discovery process is discussed further in  FIG.  5   . 
     During the discovery phase of the cloud migration readiness process, the workload module  2410  may identify multiple workloads of the enterprise network  105 . In some embodiments, a workload represents an amount of work or processing accomplished by an entity of the enterprise network  105 . Entities of the enterprise network  105  may include compute devices, network devices, network elements, and/or storage elements. In some embodiments, entities of the enterprise network  105  include enterprise network applications. In some embodiments, entities of the enterprise network  105  include virtual machines. 
     The workload module  2410  may identify workload based on the designation of server port and client ports by the migration module  2404  as discussed above. The designation of the server ports and client ports may be utilized to identify workloads. For example, referring to the port frequency distribution table  2500  of  FIG.  25   , port 161 of the entity VM-123 is designated as the server port, while port 5353 of the entity HOST-45 is designated as the client port. The migration module  2404  may identify other client ports of the enterprise network, which is in a communication tuple with the designated server port. These other client ports, as well as port 161 of the entity VM-123 and port 5353 of the entity HOST-45 may be identified as being associated with a workload. For example, the migration module  2404  may receive, from the network traffic analyzing software platform  150 , network traffic data. The migration module  2404  may identify a communication associated with network traffic data, such as the request  2330  of  FIG.  23 A . The migration module  2404  may designate port 587 of the email server  2324  as a server port, while port 49152 of the virtual machine  2310  may be designated as the client port. The migration module  2404  may identify other client ports associated with port 587 of the email server  2324 . For example, as seen in  FIG.  23 B , the migration module  2404  may designate port 49152 of the virtual machines  2312 ,  2314 , and  2316  as client ports as well. With the designations of the server port of the email server  2324  and the client ports of the virtual machines  2310 ,  2312 ,  2314 , and  2316 , the workload module  2410  may determine these entities of the enterprise network may be grouped together into a workload. 
     In various embodiments, a workload may comprise any number of workloads. For example, a workload may represent the amount of work or processing accomplished by an application of the enterprise network  105 . In one example, one workload may represent the email service application of the enterprise network comprising multiple virtual machine workloads and server workloads (which function as part of the email service application). 
     In some embodiments, during the discovery phase of the cloud migration readiness process, the workload module  2410  may determine the characteristics of the workload. Characteristics of the workload may include attributes of the entity associated with the workload. For example, a workload of a virtual machine may include attributes of the virtual machine such as applications of the enterprise network  105  associated with the virtual machine and utilization such as maximum read speed, maximum write speed, central processing unit (CPU) MHz, memory utilization percentage, storage demand, and input/output operations per second (IOPS). For example, a workload of a storage device may include attributes of the storage device such as read and write latency. 
     In some embodiments, the workload module  2410  may send a list of workloads identified by the workload module  2410  to the affinity group module  2412 . The affinity group module  2412  may receive the list of workloads and categorize or group some or all of the received workloads into one or more affinity groups. In some embodiments, each workload is categorized into a different affinity group. The affinity group module  2412  may group any number of workloads into synthetic workloads based, at least in part, on common resource utilization by the workloads (e.g., CPU utilization, memory utilization, storage utilization, and/or the like). 
     The workload module  2410  may determine workload interdependencies (e.g., how workloads depend on one another). Workload interdependencies help to determine the sequence of workloads, how services are migrated to the cloud, and what workloads and services should stay on-premise. 
     The affinity group module  2412  may categorize workloads identified by the workload module  2410  into affinity groups. As discussed herein, affinity groups (e.g., representative synthetic workloads) may consist of workloads with similar resource utilization levels and temporal or time-based characteristics. For example, workloads with similar read speed and write speeds may be grouped together into the same affinity group. In one example, workloads that have a CPU usage of greater than 90% during a particular time frame each day may be placed in the same affinity groups. In some embodiments, a workload may belong to more than one affinity group. In other embodiments, each workload may only belong to one affinity group. 
