Patent Publication Number: US-11645131-B2

Title: Distributed fault code aggregation across application centric dimensions

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of, and priority to, U.S. Provisional Patent Application No. 62/520,663, filed on Jun. 16, 2017, entitled “DISTRIBUTED FAULT CODE AGGREGATION ACROSS APPLICATION CENTRIC DIMENSIONS”, the contents of which are hereby expressly incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The present technology pertains to network configuration and troubleshooting, and more specifically to distributed fault code aggregation across application-centric dimensions. 
     BACKGROUND 
     Computer networks are becoming increasingly complex, often involving low level as well as high level configurations at various layers of the network. For example, computer networks generally include numerous access policies, forwarding policies, routing policies, security policies, quality-of-service (QoS) policies, etc., which together define the overall behavior and operation of the network. Network operators have a wide array of configuration options for tailoring the network to the needs of the users. While the different configuration options available provide network operators a great degree of flexibility and control over the network, they also add to the complexity of the network. In many cases, the configuration process can become highly complex. Not surprisingly, the network configuration process is increasingly error prone. In addition, troubleshooting errors in a highly complex network can be extremely difficult. The process of identifying the root cause of undesired behavior in the network can be a daunting task. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In order to describe the manner in which the above-recited and other advantages and features of the disclosure can be obtained, a more particular description of the principles briefly described above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only exemplary embodiments of the disclosure and are not therefore to be considered to be limiting of its scope, the principles herein are described and explained with additional specificity and detail through the use of the accompanying drawings in which: 
         FIGS.  1 A and  1 B  illustrate example network environments; 
         FIG.  2 A  illustrates an example object model for a network; 
         FIG.  2 B  illustrates an example object model for a tenant object in the example object model from  FIG.  2 A ; 
         FIG.  2 C  illustrates an example association of various objects in the example object model from  FIG.  2 A ; 
         FIG.  2 D  illustrates a schematic diagram of example models for implementing the example object model from  FIG.  2 A ; 
         FIG.  3 A  illustrates an example network assurance appliance; 
         FIG.  3 B  illustrates an example system for network assurance; 
         FIG.  3 C  illustrates a schematic diagram of an example system for static policy analysis in a network; 
         FIG.  4    illustrates an example platform for distributed fault code aggregation; 
         FIGS.  5 A and  5 B  illustrate example method embodiments for network assurance and fault code aggregation; 
         FIG.  6    illustrates an example network device in accordance with various embodiments; and 
         FIG.  7    illustrates an example computing device in accordance with various embodiments. 
     
    
    
     DESCRIPTION OF EXAMPLE EMBODIMENTS 
     Various embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the disclosure. Thus, the following description and drawings are illustrative and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding of the disclosure. However, in certain instances, well-known or conventional details are not described in order to avoid obscuring the description. References to one or an embodiment in the present disclosure can be references to the same embodiment or any embodiment; and, such references mean at least one of the embodiments. 
     Reference to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which may be exhibited by some embodiments and not by others. 
     The terms used in this specification generally have their ordinary meanings in the art, within the context of the disclosure, and in the specific context where each term is used. Alternative language and synonyms may be used for any one or more of the terms discussed herein, and no special significance should be placed upon whether or not a term is elaborated or discussed herein. In some cases, synonyms for certain terms are provided. A recital of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification including examples of any terms discussed herein is illustrative only, and is not intended to further limit the scope and meaning of the disclosure or of any example term. Likewise, the disclosure is not limited to various embodiments given in this specification. 
     Without intent to limit the scope of the disclosure, examples of instruments, apparatus, methods and their related results according to the embodiments of the present disclosure are given below. Note that titles or subtitles may be used in the examples for convenience of a reader, which in no way should limit the scope of the disclosure. Unless otherwise defined, technical and scientific terms used herein have the meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. In the case of conflict, the present document, including definitions will control. 
     Additional features and advantages of the disclosure will be set forth in the description which follows, and in part will be obvious from the description, or can be learned by practice of the herein disclosed principles. The features and advantages of the disclosure can be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the disclosure will become more fully apparent from the following description and appended claims, or can be learned by the practice of the principles set forth herein. 
     Overview 
     Disclosed herein are systems, methods, and computer-readable media for fault code aggregation across application-centric dimensions. In some examples, a system can obtain respective fault codes (e.g., hardware errors) corresponding to one or more network devices (e.g., switches) in a network and map the one or more network devices and/or the respective fault codes to respective logical policy entities (e.g., policies or entities in a software-defined network, such as contracts, endpoint groups, tenants, application profiles, etc.) defined in a logical policy model of the network to yield fault code mappings. 
     The logical model can be a model generated based on configurations defined in one or more controllers or servers in a software-defined network (SDN), such as an APIC (application policy infrastructure controller) in an ACI (application-centric infrastructure) network. The logical model can thus represent the logical configuration of the SDN network (e.g., a representation of the logical configurations in the ACI). The logical configuration of the SDN network can be based on the configurations defined by the network operator for the SDN network, such as the configurations entered into the APIC of an ACI network, and may thus reflect the intent of the network operator or the intended behavior of the SDN network. 
     The system can then aggregate one or more of the fault code mappings along respective logical policy dimensions in the network to yield an aggregation of fault codes across respective logical policy dimensions (e.g., contracts, security groups, tenants, application profiles, endpoint groups, tenants, etc.) and, based on the aggregation of fault codes across respective logical policy dimensions, present, for each of the respective logical policy dimensions, one or more hardware-level errors along the respective logical policy dimension. The system can thus identify and visualize hardware-level errors along a logical dimension, such as an application-centric dimension (i.e., the respective logical policy dimension). 
     Description 
     The disclosed technology addresses the need in the art for accurate and efficient discovery of problems in a large and complex network or data center. The present technology involves system, methods, and computer-readable media for fault code aggregation across logical or application-centric dimensions. The present technology will be described in the following disclosure as follows. The discussion begins with an introductory discussion of network assurance and fault code aggregation across application-centric dimensions. An introductory discussion of network assurance and a description of example computing environments, as illustrated in  FIGS.  1 A and  1 B , will then follow. The discussion continues with a description of systems and methods for network assurance, network modeling, and fault code aggregation across logical or application-centric dimensions, as shown in  FIGS.  2 A- 2 D,  3 A -C,  4  and  5 A-B. The discussion concludes with a description of an example network device, as illustrated in  FIG.  6   , and an example computing device, as illustrated in  FIG.  7   , including example hardware components suitable for hosting software applications and performing computing operations. 
     The disclosure now turns to a discussion of network assurance and distributed fault code aggregation across logical or application-centric dimensions. 
     Network assurance is the guarantee or determination that the network is behaving as intended by the network operator and has been configured properly (e.g., the network is doing what it is intended to do). Intent can encompass various network operations, such as bridging, routing, security, service chaining, endpoints, compliance, QoS (Quality of Service), audits, etc. Intent can be embodied in one or more policies, settings, configurations, etc., defined for the network and individual network elements (e.g., switches, routers, applications, resources, etc.). However, often times, the configurations, policies, etc., defined by a network operator are incorrect or not accurately reflected in the actual behavior of the network. For example, a network operator specifies a configuration A for one or more types of traffic but later finds out that the network is actually applying configuration B to that traffic or otherwise processing that traffic in a manner that is inconsistent with configuration A. This can be a result of many different causes, such as hardware errors, software bugs, varying priorities, configuration conflicts, misconfiguration of one or more settings, improper rule rendering by devices, unexpected errors or events, software upgrades, configuration changes, failures, etc. As another example, a network operator implements configuration C but one or more other configurations result in the network behaving in a manner that is inconsistent with the intent reflected by the implementation of configuration C. For example, such a situation can result when configuration C conflicts with other configurations in the network. 
