Patent Publication Number: US-11032126-B2

Title: Diagnostic traffic generation for automatic testing and troubleshooting

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of and claims the benefit of U.S. patent application Ser. No. 15/908,302, filed Feb. 28, 2018, which is a continuation of and claims the benefit of U.S. patent application Ser. No. 15/584,696, filed on May 2, 2017, now U.S. Pat. No. 9,935,818 issued on Apr. 3, 2018, entitled “Diagnostic Traffic Generation For Automatic Testing And Troubleshooting,” the contents of which are hereby incorporated by reference herein in its entirety. 
    
    
     TECHNICAL FIELD 
     The technical field generally relates to software-defined networks and, more specifically, to testing in software-defined networks. 
     BACKGROUND 
     Communication networks have migrated from using specialized networking equipment executing on dedicated hardware, like routers, firewalls, and gateways, to virtualized network components, such as virtual network functions (VNF) and virtual machines (VM) that may be implemented or run on general purpose hardware within a cloud infrastructure. Network management of network components—whether or not virtualized—may require implementing, from time to time, software changes across the network or across a subset of the network components. These software changes may include software patches, software updates, configuration changes, or installation/uninstallation of software. In the complex computing environment today, there are challenges with troubleshooting network issues that may be based on these software changes. 
     SUMMARY 
     Disclosed herein is a framework for diagnostic traffic generation for automatic testing and troubleshooting. Software-defined tracing and measurement (SD™), as disclosed herein, may allow for debugging at multiple vantage points at different layers (e.g., layer 2, layer 3, etc.). Advanced measurements apps may emulate or talk to virtual network functions to generate calls or packets or provide backend service for interactive exploration or analysis of collected data. 
     In an example, an apparatus may include a processor and a memory coupled with the processor that effectuates operations. The operations may include instantiating a virtual machine (VM) on one or more devices connected to a system. The operations may further include injecting test traffic into network traffic to proactively test the system, wherein the network traffic comprises normal traffic. The operations may further include capturing the network traffic using the VM. The operations may further include separating the test traffic and the normal traffic using the VM and based on the separated test traffic, determining a health of the system. 
     In an example, a computer readable storage medium may include instantiating a virtual machine (VM) on one or more devices connected to a system. The computer readable storage medium may further include injecting test traffic into network traffic to proactively test the system, wherein the network traffic comprises normal traffic. The computer readable storage medium may further include capturing the network traffic using the VM. The computer readable storage medium may further include separating the test traffic and the normal traffic using the VM and based on the separated test traffic, determining a health of the system. 
     In an example, an apparatus may include a processor and a memory coupled with the processor that effectuates operations. The operations may include detecting an occurrence of an event. The operations may further include, based on the detected occurrence of the event, activating a virtual machine, wherein the activating the virtual machine comprises providing instructions to instantiate a virtual machine on one or more devices or providing instructions to configure a virtual machine already instantiated on the one or more devices. The operations may further include monitoring the network traffic for the one or more devices, wherein the network traffic comprises test traffic and normal traffic. The operations may further include separating the test traffic and the normal traffic and based on the separated test traffic and the activated virtual machine, determining a health of a system. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Furthermore, the claimed subject matter is not limited to limitations that solve any or all disadvantages noted in any part of this disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Reference will now be made to the accompanying drawings, which are not necessarily drawn to scale. 
         FIG. 1  illustrates an exemplary layer and function implementation for software-defined tracing and measurement. 
         FIG. 2  illustrates an exemplary system that may implement software-defined tracing and measurement. 
         FIG. 3  illustrates an exemplary method for software-defined tracing and measurement. 
         FIG. 4  illustrates an exemplary method flow for software-defined tracing and measurement. 
         FIG. 5  illustrates an exemplary method for software-defined tracing and measurement. 
         FIG. 6  illustrates a schematic of an exemplary network device. 
         FIG. 7  illustrates an exemplary communication system that provides wireless telecommunication services over wireless communication networks. 
         FIG. 8A  is a representation of an exemplary network. 
         FIG. 8B  is a representation of an exemplary hardware platform for a network. 