     In one method, the CMR service system  2485  may utilize a machine learning algorithm to categorize or group workloads into affinity groups. In some embodiments, the workload module may apply a k-means clustering algorithm to the multiple workloads identified by the workload module  2410 . The k-means clustering algorithm may have a feature set represented by average, a percentile of maximum values of utilization metrics. In some embodiments, utilization metrics include CPU utilization, memory utilization, input/out (IO) read utilization, IO write utilization, IO number of reads, IO number of writes, network receive rate, and network transmit rate. 
     In another method, the affinity group module  2412  groups workloads by their maximum or percentile utilization metric fitness into available cloud instances for a specific CSP. This method may be utilized when a particular CSP to use has already been determined. 
     Once the affinity group module  2412  determines the workloads that belong to each affinity group, the affinity group module  2412  may determine the representative synthetic workload for each affinity group. The representative synthetic workload for each affinity group may be a sum of the multiple workloads which make up the affinity group. In various embodiments, the representative synthetic workload for each affinity group may be a weighted sum of the multiple workloads which make up the affinity group, with each of the multiple workloads given a weight based on attributes or characteristics of the workload. In some embodiments, the affinity group module  2412  may send a request to the workload datastore  2420  to store the representative synthetic workload associated with each affinity group. 
     The affinity group module  2412  may generate a table or chart (or any data structure) which comprises temporal data capturing utilization level for some or all of the utilization metrics. An example of this may be seen in  FIG.  15   . Table  1500  of  FIG.  15    contains temporal data capturing utilization levels of two example workloads of the enterprise network  105 . The columns of the example table  1500  are associated pairs of date/time of data and the name of the workload that is being monitored by the infrastructure performance management appliance  160  for a particular utilization metric. The number of column pairs is equal to the number of workloads being monitored by the workload module  2410 . For example, column  1510  provides a timestamp for measuring the particular utilization metric, while column  1520  provides the measurement for the particular utilization metric associated with the timestamp in the same row. 
     The flow source discovery interface module  2414  interfaces with the flow source discovery system  170 . In some embodiments, the flow source discovery interface module  2414  may send the output of the flow source discovery process to the input module  2406 . The output of the flow source discovery process may include a list (or any data structure) of flow source entries. The list of flow source entries may include attributes associated with discovered flow sources. Attributes may include the type of flow source, total byte count, incoming/outgoing byte count, incoming/outgoing bit rate, total packet rate, and/or incoming/outgoing endpoint count. In some embodiments, attributes associated with flow source entries may include applications associated with the flow source. The flow source discovery process will be discussed further in  FIG.  6   . In some embodiments, the flow source discovery interface module  2414  interfaces with the network traffic analyzing software platform  150 . 
     In some embodiments, the traffic analyzing module  2416  analyzes network traffic received from the flow source discovery system  170 . In some embodiments, the traffic analyzing module  2416  receives network traffic from the network traffic analyzing software platform  150 . The traffic analyzing module  2416  may analyze IP communications between entities of the enterprise network  105  as well as IP communications between entities of the enterprise network and entities that are external to the enterprise network  105 . The traffic analyzing module  2416  may provide the output of traffic dependency on internal and external network traffic. An example of this can be seen in  FIG.  7 A . Example chart  700  depicts the amount and type of traffic that goes through a particular virtual machine. Row  710  depicts the amount of traffic between the particular virtual machine to external entities of the enterprise network  105 . Network traffic is separated by incoming and outgoing network traffic. Similarly, row  720  depicts the amount of traffic between the particular virtual machine to other entities, which are not virtual machines, of the enterprise network  105 . Row  730  depicts the amount of traffic between the particular virtual machine and other virtual machines of the enterprise network  105 . The traffic analyzing module  2416  may output the traffic dependency in forms other than a table. An example of this can be seen in  FIG.  7 B . An example output interface  750  depicts the same information as the example chart  700  of  FIG.  7 A , but in the form of a state diagram. 