     The approaches herein can provide network assurance by modeling various aspects of the network and/or performing consistency checks as well as other network assurance checks. The approaches herein can also enable identification and visualization of hardware-level (e.g., network switch-level) errors along any software or application-centric dimension. Non-limiting example visualizations can include: 1) per-tenant error aggregation, 2) per-application profile error aggregation, 3) per-endpoint group pair aggregation, and 4) per-contract error aggregation. In this way, data center operators can quickly see hardware errors that impact particular tenants or other logical entities, across the entire network fabric, and even drill down by other dimensions, such as endpoint groups, to see only those relevant hardware errors. These visualizations speed root cause analysis, improving data center and application availability metrics. Given the scale of the network fabric, the aggregations to create these visualizations can be done in a distributed fashion. 
     In this context, a network assurance platform can run an assurance operator on each individual network device, such as a switch, and emit fault codes associated with the network device. A logical policy enricher can map the hardware IDs (e.g., scope, pcTag, etc.) to the logical policy entity that is defined in the software-defined network (SDN) fabric configuration, such as the application-centric infrastructure (ACI) fabric configuration. The mappings can yield enriched fault codes. The enriched fault codes can be sent to an aggregation layer for aggregation. For example, multiple nodes (e.g., HADOOP) can collect the enriched fault codes and emit them to an aggregation layer as (key, tag) pairs. 
     In some cases, the aggregation layer can scale horizontally by running the aggregator for each key as a separate reducer. Each key can represent a different dimension for aggregation. Non-limiting examples of dimensions include tenant, contract, application profile, endpoint group (EPG) pair, etc. This provides the operator of a large scale network fabric with an integrated view of the health of the network fabric for that particular dimension of aggregation. For example, this can provide the health of each tenant, contract, application profile, EPG pair, etc. 
     As previously noted, the fault code aggregation can implement logical models which can represent various aspects of a network. A model can include a mathematical or semantic model of the network, including, without limitation the network&#39;s policies, configurations, requirements, security, routing, topology, applications, hardware, filters, contracts, access control lists, EPGs, application profiles, tenants, etc. Models can be implemented to provide network assurance to ensure that the network is properly configured and the behavior of the network will be consistent (or is consistent) with the intended behavior reflected through specific policies, settings, definitions, etc., implemented by the network operator. Unlike traditional network monitoring which involves sending and analyzing data packets and observing network behavior, network assurance can be performed through modeling without necessarily ingesting any packet data or monitoring traffic or network behavior. This can result in foresight, insight, and hindsight: problems can be prevented before they occur, identified when they occur, and fixed immediately after they occur. 
     Properties of the network can be mathematically modeled to deterministically predict the behavior and condition of the network. A mathematical model can abstract the control, management, and data planes, and may use various techniques such as symbolic, formal verification, consistency, graph, behavioral, etc. The network can be determined to be healthy if the model(s) indicate proper behavior (e.g., no inconsistencies, conflicts, errors, etc.). The network can be determined to be functional, but not fully healthy, if the modeling indicates proper behavior but some inconsistencies. The network can be determined to be non-functional and not healthy if the modeling indicates improper behavior and errors. If inconsistencies or errors are detected by the modeling, a detailed analysis of the corresponding model(s) can allow one or more underlying or root problems to be identified with great accuracy. 
     The models can consume numerous types of data and/or events which model a large amount of behavioral aspects of the network. Such data and events can impact various aspects of the network, such as underlay services, overlay service, tenant connectivity, tenant security, tenant EP mobility, tenant policy, resources, etc. 
     Having described various aspects of network assurance and fault code aggregation across dimensions, the disclosure now turns to a discussion of example network environments for network assurance and fault code aggregation. 
       FIG.  1 A  illustrates a diagram of an example Network Environment  100 , such as a data center. The Network Environment  100  can include a Fabric  120  which can represent the physical layer or infrastructure (e.g., underlay) of the Network Environment  100 . Fabric  120  can include Spines  102  (e.g., spine routers or switches) and Leafs  104  (e.g., leaf routers or switches) which can be interconnected for routing or switching traffic in the Fabric  120 . Spines  102  can interconnect Leafs  104  in the Fabric  120 , and Leafs  104  can connect the Fabric  120  to an overlay or logical portion of the Network Environment  100 , which can include application services, servers, virtual machines, containers, endpoints, etc. Thus, network connectivity in the Fabric  120  can flow from Spines  102  to Leafs  104 , and vice versa. The interconnections between Leafs  104  and Spines  102  can be redundant (e.g., multiple interconnections) to avoid a failure in routing. In some embodiments, Leafs  104  and Spines  102  can be fully connected, such that any given Leaf is connected to each of the Spines  102 , and any given Spine is connected to each of the Leafs  104 . Leafs  104  can be, for example, top-of-rack (“ToR”) switches, aggregation switches, gateways, ingress and/or egress switches, provider edge devices, and/or any other type of routing or switching device. 
     Leafs  104  can be responsible for routing and/or bridging tenant or customer packets and applying network policies or rules. Network policies and rules can be driven by one or more Controllers  116 , and/or implemented or enforced by one or more devices, such as Leafs  104 . Leafs  104  can connect other elements to the Fabric  120 . For example, Leafs  104  can connect Servers  106 , Hypervisors  108 , Virtual Machines (VMs)  110 , Applications  112 , Network Device  114 , etc., with Fabric  120 . Such elements can reside in one or more logical or virtual layers or networks, such as an overlay network. In some cases, Leafs  104  can encapsulate and decapsulate packets to and from such elements (e.g., Servers  106 ) in order to enable communications throughout Network Environment  100  and Fabric  120 . Leafs  104  can also provide any other devices, services, tenants, or workloads with access to Fabric  120 . In some cases, Servers  106  connected to Leafs  104  can similarly encapsulate and decapsulate packets to and from Leafs  104 . For example, Servers  106  can include one or more virtual switches or routers or tunnel endpoints for tunneling packets between an overlay or logical layer hosted by, or connected to, Servers  106  and an underlay layer represented by Fabric  120  and accessed via Leafs  104 . 
     Applications  112  can include software applications, services, containers, appliances, functions, service chains, etc. For example, Applications  112  can include a firewall, a database, a CDN server, an IDS/IPS, a deep packet inspection service, a message router, a virtual switch, etc. An application from Applications  112  can be distributed, chained, or hosted by multiple endpoints (e.g., Servers  106 , VMs  110 , etc.), or may run or execute entirely from a single endpoint. 
     VMs  110  can be virtual machines hosted by Hypervisors  108  or virtual machine managers running on Servers  106 . VMs  110  can include workloads running on a guest operating system on a respective server. Hypervisors  108  can provide a layer of software, firmware, and/or hardware that creates, manages, and/or runs the VMs  110 . Hypervisors  108  can allow VMs  110  to share hardware resources on Servers  106 , and the hardware resources on Servers  106  to appear as multiple, separate hardware platforms. Moreover, Hypervisors  108  on Servers  106  can host one or more VMs  110 . 
     In some cases, VMs  110  and/or Hypervisors  108  can be migrated to other Servers  106 . Servers  106  can similarly be migrated to other locations in Network Environment  100 . For example, a server connected to a specific leaf can be changed to connect to a different or additional leaf. Such configuration or deployment changes can involve modifications to settings, configurations and policies that are applied to the resources being migrated as well as other network components. 
     In some cases, one or more Servers  106 , Hypervisors  108 , and/or VMs  110  can represent or reside in a tenant or customer space. Tenant space can include workloads, services, applications, devices, networks, and/or resources that are associated with one or more clients or subscribers. Accordingly, traffic in Network Environment  100  can be routed based on specific tenant policies, spaces, agreements, configurations, etc. Moreover, addressing can vary between one or more tenants. In some configurations, tenant spaces can be divided into logical segments and/or networks and separated from logical segments and/or networks associated with other tenants. Addressing, policy, security and configuration information between tenants can be managed by Controllers  116 , Servers  106 , Leafs  104 , etc. 
     Configurations in Network Environment  100  can be implemented at a logical level, a hardware level (e.g., physical), and/or both. For example, configurations can be implemented at a logical and/or hardware level based on endpoint or resource attributes, such as endpoint types and/or application groups or profiles, through a software-defined network (SDN) framework (e.g., Application-Centric Infrastructure (ACI) or VMWARE NSX). To illustrate, one or more administrators can define configurations at a logical level (e.g., application or software level) through Controllers  116 , which can implement or propagate such configurations through Network Environment  100 . In some examples, Controllers  116  can be Application Policy Infrastructure Controllers (APICs) in an ACI framework. In other examples, Controllers  116  can be one or more management components for associated with other SDN solutions, such as NSX Managers. 
