Patent Publication Number: US-10771344-B2

Title: Discovery of hyper-converged infrastructure devices

Description:
BACKGROUND 
     A computer network may include various interconnected computing devices and software applications, each of which may be represented by one or more configuration items. Managing the network may involve discovering and keeping track of the configuration items. Additionally, managing the network may involve organizing the configuration items into a representation or map that allows the state of the network to be visualized. Visualization, in turn, allows the network and its contents to be adjusted to meet various needs of an enterprise. 
     SUMMARY 
     A remote network management platform may seek to discover and map configuration items of a managed network. However, the managed network may include different types of configuration items. Discovery and mapping of these configurations items, such as devices, applications, and the relationships therebetween, may involve using discovery patterns. These discovery patterns may define rules and sequences of operations to be carried out by a discovery application to detect, classify, and gather information regarding the configuration items within the managed network. 
     One type of configuration item that may be deployed within the managed network is a computing cluster whose nodes implement a hyper-converged infrastructure (HCl). The nodes may be a plurality of servers that are communicatively coupled to one another and that are configured to provide virtualization, storage, and networking services to the managed network. The computing cluster may include different types of configuration items that allow the cluster to offer such services. For example, the virtualization services may be facilitated by virtual machines executed by the plurality of servers and the storage services may be facilitated by storage devices of the plurality of servers. It may be desirable to discover and map the configuration items of the computing cluster in order to properly utilize the capabilities of the computing cluster. 
     Disclosed herein is a discovery pattern for devices or systems that implement an HCl, such as a computing cluster. The discovery pattern may characterize the configuration items of the computing cluster, including the servers of the cluster, the applications being executed by the servers, and the storage devices of the cluster. Additionally, the discovery pattern may also map the computing cluster by establishing relationships between the discovered configuration items of the cluster. 
     Information indicative of the discovered configuration items and/or the relationships therebetween may be stored in a database of the remote network management platform. This information may be used to determine a status of the server cluster (e.g., operational status, configuration, and/or property of the server cluster and/or a component thereof). Additionally and/or alternatively, a graphical user interface that depicts the mapping of the server cluster may be provided to a user, perhaps so that the user may quickly determine the status of the cluster. 
     Accordingly, a first example embodiment may involve requesting and receiving, from a first controller and by a proxy server application, computing cluster data that identifies a computing cluster. The first controller is associated with one of a plurality of computing devices of the computing cluster, and the computing cluster and the proxy server application are disposed within a managed network. The computing cluster provides networking, storage, and virtualization services distributed across the plurality of computing devices, the plurality of computing devices are communicatively coupled via a local-area network, and each computing device is configured to execute one or more respective software applications and comprises: (i) a respective controller, and (ii) a respective storage device. The method further includes requesting and receiving, from the first controller and by the proxy server application, storage pool data that identifies a storage pool of the computing cluster, where the storage pool is provided by the storage devices of the plurality of computing devices. The method also includes requesting and receiving, from the first controller and by the proxy server application, storage container data that identifies storage containers of the storage pool, where a storage container includes a subset of available storage in the storage pool. Further, the method includes requesting and receiving, from the first controller and by the proxy server application, controller data that identifies the controllers of the plurality computing devices. Yet further, the method includes providing, to a database disposed within a remote network management platform and by the proxy server application, the computing cluster data, the storage pool data, the storage container data, and the controller data. 
     In a second example embodiment, an article of manufacture may include a non-transitory computer-readable medium, having stored thereon program instructions that, upon execution by a computing system, cause the computing system to perform operations in accordance with the first example embodiment. 
     In a third example embodiment, a computing system may include at least one processor, as well as memory and program instructions. The program instructions may be stored in the memory, and upon execution by the at least one processor, cause the computing system to perform operations in accordance with the first example embodiment. 
     In a fourth example embodiment, a system may include various means for carrying out each of the operations of the first example embodiment. 
     These as well as other embodiments, aspects, advantages, and alternatives will become apparent to those of ordinary skill in the art by reading the following detailed description, with reference where appropriate to the accompanying drawings. Further, this summary and other descriptions and figures provided herein are intended to illustrate embodiments by way of example only and, as such, that numerous variations are possible. For instance, structural elements and process steps can be rearranged, combined, distributed, eliminated, or otherwise changed, while remaining within the scope of the embodiments as claimed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a schematic drawing of a computing device, in accordance with example embodiments. 
         FIG. 2  illustrates a schematic drawing of a server device cluster, in accordance with example embodiments. 
         FIG. 3  depicts a remote network management architecture, in accordance with example embodiments. 
         FIG. 4  depicts a communication environment involving a remote network management architecture, in accordance with example embodiments. 
         FIG. 5A  depicts another communication environment involving a remote network management architecture, in accordance with example embodiments. 
         FIG. 5B  is a flow chart, in accordance with example embodiments. 
         FIG. 6  is a computing cluster, in accordance with example embodiments. 
         FIG. 7  is a messaging diagram, in accordance with example embodiments. 
         FIG. 8A  is a representation of configuration items discovered by initial discovery, in accordance with example embodiments. 
         FIG. 8B  is a representation of configuration items discovered by a computing cluster discovery pattern, in accordance with example embodiments. 
         FIGS. 9A and 9B  are graphical user interfaces for displaying representations of a computing cluster, in accordance with example embodiments. 
         FIG. 10  is a flow chart, in accordance with example embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Example methods, devices, and systems are described herein. It should be understood that the words “example” and “exemplary” are used herein to mean “serving as an example, instance, or illustration.” Any embodiment or feature described herein as being an “example” or “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or features unless stated as such. Thus, other embodiments can be utilized and other changes can be made without departing from the scope of the subject matter presented herein. 
     Accordingly, the example embodiments described herein are not meant to be limiting. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations. For example, the separation of features into “client” and “server” components may occur in a number of ways. 
     Further, unless context suggests otherwise, the features illustrated in each of the figures may be used in combination with one another. Thus, the figures should be generally viewed as component aspects of one or more overall embodiments, with the understanding that not all illustrated features are necessary for each embodiment. 
     Additionally, any enumeration of elements, blocks, or steps in this specification or the claims is for purposes of clarity. Thus, such enumeration should not be interpreted to require or imply that these elements, blocks, or steps adhere to a particular arrangement or are carried out in a particular order. 
     I. INTRODUCTION 
     A large enterprise is a complex entity with many interrelated operations. Some of these are found across the enterprise, such as human resources (HR), supply chain, information technology (IT), and finance. However, each enterprise also has its own unique operations that provide essential capabilities and/or create competitive advantages. 
     To support widely-implemented operations, enterprises typically use off-the-shelf software applications, such as customer relationship management (CRM) and human capital management (HCM) packages. However, they may also need custom software applications to meet their own unique requirements. A large enterprise often has dozens or hundreds of these custom software applications. Nonetheless, the advantages provided by the embodiments herein are not limited to large enterprises and may be applicable to an enterprise, or any other type of organization, of any size. 
     Many such software applications are developed by individual departments within the enterprise. These range from simple spreadsheets to custom-built software tools and databases. But the proliferation of siloed custom software applications has numerous disadvantages. It negatively impacts an enterprise&#39;s ability to run and grow its operations, innovate, and meet regulatory requirements. The enterprise may find it difficult to integrate, streamline and enhance its operations due to lack of a single system that unifies its subsystems and data. 
     To efficiently create custom applications, enterprises would benefit from a remotely-hosted application platform that eliminates unnecessary development complexity. The goal of such a platform would be to reduce time-consuming, repetitive application development tasks so that software engineers and individuals in other roles can focus on developing unique, high-value features. 
     In order to achieve this goal, the concept of Application Platform as a Service (aPaaS) is introduced, to intelligently automate workflows throughout the enterprise. An aPaaS system is hosted remotely from the enterprise, but may access data, applications, and services within the enterprise by way of secure connections. Such an aPaaS system may have a number of advantageous capabilities and characteristics. These advantages and characteristics may be able to improve the enterprise&#39;s operations and workflow for IT, HR, CRM, customer service, application development, and security. 
     The aPaaS system may support development and execution of model-view-controller (MVC) applications. MVC applications divide their functionality into three interconnected parts (model, view, and controller) in order to isolate representations of information from the manner in which the information is presented to the user, thereby allowing for efficient code reuse and parallel development. These applications may be web-based, and offer create, read, update, delete (CRUD) capabilities. This allows new applications to be built on a common application infrastructure. 
     The aPaaS system may support standardized application components, such as a standardized set of widgets for graphical user interface (GUI) development. In this way, applications built using the aPaaS system have a common look and feel. Other software components and modules may be standardized as well. In some cases, this look and feel can be branded or skinned with an enterprise&#39;s custom logos and/or color schemes. 
     The aPaaS system may support the ability to configure the behavior of applications using metadata. This allows application behaviors to be rapidly adapted to meet specific needs. Such an approach reduces development time and increases flexibility. Further, the aPaaS system may support GUI tools that facilitate metadata creation and management, thus reducing errors in the metadata. 
     The aPaaS system may support clearly-defined interfaces between applications, so that software developers can avoid unwanted inter-application dependencies. Thus, the aPaaS system may implement a service layer in which persistent state information and other data is stored. 
     The aPaaS system may support a rich set of integration features so that the applications thereon can interact with legacy applications and third-party applications. For instance, the aPaaS system may support a custom employee-onboarding system that integrates with legacy HR, IT, and accounting systems. 
     The aPaaS system may support enterprise-grade security. Furthermore, since the aPaaS system may be remotely hosted, it should also utilize security procedures when it interacts with systems in the enterprise or third-party networks and services hosted outside of the enterprise. For example, the aPaaS system may be configured to share data amongst the enterprise and other parties to detect and identify common security threats. 