     In one embodiment, the traffic analyzing module  2416  may provide a traffic dependency mapping of the various entities or workloads of the enterprise network  105 . The dependency mapping of entities or workloads of the enterprise network may be done iteratively. For example, an initial internal and external communication map may be produced and reviewed by a customer, or alternately, the external communication map may be reviewed by an analytical system. The initial communication map may provide an overall view of the enterprise network. The customer or analytical system may review the overall view of the enterprise network to identify and filter entities or workloads which are not of interest and tune priorities and data characteristics reveal during each iteration. An example of an initial communication map may be seen in  FIG.  11 A . The customer or analytical system may receive an example initial communication map  1100 . Subsequent to receiving the initial communication map  1100  of  FIG.  11 A , the customer or analytical system may refine the IP addresses or entities to focus on a particular set of entities to produce a second communication map  1110  of  FIG.  11 B . Subsequently, the third iteration of the communication map  1120  of  FIG.  11 C  may be produced. The initial communication map  1100 , the second communication map  1110 , and the third communication map  1120  may be used to determine or identify bully virtual machines and zombie virtual machines. A bully virtual machine is a virtual machine that utilizes too many resources and causes other virtual machines to be without resources. A bully virtual machine may cause cluster performance to degrade. Identifying bully virtual machines may help in analyzing whether one or more of these virtual machines need additional resources or need to be re-routed to other applications or workloads. 
     A zombie virtual machine is inactive or is used by very few or no applications or workloads. In one example, a virtual machine is considered a zombie when it is powered on but does fewer than 30 IOPS and receives or transfers fewer than 1000 bytes per day for the past 30 days. To prevent wasting host resources, zombie virtual machines may be identified and removed (or data flows of some workloads may be re-routed to other applications or network devices). 
     In some embodiments, the traffic analyzing module  2416  may receive network traffic data from the network traffic analyzing software platform  150  or the flow source discovery system  170  after the network traffic analyzing software platform  150  has performed flow source detection for a predetermined period of time. In some embodiments, the predetermined period of time is at least two weeks, a week, a month, several months, a day, several hours, and/or the like. 
     The scheduling module  2418  may receive from the input module  2406  the start time or end time of one or more of the phases of the cloud migration readiness process. In some embodiments, the scheduling module  2418  may suspend the cloud migration readiness process or any phase of the cloud migration readiness process when any number of trigger conditions is satisfied. 
     The workload datastore  2420  may be any structure and/or structures suitable for storing data entries or records (e.g., an active database, a relational database, a self-referential database, a table, a matrix, an array, a flat file, a documented-oriented storage system, a non-relational No-SQL system, an FTS-management system such as Lucene/Solar, and the like). The workload datastore  2420  may store the representative synthetic workload associated with each affinity group. In some embodiments, the workload datastore  2420  may create a workload entry for each workload identified by the migration module  2404 . Each workload entry may include metrics or attributes associated with the workload. The attributes of the workload may depend on what the workload represents. For example, if the workload represents an application, the attributes of the workload may include attributes of the application, such as identifiers of the entities associated with the enterprise network  105 , tier, or criticality of the application. In another example, if the workload represents a virtual machine, attributes of the workload may include attributes of the virtual machine such as maximum read speed, maximum write speed, central processing unit (CPU) MHz, memory utilization percentage, storage demand, and/or IOPS. 
     The cloud service provider profile datastore  2422  may be any structure and/or structures suitable for storing data entries or records (e.g., an active database, a relational database, a self-referential database, a table, a matrix, an array, a flat file, a documented-oriented storage system, a non-relational No-SQL system, an FTS-management system such as Lucene/Solar, and the like). The cloud service provider profile datastore  2422  may store a CSP profile entry for various CSPs such as Microsoft Azure, IBM Cloud, and the like. Each CSP profile entry may include attributes of the CSP such as available memory storage, types of security levels available, cost per gigabyte, methods of retrieving data stored on the cloud, and types of deployment available to a consumer. The migration module  2404  may utilize the cloud service provider profile datastore  2422  to suggest one or more CSP to the customer. 
     The reporting module  2424  may receive a request to provide an example output of the network traffic between a particular virtual machine and external entities, such as the example output  800  of  FIG.  8   . During the discovery phase of the cloud migration readiness process, the reporting module  2424  may provide the example output  800 , which depicts the virtual machine  810 , given an identifier “ORDDB02.” The example output  800  also depicts identifiers of other virtual machines that are in communication with the “ORDDB02” virtual machine. Element  802  of  FIG.  8    depicts an entity external to the enterprise network, or an external entity that is in communication with the “ORDDB02” virtual machine. The reporting module  2424  may receive a request to provide example outputs from various phases of the cloud migration readiness process, such as the example workload utilization table  1500  of  FIG.  15   . 