     Such configurations can define rules, policies, priorities, protocols, attributes, objects, etc., for routing and/or classifying traffic in Network Environment  100 . For example, such configurations can define attributes and objects for classifying and processing traffic based on Endpoint Groups (EPGs), Security Groups (SGs), VM types, bridge domains (BDs), virtual routing and forwarding instances (VRFs), tenants, priorities, firewall rules, etc. Other example network objects and configurations are further described below. Traffic policies and rules can be enforced based on tags, attributes, or other characteristics of the traffic, such as protocols associated with the traffic, EPGs associated with the traffic, SGs associated with the traffic, network address information associated with the traffic, etc. Such policies and rules can be enforced by one or more elements in Network Environment  100 , such as Leafs  104 , Servers  106 , Hypervisors  108 , Controllers  116 , etc. As previously explained, Network Environment  100  can be configured according to one or more particular software-defined network (SDN) solutions, such as CISCO ACI or VMWARE NSX. These example SDN solutions are briefly described below. 
     ACI can provide an application-centric or policy-based solution through scalable distributed enforcement. ACI supports integration of physical and virtual environments under a declarative configuration model for networks, servers, services, security, requirements, etc. For example, the ACI framework implements EPGs, which can include a collection of endpoints or applications that share common configuration requirements, such as security, QoS, services, etc. Endpoints can be virtual/logical or physical devices, such as VMs, containers, hosts, or physical servers that are connected to Network Environment  100 . Endpoints can have one or more attributes such as a VM name, guest OS name, a security tag, application profile, etc. Application configurations can be applied between EPGs, instead of endpoints directly, in the form of contracts. Leafs  104  can classify incoming traffic into different EPGs. The classification can be based on, for example, a network segment identifier such as a VLAN ID, VXLAN Network Identifier (VNID), NVGRE Virtual Subnet Identifier (VSID), MAC address, IP address, etc. 
     In some cases, classification in the ACI infrastructure can be implemented by Application Virtual Switches (AVS), which can run on a host, such as a server or switch. For example, an AVS can classify traffic based on specified attributes, and tag packets of different attribute EPGs with different identifiers, such as network segment identifiers (e.g., VLAN ID). Finally, Leafs  104  can tie packets with their attribute EPGs based on their identifiers and enforce policies, which can be implemented and/or managed by one or more Controllers  116 . Leaf  104  can classify to which EPG the traffic from a host belongs and enforce policies accordingly. 
     Another example SDN solution is based on VMWARE NSX. With VMWARE NSX, hosts can run a distributed firewall (DFW) which can classify and process traffic. Consider a case where three types of VMs, namely, application, database and web VMs, are put into a single layer-2 network segment. Traffic protection can be provided within the network segment based on the VM type. For example, HTTP traffic can be allowed among web VMs, and disallowed between a web VM and an application or database VM. To classify traffic and implement policies, VMWARE NSX can implement security groups, which can be used to group the specific VMs (e.g., web VMs, application VMs, database VMs). DFW rules can be configured to implement policies for the specific security groups. To illustrate, in the context of the previous example, DFW rules can be configured to block HTTP traffic between web, application, and database security groups. 
     Returning now to  FIG.  1 A , Network Environment  100  can deploy different hosts via Leafs  104 , Servers  106 , Hypervisors  108 , VMs  110 , Applications  112 , and Controllers  116 , such as VMWARE ESXi hosts, WINDOWS HYPER-V hosts, bare metal physical hosts, etc. Network Environment  100  may interoperate with a variety of Hypervisors  108 , Servers  106  (e.g., physical and/or virtual servers), SDN orchestration platforms, etc. Network Environment  100  may implement a declarative model to allow its integration with application design and holistic network policy. 
     Controllers  116  can provide centralized access to fabric information, application configuration, resource configuration, application-level configuration modeling for a software-defined network (SDN) infrastructure, integration with management systems or servers, etc. Controllers  116  can form a control plane that interfaces with an application plane via northbound APIs and a data plane via southbound APIs. 
     As previously noted, Controllers  116  can define and manage application-level model(s) for configurations in Network Environment  100 . In some cases, application or device configurations can also be managed and/or defined by other components in the network. For example, a hypervisor or virtual appliance, such as a VM or container, can run a server or management tool to manage software and services in Network Environment  100 , including configurations and settings for virtual appliances. 
     As illustrated above, Network Environment  100  can include one or more different types of SDN solutions, hosts, etc. For the sake of clarity and explanation purposes, various examples in the disclosure will be described with reference to an ACI framework, and Controllers  116  may be interchangeably referenced as controllers, APICs, or APIC controllers. However, it should be noted that the technologies and concepts herein are not limited to ACI solutions and may be implemented in other architectures and scenarios, including other SDN solutions as well as other types of networks which may not deploy an SDN solution. 
     Further, as referenced herein, the term “hosts” can refer to Servers  106  (e.g., physical or logical), Hypervisors  108 , VMs  110 , containers (e.g., Applications  112 ), etc., and can run or include any type of server or application solution. Non-limiting examples of “hosts” can include virtual switches or routers, such as distributed virtual switches (DVS), application virtual switches (AVS), vector packet processing (VPP) switches; VCENTER and NSX MANAGERS; bare metal physical hosts; HYPER-V hosts; VMs; DOCKER Containers; etc. 
       FIG.  1 B  illustrates another example of Network Environment  100 . In this example, Network Environment  100  includes Endpoints  122  connected to Leafs  104  in Fabric  120 . Endpoints  122  can be physical and/or logical or virtual entities, such as servers, clients, VMs, hypervisors, software containers, applications, resources, network devices, workloads, etc. For example, an Endpoint  122  can be an object that represents a physical device (e.g., server, client, switch, etc.), an application (e.g., web application, database application, etc.), a logical or virtual resource (e.g., a virtual switch, a virtual service appliance, a virtualized network function (VNF), a VM, a service chain, etc.), a container running a software resource (e.g., an application, an appliance, a VNF, a service chain, etc.), storage, a workload or workload engine, etc. Endpoints  122  can have an address (e.g., an identity), a location (e.g., host, network segment, virtual routing and forwarding (VRF) instance, domain, etc.), one or more attributes (e.g., name, type, version, patch level, OS name, OS type, etc.), a tag (e.g., security tag), a profile, etc. 
     Endpoints  122  can be associated with respective Logical Groups  118 . Logical Groups  118  can be logical entities containing endpoints (physical and/or logical or virtual) grouped together according to one or more attributes, such as endpoint type (e.g., VM type, workload type, application type, etc.), one or more requirements (e.g., policy requirements, security requirements, QoS requirements, customer requirements, resource requirements, etc.), a resource name (e.g., VM name, application name, etc.), a profile, platform or operating system (OS) characteristics (e.g., OS type or name including guest and/or host OS, etc.), an associated network or tenant, one or more policies, a tag, etc. For example, a logical group can be an object representing a collection of endpoints grouped together. To illustrate, Logical Group  1  can contain client endpoints, Logical Group  2  can contain web server endpoints, Logical Group  3  can contain application server endpoints, Logical Group N can contain database server endpoints, etc. In some examples, Logical Groups  118  are EPGs in an ACI environment and/or other logical groups (e.g., SGs) in another SDN environment. 
     Traffic to and/or from Endpoints  122  can be classified, processed, managed, etc., based Logical Groups  118 . For example, Logical Groups  118  can be used to classify traffic to or from Endpoints  122 , apply policies to traffic to or from Endpoints  122 , define relationships between Endpoints  122 , define roles of Endpoints  122  (e.g., whether an endpoint consumes or provides a service, etc.), apply rules to traffic to or from Endpoints  122 , apply filters or access control lists (ACLs) to traffic to or from Endpoints  122 , define communication paths for traffic to or from Endpoints  122 , enforce requirements associated with Endpoints  122 , implement security and other configurations associated with Endpoints  122 , etc. 