     Other features, functionality, and advantages of an aPaaS system may exist. This description is for purpose of example and is not intended to be limiting. 
     As an example of the aPaaS development process, a software developer may be tasked to create a new application using the aPaaS system. First, the developer may define the data model, which specifies the types of data that the application uses and the relationships therebetween. Then, via a GUI of the aPaaS system, the developer enters (e.g., uploads) the data model. The aPaaS system automatically creates all of the corresponding database tables, fields, and relationships, which can then be accessed via an object-oriented services layer. 
     In addition, the aPaaS system can also build a fully-functional MVC application with client-side interfaces and server-side CRUD logic. This generated application may serve as the basis of further development for the user. Advantageously, the developer does not have to spend a large amount of time on basic application functionality. Further, since the application may be web-based, it can be accessed from any Internet-enabled client device. Alternatively or additionally, a local copy of the application may be able to be accessed, for instance, when Internet service is not available. 
     The aPaaS system may also support a rich set of pre-defined functionality that can be added to applications. These features include support for searching, email, templating, workflow design, reporting, analytics, social media, scripting, mobile-friendly output, and customized GUIs. 
     The following embodiments describe architectural and functional aspects of example aPaaS systems, as well as the features and advantages thereof. 
     II. EXAMPLE COMPUTING DEVICES AND CLOUD-BASED COMPUTING ENVIRONMENTS 
       FIG. 1  is a simplified block diagram exemplifying a computing device  100 , illustrating some of the components that could be included in a computing device arranged to operate in accordance with the embodiments herein. Computing device  100  could be a client device (e.g., a device actively operated by a user), a server device (e.g., a device that provides computational services to client devices), or some other type of computational platform. Some server devices may operate as client devices from time to time in order to perform particular operations, and some client devices may incorporate server features. 
     In this example, computing device  100  includes processor  102 , memory  104 , network interface  106 , and an input/output unit  108 , all of which may be coupled by a system bus  110  or a similar mechanism. In some embodiments, computing device  100  may include other components and/or peripheral devices (e.g., detachable storage, printers, and so on). 
     Processor  102  may be one or more of any type of computer processing element, such as a central processing unit (CPU), a co-processor (e.g., a mathematics, graphics, or encryption co-processor), a digital signal processor (DSP), a network processor, and/or a form of integrated circuit or controller that performs processor operations. In some cases, processor  102  may be one or more single-core processors. In other cases, processor  102  may be one or more multi-core processors with multiple independent processing units. Processor  102  may also include register memory for temporarily storing instructions being executed and related data, as well as cache memory for temporarily storing recently-used instructions and data. 
     Memory  104  may be any form of computer-usable memory, including but not limited to random access memory (RAM), read-only memory (ROM), and non-volatile memory (e.g., flash memory, hard disk drives, solid state drives, compact discs (CDs), digital video discs (DVDs), and/or tape storage). Thus, memory  104  represents both main memory units, as well as long-term storage. Other types of memory may include biological memory. 
     Memory  104  may store program instructions and/or data on which program instructions may operate. By way of example, memory  104  may store these program instructions on a non-transitory, computer-readable medium, such that the instructions are executable by processor  102  to carry out any of the methods, processes, or operations disclosed in this specification or the accompanying drawings. 
     As shown in  FIG. 1 , memory  104  may include firmware  104 A, kernel  104 B, and/or applications  104 C. Firmware  104 A may be program code used to boot or otherwise initiate some or all of computing device  100 . Kernel  104 B may be an operating system, including modules for memory management, scheduling and management of processes, input/output, and communication. Kernel  104 B may also include device drivers that allow the operating system to communicate with the hardware modules (e.g., memory units, networking interfaces, ports, and busses), of computing device  100 . Applications  104 C may be one or more user-space software programs, such as web browsers or email clients, as well as any software libraries used by these programs. Memory  104  may also store data used by these and other programs and applications. 
     Network interface  106  may take the form of one or more wireline interfaces, such as Ethernet (e.g., Fast Ethernet, Gigabit Ethernet, and so on). Network interface  106  may also support communication over one or more non-Ethernet media, such as coaxial cables or power lines, or over wide-area media, such as Synchronous Optical Networking (SONET) or digital subscriber line (DSL) technologies. Network interface  106  may additionally take the form of one or more wireless interfaces, such as IEEE 802.11 (Wifi), BLUETOOTH®, global positioning system (GPS), or a wide-area wireless interface. However, other forms of physical layer interfaces and other types of standard or proprietary communication protocols may be used over network interface  106 . Furthermore, network interface  106  may comprise multiple physical interfaces. For instance, some embodiments of computing device  100  may include Ethernet, BLUETOOTH®, and Wifi interfaces. 
     Input/output unit  108  may facilitate user and peripheral device interaction with computing device  100 . Input/output unit  108  may include one or more types of input devices, such as a keyboard, a mouse, a touch screen, and so on. Similarly, input/output unit  108  may include one or more types of output devices, such as a screen, monitor, printer, and/or one or more light emitting diodes (LEDs). Additionally or alternatively, computing device  100  may communicate with other devices using a universal serial bus (USB) or high-definition multimedia interface (HDMI) port interface, for example. 
     In some embodiments, one or more computing devices like computing device  100  may be deployed to support an aPaaS architecture. The exact physical location, connectivity, and configuration of these computing devices may be unknown and/or unimportant to client devices. Accordingly, the computing devices may be referred to as “cloud-based” devices that may be housed at various remote data center locations. 
       FIG. 2  depicts a cloud-based server cluster  200  in accordance with example embodiments. In  FIG. 2 , operations of a computing device (e.g., computing device  100 ) may be distributed between server devices  202 , data storage  204 , and routers  206 , all of which may be connected by local cluster network  208 . The number of server devices  202 , data storages  204 , and routers  206  in server cluster  200  may depend on the computing task(s) and/or applications assigned to server cluster  200 . 
     For example, server devices  202  can be configured to perform various computing tasks of computing device  100 . Thus, computing tasks can be distributed among one or more of server devices  202 . To the extent that these computing tasks can be performed in parallel, such a distribution of tasks may reduce the total time to complete these tasks and return a result. For purpose of simplicity, both server cluster  200  and individual server devices  202  may be referred to as a “server device.” This nomenclature should be understood to imply that one or more distinct server devices, data storage devices, and cluster routers may be involved in server device operations. 
     Data storage  204  may be data storage arrays that include drive array controllers configured to manage read and write access to groups of hard disk drives and/or solid state drives. The drive array controllers, alone or in conjunction with server devices  202 , may also be configured to manage backup or redundant copies of the data stored in data storage  204  to protect against drive failures or other types of failures that prevent one or more of server devices  202  from accessing units of data storage  204 . Other types of memory aside from drives may be used. 
     Routers  206  may include networking equipment configured to provide internal and external communications for server cluster  200 . For example, routers  206  may include one or more packet-switching and/or routing devices (including switches and/or gateways) configured to provide (i) network communications between server devices  202  and data storage  204  via local cluster network  208 , and/or (ii) network communications between the server cluster  200  and other devices via communication link  210  to network  212 . 
     Additionally, the configuration of routers  206  can be based at least in part on the data communication requirements of server devices  202  and data storage  204 , the latency and throughput of the local cluster network  208 , the latency, throughput, and cost of communication link  210 , and/or other factors that may contribute to the cost, speed, fault-tolerance, resiliency, efficiency and/or other design goals of the system architecture. 
     As a possible example, data storage  204  may include any form of database, such as a structured query language (SQL) database. Various types of data structures may store the information in such a database, including but not limited to tables, arrays, lists, trees, and tuples. Furthermore, any databases in data storage  204  may be monolithic or distributed across multiple physical devices. 
     Server devices  202  may be configured to transmit data to and receive data from data storage  204 . This transmission and retrieval may take the form of SQL queries or other types of database queries, and the output of such queries, respectively. Additional text, images, video, and/or audio may be included as well. Furthermore, server devices  202  may organize the received data into web page representations. Such a representation may take the form of a markup language, such as the hypertext markup language (HTML), the extensible markup language (XML), or some other standardized or proprietary format. Moreover, server devices  202  may have the capability of executing various types of computerized scripting languages, such as but not limited to Perl, Python, PHP Hypertext Preprocessor (PHP), Active Server Pages (ASP), JavaScript, and so on. Computer program code written in these languages may facilitate the providing of web pages to client devices, as well as client device interaction with the web pages. 
     III. EXAMPLE REMOTE NETWORK MANAGEMENT ARCHITECTURE 
       FIG. 3  depicts a remote network management architecture, in accordance with example embodiments. This architecture includes three main components, managed network  300 , remote network management platform  320 , and third-party networks  340 , all connected by way of Internet  350 . 
     Managed network  300  may be, for example, an enterprise network used by an entity for computing and communications tasks, as well as storage of data. Thus, managed network  300  may include various client devices  302 , server devices  304 , routers  306 , virtual machines  308 , firewall  310 , and/or proxy servers  312 . Client devices  302  may be embodied by computing device  100 , server devices  304  may be embodied by computing device  100  or server cluster  200 , and routers  306  may be any type of router, switch, or gateway. 
     Virtual machines  308  may be embodied by one or more of computing device  100  or server cluster  200 . In general, a virtual machine is an emulation of a computing system, and mimics the functionality (e.g., processor, memory, and communication resources) of a physical computer. One physical computing system, such as server cluster  200 , may support up to thousands of individual virtual machines. In some embodiments, virtual machines  308  may be managed by a centralized server device or application that facilitates allocation of physical computing resources to individual virtual machines, as well as performance and error reporting. Enterprises often employ virtual machines in order to allocate computing resources in an efficient, as needed fashion. Providers of virtualized computing systems include VMWARE® and MICROSOFT®. 