     
    
    
     DETAILED DESCRIPTION 
     With the development of software defined networks (SDNs), there are an increasing number of indirections, service chaining, application programming interfaces (APIs), and layering which may create more need to independently verify connectivity, service path, or performance. Disclosed herein is a framework for debugging of operational use cases that may be in an SDN. Software-defined tracing and measurement (SD™), as disclosed herein, may allow for debugging at multiple vantage points at different layers (e.g., layer 2, layer 3, etc.), such as shown in  FIG. 1 . SD™ (e.g., diagnostic traffic generation for automatic testing and troubleshooting) may allow for tracing and measuring that may be run as a virtual network function (VNF) with flexibility and scale. SD™ service may provide tracer services that filter, capture, or forward flows that may include packets (e.g., requests or responses), commands, calls, or protocols to look for particular signatures. Tracers  94  may be invoked on-demand to generate test traffic. SD™ service may provide measurement services that may include deploying or controlling tracers (and collecting results), deducing expected topologies or connectivity to verify independently or interface with standard stats from other systems and network devices. Advanced measurements apps  91  (e.g.,  FIG. 1 ) may 1) may emulate or talk to virtual network functions to generate calls or packets; 2) provide backend service for interactive exploration or analysis of collected data; or 3) automate measurement or tracing operations. 
       FIG. 2  illustrates an exemplary system  100  that may implement VNF diagnostic traffic generation for automatic testing and troubleshooting. In system  100 , there may be a communications network  112  that may connect several devices. Exemplary devices may include switch  104 , switch  107 , router  110 , base station  103 , mobile device  101 , desktop computer  104 , or server  114 . Each device may include a hypervisor or the like that may be used to generate a virtual machine (VM) and corresponding virtual network functions (VNF). For example, there may be VM  113  in mobile device  101 , VM  102  in base station  103 , VM  104  in switch  105 , VM  106  in switch  107 , VM  110  in router  111 , and VM  108  in desktop computer  109 . As provided in more detail herein, VMs may be dynamically generated or removed, as needed. 
       FIG. 3  illustrates an exemplary method for implementing software-defined tracing and measurements (SD™) as disclosed herein. At step  131 , server  114  may detect an event that triggers SD™ service. Events that may trigger SD™ services for step  131  may be based on types of packets traversing a network (e.g., network  112 ), types of errors, reaching a threshold error rate, reaching a threshold traffic load, reaching a threshold packet loss, number of times an application resets or crashes, an operating system change (e.g., version), a network device change (e.g., hardware or software version), indication of a type of customer complaint, reaching a threshold number of customer complaints, date, time, location of a device (e.g., mobile device  101 ), a billing issue, or a request from an administrator (e.g., user), among other things. 
     At step  132 , based on the event of step  131 , server  114  may determine one or more devices of interest, which may be further based on network paths of interest (e.g., one or more communication paths for uploading or downloading data). For example, in  FIG. 2 , cloud server  105 , mobile device  101 , or desktop computer  109  may be identified as devices of interest based on detecting threshold number of errors. At step  133 , server  114  may provide instructions for a VM or VNF to be activated on the one or more devices of interest. Activating the VM in step  133  may include providing instructions to generate a VM (or VNF) that was not already instantiated on the one more devices of interest or providing instructions to configure a VM already on the one or more devices to process or generate traffic for testing based on the situation. In an example, VM  102  may be configured to generate a particular type of traffic or process data as if it were a particular type of device (e.g., a mobile device or gaming server) running a particular type of application (e.g., online gaming application). In another example, VM  113  may generate data or functions that emulate processes that occurred when communicating with or through VM  102 . In this example, VM  102  may be an instantiated test process that simulates the functions of another VM or be the normal VM  102  not instantiated for testing purposes. 
     With continued reference to  FIG. 3 , at step  134 , server  114  may provide instructions for VM  113  on mobile device  101  and other VMs of interest to generate or otherwise process test traffic using the SD™ service for a test period. At step  135 , server  114  may obtain statistical information associated with the test period of step  134 . The statistical information may include network performance statistics, accounting data for the purpose of billing, usage data of the network or devices in the network. At step  136 , server  114 , based on the statistics of step  135 , may determine that one or more devices, network paths, or software applications, for example, are the source of a problem. 