     The discovery datastore  2426  may be any structure and/or structures suitable for storing data entries or records (e.g., an active database, a relational database, a self-referential database, a table, a matrix, an array, a flat file, a documented-oriented storage system, a non-relational No-SQL system, an FTS-management system such as Lucene/Solar, and the like). The discovery datastore  2426  may store the port frequency distribution table  2500 . In some embodiments, the discovery datastore  2426  may receive a request from the migration module  2404  to generate the port frequency distribution table  2500 . The port frequency distribution table  2500  include multiple port entries for each port identified by the migration module  2404 . In some embodiments, the discovery datastore  2426  stores the port frequency distribution table  2500  for each identified entity of the enterprise network. In various embodiments, the discovery datastore  2426  may generate one port frequency distribution table  2500  for the entire enterprise network. Each port entry includes a port number, the transport protocol associated with the port number, and a count of the number of times data traffic traveled into or out of a particular port. 
       FIG.  26    depicts in further detail another example of the discovery phase  2600  of the cloud migration process according to some embodiments. In optional step  2602 , the application discovery interface module  2408  may interface with the application discovery system  180  and/or receive the output of the application discovery system  180 . In some embodiments, the application discovery interface module  2408  may send the output of the application discovery process to the input module  2406 . The output of the application discovery process may include a list of applications found in the enterprise network  105 . Each application discovered may include attributes or characteristics (e.g., metrics) of the discovered applications, such as a suggested name of each of the discovered application, as well as attributes associated with the entities which make up the discovered application. In some embodiments, attributes or characteristics of the discovery application may include a tier of service associated with the application. The migration module  2404  may utilize the output of the application discovery process to identify workloads. In some embodiments, attributes or characteristics of the discovered application may include entities and attributes associated with any number of the entities. Such attributes and/or characteristics may include, for example, communication ports of each entity associated with the discovered application. In one embodiment, attributes or characteristics of the discovered application may include a server port and a client port associated with the discovered application. 
     In step  2604 , the communication module  2402  may provide a request to receive from the network traffic analyzing software platform  150  any amount of data. In some embodiments, the input module  2406  may receive data from the network traffic analyzing software platform  150 . 
     In step  2606 , the migration module  2404  may determine a communication message based on the data packets received by the network traffic analyzing software platform  150 . In various embodiments, the migration module  2404  may receive flow information regarding any number of data packets (e.g., flow packets) between components and/or entities of the enterprise network. The flow information may include a list of data packets and/or attributes of data packets including, for example, a source IP address, a source port number, a destination IPO address, and/or a destination port number 
     The communication message may include a source IP address, a source port number, a destination IPO address, and a destination port number. The source IP address and the source port number may be associated with a source entity. The destination IP address and the destination port number may be associated with a destination entity. In some embodiments, one or both of the source entity and the destination entity may be an entity of the enterprise network. 
     In various embodiments, the migration module  2404  may receive a message with information regarding any number of source IP addresses, source port numbers, destination IPO addresses, and/or destination port numbers regarding any number of communications. For example, the network traffic analyzing software platform  150  may monitor and collect data (e.g., attributes) of communication over time. The information may be collected in one or more messages and sent to the migration module  2404 . A message may contain any number of tuples and may include attribute information (as discussed herein) regarding any number of communications (e.g., sufficient to support or assist in supporting creation of the frequency distribution table as discussed herein). 
     In some embodiments, step  2608 , the migration module  2404  may identify entities of the enterprise network based on the data packets and/or messages received from the network traffic analyzing software platform  150 . Attributes of the data packets or contained within the message, such as an IP addresses, may be utilized to identify entities of the enterprise network. The migration module  2404  may build a port frequency distribution table for the entities of the enterprise network. The port frequency distribution table may identify an entity name, a port number, the transport protocol associated with the port number, and a count of the number of times data traffic traveled into or out of a particular port. The port frequency distribution table  2500  includes the port frequency distribution table for the enterprise network. Each row of the port frequency distribution table  2500  represents a numbered port of an entity of the enterprise network and an access frequency of the particular port. 