     Firewall  310  may be one or more specialized routers or server devices that protect managed network  300  from unauthorized attempts to access the devices, applications, and services therein, while allowing authorized communication that is initiated from managed network  300 . Firewall  310  may also provide intrusion detection, web filtering, virus scanning, application-layer gateways, and other applications or services. In some embodiments not shown in  FIG. 3 , managed network  300  may include one or more virtual private network (VPN) gateways with which it communicates with remote network management platform  320  (see below). 
     Managed network  300  may also include one or more proxy servers  312 . An embodiment of proxy servers  312  may be a server device that facilitates communication and movement of data between managed network  300 , remote network management platform  320 , and third-party networks  340 . In particular, proxy servers  312  may be able to establish and maintain secure communication sessions with one or more computational instances of remote network management platform  320 . By way of such a session, remote network management platform  320  may be able to discover and manage aspects of the architecture and configuration of managed network  300  and its components. Possibly with the assistance of proxy servers  312 , remote network management platform  320  may also be able to discover and manage aspects of third-party networks  340  that are used by managed network  300 . 
     Firewalls, such as firewall  310 , typically deny all communication sessions that are incoming by way of Internet  350 , unless such a session was ultimately initiated from behind the firewall (i.e., from a device on managed network  300 ) or the firewall has been explicitly configured to support the session. By placing proxy servers  312  behind firewall  310  (e.g., within managed network  300  and protected by firewall  310 ), proxy servers  312  may be able to initiate these communication sessions through firewall  310 . Thus, firewall  310  might not have to be specifically configured to support incoming sessions from remote network management platform  320 , thereby avoiding potential security risks to managed network  300 . 
     In some cases, managed network  300  may consist of a few devices and a small number of networks. In other deployments, managed network  300  may span multiple physical locations and include hundreds of networks and hundreds of thousands of devices. Thus, the architecture depicted in  FIG. 3  is capable of scaling up or down by orders of magnitude. 
     Furthermore, depending on the size, architecture, and connectivity of managed network  300 , a varying number of proxy servers  312  may be deployed therein. For example, each one of proxy servers  312  may be responsible for communicating with remote network management platform  320  regarding a portion of managed network  300 . Alternatively or additionally, sets of two or more proxy servers may be assigned to such a portion of managed network  300  for purposes of load balancing, redundancy, and/or high availability. 
     Remote network management platform  320  is a hosted environment that provides aPaaS services to users, particularly to the operators of managed network  300 . These services may take the form of web-based portals, for instance. Thus, a user can securely access remote network management platform  320  from, for instance, client devices  302 , or potentially from a client device outside of managed network  300 . By way of the web-based portals, users may design, test, and deploy applications, generate reports, view analytics, and perform other tasks. 
     As shown in  FIG. 3 , remote network management platform  320  includes four computational instances  322 ,  324 ,  326 , and  328 . Each of these instances may represent one or more server devices and/or one or more databases that provide a set of web portals, services, and applications (e.g., a wholly-functioning aPaaS system) available to a particular customer. In some cases, a single customer may use multiple computational instances. For example, managed network  300  may be an enterprise customer of remote network management platform  320 , and may use computational instances  322 ,  324 , and  326 . The reason for providing multiple instances to one customer is that the customer may wish to independently develop, test, and deploy its applications and services. Thus, computational instance  322  may be dedicated to application development related to managed network  300 , computational instance  324  may be dedicated to testing these applications, and computational instance  326  may be dedicated to the live operation of tested applications and services. A computational instance may also be referred to as a hosted instance, a remote instance, a customer instance, or by some other designation. Any application deployed onto a computational instance may be a scoped application, in that its access to databases within the computational instance can be restricted to certain elements therein (e.g., one or more particular database tables or particular rows with one or more database tables). 
     For purpose of clarity, the disclosure herein refers to the physical hardware, software, and arrangement thereof as a “computational instance.” Note that users may colloquially refer to the graphical user interfaces provided thereby as “instances.” But unless it is defined otherwise herein, a “computational instance” is a computing system disposed within remote network management platform  320 . 
     The multi-instance architecture of remote network management platform  320  is in contrast to conventional multi-tenant architectures, over which multi-instance architectures have several advantages. In multi-tenant architectures, data from different customers (e.g., enterprises) are comingled in a single database. While these customers&#39; data are separate from one another, the separation is enforced by the software that operates the single database. As a consequence, a security breach in this system may impact all customers&#39; data, creating additional risk, especially for entities subject to governmental, healthcare, and/or financial regulation. Furthermore, any database operations that impact one customer will likely impact all customers sharing that database. Thus, if there is an outage due to hardware or software errors, this outage affects all such customers. Likewise, if the database is to be upgraded to meet the needs of one customer, it will be unavailable to all customers during the upgrade process. Often, such maintenance windows will be long, due to the size of the shared database. 
     In contrast, the multi-instance architecture provides each customer with its own database in a dedicated computing instance. This prevents comingling of customer data, and allows each instance to be independently managed. For example, when one customer&#39;s instance experiences an outage due to errors or an upgrade, other computational instances are not impacted. Maintenance down time is limited because the database only contains one customer&#39;s data. Further, the simpler design of the multi-instance architecture allows redundant copies of each customer database and instance to be deployed in a geographically diverse fashion. This facilitates high availability, where the live version of the customer&#39;s instance can be moved when faults are detected or maintenance is being performed. 
     In some embodiments, remote network management platform  320  may include one or more central instances, controlled by the entity that operates this platform. Like a computational instance, a central instance may include some number of physical or virtual servers and database devices. Such a central instance may serve as a repository for data that can be shared amongst at least some of the computational instances. For instance, definitions of common security threats that could occur on the computational instances, software packages that are commonly discovered on the computational instances, and/or an application store for applications that can be deployed to the computational instances may reside in a central instance. Computational instances may communicate with central instances by way of well-defined interfaces in order to obtain this data. 
     In order to support multiple computational instances in an efficient fashion, remote network management platform  320  may implement a plurality of these instances on a single hardware platform. For example, when the aPaaS system is implemented on a server cluster such as server cluster  200 , it may operate a virtual machine that dedicates varying amounts of computational, storage, and communication resources to instances. But full virtualization of server cluster  200  might not be necessary, and other mechanisms may be used to separate instances. In some examples, each instance may have a dedicated account and one or more dedicated databases on server cluster  200 . Alternatively, computational instance  322  may span multiple physical devices. 
     In some cases, a single server cluster of remote network management platform  320  may support multiple independent enterprises. Furthermore, as described below, remote network management platform  320  may include multiple server clusters deployed in geographically diverse data centers in order to facilitate load balancing, redundancy, and/or high availability. 
     Third-party networks  340  may be remote server devices (e.g., a plurality of server clusters such as server cluster  200 ) that can be used for outsourced computational, data storage, communication, and service hosting operations. These servers may be virtualized (i.e., the servers may be virtual machines). Examples of third-party networks  340  may include AMAZON WEB SERVICES® and MICROSOFT® Azure. Like remote network management platform  320 , multiple server clusters supporting third-party networks  340  may be deployed at geographically diverse locations for purposes of load balancing, redundancy, and/or high availability. 
     Managed network  300  may use one or more of third-party networks  340  to deploy applications and services to its clients and customers. For instance, if managed network  300  provides online music streaming services, third-party networks  340  may store the music files and provide web interface and streaming capabilities. In this way, the enterprise of managed network  300  does not have to build and maintain its own servers for these operations. 
     Remote network management platform  320  may include modules that integrate with third-party networks  340  to expose virtual machines and managed services therein to managed network  300 . The modules may allow users to request virtual resources and provide flexible reporting for third-party networks  340 . In order to establish this functionality, a user from managed network  300  might first establish an account with third-party networks  340 , and request a set of associated resources. Then, the user may enter the account information into the appropriate modules of remote network management platform  320 . These modules may then automatically discover the manageable resources in the account, and also provide reports related to usage, performance, and billing. 
     Internet  350  may represent a portion of the global Internet. However, Internet  350  may alternatively represent a different type of network, such as a private wide-area or local-area packet-switched network. 
       FIG. 4  further illustrates the communication environment between managed network  300  and computational instance  322 , and introduces additional features and alternative embodiments. In  FIG. 4 , computational instance  322  is replicated across data centers  400 A and  400 B. These data centers may be geographically distant from one another, perhaps in different cities or different countries. Each data center includes support equipment that facilitates communication with managed network  300 , as well as remote users. 
     In data center  400 A, network traffic to and from external devices flows either through VPN gateway  402 A or firewall  404 A. VPN gateway  402 A may be peered with VPN gateway  412  of managed network  300  by way of a security protocol such as Internet Protocol Security (IPSEC) or Transport Layer Security (TLS). Firewall  404 A may be configured to allow access from authorized users, such as user  414  and remote user  416 , and to deny access to unauthorized users. By way of firewall  404 A, these users may access computational instance  322 , and possibly other computational instances. Load balancer  406 A may be used to distribute traffic amongst one or more physical or virtual server devices that host computational instance  322 . Load balancer  406 A may simplify user access by hiding the internal configuration of data center  400 A, (e.g., computational instance  322 ) from client devices. For instance, if computational instance  322  includes multiple physical or virtual computing devices that share access to multiple databases, load balancer  406 A may distribute network traffic and processing tasks across these computing devices and databases so that no one computing device or database is significantly busier than the others. In some embodiments, computational instance  322  may include VPN gateway  402 A, firewall  404 A, and load balancer  406 A. 