       FIG. 4  illustrates an exemplary method flow for a scenario implementing VNF diagnostic traffic generation for automatic testing and troubleshooting disclosed herein. At step  141  there may be general communication between mobile device  101  and cloud server  105 . Exemplary communications may be associated with e-mail, gaming, or cloud networking services, among other things. At step  142 , server  141  may detect an event. In an example, server  114  may periodically check statistical information associated with the general communication for mobile device  101  and cloud server  105  may send a message to server  114  upon detecting the event. This event may be any event, such as the events disclosed herein with reference to  FIG. 3 . At step  143 , based on the event, server  114  may monitor or record the network traffic (or “operations” which is a term that may generally be substituted for “traffic” as disclosed herein) of mobile device  101 , cloud server  105 , or other devices along the communication path or tangentially effect the communication between mobile device  101  and cloud server  105 , such as base station  103  or router  110 . The recorded operations may include commands or messages sent or otherwise executed by mobile device  101  or cloud server  105 . The operations or traffic may include packets (e.g., requests or responses), commands, calls, or protocols. At step  144 , based on the event of step  142  or the monitored (or recorded) traffic of step  143 , server  114  may determine devices of interest to generate test traffic. The test traffic may specifically mimic the commands recorded in step  143  or may generally simulate traffic associated with applications of the type of mobile device  101  or cloud server  105 . The general simulation, for example, may be a random selection of operations based on the average (or median or mode, etc.) location or other event of mobile device, cloud server, or other device of interest. Here, for example, it is determined that mobile device  101  and cloud server  105  are the devices of interest (e.g., devices that generate traffic for the SD™ service). It is contemplated herein that other devices may be selected, such as the other devices of system  100 . The devices of interest may be determined based on multiple factors. Factors include the events of step  142  or step  143 , or otherwise disclosed herein. Another exemplary factor may include a determination of the location, such as farthest device closest to the originator of traffic that has the device capabilities (e.g., processor speed, memory amount, bandwidth of network connection, power to the device, etc.) of creating a VM for test traffic or generating test traffic. Another example, with regard to location, may be associated with the base station in which mobile device is connected or not connected with. Another factor may be based on the intersection of complaints (or detected errors) associated with a plurality of users. For example, base station  103  may be selected over mobile device  101  based on an intersection of complaints (e.g., the device that is commonly involved in different situations) or device capability. 
     With continued reference to  FIG. 4 , at step  145  and step  146 , server  114  may send a message to cloud server  105  or may send a message to mobile device  101  to create VM  104  of cloud server  105  or VM  113  of mobile device  101  for generating or processing test traffic. Scenarios are contemplated herein in which VM  113  may be created particularly for generating test traffic for the SD™ service, while cloud server  105  does not create another VM (e.g., uses a previously instantiated VM used for testing) or cloud server  105  processes messages from VM  113  as it would for any other device (e.g., a VM of cloud server  105  already serving real traffic and is not generally aware that it is test traffic). At this step  145  or step  146 , server  114  may also provide instructions for the created VMS to mark the test traffic in order to recognize it at a later point in time (e.g., diagnosis at step  148 ). In addition, server  114  may provide instructions to create VM or generate test traffic during a particular period. The particular period may be a period of relatively low network traffic or device usage, in order to minimize impact to a user. Or the particular period may be period of relatively high traffic or device usage, in order to more effectively diagnose problems that occur during those periods of relatively high traffic or device usage. 
     At step  147 , tests (e.g., generation of traffic and monitoring results) may occur between mobile device  101  and cloud server  105  for a period that may have been communicated at step  145  or step  146 . It is contemplated herein per-hop one-way active or passive performance measurements on each hop using the same ports and protocols as in ordinary traffic (e.g., simulate traffic that is processed as normal, but measured more discretely). At step  148 , server  114  may determine possible issues that may have caused a previous problem (e.g., problem experienced and reported by a user associated with mobile device  101 ), may determine possible issues that may cause a problem for mobile device  101  (or cloud server  105 ) in the future (e.g., after installation of a software update), or other indicators associated with health of the entire or subset of system  100 . Subsequently server  114  may provide instructions to display the determined results or transmit the results of the diagnosis of step  148  to a device. 