     In an ACI environment, Logical Groups  118  can be EPGs used to define contracts in the ACI. Contracts can include rules specifying what and how communications between EPGs take place. For example, a contract can define what provides a service, what consumes a service, and what policy objects are related to that consumption relationship. A contract can include a policy that defines the communication path and all related elements of a communication or relationship between endpoints or EPGs. For example, a Web EPG can provide a service that a Client EPG consumes, and that consumption can be subject to a filter (ACL) and a service graph that includes one or more services, such as firewall inspection services and server load balancing. 
       FIG.  2 A  illustrates a diagram of an example Management Information Model  200  for an SDN network, such as Network Environment  100 . The following discussion of Management Information Model  200  references various terms which shall also be used throughout the disclosure. Accordingly, for clarity, the disclosure shall first provide below a list of terminology, which will be followed by a more detailed discussion of Management Information Model  200 . 
     As used herein, an “Alias” can refer to a changeable name for a given object. Thus, even if the name of an object, once created, cannot be changed, the Alias can be a field that can be changed. 
     As used herein, the term “Aliasing” can refer to a rule (e.g., contracts, policies, configurations, etc.) that overlaps one or more other rules. For example, Contract  1  defined in a logical model of a network can be said to be aliasing Contract  2  defined in the logical model of the network if Contract  1  overlaps Contract  1 . In this example, by aliasing Contract  2 , Contract  1  may render Contract  2  redundant or inoperable. For example, if Contract  1  has a higher priority than Contract  2 , such aliasing can render Contract  2  redundant based on Contract  1 &#39;s overlapping and higher priority characteristics. 
     As used herein, the term “APIC” can refer to one or more controllers (e.g., Controllers  116 ) in an ACI framework. The APIC can provide a unified point of automation and management, policy programming, application deployment, health monitoring for an ACI multitenant fabric. The APIC can be implemented as a single controller, a distributed controller, or a replicated, synchronized, and/or clustered controller. 
     As used herein, the term “BDD” can refer to a binary decision tree. A binary decision tree can be a data structure representing functions, such as Boolean functions. 
     As used herein, the term “BD” can refer to a bridge domain. A bridge domain can be a set of logical ports that share the same flooding or broadcast characteristics. Like a virtual LAN (VLAN), bridge domains can span multiple devices. A bridge domain can be a L2 (Layer 2) construct. 
     As used herein, a “Consumer” can refer to an endpoint, resource, and/or EPG that consumes a service. 
     As used herein, a “Context” can refer to an L3 (Layer 3) address domain that allows multiple instances of a routing table to exist and work simultaneously. This increases functionality by allowing network paths to be segmented without using multiple devices. Non-limiting examples of a context or L3 address domain can include a Virtual Routing and Forwarding (VRF) instance, a private network, and so forth. 
     As used herein, the term “Contract” can refer to rules or configurations that specify what and how communications in a network are conducted (e.g., allowed, denied, filtered, processed, etc.). In an ACI network, contracts can specify how communications between endpoints and/or EPGs take place. In some examples, a contract can provide rules and configurations akin to an Access Control List (ACL). 
     As used herein, the term “Distinguished Name” (DN) can refer to a unique name that describes an object, such as an MO, and locates its place in Management Information Model  200 . In some cases, the DN can be (or equate to) a Fully Qualified Domain Name (FQDN). 
     As used herein, the term “Endpoint Group” (EPG) can refer to a logical entity or object associated with a collection or group of endoints as previously described with reference to  FIG.  1 B . 
     As used herein, the term “Filter” can refer to a parameter or configuration for allowing communications. For example, in a whitelist model where all communications are blocked by default, a communication must be given explicit permission to prevent such communication from being blocked. A filter can define permission(s) for one or more communications or packets. A filter can thus function similar to an ACL or Firewall rule. In some examples, a filter can be implemented in a packet (e.g., TCP/IP) header field, such as L3 protocol type, L4 (Layer 4) ports, and so on, which is used to allow inbound or outbound communications between endpoints or EPGs, for example. 
     As used herein, the term “L2 Out” can refer to a bridged connection. A bridged connection can connect two or more segments of the same network so that they can communicate. In an ACI framework, an L2 out can be a bridged (Layer 2) connection between an ACI fabric (e.g., Fabric  120 ) and an outside Layer 2 network, such as a switch. 
     As used herein, the term “L3 Out” can refer to a routed connection. A routed Layer 3 connection uses a set of protocols that determine the path that data follows in order to travel across networks from its source to its destination. Routed connections can perform forwarding (e.g., IP forwarding) according to a protocol selected, such as BGP (border gateway protocol), OSPF (Open Shortest Path First), EIGRP (Enhanced Interior Gateway Routing Protocol), etc. 
     As used herein, the term “Managed Object” (MO) can refer to an abstract representation of objects that are managed in a network (e.g., Network Environment  100 ). The objects can be concrete objects (e.g., a switch, server, adapter, etc.), or logical objects (e.g., an application profile, an EPG, a fault, etc.). The MOs can be network resources or elements that are managed in the network. For example, in an ACI environment, an MO can include an abstraction of an ACI fabric (e.g., Fabric  120 ) resource. 
     As used herein, the term “Management Information Tree” (MIT) can refer to a hierarchical management information tree containing the MOs of a system. For example, in ACI, the MIT contains the MOs of the ACI fabric (e.g., Fabric  120 ). The MIT can also be referred to as a Management Information Model (MIM), such as Management Information Model  200 . 
     As used herein, the term “Policy” can refer to one or more specifications for controlling some aspect of system or network behavior. For example, a policy can include a named entity that contains specifications for controlling some aspect of system behavior. To illustrate, a Layer 3 Outside Network Policy can contain the BGP protocol to enable BGP routing functions when connecting Fabric  120  to an outside Layer 3 network. 
     As used herein, the term “Profile” can refer to the configuration details associated with a policy. For example, a profile can include a named entity that contains the configuration details for implementing one or more instances of a policy. To illustrate, a switch node profile for a routing policy can contain the switch-specific configuration details to implement the BGP routing protocol. 
     As used herein, the term “Provider” refers to an object or entity providing a service. For example, a provider can be an EPG that provides a service. 
     As used herein, the term “Subject” refers to one or more parameters in a contract for defining communications. For example, in ACI, subjects in a contract can specify what information can be communicated and how. Subjects can function similar to ACLs. 
     As used herein, the term “Tenant” refers to a unit of isolation in a network. For example, a tenant can be a secure and exclusive virtual computing environment. In ACI, a tenant can be a unit of isolation from a policy perspective, but does not necessarily represent a private network. Indeed, ACI tenants can contain multiple private networks (e.g., VRFs). Tenants can represent a customer in a service provider setting, an organization or domain in an enterprise setting, or just a grouping of policies. 
     As used herein, the term “VRF” refers to a virtual routing and forwarding instance. The VRF can define a Layer 3 address domain that allows multiple instances of a routing table to exist and work simultaneously. This increases functionality by allowing network paths to be segmented without using multiple devices. Also known as a context or private network. 
     Having described various terms used herein, the disclosure now returns to a discussion of Management Information Model (MIM)  200  in  FIG.  2 A . As previously noted, MIM  200  can be a hierarchical management information tree or MIT. Moreover, MIM  200  can be managed and processed by Controllers  116 , such as APICs in an ACI. Controllers  116  can enable the control of managed resources by presenting their manageable characteristics as object properties that can be inherited according to the location of the object within the hierarchical structure of the model. 
     The hierarchical structure of MIM  200  starts with Policy Universe  202  at the top (Root) and contains parent and child nodes  116 ,  204 ,  206 ,  208 ,  210 ,  212 . Nodes  116 ,  202 ,  204 ,  206 ,  208 ,  210 ,  212  in the tree represent the managed objects (MOs) or groups of objects. Each object in the fabric (e.g., Fabric  120 ) has a unique distinguished name (DN) that describes the object and locates its place in the tree. The Nodes  116 ,  202 ,  204 ,  206 ,  208 ,  210 ,  212  can include the various MOs, as described below, which contain policies that govern the operation of the system. 