     In some embodiments, there may be any number of TAPs (e.g., TAP  140 ) coupled to fiber optic cables to assess information passing through a fiber optic cable. In one example, the TAP  140  is an optical splitter that provides a copy of data passing through a fiber optic channel of the enterprise network  105  without affecting the integrity of the data. There may be any number of Taps, appliances, software, or the like that tracks or monitors attributes of data as discussed herein. The migration module  2404  may receive data from the TAPs  140  and/or receive information from any number of appliances, software, or the like to assist in collecting the attributes and create the port frequency distribution table. 
     In step  2610 , the migration module  2404  may optionally determine if one or more of the identified ports of entities of the enterprise network is registered with an organization such as IANA. In some embodiments, if the migration module  2404  determines that a particular port is registered, the migration module  2404  may send a request to the discovery datastore  2426  to update a ‘registered’ attribute of the particular port. 
     In step  2612 , the migration module  2404  may optionally determine if each of the ports identified based on the data packets received from the network traffic analyzing software platform  150  is a relevant port. A port that is not identified as a relevant port may be categorized as an irrelevant port. In some embodiments, only ports that are deemed relevant may be designated as a server port or a client port. Ports which are categorized as irrelevant may be ignored for the next step of the discovery phase  2600 , and may be left out of the discovery report. 
     The relevance of a particular port of an entity of the enterprise network may be based on one or more relevancy factors. Relevancy factors may include an access frequency being greater than a predetermined access threshold. The access threshold may depend on a tier of service of an application associated with the entity of the enterprise network associated with the particular port. For example, a port associated with a tier 0, or mission-critical application may have a lower predetermined access threshold than another port associated with a tier 4 application. In some embodiments, the predetermined access threshold may depend on whether or not the port is registered. For example, a port registered with IANA may have a lower predetermined access threshold than another port that is not registered with IANA. 
     The relevancy factors, for example, may include whether or not the particular port of the entity of the enterprise network has been identified as the server port or the client port of an application discovered by the application discovery system  180 . In optional step  2602  of the discovery phase  2600 , the application discovery interface module  2408  may receive output of the application discovery system  180 . The output of the application discovery system  180  may include attributes or characteristics of the discovered application such as the server port or the client port associated with the discovered application, ports used in communication, data flow information, and/or the like. 
     In some embodiments, a particular port of an entity of the enterprise network may be categorized as a relevant port if a substantial part of the access frequency of the particular port occurs over specific time ranges. For example, if the migration module  2404  determines that more than 50% of the access frequency of a particular port of the enterprise network occurs between the hours of 9 a.m. through 4 p.m. Monday through Friday. 
     In various embodiments, if one port of a communication tuple is categorized as relevant, the other port of the communication tuple is also identified or categorized as relevant. In one embodiment, ports of the communication tuple are categorized independently of each other. In some embodiments, an entity of the enterprise network may include both relevant and irrelevant ports. In other words, just because one port of an entity of the enterprise network does not mean that other ports of the entity of the enterprise network are relevant ports. 
     In step  2614 , for each of the determined communication message (e.g., tuple), the migration module  2404  may designate one of the source port and the destination port as a server port and the other of the source port and the destination port as a client port. The designation of the server port and the client port may be accomplished using a heuristic algorithm. The algorithm may include: 1) for a communication tuple, (source IP address, source port, destination IP address, destination port), the port with a higher port access frequency is designated the server port; 2) if the source port and the destination port has the same access frequency, then the port that is registered with an organization such as IANA is designated as the server port; 3) if neither the source port and the destination port have the same access frequency and if neither the source port and the destination port are registered, then the port with the lowest port number is designated as the server port. 
     In some embodiments, the heuristic algorithm to designate the server port and the client port may only be applied to ports that are categorized as relevant. 