     Data center  400 B may include its own versions of the components in data center  400 A. Thus, VPN gateway  402 B, firewall  404 B, and load balancer  406 B may perform the same or similar operations as VPN gateway  402 A, firewall  404 A, and load balancer  406 A, respectively. Further, by way of real-time or near-real-time database replication and/or other operations, computational instance  322  may exist simultaneously in data centers  400 A and  400 B. 
     Data centers  400 A and  400 B as shown in  FIG. 4  may facilitate redundancy and high availability. In the configuration of  FIG. 4 , data center  400 A is active and data center  400 B is passive. Thus, data center  400 A is serving all traffic to and from managed network  300 , while the version of computational instance  322  in data center  400 B is being updated in near-real-time. Other configurations, such as one in which both data centers are active, may be supported. 
     Should data center  400 A fail in some fashion or otherwise become unavailable to users, data center  400 B can take over as the active data center. For example, domain name system (DNS) servers that associate a domain name of computational instance  322  with one or more Internet Protocol (IP) addresses of data center  400 A may re-associate the domain name with one or more IP addresses of data center  400 B. After this re-association completes (which may take less than one second or several seconds), users may access computational instance  322  by way of data center  400 B. 
       FIG. 4  also illustrates a possible configuration of managed network  300 . As noted above, proxy servers  312  and user  414  may access computational instance  322  through firewall  310 . Proxy servers  312  may also access configuration items  410 . In  FIG. 4 , configuration items  410  may refer to any or all of client devices  302 , server devices  304 , routers  306 , and virtual machines  308 , any applications or services executing thereon, as well as relationships between devices, applications, and services. Thus, the term “configuration items” may be shorthand for any physical or virtual device, or any application or service remotely discoverable or managed by computational instance  322 , or relationships between discovered devices, applications, and services. Configuration items may be represented in a configuration management database (CMDB) of computational instance  322 . 
     As noted above, VPN gateway  412  may provide a dedicated VPN to VPN gateway  402 A. Such a VPN may be helpful when there is a significant amount of traffic between managed network  300  and computational instance  322 , or security policies otherwise suggest or require use of a VPN between these sites. In some embodiments, any device in managed network  300  and/or computational instance  322  that directly communicates via the VPN is assigned a public IP address. Other devices in managed network  300  and/or computational instance  322  may be assigned private IP addresses (e.g., IP addresses selected from the 10.0.0.0-10.255.255.255 or 192.168.0.0-192.168.255.255 ranges, represented in shorthand as subnets 10.0.0.0/8 and 192.168.0.0/16, respectively). 
     IV. EXAMPLE DEVICE, APPLICATION, AND SERVICE DISCOVERY 
     In order for remote network management platform  320  to administer the devices, applications, and services of managed network  300 , remote network management platform  320  may first determine what devices are present in managed network  300 , the configurations and operational statuses of these devices, and the applications and services provided by the devices, and well as the relationships between discovered devices, applications, and services. As noted above, each device, application, service, and relationship may be referred to as a configuration item. The process of defining configuration items within managed network  300  is referred to as discovery, and may be facilitated at least in part by proxy servers  312 . 
     For purpose of the embodiments herein, an “application” may refer to one or more processes, threads, programs, client modules, server modules, or any other software that executes on a device or group of devices. A “service” may refer to a high-level capability provided by multiple applications executing on one or more devices working in conjunction with one another. For example, a high-level web service may involve multiple web application server threads executing on one device and accessing information from a database application that executes on another device. 
       FIG. 5A  provides a logical depiction of how configuration items can be discovered, as well as how information related to discovered configuration items can be stored. For sake of simplicity, remote network management platform  320 , third-party networks  340 , and Internet  350  are not shown. 
     In  FIG. 5A , CMDB  500  and task list  502  are stored within computational instance  322 . Computational instance  322  may transmit discovery commands to proxy servers  312 . In response, proxy servers  312  may transmit probes to various devices, applications, and services in managed network  300 . These devices, applications, and services may transmit responses to proxy servers  312 , and proxy servers  312  may then provide information regarding discovered configuration items to CMDB  500  for storage therein. Configuration items stored in CMDB  500  represent the environment of managed network  300 . 
     Task list  502  represents a list of activities that proxy servers  312  are to perform on behalf of computational instance  322 . As discovery takes place, task list  502  is populated. Proxy servers  312  repeatedly query task list  502 , obtain the next task therein, and perform this task until task list  502  is empty or another stopping condition has been reached. 
     To facilitate discovery, proxy servers  312  may be configured with information regarding one or more subnets in managed network  300  that are reachable by way of proxy servers  312 . For instance, proxy servers  312  may be given the IP address range 192.168.0/24 as a subnet. Then, computational instance  322  may store this information in CMDB  500  and place tasks in task list  502  for discovery of devices at each of these addresses. 
       FIG. 5A  also depicts devices, applications, and services in managed network  300  as configuration items  504 ,  506 ,  508 ,  510 , and  512 . As noted above, these configuration items represent a set of physical and/or virtual devices (e.g., client devices, server devices, routers, or virtual machines), applications executing thereon (e.g., web servers, email servers, databases, or storage arrays), relationships therebetween, as well as services that involve multiple individual configuration items. 
     Placing the tasks in task list  502  may trigger or otherwise cause proxy servers  312  to begin discovery. Alternatively or additionally, discovery may be manually triggered or automatically triggered based on triggering events (e.g., discovery may automatically begin once per day at a particular time). 
     In general, discovery may proceed in four logical phases: scanning, classification, identification, and exploration. Each phase of discovery involves various types of probe messages being transmitted by proxy servers  312  to one or more devices in managed network  300 . The responses to these probes may be received and processed by proxy servers  312 , and representations thereof may be transmitted to CMDB  500 . Thus, each phase can result in more configuration items being discovered and stored in CMDB  500 . 
     In the scanning phase, proxy servers  312  may probe each IP address in the specified range of IP addresses for open Transmission Control Protocol (TCP) and/or User Datagram Protocol (UDP) ports to determine the general type of device. The presence of such open ports at an IP address may indicate that a particular application is operating on the device that is assigned the IP address, which in turn may identify the operating system used by the device. For example, if TCP port  135  is open, then the device is likely executing a WINDOWS® operating system. Similarly, if TCP port  22  is open, then the device is likely executing a UNIX® operating system, such as LINUX®. If UDP port  161  is open, then the device may be able to be further identified through the Simple Network Management Protocol (SNMP). Other possibilities exist. Once the presence of a device at a particular IP address and its open ports have been discovered, these configuration items are saved in CMDB  500 . 
     In the classification phase, proxy servers  312  may further probe each discovered device to determine the version of its operating system. The probes used for a particular device are based on information gathered about the devices during the scanning phase. For example, if a device is found with TCP port  22  open, a set of UNIX®-specific probes may be used. Likewise, if a device is found with TCP port  135  open, a set of WINDOWS®-specific probes may be used. For either case, an appropriate set of tasks may be placed in task list  502  for proxy servers  312  to carry out. These tasks may result in proxy servers  312  logging on, or otherwise accessing information from the particular device. For instance, if TCP port  22  is open, proxy servers  312  may be instructed to initiate a Secure Shell (SSH) connection to the particular device and obtain information about the operating system thereon from particular locations in the file system. Based on this information, the operating system may be determined. As an example, a UNIX® device with TCP port  22  open may be classified as AIX®, HPUX, LINUX®, MACOS®, or SOLARIS®. This classification information may be stored as one or more configuration items in CMDB  500 . 
     In the identification phase, proxy servers  312  may determine specific details about a classified device. The probes used during this phase may be based on information gathered about the particular devices during the classification phase. For example, if a device was classified as LINUX®, a set of LINUX®-specific probes may be used. Likewise if a device was classified as WINDOWS® 2012, as a set of WINDOWS®-2012-specific probes may be used. As was the case for the classification phase, an appropriate set of tasks may be placed in task list  502  for proxy servers  312  to carry out. These tasks may result in proxy servers  312  reading information from the particular device, such as basic input/output system (BIOS) information, serial numbers, network interface information, media access control address(es) assigned to these network interface(s), IP address(es) used by the particular device and so on. This identification information may be stored as one or more configuration items in CMDB  500 . 
     In the exploration phase, proxy servers  312  may determine further details about the operational state of a classified device. The probes used during this phase may be based on information gathered about the particular devices during the classification phase and/or the identification phase. Again, an appropriate set of tasks may be placed in task list  502  for proxy servers  312  to carry out. These tasks may result in proxy servers  312  reading additional information from the particular device, such as processor information, memory information, lists of running processes (applications), and so on. Once more, the discovered information may be stored as one or more configuration items in CMDB  500 . 
     Running discovery on a network device, such as a router, may utilize SNMP. Instead of or in addition to determining a list of running processes or other application-related information, discovery may determine additional subnets known to the router and the operational state of the router&#39;s network interfaces (e.g., active, inactive, queue length, number of packets dropped, etc.). The IP addresses of the additional subnets may be candidates for further discovery procedures. Thus, discovery may progress iteratively or recursively. 
     Once discovery completes, a snapshot representation of each discovered device, application, and service is available in CMDB  500 . For example, after discovery, operating system version, hardware configuration and network configuration details for client devices, server devices, and routers in managed network  300 , as well as applications executing thereon, may be stored. This collected information may be presented to a user in various ways to allow the user to view the hardware composition and operational status of devices, as well as the characteristics of services that span multiple devices and applications. 