       FIG. 5  illustrates another exemplary method for implementation of SD™. At step  151 , Openstack (or the like) may be used to instantiate virtual infrastructure (e.g., VMs) and inject or capture test traffic. For example, VMs and VNFs may be placed within mobile device  101 , base station  103 , router  111 , and desktop computer  109  for testing. This step  151  may be proactive testing of system  100  and may not be in response to a detected error. This method (or methods herein) may be a periodic (e.g., may occur at peak times or alternatively non-peak times). At step  152 , a library may be created for other VNFs to incorporate test traffic generation on command. For example, libraries to implement SD™ may be created in existing or new VNFs. At step  153 , vRouter (or the like) may separate test traffic (e.g., injected traffic) from real traffic (e.g., normal traffic from users). At step  154 , using instantiated VMs of step  152  in the context of SD™ to determine possible problems and suggest solutions (e.g., a configuration window may pop-up with a highlighted possible configuration at issue) or provide an indication that there are no detected problems. In other words, at step  154 , the health of system  100  may be determined and communicated. 
     It is contemplated herein that one or more steps of SD™ (e.g.,  FIG. 3 ,  FIG. 4 , or  FIG. 5 ) may occur on one device or may be distributed across multiple devices. Exemplary devices that may execute the disclosed methods may include an operation, administration, and maintenance (OAM) server, SDN controller, router, or switch, among other things. In an exemplary implementation, distributed processing and data structures or algorithms may be used to reduce the collection traffic. 
       FIG. 6  is a block diagram of network device  300  that may be connected to or comprise a component of system  100 . Network device  300  may comprise hardware or a combination of hardware and software. The functionality to facilitate telecommunications via a telecommunications network may reside in one or combination of network devices  300 . Network device  300  depicted in  FIG. 6  may represent or perform functionality of an appropriate network device  300 , or combination of network devices  300 , such as, for example, a component or various components of a cellular broadcast system wireless network, a processor, a server, a gateway, a node, a mobile switching center (MSC), a short message service center (SMSC), an automatic location function server (ALFS), a gateway mobile location center (GMLC), a radio access network (RAN), a serving mobile location center (SMLC), or the like, or any appropriate combination thereof. It is emphasized that the block diagram depicted in  FIG. 6  is exemplary and not intended to imply a limitation to a specific implementation or configuration. Thus, network device  300  may be implemented in a single device or multiple devices (e.g., single server or multiple servers, single gateway or multiple gateways, single controller, or multiple controllers). Multiple network entities may be distributed or centrally located. Multiple network entities may communicate wirelessly, via hard wire, or any appropriate combination thereof. 
     Network device  300  may comprise a processor  302  and a memory  304  coupled to processor  302 . Memory  304  may contain executable instructions that, when executed by processor  302 , cause processor  302  to effectuate operations associated with mapping wireless signal strength. As evident from the description herein, network device  300  is not to be construed as software per se. 
     In addition to processor  302  and memory  304 , network device  300  may include an input/output system  306 . Processor  302 , memory  304 , and input/output system  306  may be coupled together (coupling not shown in  FIG. 6 ) to allow communications between them. Each portion of network device  300  may comprise circuitry for performing functions associated with each respective portion. Thus, each portion may comprise hardware, or a combination of hardware and software. Accordingly, each portion of network device  300  is not to be construed as software per se. Input/output system  306  may be capable of receiving or providing information from or to a communications device or other network entities configured for telecommunications. For example, input/output system  306  may include a wireless communications (e.g., 3G/4G/GPS) card. Input/output system  306  may be capable of receiving or sending video information, audio information, control information, image information, data, or any combination thereof. Input/output system  306  may be capable of transferring information with network device  300 . In various configurations, input/output system  306  may receive or provide information via any appropriate means, such as, for example, optical means (e.g., infrared), electromagnetic means (e.g., RF, Wi-Fi, Bluetooth®, ZigBee®), acoustic means (e.g., speaker, microphone, ultrasonic receiver, ultrasonic transmitter), or a combination thereof. In an example configuration, input/output system  306  may comprise a Wi-Fi finder, a two-way GPS chipset or equivalent, or the like, or a combination thereof. 