     In some embodiments, the migration module  2404  may confirm the designation of the server port and the client port from the heuristic algorithm using the designation of the server port and the client port from the application discovery system  180 . In one example, the heuristic algorithm determines that port 587 of the email server  2324  of  FIG.  23 B  is the client server of the enterprise network application, while port 49152 of the virtual machines  2310  is the server port. The migration module  2404  may confirm this designation with the application discovery system  180 , which determines that port 587 of the email server  2324  is the server port, while port 49152 of the virtual machines  2310  is the client server of the email application. In the case of a mismatch or conflict between the designation of the heuristic algorithm and the application discovery system  180 , a rule may use to prefer one over the other (e.g., there may be a rule that the designation of the application discovery system  180  may be used). In some embodiments, in the case of a conflict between the application discovery system  180  and the migration module  2404 , the designation of the migration module  2404  may be used. In various embodiments, the conflict may be provided to the reporting module  2424 . 
     In some embodiments, in the case of a conflict between the application discovery system  180  and the migration module  2404 , the CMR service system  2485  may repeat any number of the steps of the discovery phase  2600 . For example, the CMR service system  2485  may repeat steps  2604  through  2616  with data packets from the network traffic analyzing software platform  150 . In step  2604 , the CMR service system  2485  may send a request to receive data packets from the network traffic analyzing software platform  150 . The CMR service system  2485  may receive data packets of the enterprise network collected over a period of time different from the previous round of the discovery phase  2600 . In step  2606 , the migration module  2404  may determine a communication message based on the data packets received from the network traffic analyzing software platform  150 . In step  2608 , the migration module  2404  may identify entities of the enterprise network based on the data packets and/or messages received from the network traffic analyzing software platform  150 , and build a different port frequency distribution table for entities of the enterprise network. In some embodiments, the migration module  2404  may update rows of the port frequency distribution table  2500  based on the data packets received from the network traffic analyzing software platform  150 , where each row of the port frequency distribution table  2500  represents a numbered port of an entity of the enterprise network and an access frequency of the particular port. In step  2612 , the relevancy of each port may be determined based on one or more relevancy factors. In step  2614 , the migration module  2404  may designate one of the source port and the destination port as a server port and the other of the source port and the destination port as a client port using the heuristic algorithm. 
     In step  2616 , the workload module  2410  may identify workload(s) based on the designation of server port and client ports by the migration module  2404 . Client ports communicating with the same server port may be identified as a workload, such as email server  2324  and the virtual machines  2310  through  2316  of  FIG.  23 B . These entities of the enterprise network may be identified or categorized into one workload. 
     In some embodiments, a workload represents the amount of work or processing accomplished by an entity of the enterprise network  105 . Entities of the enterprise network  105  may include compute devices, network elements, and storage elements. In one embodiment, entities of the enterprise network  105  include enterprise network applications. In some embodiments, entities of the enterprise network include virtual machines. 
     In various embodiments, a workload may comprise multiple workloads. For example, the workload represents the amount of work or processing accomplished by an application of the enterprise network  105 . Thereby, one workload representing the email service application of the enterprise network comprises multiple virtual machine workloads and server workloads, which function as part of the email service application. 
     In some embodiments, during the discovery phase of the cloud migration readiness process, the workload module  2410  may determine the characteristics of the workload. Characteristics of the workload may include attributes of the entity associated with the workload. For example, a workload of a virtual machine may include attributes of the virtual machine such as applications of the enterprise network  105  associated with the virtual machine and utilization such as maximum read speed, maximum write speed, central processing unit (CPU) MHz, memory utilization percentage, storage demand, and input/output operations per second (IOPS). For example, a workload of a storage device may include attributes of the storage device such as read and write latency. In some embodiments, characteristics of the workload include the communication ports associated with the particular workload. 
     Referring to the examples  FIG.  23 A  and  FIG.  23 B  which represent communication between the email server  2324  and the virtual machines  2310 ,  2312 ,  2314 , and  2316 , the migration module  2404  may determine that these entities of the enterprise network  105  make up at least a part of an email application of the enterprise network  105 . The workload module  2410  may determine that the server port of the email application is port number 587 of the email server  2324 , while the client port of the email application is port 30 of a number of virtual machines (including virtual machines  2310 ,  2312 ,  2314 , and  2316 ). 
     In various embodiments, the migration module  2404  may send a request to the workload datastore  2420  to store workloads discovered in the discovery phase  2600 . The discovery output may include workload characteristics and interdependencies among compute, network, and storage elements of an on-premises enterprise system. 