     Furthermore, CMDB  500  may include entries regarding dependencies and relationships between configuration items. More specifically, an application that is executing on a particular server device, as well as the services that rely on this application, may be represented as such in CMDB  500 . For instance, suppose that a database application is executing on a server device, and that this database application is used by a new employee onboarding service as well as a payroll service. Thus, if the server device is taken out of operation for maintenance, it is clear that the employee onboarding service and payroll service will be impacted. Likewise, the dependencies and relationships between configuration items may be able to represent the services impacted when a particular router fails. 
     In general, dependencies and relationships between configuration items may be displayed on a web-based interface and represented in a hierarchical fashion. Thus, adding, changing, or removing such dependencies and relationships may be accomplished by way of this interface. 
     Furthermore, users from managed network  300  may develop workflows that allow certain coordinated activities to take place across multiple discovered devices. For instance, an IT workflow might allow the user to change the common administrator password to all discovered LINUX® devices in single operation. 
     In order for discovery to take place in the manner described above, proxy servers  312 , CMDB  500 , and/or one or more credential stores may be configured with credentials for one or more of the devices to be discovered. Credentials may include any type of information needed in order to access the devices. These may include userid/password pairs, certificates, and so on. In some embodiments, these credentials may be stored in encrypted fields of CMDB  500 . Proxy servers  312  may contain the decryption key for the credentials so that proxy servers  312  can use these credentials to log on to or otherwise access devices being discovered. 
     The discovery process is depicted as a flow chart in  FIG. 5B . At block  520 , the task list in the computational instance is populated, for instance, with a range of IP addresses. At block  522 , the scanning phase takes place. Thus, the proxy servers probe the IP addresses for devices using these IP addresses, and attempt to determine the operating systems that are executing on these devices. At block  524 , the classification phase takes place. The proxy servers attempt to determine the operating system version of the discovered devices. At block  526 , the identification phase takes place. The proxy servers attempt to determine the hardware and/or software configuration of the discovered devices. At block  528 , the exploration phase takes place. The proxy servers attempt to determine the operational state and applications executing on the discovered devices. At block  530 , further editing of the configuration items representing the discovered devices and applications may take place. This editing may be automated and/or manual in nature. 
     The blocks represented in  FIG. 5B  are for purpose of example. Discovery may be a highly configurable procedure that can have more or fewer phases, and the operations of each phase may vary. In some cases, one or more phases may be customized, or may otherwise deviate from the exemplary descriptions above. 
     V. CMDB IDENTIFICATION RULES AND RECONCILIATION 
     A CMDB, such as CMDB  500 , provides a repository of configuration items, and when properly provisioned, can take on a key role in higher-layer applications deployed within or involving a computational instance. These applications may relate to enterprise IT service management, operations management, asset management, configuration management, compliance, and so on. 
     For example, an IT service management application may use information in the CMDB to determine applications and services that may be impacted by a component (e.g., a server device) that has malfunctioned, crashed, or is heavily loaded. Likewise, an asset management application may use information in the CMDB to determine which hardware and/or software components are being used to support particular enterprise applications. As a consequence of the importance of the CMDB, it is desirable for the information stored therein to be accurate, consistent, and up to date. 
     A CMDB may be populated in various ways. As discussed above, a discovery procedure may automatically store information related to configuration items in the CMDB. However, a CMDB can also be populated, as a whole or in part, by manual entry, configuration files, and third-party data sources. Given that multiple data sources may be able to update the CMDB at any time, it is possible that one data source may overwrite entries of another data source. Also, two data sources may each create slightly different entries for the same configuration item, resulting in a CMDB containing duplicate data. When either of these occurrences takes place, they can cause the health and utility of the CMDB to be reduced. 
     In order to mitigate this situation, these data sources might not write configuration items directly to the CMDB. Instead, they may write to an identification and reconciliation application programming interface (API). This API may use a set of configurable identification rules that can be used to uniquely identify configuration items and determine whether and how they are written to the CMDB. 
     In general, an identification rule specifies a set of configuration item attributes that can be used for this unique identification. Identification rules may also have priorities so that rules with higher priorities are considered before rules with lower priorities. Additionally, a rule may be independent, in that the rule identifies configuration items independently of other configuration items. Alternatively, the rule may be dependent, in that the rule first uses a metadata rule to identify a dependent configuration item. 
     Metadata rules describe which other configuration items are contained within a particular configuration item, or the host on which a particular configuration item is deployed. For example, a network directory service configuration item may contain a domain controller configuration item, while a web server application configuration item may be hosted on a server device configuration item. 
     A goal of each identification rule is to use a combination of attributes that can unambiguously distinguish a configuration item from all other configuration items, and is expected not to change during the lifetime of the configuration item. Some possible attributes for an example server device may include serial number, location, operating system, operating system version, memory capacity, and so on. If a rule specifies attributes that do not uniquely identify the configuration item, then multiple components may be represented as the same configuration item in the CMDB. Also, if a rule specifies attributes that change for a particular configuration item, duplicate configuration items may be created. 
     Thus, when a data source provides information regarding a configuration item to the identification and reconciliation API, the API may attempt to match the information with one or more rules. If a match is found, the configuration item is written to the CMDB. If a match is not found, the configuration item may be held for further analysis. 
     Configuration item reconciliation procedures may be used to ensure that only authoritative data sources are allowed to overwrite configuration item data in the CMDB. This reconciliation may also be rules-based. For instance, a reconciliation rule may specify that a particular data source is authoritative for a particular configuration item type and set of attributes. Then, the identification and reconciliation API will only permit this authoritative data source to write to the particular configuration item, and writes from unauthorized data sources may be prevented. Thus, the authorized data source becomes the single source of truth regarding the particular configuration item. In some cases, an unauthorized data source may be allowed to write to a configuration item if it is creating the configuration item or the attributes to which it is writing are empty. 
     Additionally, multiple data sources may be authoritative for the same configuration item or attributes thereof. To avoid ambiguities, these data sources may be assigned precedences that are taken into account during the writing of configuration items. For example, a secondary authorized data source may be able to write to a configuration item&#39;s attribute until a primary authorized data source writes to this attribute. Afterward, further writes to the attribute by the secondary authorized data source may be prevented. 
     In some cases, duplicate configuration items may be automatically detected by reconciliation procedures or in another fashion. These configuration items may be flagged for manual de-duplication. 
     VI. ILLUSTRATIVE SERVER CLUSTER 
     In line with the discussion above, the managed network  300  may include a device (e.g., a server) that may implement a full-stack infrastructure that integrates computation, virtualization, storage, and networking capabilities. In an example, the infrastructure that achieves such integration may be known as a hyper-converged infrastructure (HCl). HCl is a software-defined mechanism that virtualizes one or more elements of a traditional hardware system. Generally, an HCl server may deliver virtualized computing (e.g., by way of a hypervisor), virtualized software-defined storage, and virtualized networking (e.g., software-defined networking). The benefits of such a system, e.g., over a more traditional three-tiered hardware system, include simplicity, scalability, and cost-effectiveness. 
     In an embodiment, a plurality of the servers may be arranged to form a server cluster, perhaps by communicatively coupling the servers via a network. Each server may serve as a node of the server cluster and may provide computation, virtualization, storage, and networking capabilities. The server cluster may be configured to distribute operating functions across the plurality of servers for purposes of performance and resilience. Furthermore, in this arrangement, the server cluster may be scaled to integrate additional nodes up to an unlimited number of nodes. 
       FIG. 6  illustrates a server cluster  600 , according to an example embodiment. The server cluster  600  may include a plurality of servers, which include identical or different types of servers, such as an x86 based server. Furthermore, the servers may be communicatively coupled to one another, perhaps via a local-area network (LAN)  614 . In the example shown in  FIG. 6 , the server cluster  600  includes three servers  602 A,  602 B, and  602 C. In other examples, the server cluster  600  may include fewer than or more than three servers. 
     In an embodiment, the servers  602 A,  602 B,  602 C may each include a hypervisor that executes one or more virtual machines, local storage in the form of flash storage device(s) and/or hard-disk storage device(s), and a controller (also referred to herein as a “controller virtual machine (CVM)” or “an application controller”). As shown in  FIG. 6 , server  602 A may include hypervisor  604 A, controller  606 A, flash storage  608 A, and hard-disk storage  610 A; server  602 B may include hypervisor  604 B, controller  606 B, flash storage  608 B, and hard-disk storage  610 B; and server  602 C may include hypervisor  604 C, controller  606 C, flash storage  608 C, and hard-disk storage  610 C. As described herein, these components may enable the server cluster  600  to provide the computation, virtualization, storage, and networking services. 
     More specifically, the hypervisors may enable the server cluster  600  to provide virtualization services. Each hypervisor may include software, firmware, and/or hardware that enable the hypervisor to execute and manage one or more respective virtual machines on a single physical host system (i.e., a server). The hardware may include one or more processors (e.g., single-core processors and/or multi-core processors), computer-usable memory (e.g., RAM and/or ROM), among other hardware. The software may implement a virtualization technique that enables the virtual machines to run on the host system simultaneously. For instance, as shown in  FIG. 6 , hypervisor  604 A may execute virtual machines  612 A, hypervisor  604 B may execute virtual machines  612 B, and hypervisor  604 C may execute virtual machines  612 C. In this arrangement, the operating systems of the virtual machines may share the hardware such that each virtual machine appears to have its own hardware. Accordingly, the virtual machines may effectively share the hardware resources (e.g., processor cycles, memory space, network bandwidth, etc.) of the respective hypervisor. In some examples, the hypervisor may also manage the hardware resources that are allocated to each virtual machine. 