     Controllers  116   
     Controllers  116  (e.g., APIC controllers) can provide management, policy programming, application deployment, and health monitoring for Fabric  120 . 
     Node  204   
     Node  204  includes a tenant container for policies that enable an administrator to exercise domain-based access control. Non-limiting examples of tenants can include: 
     User tenants defined by the administrator according to the needs of users. They contain policies that govern the operation of resources such as applications, databases, web servers, network-attached storage, virtual machines, and so on. 
     The common tenant is provided by the system but can be configured by the administrator. It contains policies that govern the operation of resources accessible to all tenants, such as firewalls, load balancers, Layer 4 to Layer 7 services, intrusion detection appliances, and so on. 
     The infrastructure tenant is provided by the system but can be configured by the administrator. It contains policies that govern the operation of infrastructure resources such as the fabric overlay (e.g., VXLAN). It also enables a fabric provider to selectively deploy resources to one or more user tenants. Infrastructure tenant polices can be configurable by the administrator. 
     The management tenant is provided by the system but can be configured by the administrator. It contains policies that govern the operation of fabric management functions used for in-band and out-of-band configuration of fabric nodes. The management tenant contains a private out-of-bound address space for the Controller/Fabric internal communications that is outside the fabric data path that provides access through the management port of the switches. The management tenant enables discovery and automation of communications with virtual machine controllers. 
     Node  206   
     Node  206  can contain access policies that govern the operation of switch access ports that provide connectivity to resources such as storage, compute, Layer 2 and Layer 3 (bridged and routed) connectivity, virtual machine hypervisors, Layer 4 to Layer 7 devices, and so on. If a tenant requires interface configurations other than those provided in the default link, Cisco Discovery Protocol (CDP), Link Layer Discovery Protocol (LLDP), Link Aggregation Control Protocol (LACP), or Spanning Tree Protocol (STP), an administrator can configure access policies to enable such configurations on the access ports of Leafs  104 . 
     Node  206  can contain fabric policies that govern the operation of the switch fabric ports, including such functions as Network Time Protocol (NTP) server synchronization, Intermediate System-to-Intermediate System Protocol (IS-IS), Border Gateway Protocol (BGP) route reflectors, Domain Name System (DNS) and so on. The fabric MO contains objects such as power supplies, fans, chassis, and so on. 
     Node  208   
     Node  208  can contain VM domains that group VM controllers with similar networking policy requirements. VM controllers can share virtual space (e.g., VLAN or VXLAN space) and application EPGs. Controllers  116  communicate with the VM controller to publish network configurations such as port groups that are then applied to the virtual workloads. 
     Node  210   
     Node  210  can contain Layer 4 to Layer 7 service integration life cycle automation framework that enables the system to dynamically respond when a service comes online or goes offline. Policies can provide service device package and inventory management functions. 
     Node  212   
     Node  212  can contain access, authentication, and accounting (AAA) policies that govern user privileges, roles, and security domains of Fabric  120 . 
     The hierarchical policy model can fit well with an API, such as a REST API interface. When invoked, the API can read from or write to objects in the MIT. URLs can map directly into distinguished names that identify objects in the MIT. Data in the MIT can be described as a self-contained structured tree text document encoded in XML or JSON, for example. 
       FIG.  2 B  illustrates an example object model  220  for a tenant portion of MIM  200 . As previously noted, a tenant is a logical container for application policies that enable an administrator to exercise domain-based access control. A tenant thus represents a unit of isolation from a policy perspective, but it does not necessarily represent a private network. Tenants can represent a customer in a service provider setting, an organization or domain in an enterprise setting, or just a convenient grouping of policies. Moreover, tenants can be isolated from one another or can share resources. 
     Tenant portion  204 A of MIM  200  can include various entities, and the entities in Tenant Portion  204 A can inherit policies from parent entities. Non-limiting examples of entities in Tenant Portion  204 A can include Filters  240 , Contracts  236 , Outside Networks  222 , Bridge Domains  230 , VRF Instances  234 , and Application Profiles  224 . 
     Bridge Domains  230  can include Subnets  232 . Contracts  236  can include Subjects  238 . Application Profiles  224  can contain one or more EPGs  226 . Some applications can contain multiple components. For example, an e-commerce application could require a web server, a database server, data located in a storage area network, and access to outside resources that enable financial transactions. Application Profile  224  contains as many (or as few) EPGs as necessary that are logically related to providing the capabilities of an application. 
     EPG  226  can be organized in various ways, such as based on the application they provide, the function they provide (such as infrastructure), where they are in the structure of the data center (such as DMZ), or whatever organizing principle that a fabric or tenant administrator chooses to use. 
     EPGs in the fabric can contain various types of EPGs, such as application EPGs, Layer 2 external outside network instance EPGs, Layer 3 external outside network instance EPGs, management EPGs for out-of-band or in-band access, etc. EPGs  226  can also contain Attributes  228 , such as encapsulation-based EPGs, IP-based EPGs, or MAC-based EPGs. 
     As previously mentioned, EPGs can contain endpoints (e.g., EPs  122 ) that have common characteristics or attributes, such as common policy requirements (e.g., security, virtual machine mobility (VMM), QoS, or Layer 4 to Layer 7 services). Rather than configure and manage endpoints individually, they can be placed in an EPG and managed as a group. 
     Policies apply to EPGs, including the endpoints they contain. An EPG can be statically configured by an administrator in Controllers  116 , or dynamically configured by an automated system such as VCENTER or OPENSTACK. 
     To activate tenant policies in Tenant Portion  204 A, fabric access policies should be configured and associated with tenant policies. Access policies enable an administrator to configure other network configurations, such as port channels and virtual port channels, protocols such as LLDP, CDP, or LACP, and features such as monitoring or diagnostics. 
       FIG.  2 C  illustrates an example Association  260  of tenant entities and access entities in MIM  200 . Policy Universe  202  contains Tenant Portion  204 A and Access Portion  206 A. Thus, Tenant Portion  204 A and Access Portion  206 A are associated through Policy Universe  202 . 
     Access Portion  206 A can contain fabric and infrastructure access policies. Typically, in a policy model, EPGs are coupled with VLANs. For traffic to flow, an EPG is deployed on a leaf port with a VLAN in a physical, VMM, L2 out, L3 out, or Fiber Channel domain, for example. 
     Access Portion  206 A thus contains Domain Profile  236  which can define a physical, VMM, L2 out, L3 out, or Fiber Channel domain, for example, to be associated to the EPGs. Domain Profile  236  contains VLAN Instance Profile  238  (e.g., VLAN pool) and Attacheable Access Entity Profile (AEP)  240 , which are associated directly with application EPGs. The AEP  240  deploys the associated application EPGs to the ports to which it is attached, and automates the task of assigning VLANs. While a large data center can have thousands of active VMs provisioned on hundreds of VLANs, Fabric  120  can automatically assign VLAN IDs from VLAN pools. This saves time compared with trunking down VLANs in a traditional data center. 
       FIG.  2 D  illustrates a schematic diagram of example models for a network, such as Network Environment  100 . The models can be generated based on specific configurations and/or network state parameters associated with various objects, policies, properties, and elements defined in MIM  200 . The models can be implemented for network analysis and assurance, and may provide a depiction of the network at various stages of implementation and levels of the network. 
     As illustrated, the models can include L_Model  270 A (Logical Model), LR_Model  270 B (Logical Rendered Model or Logical Runtime Model), Li_Model  272  (Logical Model for i), Ci_Model  274  (Concrete model for i), and/or Hi_Model  276  (Hardware model or TCAM Model for i). 
     L_Model  270 A is the logical representation of various elements in MIM  200  as configured in a network (e.g., Network Environment  100 ), such as objects, object properties, object relationships, and other elements in MIM  200  as configured in a network. L_Model  270 A can be generated by Controllers  116  based on configurations entered in Controllers  116  for the network, and thus represents the logical configuration of the network at Controllers  116 . This is the declaration of the “end-state” expression that is desired when the elements of the network entities (e.g., applications, tenants, etc.) are connected and Fabric  120  is provisioned by Controllers  116 . Because L_Model  270 A represents the configurations entered in Controllers  116 , including the objects and relationships in MIM  200 , it can also reflect the “intent” of the administrator: how the administrator wants the network and network elements to behave. 