     Input/output system  306  of network device  300  also may contain a communication connection  308  that allows network device  300  to communicate with other devices, network entities, or the like. Communication connection  308  may comprise communication media. Communication media typically embody computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, or wireless media such as acoustic, RF, infrared, or other wireless media. The term computer-readable media as used herein includes both storage media and communication media. Input/output system  306  also may include an input device  310  such as keyboard, mouse, pen, voice input device, or touch input device. Input/output system  306  may also include an output device  312 , such as a display, speakers, or a printer. 
     Processor  302  may be capable of performing functions associated with telecommunications, such as functions for processing broadcast messages, as described herein. For example, processor  302  may be capable of, in conjunction with any other portion of network device  300 , determining a type of broadcast message and acting according to the broadcast message type or content, as described herein. 
     Memory  304  of network device  300  may comprise a storage medium having a concrete, tangible, physical structure. As is known, a signal does not have a concrete, tangible, physical structure. Memory  304 , as well as any computer-readable storage medium described herein, is not to be construed as a signal. Memory  304 , as well as any computer-readable storage medium described herein, is not to be construed as a transient signal. Memory  304 , as well as any computer-readable storage medium described herein, is not to be construed as a propagating signal. Memory  304 , as well as any computer-readable storage medium described herein, is to be construed as an article of manufacture. 
     Memory  304  may store any information utilized in conjunction with telecommunications. Depending upon the exact configuration or type of processor, memory  304  may include a volatile storage  314  (such as some types of RAM), a nonvolatile storage  316  (such as ROM, flash memory), or a combination thereof. Memory  304  may include additional storage (e.g., a removable storage  318  or a non-removable storage  320 ) including, for example, tape, flash memory, smart cards, CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, USB-compatible memory, or any other medium that can be used to store information and that can be accessed by network device  300 . Memory  304  may comprise executable instructions that, when executed by processor  302 , cause processor  302  to effectuate operations to map signal strengths in an area of interest. 
       FIG. 7  depicts an exemplary diagrammatic representation of a machine in the form of a computer system  500  within which a set of instructions, when executed, may cause the machine to perform any one or more of the methods described above. One or more instances of the machine can operate, for example, as processor  302 , mobile device  101 , server  114 , cloud server  105 , switch  107 , and other devices of  FIG. 2 . In some embodiments, the machine may be connected (e.g., using a network  100 ) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client user machine in a server-client user network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. 
     The machine may comprise a server computer, a client user computer, a personal computer (PC), a tablet, a smart phone, a laptop computer, a desktop computer, a control system, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. It will be understood that a communication device of the subject disclosure includes broadly any electronic device that provides voice, video, or data communication. Further, while a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein. 
     Computer system  500  may include a processor (or controller)  504  (e.g., a central processing unit (CPU)), a graphics processing unit (GPU, or both), a main memory  506  and a static memory  508 , which communicate with each other via a bus  510 . The computer system  500  may further include a display unit  512  (e.g., a liquid crystal display (LCD), a flat panel, or a solid-state display). Computer system  500  may include an input device  514  (e.g., a keyboard), a cursor control device  516  (e.g., a mouse), a disk drive unit  518 , a signal generation device  520  (e.g., a speaker or remote control) and a network interface device  522 . In distributed environments, the embodiments described in the subject disclosure can be adapted to utilize multiple display units  512  controlled by two or more computer systems  500 . In this configuration, presentations described by the subject disclosure may in part be shown in a first of display units  512 , while the remaining portion is presented in a second of display units  512 . 
     The disk drive unit  518  may include a tangible computer-readable storage medium  524  on which is stored one or more sets of instructions (e.g., software  526 ) embodying any one or more of the methods or functions described herein, including those methods illustrated above. Instructions  526  may also reside, completely or at least partially, within main memory  506 , static memory  508 , or within processor  504  during execution thereof by the computer system  500 . Main memory  506  and processor  504  also may constitute tangible computer-readable storage media. 