     In step  2618 , the migration module  2404  may send a request to the workload datastore  2420  for some or all of the workloads discovered in the discovery phase  2600 . 
     In some embodiments, the migration module  2404  may send a request to the reporting module  2424  to provide some or all components of the discovery report. For example, the reporting module  2424  may provide a port frequency distribution table such as the example port frequency distribution table  2500  of  FIG.  25   . The port frequency distribution table  2500  include multiple port entries for each port identified by the migration module  2404 . Each port entry includes a port number, the transport protocol associated with the port number, and a count of the number of times data traffic traveled into or out of a particular port. In one embodiment, the reporting module  2424  may provide a visual representation of the infrastructure corresponding to one identified workload, an example of which can be seen in  FIG.  23 B  which illustrates at least some entities of the enterprise network  105  which make up the email application. 
     In various embodiments, for each of the workloads identified during the discovery phase  2600 , the migration module  2404  may send a request to access or configure any number of network devices (e.g., virtual, hardware, or software router, firewall, switch, and/or the like) top open server port(s) and client port(s) of an identified workload. For example, the migration module  2404  may send a request to a router serving the email server  2324  to open port 587. Furthermore, the migration module  2404  may send a request to the same or different virtual or hardware network device to open port 30 of the virtual machine  2310 . In some embodiments, the migration module  2404  may confirm that any number of ports are open or closed by accessing the virtual, hardware, or software network device (e.g., virtual, hardware, or software router, firewall, switch, and/or the like). 
     The profiling phase, playback phase, and the monitor phase of the cloud migration readiness process  300  may be similar to the profiling phase  304 , the playback phase  306 , and the monitor phase  308 , as described in  FIG.  13   ,  FIG.  18   , and  FIG.  21    respectively. The profiling phase  304  may, in some embodiments, utilize the identified workloads, server ports, client ports, entities, applications, and servers discovered through one or more steps of the method discussed regarding  FIG.  26   . The CMR service system  185  may send attributes associated with enterprise network applications identified during the profiling phase to the application discovery system  180 . Attributes associated with the application may include such as a suggested name for the discovered application, tier of service of the application. In some embodiments, attributes associated with the application includes entities, as well as attributes associated with the entities which make up the discovered application. The application discovery system  180  may receive information from the CMR service system  185  and create or update an application entry stored in the application discovery system  180 . 
     In some embodiments, the affinity group module  212  utilizes a machine learning algorithm to categorize or group workloads identified in the discovery phase  2600  into affinity groups. The affinity group module  212  determines the workloads that belong to each affinity group. The affinity group module  212  may determine the representative synthetic workload for each affinity group. The representative synthetic workload may reduce playback time by capturing relevant utilization attributes of the workloads within an affinity group. 
     In some embodiments, the migration module  2404  sends a request to access or configure particular ports of routers or switches according to server ports or client ports associated with an identified workload. The migration module  2404  may send a request to determine if the router or switches associated with the identified workload are correctly configured. In various embodiments, the migration module  2404  sends the request to access or configure particular ports of routers or switches associated with workloads identified in the discovery phase  2600 . In some embodiments, the migration module  2404  sends the request to access or configure particular ports of routers or switches for simulation of workloads, during the migration process, and/or after the migration process for a particular workload has been completed in order to determine if the ports of routers or switches were correctly migrated to cloud-based infrastructure. It may be important that pre-migration and post-migration, that the correct ports of routers or switches are kept open for a migrating workload such that after the workload has been migrated to the cloud-based infrastructure, network traffic will be able to travel from the server port to the client port of the workload. 
     In various embodiments, the migration module  2404  may determine if particular ports of a network device was or will be correctly configured for an identified workload. The migration module  2404  may send a request to determine if the particular ports of the network device associated with the identified workload are correctly configured. In various embodiments, the migration module  2404  configures particular ports of a firewall associated with workloads identified in the discovery phase  2600 . In some embodiments, the migration module  2404  sends the request to one or more firewalls (or any number of network devices) during the migration process or after the migration process for a particular workload has been completed in order to determine if the ports of routers or switches were correctly migrated to cloud-based infrastructure. It is important that pre-migration and post-migration, that the correct ports of network devices are kept open for a migrating workload such that after the workload has been migrated to the cloud-based infrastructure, network traffic will be able to travel from the server port to the client port of the workload. 