     L_Model  270 A can be a fabric or network-wide logical model. For example, L_Model  270 A can account configurations and objects from each of Controllers  116 . As previously explained, Network Environment  100  can include multiple Controllers  116 . In some cases, two or more Controllers  116  may include different configurations or logical models for the network. In such cases, L_Model  270 A can obtain any of the configurations or logical models from Controllers  116  and generate a fabric or network wide logical model based on the configurations and logical models from all Controllers  116 . L_Model  270 A can thus incorporate configurations or logical models between Controllers  116  to provide a comprehensive logical model. L_Model  270 A can also address or account for any dependencies, redundancies, conflicts, etc., that may result from the configurations or logical models at the different Controllers  116 . 
     LR_Model  270 B is the abstract model expression that Controllers  116  (e.g., APICs in ACI) resolve from L_Model  270 A. LR_Model  270 B can provide the configuration components that would be delivered to the physical infrastructure (e.g., Fabric  120 ) to execute one or more policies. For example, LR_Model  270 B can be delivered to Leafs  104  in Fabric  120  to configure Leafs  104  for communication with attached Endpoints  122 . LR_Model  270 B can also incorporate state information to capture a runtime state of the network (e.g., Fabric  120 ). 
     In some cases, LR_Model  270 B can provide a representation of L_Model  270 A that is normalized according to a specific format or expression that can be propagated to, and/or understood by, the physical infrastructure of Fabric  120  (e.g., Leafs  104 , Spines  102 , etc.). For example, LR_Model  270 B can associate the elements in L_Model  270 A with specific identifiers or tags that can be interpreted and/or compiled by the switches in Fabric  120 , such as hardware plane identifiers used as classifiers. 
     Li_Model  272  is a switch-level or switch-specific model obtained from L_Model  270 A and/or LR_Model  270 B. Li_Model  272  can project L_Model  270 A and/or LR_Model  270 B on a specific switch or device i, and thus can convey how L_Model  270 A and/or LR_Model  270 B should appear or be implemented at the specific switch or device i. 
     For example, Li_Model  272  can project L_Model  270 A and/or LR_Model  270 B pertaining to a specific switch i to capture a switch-level representation of L_Model  270 A and/or LR_Model  270 B at switch i. To illustrate, Li_Model  272  L 1  can represent L_Model  270 A and/or LR_Model  270 B projected to, or implemented at, Leaf  1  ( 104 ). Thus, Li_Model  272  can be generated from L_Model  270 A and/or LR_Model  270 B for individual devices (e.g., Leafs  104 , Spines  102 , etc.) on Fabric  120 . 
     In some cases, Li_Model  272  can be represented using JSON (JavaScript Object Notation). For example, Li_Model  272  can include JSON objects, such as Rules, Filters, Entries, and Scopes. 
     Ci_Model  274  is the actual in-state configuration at the individual fabric member i (e.g., switch i). In other words, Ci_Model  274  is a switch-level or switch-specific model that is based on Li_Model  272 . For example, Controllers  116  can deliver Li_Model  272  to Leaf  1  ( 104 ). Leaf  1  ( 104 ) can take Li_Model  272 , which can be specific to Leaf  1  ( 104 ), and render the policies in Li_Model  272  into a concrete model, Ci_Model  274 , that runs on Leaf  1  ( 104 ). Leaf  1  ( 104 ) can render Li_Model  272  via the OS on Leaf  1  ( 104 ), for example. Thus, Ci_Model  274  can be analogous to compiled software, as it is the form of Li_Model  272  that the switch OS at Leaf  1  ( 104 ) can execute. 
     In some cases, Li_Model  272  and Ci_Model  274  can have a same or similar format. For example, Li_Model  272  and Ci_Model  274  can be based on JSON objects. Having the same or similar format can facilitate objects in Li_Model  272  and Ci_Model  274  to be compared for equivalence or congruence. Such equivalence or congruence checks can be used for network analysis and assurance, as further described herein. 
     Hi_Model  276  is also a switch-level or switch-specific model for switch i, but is based on Ci_Model  274  for switch i. Hi_Model  276  is the actual configuration (e.g., rules) stored or rendered on the hardware or memory (e.g., TCAM memory) at the individual fabric member i (e.g., switch i). For example, Hi_Model  276  can represent the configurations (e.g., rules) which Leaf  1  ( 104 ) stores or renders on the hardware (e.g., TCAM memory) of Leaf  1  ( 104 ) based on Ci_Model  274  at Leaf  1  ( 104 ). The switch OS at Leaf  1  ( 104 ) can render or execute Ci_Model  274 , and Leaf  1  ( 104 ) can store or render the configurations from Ci_Model  274  in storage, such as the memory or TCAM at Leaf  1  ( 104 ). The configurations from Hi_Model  276  stored or rendered by Leaf  1  ( 104 ) represent the configurations that will be implemented by Leaf  1  ( 104 ) when processing traffic. 
     While Models  272 ,  274 ,  276  are shown as device-specific models, similar models can be generated or aggregated for a collection of fabric members (e.g., Leafs  104  and/or Spines  102 ) in Fabric  120 . When combined, device-specific models, such as Model  272 , Model  274 , and/or Model  276 , can provide a representation of Fabric  120  that extends beyond a particular device. For example, in some cases, Li_Model  272 , Ci_Model  274 , and/or Hi_Model  276  associated with some or all individual fabric members (e.g., Leafs  104  and Spines  102 ) can be combined or aggregated to generate one or more aggregated models based on the individual fabric members. 
     As referenced herein, the terms H Model, T Model, and TCAM Model can be used interchangeably to refer to a hardware model, such as Hi_Model  276 . For example, Ti Model, Hi Model and TCAMi Model may be used interchangeably to refer to Hi_Model  276 . 
     Models  270 A,  270 B,  272 ,  274 ,  276  can provide representations of various aspects of the network or various configuration stages for MIM  200 . For example, one or more of Models  270 A,  270 B,  272 ,  274 ,  276  can be used to generate Underlay Model  278  representing one or more aspects of Fabric  120  (e.g., underlay topology, routing, etc.), Overlay Model  280  representing one or more aspects of the overlay or logical segment(s) of Network Environment  100  (e.g., COOP, MPBGP, tenants, VRFs, VLANs, VXLANs, virtual applications, VMs, hypervisors, virtual switching, etc.), Tenant Model  282  representing one or more aspects of Tenant portion  204 A in MIM  200  (e.g., security, forwarding, service chaining, QoS, VRFs, BDs, Contracts, Filters, EPGs, subnets, etc.), Resources Model  284  representing one or more resources in Network Environment  100  (e.g., storage, computing, VMs, port channels, physical elements, etc.), etc. 
     In general, L_Model  270 A can be the high-level expression of what exists in the LR_Model  270 B, which should be present on the concrete devices as Ci_Model  274  and Hi_Model  276  expression. If there is any gap between the models, there may be inconsistent configurations or problems. 
       FIG.  3 A  illustrates a diagram of an example Assurance Appliance  300  for network assurance. In this example, Assurance Appliance  300  can include k VMs  110  operating in cluster mode. VMs are used in this example for explanation purposes. However, it should be understood that other configurations are also contemplated herein, such as use of containers, bare metal devices, Endpoints  122 , or any other physical or logical systems. Moreover, while  FIG.  3 A  illustrates a cluster mode configuration, other configurations are also contemplated herein, such as a single mode configuration (e.g., single VM, container, or server) or a service chain for example. 
     Assurance Appliance  300  can run on one or more Servers  106 , VMs  110 , Hypervisors  108 , EPs  122 , Leafs  104 , Controllers  116 , or any other system or resource. For example, Assurance Appliance  300  can be a logical service or application running on one or more VMs  110  in Network Environment  100 . 
     The Assurance Appliance  300  can include Data Framework  308 , which can be based on, for example, APACHE APEX and HADOOP. In some cases, assurance checks can be written as individual operators that reside in Data Framework  308 . This enables a natively horizontal scale-out architecture that can scale to arbitrary number of switches in Fabric  120  (e.g., ACI fabric). 