       FIG. 8A  is a representation of an exemplary network  600 . Network  600  (e.g., system  100 ) may comprise an SDN—for example, network  600  may include one or more virtualized functions implemented on general purpose hardware, such as in lieu of having dedicated hardware for every network function. For example, general purpose hardware of network  600  may be configured to run virtual network elements to support communication services, such as mobility services, including consumer services and enterprise services. These services may be provided or measured in sessions. 
     A virtual network functions (VNFs)  602  may be able to support a limited number of sessions. Each VNF  602  may have a VNF type that indicates its functionality or role. For example,  FIG. 8A  illustrates a gateway VNF  602   a  and a policy and charging rules function (PCRF) VNF  602   b . Additionally or alternatively, VNFs  602  may include other types of VNFs. Each VNF  602  may use one or more virtual machines (VMs)  604  to operate. Each VM  604  may have a VM type that indicates its functionality or role. For example,  FIG. 8A  illustrates a management control module (MCM) VM  604   a , an advanced services module (ASM) VM  604   b , and a DEP VM  604   c . Additionally or alternatively, VMs  604  may include other types of VMs. Each VM  604  may consume various network resources from a hardware platform  606 , such as a resource  608 , a virtual central processing unit (vCPU)  608   a , memory  608   b , or a network interface card (NIC)  608   c . Additionally or alternatively, hardware platform  606  may include other types of resources  608 . 
     While  FIG. 8A  illustrates resources  608  as collectively contained in hardware platform  606 , the configuration of hardware platform  606  may isolate, for example, certain memory  608   c  from other memory  608   c .  FIG. 8B  provides an exemplary implementation of hardware platform  606 . 
     Hardware platform  606  may comprise one or more chasses  610 . Chassis  610  may refer to the physical housing or platform for multiple servers or other network equipment. In an aspect, chassis  610  may also refer to the underlying network equipment. Chassis  610  may include one or more servers  612 . Server  612  may comprise general purpose computer hardware or a computer. In an aspect, chassis  610  may comprise a metal rack, and servers  612  of chassis  610  may comprise blade servers that are physically mounted in or on chassis  610 . 
     Each server  612  may include one or more network resources  608 , as illustrated. Servers  612  may be communicatively coupled together (not shown) in any combination or arrangement. For example, all servers  612  within a given chassis  610  may be communicatively coupled. As another example, servers  612  in different chasses  610  may be communicatively coupled. Additionally or alternatively, chasses  610  may be communicatively coupled together (not shown) in any combination or arrangement. 
     The characteristics of each chassis  610  and each server  612  may differ. For example,  FIG. 8B  illustrates that the number of servers  612  within two chasses  610  may vary. Additionally or alternatively, the type or number of resources  610  within each server  612  may vary. In an aspect, chassis  610  may be used to group servers  612  with the same resource characteristics. In another aspect, servers  612  within the same chassis  610  may have different resource characteristics. 
     Given hardware platform  606 , the number of sessions that may be instantiated may vary depending upon how efficiently resources  608  are assigned to different VMs  604 . For example, assignment of VMs  604  to particular resources  608  may be constrained by one or more rules. For example, a first rule may require that resources  608  assigned to a particular VM  604  be on the same server  612  or set of servers  612 . For example, if VM  604  uses eight vCPUs  608   a,  1 GB of memory  608   b , and 2 NICs  608   c , the rules may require that all of these resources  608  be sourced from the same server  612 . Additionally or alternatively, VM  604  may require splitting resources  608  among multiple servers  612 , but such splitting may need to conform with certain restrictions. For example, resources  608  for VM  604  may be able to be split between two servers  612 . Default rules may apply. For example, a default rule may require that all resources  608  for a given VM  604  must come from the same server  612 . 
     An affinity rule may restrict assignment of resources  608  for a particular VM  604  (or a particular type of VM  604 ). For example, an affinity rule may require that certain VMs  604  be instantiated on (e.g., consume resources from) the same server  612  or chassis  610 . For example, if VNF  602  uses six MCM VMs  604   a , an affinity rule may dictate that those six MCM VMs  604   a  be instantiated on the same server  612  (or chassis  610 ). As another example, if VNF  602  uses MCM VMs  604   a , ASM VMs  604   b , and a third type of VMs  604 , an affinity rule may dictate that at least the MCM VMs  604   a  and the ASM VMs  604   b  be instantiated on the same server  612  (or chassis  610 ). Affinity rules may restrict assignment of resources  608  based on the identity or type of resource  608 , VNF  602 , VM  604 , chassis  610 , server  612 , or any combination thereof. 