     In some embodiments, the output of the profiling phase may be utilized to model data traffic of the enterprise network. The CMR service system  2485  may utilized data packets received from the software platform  150  to model data traffic. Data packets received from the software platform  150  includes information such as a byte count, packet count, source IP address, destination IP address, transport protocol. The CMR service system  2485  may utilize the data packets to simulate network traffic. 
     In some embodiments, during the playback phase  306 , the migration module  204  may send a request to the workload datastore  220  to simulate one or more workloads identified during the discovery phase  2600 . In some embodiments, the customer may select one or more workloads to simulate in the playback phase  306 . Before or while simulating workloads during the playback phase  306 , the migration module  2404  may access or configure particular ports of routers or switches (e.g., virtual, hardware, or software) associated with a particular simulated workload. In various embodiments, the migration module  2404  configures particular ports of network devices associated with a particular simulated workload. It is important to determine if the correct ports of routers, switches, or firewalls associated with an identified workload are opened or configured. If the wrong ports of routers, switches, or firewalls associated with the identified workload are opened, then the data traffic associated with the identified workload may not be able to travel from the server port to the client port. 
     In some embodiments, one or more of the steps of  2606  through  2614  of the discovery phase  2600  may be performed in step  408  of the discovery phase  302  of  FIG.  4   . In step  408 , the traffic analyzing module  2416  analyzes network traffic received from the flow source discovery system  170  and the application discovery system  180 . For example, similar to step  2606  of the discovery phase  2600 , the traffic analyzing module  2416  may send a request to the migration module  2404  to determine a communication message based on the data packets received from the network traffic analyzing software platform  150 . 
     Step  408  may include step  2608 . The traffic analyzing module  2416  may identify entities of the enterprise network based on the data packets and/or messages received from the network traffic analyzing software platform  150 , and build a different port frequency distribution table for entities of the enterprise network. In some embodiments, the traffic analyzing module  2416  may update rows of the port frequency distribution table  2500  based on the data packets received from the network traffic analyzing software platform  150 , where each row of the port frequency distribution table  2500  represents a numbered port of an entity of the enterprise network and an access frequency of the particular port. Step  408  may include optional step  2610 . The traffic analyzing module  2416  may determine if one or more of the identified ports of entities of the enterprise network is registered with an organization such as IANA. In some embodiments, if the migration module  2404  determines that a particular port is registered, the migration module  2404  may send a request to the discovery datastore  2426  to update a ‘registered’ attribute of the particular port. Step  408  may include optional step  2612 , and the relevancy of each port may be determined based on one or more relevancy factors. In step  2614 , the traffic analyzing module  2416  may designate one of the source port and the destination port as a server port and the other of the source port and the destination port as a client port using the heuristic algorithm. 
     In this description, the term “module” refers to computational logic for providing the specified functionality. A module can be implemented in hardware, firmware, and/or software. Where the modules described herein are implemented as software, the module can be implemented as a standalone program, but can also be implemented through other means, for example, as part of a larger program, as any number of separate programs, or as one or more statically or dynamically linked libraries. It will be understood that the named modules described herein represent one embodiment, and other embodiments may include other modules. In addition, other embodiments may lack the modules described herein and/or distribute the described functionality among the modules in a different manner. Additionally, the functionalities attributed to more than one module can be incorporated into a single module. In an embodiment where the modules as implemented by software, they are stored on a computer-readable persistent storage device (e.g., hard disk), loaded into the memory, and executed by one or more processors as described above in connection with  FIG.  22   . Alternatively, hardware or software modules may be stored elsewhere within a computing system. 
     As referenced herein, a computer or computing system includes hardware elements used for the operations described here regardless of specific reference in  FIG.  22    to such elements, including, for example, one or more processors, high-speed memory, hard disk storage, and backup, network interfaces and protocols, input devices for data entry, and output devices for display, printing, or other presentations of data. Numerous variations from the system architecture specified herein are possible. The entities of such systems and their respective functionalities can be combined or redistributed.