     Assurance Appliance  300  can poll Fabric  120  at a configurable periodicity (e.g., an epoch). The analysis workflow can be setup as a DAG (Directed Acyclic Graph) of Operators  310 , where data flows from one operator to another and eventually results are generated and persisted to Database  302  for each interval (e.g., each epoch). 
     The north-tier implements API Server (e.g., APACHE Tomcat and Spring framework)  304  and Web Server  306 . A graphical user interface (GUI) interacts via the APIs exposed to the customer. These APIs can also be used by the customer to collect data from Assurance Appliance  300  for further integration into other tools. 
     Operators  310  in Data Framework  308  (e.g., APEX/Hadoop) can together support assurance operations. Below are non-limiting examples of assurance operations that can be performed by Assurance Appliance  300  via Operators  310 . 
     Security Policy Adherence 
     Assurance Appliance  300  can check to make sure the configurations or specification from L_Model  270 A, which may reflect the user&#39;s intent for the network, including for example the security policies and customer-configured contracts, are correctly implemented and/or rendered in Li_Model  272 , Ci_Model  274 , and Hi_Model  276 , and thus properly implemented and rendered by the fabric members (e.g., Leafs  104 ), and report any errors, contract violations, or irregularities found. 
     Static Policy Analysis 
     Assurance Appliance  300  can check for issues in the specification of the user&#39;s intent or intents (e.g., identify contradictory or conflicting policies in L_Model  270 A). 
     TCAM Utilization 
     TCAM is a scarce resource in the fabric (e.g., Fabric  120 ). However, Assurance Appliance  300  can analyze the TCAM utilization by the network data (e.g., Longest Prefix Match (LPM) tables, routing tables, VLAN tables, BGP updates, etc.), Contracts, Logical Groups  118  (e.g., EPGs), Tenants, Spines  102 , Leafs  104 , and other dimensions in Network Environment  100  and/or objects in MIM  200 , to provide a network operator or user visibility into the utilization of this scarce resource. This can greatly help for planning and other optimization purposes. 
     Endpoint Checks 
     Assurance Appliance  300  can validate that the fabric (e.g. fabric  120 ) has no inconsistencies in the Endpoint information registered (e.g., two leafs announcing the same endpoint, duplicate subnets, etc.), among other such checks. 
     Tenant Routing Checks 
     Assurance Appliance  300  can validate that BDs, VRFs, subnets (both internal and external), VLANs, contracts, filters, applications, EPGs, etc., are correctly programmed. 
     Infrastructure Routing 
     Assurance Appliance  300  can validate that infrastructure routing (e.g., IS-IS protocol) has no convergence issues leading to black holes, loops, flaps, and other problems. 
     MP-BGP Route Reflection Checks 
     The network fabric (e.g., Fabric  120 ) can interface with other external networks and provide connectivity to them via one or more protocols, such as Border Gateway Protocol (BGP), Open Shortest Path First (OSPF), etc. The learned routes are advertised within the network fabric via, for example, MP-BGP. These checks can ensure that a route reflection service via, for example, MP-BGP (e.g., from Border Leaf) does not have health issues. 
     Logical Lint and Real-Time Change Analysis 
     Assurance Appliance  300  can validate rules in the specification of the network (e.g., L_Model  270 A) are complete and do not have inconsistencies or other problems. MOs in the MIM  200  can be checked by Assurance Appliance  300  through syntactic and semantic checks performed on L_Model  270 A and/or the associated configurations of the MOs in MIM  200 . Assurance Appliance  300  can also verify that unnecessary, stale, unused or redundant configurations, such as contracts, are removed. 
       FIG.  3 B  illustrates an architectural diagram of an example system  350  for network assurance, such as Assurance Appliance  300 . In some cases, system  350  can correspond to the DAG of Operators  310  previously discussed with respect to  FIG.  3 A   
     In this example, Topology Explorer  312  communicates with Controllers  116  (e.g., APIC controllers) in order to discover or otherwise construct a comprehensive topological view of Fabric  120  (e.g., Spines  102 , Leafs  104 , Controllers  116 , Endpoints  122 , and any other components as well as their interconnections). While various architectural components are represented in a singular, boxed fashion, it is understood that a given architectural component, such as Topology Explorer  312 , can correspond to one or more individual Operators  310  and may include one or more nodes or endpoints, such as one or more servers, VMs, containers, applications, service functions (e.g., functions in a service chain or virtualized network function), etc. 
     Topology Explorer  312  is configured to discover nodes in Fabric  120 , such as Controllers  116 , Leafs  104 , Spines  102 , etc. Topology Explorer  312  can additionally detect a majority election performed amongst Controllers  116 , and determine whether a quorum exists amongst Controllers  116 . If no quorum or majority exists, Topology Explorer  312  can trigger an event and alert a user that a configuration or other error exists amongst Controllers  116  that is preventing a quorum or majority from being reached. Topology Explorer  312  can detect Leafs  104  and Spines  102  that are part of Fabric  120  and publish their corresponding out-of-band management network addresses (e.g., IP addresses) to downstream services. This can be part of the topological view that is published to the downstream services at the conclusion of Topology Explorer&#39;s  312  discovery epoch (e.g., 5 minutes, or some other specified interval). 
     In some examples, Topology Explorer  312  can receive as input a list of Controllers  116  (e.g., APIC controllers) that are associated with the network/fabric (e.g., Fabric  120 ). Topology Explorer  312  can also receive corresponding credentials to login to each controller. Topology Explorer  312  can retrieve information from each controller using, for example, REST calls. Topology Explorer  312  can obtain from each controller a list of nodes (e.g., Leafs  104  and Spines  102 ), and their associated properties, that the controller is aware of. Topology Explorer  312  can obtain node information from Controllers  116  including, without limitation, an IP address, a node identifier, a node name, a node domain, a node URI, a node_dm, a node role, a node version, etc. 
     Topology Explorer  312  can also determine if Controllers  116  are in quorum, or are sufficiently communicatively coupled amongst themselves. For example, if there are n controllers, a quorum condition might be met when (n/2+1) controllers are aware of each other and/or are communicatively coupled. Topology Explorer  312  can make the determination of a quorum (or identify any failed nodes or controllers) by parsing the data returned from the controllers, and identifying communicative couplings between their constituent nodes. Topology Explorer  312  can identify the type of each node in the network, e.g. spine, leaf, APIC, etc., and include this information in the topology information generated (e.g., topology map or model). 
     If no quorum is present, Topology Explorer  312  can trigger an event and alert a user that reconfiguration or suitable attention is required. If a quorum is present, Topology Explorer  312  can compile the network topology information into a JSON object and pass it downstream to other operators or services, such as Unified Collector  314 . 
     Unified Collector  314  can receive the topological view or model from Topology Explorer  312  and use the topology information to collect information for network assurance from Fabric  120 . Unified Collector  314  can poll nodes (e.g., Controllers  116 , Leafs  104 , Spines  102 , etc.) in Fabric  120  to collect information from the nodes. 
     Unified Collector  314  can include one or more collectors (e.g., collector devices, operators, applications, VMs, etc.) configured to collect information from Topology Explorer  312  and/or nodes in Fabric  120 . For example, Unified Collector  314  can include a cluster of collectors, and each of the collectors can be assigned to a subset of nodes within the topological model and/or Fabric  120  in order to collect information from their assigned subset of nodes. For performance, Unified Collector  314  can run in a parallel, multi-threaded fashion. 
     Unified Collector  314  can perform load balancing across individual collectors in order to streamline the efficiency of the overall collection process. Load balancing can be optimized by managing the distribution of subsets of nodes to collectors, for example by randomly hashing nodes to collectors. 
     In some cases, Assurance Appliance  300  can run multiple instances of Unified Collector  314 . This can also allow Assurance Appliance  300  to distribute the task of collecting data for each node in the topology (e.g., Fabric  120  including Spines  102 , Leafs  104 , Controllers  116 , etc.) via sharding and/or load balancing, and map collection tasks and/or nodes to a particular instance of Unified Collector  314  with data collection across nodes being performed in parallel by various instances of Unified Collector  314 . Within a given node, commands and data collection can be executed serially. Assurance Appliance  300  can control the number of threads used by each instance of Unified Collector  314  to poll data from Fabric  120 . 