     An anti-affinity rule may restrict assignment of resources  608  for a particular VM  604  (or a particular type of VM  604 ). In contrast to an affinity rule—which may require that certain VMs  604  be instantiated on the same server  612  or chassis  610 —an anti-affinity rule requires that certain VMs  604  be instantiated on different servers  612  (or different chasses  610 ). For example, an anti-affinity rule may require that MCM VM  604   a  be instantiated on a particular server  612  that does not contain any ASM VMs  604   b . As another example, an anti-affinity rule may require that MCM VMs  604   a  for a first VNF  602  be instantiated on a different server  612  (or chassis  610 ) than MCM VMs  604   a  for a second VNF  602 . Anti-affinity rules may restrict assignment of resources  608  based on the identity or type of resource  608 , VNF  602 , VM  604 , chassis  610 , server  612 , or any combination thereof. 
     Within these constraints, resources  608  of hardware platform  606  may be assigned to be used to instantiate VMs  604 , which in turn may be used to instantiate VNFs  602 , which in turn may be used to establish sessions. The different combinations for how such resources  608  may be assigned may vary in complexity and efficiency. For example, different assignments may have different limits of the number of sessions that can be established given a particular hardware platform  606 . 
     For example, consider a session that may require gateway VNF  602   a  and PCRF VNF  602   b . Gateway VNF  602   a  may require five VMs  604  instantiated on the same server  612 , and PCRF VNF  602   b  may require two VMs  604  instantiated on the same server  612 . (Assume, for this example, that no affinity or anti-affinity rules restrict whether VMs  604  for PCRF VNF  602   b  may or must be instantiated on the same or different server  612  than VMs  604  for gateway VNF  602   a .) In this example, each of two servers  612  may have sufficient resources  608  to support 10 VMs  604 . To implement sessions using these two servers  612 , first server  612  may be instantiated with 10 VMs  604  to support two instantiations of gateway VNF  602   a , and second server  612  may be instantiated with 9 VMs: five VMs  604  to support one instantiation of gateway VNF  602   a  and four VMs  604  to support two instantiations of PCRF VNF  602   b . This may leave the remaining resources  608  that could have supported the tenth VM  604  on second server  612  unused (and unusable for an instantiation of either a gateway VNF  602   a  or a PCRF VNF  602   b ). Alternatively, first server  612  may be instantiated with 10 VMs  604  for two instantiations of gateway VNF  602   a  and second server  612  may be instantiated with 10 VMs  604  for five instantiations of PCRF VNF  602   b , using all available resources  608  to maximize the number of VMs  604  instantiated. 
     Consider, further, how many sessions each gateway VNF  602   a  and each PCRF VNF  602   b  may support. This may factor into which assignment of resources  608  is more efficient. For example, consider if each gateway VNF  602   a  supports two million sessions, and if each PCRF VNF  602   b  supports three million sessions. For the first configuration—three total gateway VNFs  602   a  (which satisfy the gateway requirement for six million sessions) and two total PCRF VNFs  602   b  (which satisfy the PCRF requirement for six million sessions)—would support a total of six million sessions. For the second configuration—two total gateway VNFs  602   a  (which satisfy the gateway requirement for four million sessions) and five total PCRF VNFs  602   b  (which satisfy the PCRF requirement for 15 million sessions)—would support a total of four million sessions. Thus, while the first configuration may seem less efficient looking only at the number of available resources  608  used (as resources  608  for the tenth possible VM  604  are unused), the second configuration is actually more efficient from the perspective of being the configuration that can support more the greater number of sessions. 