     The data storage devices may enable the server cluster  600  to provide storage capabilities. In an implementation, the storage devices of the servers  602 A,  602 B,  602 C may converge to deliver one or more unified pools of storage (also referred to herein as a “storage pools”). In this implementation, a storage pool may span a plurality of servers, and may be expanded to include the storage devices of servers that are later added to the server cluster  600 . Additionally, a storage pool may implement a tiered storage structure and/or may be segmented into logical segments called “storage containers.” Each storage container may be a defined subset of storage within the storage pool. In an example, the storage containers may have a 1-to-1 mapping with virtual machine datastores. 
     A storage pool may provide data storage to the virtual machines that are being executed by the server cluster  600 . In order to access the storage pool, a virtual machine may interface with a controller of the server that is executing the virtual machine. As shown in  FIG. 6 , server  602 A may include a controller  606 A that may interface between the virtual machines  612 A and the storage pool, server  602 B may include a controller  606 B that may interface between the virtual machines  612 B and the storage pool, and server  602 C may include a controller  606 C that may interface between the virtual machines  612 C and the storage pool. 
     In an implementation, a virtual machine may store data in the local storage devices of the server that is executing the virtual machine. Additionally, the data may be replicated in the storage devices of other servers to protect against hardware failure. To replicate the data, the data may be transferred between servers, perhaps via a network  614  (e.g., a local-area network) that communicatively couples the servers. In particular, the controllers of the servers may communicate with one another in order to exchange information (e.g., instructions, requests, data, etc.). 
     When a virtual machine submits a write request through its respective hypervisor, the request may be sent to the controller of the server that is executing the virtual machine. In order to provide a rapid response, the controller may store the data in local flash storage. The controller may then periodically transfer the stored data to the local hard-disk storage for longer-term storage. Additionally and/or alternatively, the controller may periodically transfer the data to other servers in the cluster for storage so that the data may be replicated in multiple nodes for higher data reliability and availability. The controllers of the other nodes may receive the data and may store the data in their respective local storage. 
     When a virtual machine submits a read request through the hypervisor, the request may be sent to the controller. The controller may search for a local copy of the requested data, and if present, may provide the hypervisor with the requested data. However, if the controller does not find a local copy, the controller may request the data from another node. Once the controller receives the data, the controller may provide the hypervisor with the requested data. Additionally, the controller may store the received data in local storage so that the data may be accessed more rapidly in the future. 
     VII. DISCOVERY PATTERN TO DISCOVER A SERVER CLUSTER 
     As described above, the remote network management platform  320  may discover configuration items present in the managed network  300 . The remote network management platform  320  may periodically perform discovery in order to detect any new configuration items that have been added to the managed network  300  since the last discovery. Additionally, the remote network management platform  320 , in a process referred to as “mapping,” may generate a map that illustrates relationships between the discovered configuration items. 
     The remote network management platform  320  may use discovery and mapping to create and maintain an inventory of configuration items of the managed network  300 . Such an inventory may provide clear and concise topology information of the managed network  300 . This information may be used by the remote network management platform  320  to administer the devices, applications, and services of the managed network  300 . Additionally, this information may be useful when determining how one configuration item may affect another. For instance, a comprehensive map may be used to determine an impact that a problematic configuration item may have on other configuration items. 
     In an embodiment, the managed network  300  may obtain service from a server cluster, such as the server cluster  600 . The server cluster  600  may be disposed within the managed network  300  to provide the managed network  300  with services such as computation, virtualization, storage, and networking. When the managed network  300  includes the server cluster  600 , it may be useful to discover and/or map the server cluster  600 . 
     In an embodiment, the remote network management platform  320  may detect the server cluster  600  after performing an initial discovery (described above in  FIG. 5A, 5B ) that probes computing devices within managed network  300  according to one or more rule-based discovery patterns. Such probes may instruct the computing devices to identify software processes executing thereon. The software processes and the parameters associated therewith may be used to detect and/or identify the virtual machines of the server cluster  600 . Alternatively or additionally, the one or more virtual machines may be discovered even when they are not being executed by, for example, scanning a file system of the server cluster  600  for files associated with the one or more virtual machines. 
     Performing the initial discovery may allow the remote network management platform  320  to detect the virtual machines that are being executed by the server cluster  600 . Additionally, the initial discovery may detect the servers and the storage devices of the server cluster  600 . The remote management network  320  may store, perhaps in CMDB  500 , information indicative of the virtual machines as virtual machine data, information indicative of the discovered servers as server data, and information indicative of the discovered storage devices as storage device data. The virtual machine data may include an identifier (e.g., a name or Unique Identifier (UID)) for each virtual machine, identifiers of the servers that are executing the virtual machines, and performance data of the virtual machines. The server data may include an identifier (e.g., name or UID) for each server, the properties and specifications of each server, among other data. And the storage device data may include an identifier for each storage device (e.g., name or UID), a respective server identifier for each server that houses one of the storage devices, the properties and specifications of the storage devices, among other data. Additionally, the initial discovery may establish relationships between the discovered configuration items. For instance, the initial discovery may map each virtual machine to a corresponding server and may map each storage device to a corresponding server. 
     However, this initial discovery might not detect or determine additional information about the server cluster  600 , such as information indicative of storage pools of the server cluster  600 , information indicative of the relationships between the components of the server cluster  600 , or information indicative of storage containers of the server cluster  600 . However, determining such information may be desirable in order to fully discover the server cluster  600  and/or to generate a comprehensive map of the server cluster  600 . 
     Disclosed herein is a server cluster discovery pattern for discovering and/or generating a comprehensive map of the server cluster  600 . The server cluster discovery pattern may be stored and executed by a computational instance (e.g., computational instance  322 ) within the remote network management platform  320 , a computing device (e.g., proxy servers  312 ) within the managed network  300 , or a combination thereof. That is, functions of the server cluster discovery pattern may be distributed among different computing devices that form part of different computer networks. 
     In an embodiment, the remote network management platform  320  may initiate the server cluster discovery pattern after the initial discovery. During or after performing the initial discovery, the remote network management platform  320  may perform one or more additional discovery patterns in an attempt to classify the configuration items discovered by the initial discovery. For instance, the remote network management platform  320  may perform the server cluster discovery pattern in order to classify the discovered configuration items of the server cluster  600 . 
       FIG. 7  depicts a message diagram  700  that illustrates steps of a server cluster discovery pattern, according to an example embodiment. As illustrated in  FIG. 7 , the steps of the server cluster discovery pattern may be carried out by the computational instance  322 , proxy servers  312 , and the server cluster  600 . Unless specifically indicated, steps in the diagram  700  may be executed out of order from that shown or discussed, including substantially concurrent execution of separately described steps, or even in reverse order in some examples, depending on the functionality involved, so long as the overall functionality of the described pattern is maintained. Additionally, some requests and related responses may involve multiple transactions between the entities. 
     In step  702 , the computational instance  322  may provide the proxy server  312  with an instruction to perform the server cluster discovery pattern. The computational instance  322  may provide the instruction in response to discovering configuration items associated with the server cluster  600 . Specifically, various attributes and parameters of the discovered configuration items determined from software processes and/or files associated with the discovered configuration items may be used to select the server cluster discovery pattern. In some cases, these attributes and parameters may unambiguously identify the server cluster discovery pattern to be used for the configuration items. 
     For example, from information indicative of the discovered configuration items associated with the server cluster  600 , the computational instance  322  may determine a type of the server cluster  600 . The type of the server cluster  600  may be the type of architecture implemented by the cluster (e.g., a hyper-converged infrastructure) or may be a brand name of the cluster. The computational instance  322  may then provide the proxy server  312  with the instruction to perform a discovery pattern associated with the determined type of the server cluster  600 . Here, the discovery pattern associated with the type of the server cluster  600  is the server cluster discovery pattern. 
     In response to receiving the instruction, the proxy server  312  may initiate the server cluster discovery pattern. As shown in  FIG. 7 , at least a portion of the server cluster discovery pattern may involve the proxy server  312  requesting and receiving data from the server cluster  600 . In an implementation, the proxy servers  312  may request and receive data from one of the controllers  606 A,  606 B, and  606 C of the server cluster  600 . In an example, the requests from the proxy server  312  may be API requests to an API of the server cluster  600 , such as a Representational State Transfer (REST) API. 
     As shown by step  704 , the proxy server  312  may provide the server cluster  600  with a request for server cluster data. The server cluster data may include information indicative of the server cluster  600 , such as a UID and a version of the server cluster  600 . Other information in the server cluster data may include a domain, hypervisor types, and a number of nodes of the server cluster  600 . In response to receiving the request, the server cluster  600  may provide the proxy server  312  with the server cluster data in step  706 . 
     In step  708 , the proxy server  312  may provide the server cluster  600  with a request for storage pool data. In an implementation, the proxy server  312  may first request and receive from the proxy server  312  a list of the storage pools of the server cluster  600 . The proxy server  312  may then request respective storage pool data for each of the storage pools. In response to receiving the request, the server cluster  600  may provide the proxy server  312 , in step  710 , with the respective storage pool data. 
     Storage pool data may include information indicative of a storage pool, such as a name, available storage, used storage, information indicative of the servers (e.g., names or UIDs) that the storage pool is a part of, storage containers of the storage pool, and capacity. The name may indicate a name or UID of the storage pool, the available storage may indicate a total amount of physical storage space available in the storage pool, the used storage may indicate a total amount of physical storage space used in the storage pool, the information indicative of the servers may include names or UIDs of the one or more servers, the storage container data may include information (e.g., a name or UID) of the storage containers of the storage pool, and the capacity may indicate a total physical storage space capacity in the storage pool. 