     To solve the problem of determining a capacity (or, number of sessions) that can be supported by a given hardware platform  605 , a given requirement for VNFs  602  to support a session, a capacity for the number of sessions each VNF  602  (e.g., of a certain type) can support, a given requirement for VMs  604  for each VNF  602  (e.g., of a certain type), a give requirement for resources  608  to support each VM  604  (e.g., of a certain type), rules dictating the assignment of resources  608  to one or more VMs  604  (e.g., affinity and anti-affinity rules), the chasses  610  and servers  612  of hardware platform  606 , and the individual resources  608  of each chassis  610  or server  612  (e.g., of a certain type), an integer programming problem may be formulated. 
     As described herein, a telecommunications system wherein management and control utilizing a software defined network (SDN) and a simple IP are based, at least in part, on user equipment, may provide a wireless management and control framework that enables common wireless management and control, such as mobility management, radio resource management, QoS, load balancing, etc., across many wireless technologies, e.g. LTE, Wi-Fi, and future 5G access technologies; decoupling the mobility control from data planes to let them evolve and scale independently; reducing network state maintained in the network based on user equipment types to reduce network cost and allow massive scale; shortening cycle time and improving network upgradability; flexibility in creating end-to-end services based on types of user equipment and applications, thus improve customer experience; or improving user equipment power efficiency and battery life—especially for simple M2M devices—through enhanced wireless management. 
     While examples of a telecommunications system in which diagnostic traffic generation for automatic testing and troubleshooting may be processed and managed have been described in connection with various computing devices/processors, the underlying concepts may be applied to any computing device, processor, or system capable of facilitating a telecommunications system. The various techniques described herein may be implemented in connection with hardware or software or, where appropriate, with a combination of both. Thus, the methods and devices may take the form of program code (i.e., instructions) embodied in concrete, tangible, storage media having a concrete, tangible, physical structure. Examples of tangible storage media include floppy diskettes, CD-ROMs, DVDs, hard drives, or any other tangible machine-readable storage medium (computer-readable storage medium). Thus, a computer-readable storage medium is not a signal. A computer-readable storage medium is not a transient signal. Further, a computer-readable storage medium is not a propagating signal. A computer-readable storage medium as described herein is an article of manufacture. When the program code is loaded into and executed by a machine, such as a computer, the machine becomes a device for telecommunications. In the case of program code execution on programmable computers, the computing device will generally include a processor, a storage medium readable by the processor (including volatile or nonvolatile memory or storage elements), at least one input device, and at least one output device. The program(s) can be implemented in assembly or machine language, if desired. The language can be a compiled or interpreted language, and may be combined with hardware implementations. 
     The methods and devices associated with a telecommunications system as described herein also may be practiced via communications embodied in the form of program code that is transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via any other form of transmission, wherein, when the program code is received and loaded into and executed by a machine, such as an EPROM, a gate array, a programmable logic device (PLD), a client computer, or the like, the machine becomes a device for implementing telecommunications as described herein. When implemented on a general-purpose processor, the program code combines with the processor to provide a unique device that operates to invoke the functionality of a telecommunications system. 
     While a telecommunications system has been described in connection with the various examples of the various figures, it is to be understood that other similar implementations may be used or modifications and additions may be made to the described examples of a telecommunications system without deviating therefrom. For example, one skilled in the art will recognize that a telecommunications system as described in the instant application may apply to any environment, whether wired or wireless, and may be applied to any number of such devices connected via a communications network and interacting across the network. Therefore, a telecommunications system as described herein should not be limited to any single example, but rather should be construed in breadth and scope in accordance with the appended claims. 
     In describing preferred methods, systems, or apparatuses of the subject matter of the present disclosure—diagnostic traffic generation for automatic testing and troubleshooting—as illustrated in the Figures, specific terminology is employed for the sake of clarity. The claimed subject matter, however, is not intended to be limited to the specific terminology so selected, and it is to be understood that each specific element includes all technical equivalents that operate in a similar manner to accomplish a similar purpose. In addition, the use of the word “or” is generally used inclusively unless otherwise provided herein. It is contemplated that steps of  FIG. 3 ,  FIG. 4 , or  FIG. 5 , for example, may be skipped or combined. 
     This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art (e.g., skipping steps, combining steps, or adding steps between exemplary methods disclosed herein). Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.