     In step  712 , the proxy server  312  may provide the server cluster  600  with a request for storage container data of the storage containers associated with each of the discovered storage pools. In response to receiving the request, the server cluster  600  may provide the proxy server  312 , in step  714 , with respective container data for each storage container. The container data may include a name of the container, a UID of the container, free storage space in the container, used storage space in the container, max storage capacity of the container, total reserved storage capacity, replication factor (e.g., a number of maintained data copies of data stored in the container, such as 2 or 3), compression, compression delay, compression space saved, disk duplication state (e.g., enabled or not enabled), erasure coding, information indicative of the storage pool (e.g., name or UID) that the storage container is a part of, among other data. 
     In step  716 , the proxy server  312  may provide the server cluster  600  with a request for respective controller data of each controller of each server. In response to receiving the request, the server cluster  600  may provide the server proxy  312 , in step  718 , with the respective controller data. The controller data may include a given name of a controller, information indicative of a server (e.g., a name or UID) that is executing the controller, a host IP address, an operating system of the controller, an amount of memory available to the controller, an amount of memory reserved for the controller, an amount of dynamic memory currently assigned to the controller, a number of CPU cores being used by the controller, an amount of CPU power reserved for the controller, a total disk capacity available to the controller, an IP address assigned to the controller, among other data. In some examples, the host IP address and the IP address assigned to the controller may be the same, and in other examples, they may be different. 
     Once the proxy server  312  receives the controller data, the proxy server  312 , in step  720 , may provide the computational instance  322  with some or all of the data received from the server cluster  600 . Alternatively, the proxy server  312  may provide each data set upon receipt of the data from the server cluster  600  (as opposed to sending the data collectively once all of the data is received from the server cluster  600 ). 
     The computational instance  322  may store the received data in a storage device, perhaps CMDB  500 . For instance, the data may be stored in database tables, each of which may be associated with a respective type of configuration item. The database tables may list discovered configuration items of a particular type and any determined properties thereof. For example, a database table may be associated with a “server” type of configuration item. Such a table may list the discovered servers and any determined properties thereof. 
     Additionally, the computational instance  322  may determine the dependencies and relationships between the discovered configuration items. In an embodiment, the computational instance  322  may do so using the data received from the server cluster  600 . For example, the computational instance  322  may extract, from the data, identifying information indicative of the configuration items and may cross-reference the identifying information in order to determine the relationships between the configuration items. The computational instance  322  may then use these relationships to determine a hierarchy of the configuration items. 
     To illustrate this process, consider the server cluster  600 . From server cluster data indicative of the server cluster  600 , the computational instance  322  may determine an identifier of the server cluster  600  (e.g., a name or UID). Additionally, the computational instance  322  may determine, from the server data of the discovered servers, a respective identifier of a respective server cluster to which each server corresponds (if any). The computational instance  322  may then cross-reference each respective identifier with the identifier of the server cluster  600  (that was determined from the server cluster data). The computational instance  322  may determine that the servers whose respective server cluster identifier matches the identifier of the server cluster  600  are part of the server cluster  600 . As such, by cross-referencing the identifier of the server cluster  600  from server cluster data with server cluster identifiers from the server data, the computational instance  322  may determine the relationship between the discovered servers and the server cluster  600  (e.g., that the servers are nodes of the server cluster  600 ). 
     Similarly, the computational instance  322  may cross-reference storage device identifiers from storage device data and storage pool data to establish a relationship between the storage devices and the storage pools. By doing so, the computational instance  322  may map each storage pool to one or more corresponding storage devices on which the storage pool is stored. The computational instance  322  may also cross-reference storage pool identifiers from storage pool data and storage container data to establish a relationship between the storage pools and the storage containers. By doing so, the computational instance  322  may map each storage container to a particular storage pool to which the storage container corresponds. Further, the computational instance  322  may cross-reference server identifiers from controller data and server data to determine a relationship between the controller virtual machines and the servers. By doing so, the computational instance may map each controller virtual machine to a corresponding server that executes the virtual machine. Yet further, the computational instance  322  may cross-reference server identifiers from controller data and virtual machine data to determine a relationship between the controller virtual machines and the virtual machines. By doing so, the computational instance may map each controller virtual machine to a corresponding group of virtual machines that are being executed by the same server as the controller virtual machine. 
       FIGS. 8A and 8B  illustrate representations of the relationships that the computational instance  322  may establish between discovered configuration items, according to example embodiments. 
     As described above, an initial discovery may discover the servers of the server cluster  600 , the virtual machines that are being executed by the servers, and/or the storage devices of the servers. Data indicative of these discovered configuration items may be stored in tables, where each table may include information indicative of a particular type of configuration item. For instance, a “server” table may list the discovered servers, a “virtual machine” table may list the virtual machines that are being executed by a respective server, and a “storage device” table may list the storage devices that are included in a respective server. Furthermore, the tables may map to one another, thereby representing the relationships between the configuration items. 
       FIG. 8A  depicts a representation  800  of the configuration items discovered by the initial discovery. In this representation, “CMDB CI SERVER”  802  may represent a table that lists the servers that are included in the server cluster  600 , “CMDB_CI_VM”  804  may represent the tables that list the virtual machines that are being executed by the servers, and “CMDB_CI_STORAGE_DEVICE”  806  may represent the tables that list the storage devices of the servers. Additionally,  FIG. 8A  depicts the relationships between the configuration items by arrows  808  and  810 . Arrow  808  is indicative of the relationship between the virtual machines and the servers (e.g., that the virtual machines are being executed by the servers), and arrow  810  is indicative of the relationship between the storage devices and the servers (e.g., that the storage devices are part of the servers). Additionally, each configuration item listed in a table may map to a corresponding configuration item in another table. For example, a virtual machine in the virtual machines table may point to the server, in the server table, that executes the virtual machine. 
     As described above, the server cluster discovery pattern may discover one or more additional configuration items of the server cluster  600 , perhaps after the initial discovery has been completed. Data indicative of the additionally discovered configuration items may be stored in tables. For instance, a “controller virtual machine” table may list the controllers of the servers, a “storage pool” table may list storage pools of the server cluster, a “storage container” table may list the storage containers of a respective storage pool, and a “server cluster” table may list an identifier of the server cluster. In addition to listing the different configuration items in each table, the tables may also include discovered properties of the configuration items. Furthermore, the relationships between the configuration items may be stored as relationships between the stored tables. 
       FIG. 8B  illustrates a representation  812  that is an updated version of the representation  800 . In addition to the tables of representation  800 , the representation  812  also depicts the tables that are generated by server cluster discovery pattern. As shown in  FIG. 8B , “CMDB_CI_CONTROLLER_VM”  816  may represent a table that lists the controller virtual machines of the servers, “CMDB_CI_STORAGE_POOL”  818  may represent a table that lists the storage pools of the server cluster  600 , and “CMDB_CI_STORAGE_CONTAINER”  820  may represent table that list the storage containers of each storage pool. 
     Like in the representation  800 , the relationships between the configuration items may be represented by arrows. More specifically, arrow  822  may indicate that the servers listed in table  802  are part of the server cluster listed in table  814 . Furthermore, arrow  824  may indicate that the controller virtual machines are a subset of the virtual machines that are being executed by the servers listed in table  802 . Further, arrow  826  may indicate that the storage pools are stored in the storage devices that are listed in table  806 . Yet further, arrow  828  may indicate that storage containers listed in table  820  are part of the storage pools listed in table  818 . 
     In an embodiment, the computational instance  322  may also graphically represent the information that is stored in the tables of the representation  812 . The computational instance  322  may do so to allow the user to view the hardware composition and operational status of configuration items of the server cluster  600 . For example, after discovering the server cluster  600 , the computational instance may generate a graphical representation of the components of the server cluster  600  (as shown in  FIG. 6 ) and may provide the graphical representation to the user, perhaps by way of a graphical user interface. 
       FIG. 9A  illustrates graphical user interface  900  that includes a graphical representation  922  of the server cluster  600 , according to an example embodiment. The graphical representation  922  may be created based on the data generated during the discovery of the server cluster  600 . In particular, the graphical representation  922  may mirror the infrastructure representation of the server cluster as depicted in  FIG. 6 . 
     Like the representation in  FIG. 6 , the graphical representation  922  may depict each server of the server cluster and the components thereof. As shown in  FIG. 9A , the graphical representation may depict servers  924 A,  924 B, and  924 C of the discovered server cluster. For example, if the discovered server cluster is the server cluster  600 , the servers  924 A,  924 B,  924 C may represent servers  602 A,  602 B,  602 C, respectively (as shown in  FIG. 6 ). Additionally, the graphical representation  922  may depict the configuration items of each server. As shown in  FIG. 9A , the server  924 A may include hypervisor  926 A that executes virtual machines  928 A. Additionally, the server  924 A may include flash storage  930 A, hard-disk storage  932 A, and controller  934 A. Similarly, the graphical representation  922  may depict hypervisor  926 B that includes virtual machines  928 B, flash storage  930 B, hard-disk storage  932 B, and controller  934 B of server  924 B; and hypervisor  926 C that includes virtual machines  928 C, flash storage  930 C, hard-disk storage  932 C, and controller  934 C of server  924 C. 
     Additionally, the graphical representation  922  may depict a storage pool  936  of the server cluster  600 . As shown in  FIG. 9A , the storage pool may be part of several storage devices across a plurality of servers. The graphical representation  922  may also depict connection  938 , which may represent that the servers  924 A,  924 B,  924 C are communicatively coupled to one other, perhaps by way of their respective controllers. 
     In an embodiment, the graphical user interface  900  may allow a user to interact with the map  922 . For example, in addition to allowing the user to view the graphical representation  922 , the graphical user interface  902  may also receive an input from the user. The user input may allow the user to view additional information associated with one or more of the configuration items, modify the properties of one or more of the configuration items, and/or provide other inputs and instructions. 
     Furthermore, the graphical representation  922  is merely one example of the graphical representations that may be generated from the data generated during discovery of the server cluster  600 . As another example, a hierarchical representation of the components of the server cluster  600  may be generated based on the generated data. 
       FIG. 9B  illustrates graphical user interface  920  that includes a hierarchical representation  940  of configuration items of a server cluster  600 , according to an example embodiment. As shown in  FIG. 9B , the broadest category in the representation is a category “CMDB_CI”  902  that includes all configuration items of a managed network. This category  902  may include three subcategories that are relevant to the server cluster  600 : “CMDB_CI_VM_OBJECT”  904 , “CMDB_CI_STORAGE_POOL”  910 , and “CMDB_CI_STORAGE_VOLUME”  912 . 
     The category “CMDB_CI_VM_OBJECT”  904  includes all of the virtual machine objects in the managed network and includes two subcategories relevant to the server cluster  600 : “CMDB_CI_SERVER_CLUSTER”  906  and “CMDB_CI_VM_INSTANCE”  908 . The subcategory  906  may include the server clusters that are disposed in the managed network. The subcategory  908  may include a subcategory “CMDB_CI_SERVER_CONTROLLER_VM”  914  that includes the controllers of the server clusters included in subcategory  906 . 
     The category  910  may include a subcategory that is related to the server cluster  600 . This subcategory, labelled “CMDB_CI_SERVER_STORAGE_POOL”  916 , includes the storage pools of the servers included in subcategory  906 . Furthermore, the category  912  may include a subcategory that is related to the server cluster  600 . This subcategory, labelled “CMDB_CI_SERVER_STORAGE_CONTAINER”  918 , may include the storage containers of the server clusters included in the subcategory  906 . 
     VIII. EXAMPLE OPERATIONS 
       FIG. 10  is a flow chart illustrating an example embodiment. The process illustrated by  FIG. 10  may be carried out by a computing device, such as computing device  100 , and/or a cluster of computing devices, such as server cluster  200 . However, the process can be carried out by other types of devices or device subsystems. For example, the process could be carried out by a portable computer, such as a laptop or a tablet device. 
     The embodiments of  FIG. 10  may be simplified by the removal of any one or more of the features shown therein. Further, these embodiments may be combined with features, aspects, and/or implementations of any of the previous figures or otherwise described herein. 
     Block  1000  may involve requesting and receiving, from a first controller and by a proxy server application, computing cluster data that identifies a computing cluster, wherein the first controller is associated with one of a plurality of computing devices of the computing cluster, wherein the computing cluster and the proxy server application are disposed within a managed network, wherein the computing cluster provides networking, storage, and virtualization services distributed across the plurality of computing devices, wherein the plurality of computing devices are communicatively coupled via a local-area network, and wherein each computing device is configured to execute one or more respective software applications and comprises: (i) a respective controller, and (ii) a respective storage device. 
     Block  1002  may involve requesting and receiving, from the first controller and by the proxy server application, storage pool data that identifies a storage pool of the computing cluster, wherein the storage pool is provided by the storage devices of the plurality of computing devices. 
     Block  1004  may involve requesting and receiving, from the first controller and by the proxy server application, storage pool data that identifies a storage pool of the computing cluster, wherein the storage pool is provided by the storage devices of the plurality of computing devices. 
     Block  1006  may involve requesting and receiving, from the first controller and by the proxy server application, controller data that identifies the controllers of the plurality computing devices. 
     Block  1008  may involve providing, to a database disposed within a remote network management platform and by the proxy server application, the computing cluster data, the storage pool data, the storage container data, and the controller data. 
     In some embodiments, a proxy server application disposed in a managed network may carry out the process of blocks  1100 - 1108 . 
     In some embodiments, the database may store: (i) software application data that identifies software applications that are executed by the plurality of computing devices, (ii) computing device data that identifies the plurality of computing devices, and (iii) storage device data that identifies the storage devices of the plurality of computing devices. 
     In some embodiments, the software application data, the computing device data, and the storage device data may be determined by an initial discovery performed by the proxy server application. 
     In some embodiments, the remote network management platform may include a server device configured to, based on the software application data, the computing device data, and the storage device data, map each software application to a corresponding computing device that executes the software application and each storage device to a corresponding computing device that houses the storage device. 
     In some embodiments, the remote network management platform may include a server device configured to: use the software application data to determine a type of the computing cluster; based on the type of the computing cluster, select the computing cluster discovery pattern to discover the computing cluster; and responsive to selecting the computing cluster discovery pattern, send an instruction to the proxy server application to perform the operations of the computing cluster discovery pattern. 
     In some embodiments, the remote network management platform may include a server device configured to, based on the computing cluster data, the storage pool data, the storage container data, and the controller data, generating a hierarchical representation of the computing cluster, wherein the hierarchical representation is indicative of relationships between components of the computing cluster. 
     In some embodiments, generating a hierarchical representation of the computing cluster may involve: mapping the plurality of computing devices to the computing cluster; mapping the storage pool to the storage devices; mapping the storage containers to the storage pool; and mapping each respective controller to a corresponding computing device associated with the respective controller. 
     In some embodiments, mapping the plurality of computing devices to the computing cluster may involve: determining, from the computing cluster data, a first computing cluster unique identifier (UID) that identifies the computing cluster; determining, from computing device data stored in the database, a second computing cluster UID that identifies a particular computing cluster associated with the plurality of computing devices; determining that the first computing cluster UID and the second computing cluster UID are identical; and responsively mapping the plurality of computing devices to the computing cluster. 
     In some embodiments, mapping the storage pool to the storage devices may involve: determining, from the storage pool data, a first storage device unique identifier (UID) of a particular storage device associated with the storage pool; determining, from storage device data stored in the database, a second storage device UID that identifies a storage device of the computing cluster; determining that the first storage device UID and the second storage device UID are identical; and responsively mapping the storage pool to the storage device. 
     In some embodiments, mapping the storage containers to the storage pool may involve: determining, from the storage pool data, a first storage pool unique identifier (UID) that identifies the storage pool; determining, from the storage container data, a second storage pool UID of a particular storage pool associated with the storage containers; determining that the first storage pool UID and the second storage pool UID are identical; and responsively mapping the storage containers to the storage pool. 
     In some embodiments, mapping each respective controller to a corresponding computing device that is associated with the respective controller may involve: determining, from the controller data, a first computing device unique identifier (UID) of a particular computing device that is associated with the respective controller; determining, from computing device data stored in the database, a second respective computing device UID for each of the plurality of computing devices; selecting from the plurality of computing devices a first computing device whose second respective computing device UID is identical to the first computing device UID; and mapping the respective storage device to the particular computing device. 
     IX. CONCLUSION 
     The present disclosure is not to be limited in terms of the particular embodiments described in this application, which are intended as illustrations of various aspects. Many modifications and variations can be made without departing from its scope, as will be apparent to those skilled in the art. Functionally equivalent methods and apparatuses within the scope of the disclosure, in addition to those described herein, will be apparent to those skilled in the art from the foregoing descriptions. Such modifications and variations are intended to fall within the scope of the appended claims. 
     The above detailed description describes various features and operations of the disclosed systems, devices, and methods with reference to the accompanying figures. The example embodiments described herein and in the figures are not meant to be limiting. Other embodiments can be utilized, and other changes can be made, without departing from the scope of the subject matter presented herein. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations. 
     With respect to any or all of the message flow diagrams, scenarios, and flow charts in the figures and as discussed herein, each step, block, and/or communication can represent a processing of information and/or a transmission of information in accordance with example embodiments. Alternative embodiments are included within the scope of these example embodiments. In these alternative embodiments, for example, operations described as steps, blocks, transmissions, communications, requests, responses, and/or messages can be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved. Further, more or fewer blocks and/or operations can be used with any of the message flow diagrams, scenarios, and flow charts discussed herein, and these message flow diagrams, scenarios, and flow charts can be combined with one another, in part or in whole. 
     A step or block that represents a processing of information can correspond to circuitry that can be configured to perform the specific logical functions of a herein-described method or technique. Alternatively or additionally, a step or block that represents a processing of information can correspond to a module, a segment, or a portion of program code (including related data). The program code can include one or more instructions executable by a processor for implementing specific logical operations or actions in the method or technique. The program code and/or related data can be stored on any type of computer readable medium such as a storage device including RAM, a disk drive, a solid state drive, or another storage medium. 
     The computer readable medium can also include non-transitory computer readable media such as computer readable media that store data for short periods of time like register memory and processor cache. The computer readable media can further include non-transitory computer readable media that store program code and/or data for longer periods of time. Thus, the computer readable media may include secondary or persistent long term storage, like ROM, optical or magnetic disks, solid state drives, compact-disc read only memory (CD-ROM), for example. The computer readable media can also be any other volatile or non-volatile storage systems. A computer readable medium can be considered a computer readable storage medium, for example, or a tangible storage device. 
     Moreover, a step or block that represents one or more information transmissions can correspond to information transmissions between software and/or hardware modules in the same physical device. However, other information transmissions can be between software modules and/or hardware modules in different physical devices. 
     The particular arrangements shown in the figures should not be viewed as limiting. It should be understood that other embodiments can include more or less of each element shown in a given figure. Further, some of the illustrated elements can be combined or omitted. Yet further, an example embodiment can include elements that are not illustrated in the figures. 
     While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purpose of illustration and are not intended to be limiting, with the true scope being indicated by the following claims.