     Unified Collector  314  can collect models (e.g., L_Model  270 A and/or LR_Model  270 B) from Controllers  116 , switch software configurations and models (e.g., Ci_Model  274 ) from nodes (e.g., Leafs  104  and/or Spines  102 ) in Fabric  120 , hardware configurations and models (e.g., Hi_Model  276 ) from nodes (e.g., Leafs  104  and/or Spines  102 ) in Fabric  120 , etc. Unified Collector  314  can collect Ci_Model  274  and Hi_Model  276  from individual nodes or fabric members, such as Leafs  104  and Spines  102 , and L_Model  270 A and/or LR_Model  270 B from one or more controllers (e.g., Controllers  116 ) in Network Environment  100 . 
     Unified Collector  314  can poll the devices that Topology Explorer  312  discovers in order to collect data from Fabric  120  (e.g., from the constituent members of the fabric). Unified Collector  314  can collect the data using interfaces exposed by Controllers  116  and/or switch software (e.g., switch OS), including, for example, a Representation State Transfer (REST) Interface and a Secure Shell (SSH) Interface. 
     In some cases, Unified Collector  314  collects L_Model  270 A, LR_Model  270 B, and/or Ci_Model  274  via a REST API, and the hardware information (e.g., configurations, tables, fabric card information, rules, routes, etc.) via SSH using utilities provided by the switch software, such as virtual shell (VSH or VSHELL) for accessing the switch command-line interface (CLI) or VSH_LC shell for accessing runtime state of the line card. 
     Unified Collector  314  can poll other information from Controllers  116 , including, without limitation: topology information, tenant forwarding/routing information, tenant security policies, contracts, interface policies, physical domain or VMM domain information, OOB (out-of-band) management IP&#39;s of nodes in the fabric, etc. 
     Unified Collector  314  can also poll information from nodes (e.g., Leafs  104  and Spines  102 ) in Fabric  120 , including without limitation: Ci_Models  274  for VLANs, BDs, and security policies; Link Layer Discovery Protocol (LLDP) connectivity information of nodes (e.g., Leafs  104  and/or Spines  102 ); endpoint information from EPM/COOP; fabric card information from Spines  102 ; routing information base (RIB) tables from nodes in Fabric  120 ; forwarding information base (FIB) tables from nodes in Fabric  120 ; security group hardware tables (e.g., TCAM tables) from nodes in Fabric  120 ; etc. 
     In some cases, Unified Collector  314  can obtain runtime state from the network and incorporate runtime state information into L_Model  270 A and/or LR_Model  270 B. Unified Collector  314  can also obtain multiple logical models from Controllers  116  and generate a comprehensive or network-wide logical model (e.g., L_Model  270 A and/or LR_Model  270 B) based on the logical models. Unified Collector  314  can compare logical models from Controllers  116 , resolve dependencies, remove redundancies, etc., and generate a single L_Model  270 A and/or LR_Model  270 B for the entire network or fabric. 
     Unified Collector  314  can collect the entire network state across Controllers  116  and fabric nodes or members (e.g., Leafs  104  and/or Spines  102 ). For example, Unified Collector  314  can use a REST interface and an SSH interface to collect the network state. This information collected by Unified Collector  314  can include data relating to the link layer, VLANs, BDs, VRFs, security policies, etc. The state information can be represented in LR_Model  270 B, as previously mentioned. Unified Collector  314  can then publish the collected information and models to any downstream operators that are interested in or require such information. Unified Collector  314  can publish information as it is received, such that data is streamed to the downstream operators. 
     Data collected by Unified Collector  314  can be compressed and sent to downstream services. In some examples, Unified Collector  314  can collect data in an online fashion or real-time fashion, and send the data downstream, as it is collected, for further analysis. In some examples, Unified Collector  314  can collect data in an offline fashion, and compile the data for later analysis or transmission. 
     Assurance Appliance  300  can contact Controllers  116 , Spines  102 , Leafs  104 , and other nodes to collect various types of data. In some scenarios, Assurance Appliance  300  may experience a failure (e.g., connectivity problem, hardware or software error, etc.) that prevents it from being able to collect data for a period of time. Assurance Appliance  300  can handle such failures seamlessly, and generate events based on such failures. 
     Switch Logical Policy Generator  316  can receive L_Model  270 A and/or LR_Model  270 B from Unified Collector  314  and calculate Li_Model  272  for each network device i (e.g., switch i) in Fabric  120 . For example, Switch Logical Policy Generator  316  can receive L_Model  270 A and/or LR_Model  270 B and generate Li_Model  272  by projecting a logical model for each individual node i (e.g., Spines  102  and/or Leafs  104 ) in Fabric  120 . Switch Logical Policy Generator  316  can generate Li_Model  272  for each switch in Fabric  120 , thus creating a switch logical model based on L_Model  270 A and/or LR_Model  270 B for each switch. 
     Each Li_Model  272  can represent L_Model  270 A and/or LR_Model  270 B as projected or applied at the respective network device i (e.g., switch i) in Fabric  120 . In some cases, Li_Model  272  can be normalized or formatted in a manner that is compatible with the respective network device. For example, Li_Model  272  can be formatted in a manner that can be read or executed by the respective network device. To illustrate, Li_Model  272  can included specific identifiers (e.g., hardware plane identifiers used by Controllers  116  as classifiers, etc.) or tags (e.g., policy group tags) that can be interpreted by the respective network device. In some cases, Li_Model  272  can include JSON objects. For example, Li_Model  272  can include JSON objects to represent rules, filters, entries, scopes, etc. 
     The format used for Li_Model  272  can be the same as, or consistent with, the format of Ci_Model  274 . For example, both Li_Model  272  and Ci_Model  274  may be based on JSON objects. Similar or matching formats can enable Li_Model  272  and Ci_Model  274  to be compared for equivalence or congruence. Such equivalency checks can aid in network analysis and assurance as further explained herein. 
     Switch Logical Configuration Generator  316  can also perform change analysis and generate lint events or records for problems discovered in L_Model  270 A and/or LR_Model  270 B. The lint events or records can be used to generate alerts for a user or network operator. 
     Policy Operator  318  can receive Ci_Model  274  and Hi_Model  276  for each switch from Unified Collector  314 , and Li_Model  272  for each switch from Switch Logical Policy Generator  316 , and perform assurance checks and analysis (e.g., security adherence checks, TCAM utilization analysis, etc.) based on Ci_Model  274 , Hi_Model  276 , and Li_Model  272 . Policy Operator  318  can perform assurance checks on a switch-by-switch basis by comparing one or more of the models. 
     Returning to Unified Collector  314 , Unified Collector  314  can also send L_Model  270 A and/or LR_Model  270 B to Routing Policy Parser  320 , and Ci_Model  274  and Hi_Model  276  to Routing Parser  326 . 
     Routing Policy Parser  320  can receive L_Model  270 A and/or LR_Model  270 B and parse the model(s) for information that may be relevant to downstream operators, such as Endpoint Checker  322  and Tenant Routing Checker  324 . Similarly, Routing Parser  326  can receive Ci_Model  274  and Hi_Model  276  and parse each model for information for downstream operators, Endpoint Checker  322  and Tenant Routing Checker  324 . 
     After Ci_Model  274 , Hi_Model  276 , L_Model  270 A and/or LR_Model  270 B are parsed, Routing Policy Parser  320  and/or Routing Parser  326  can send cleaned-up protocol buffers (Proto Buffs) to the downstream operators, Endpoint Checker  322  and Tenant Routing Checker  324 . Endpoint Checker  322  can then generate events related to Endpoint violations, such as duplicate IPs, APIPA, etc., and Tenant Routing Checker  324  can generate events related to the deployment of BDs, VRFs, subnets, routing table prefixes, etc. 
       FIG.  3 C  illustrates a schematic diagram of an example system for static policy analysis in a network (e.g., Network Environment  100 ). Static Policy Analyzer  360  can perform assurance checks to detect configuration violations, logical lint events, contradictory or conflicting policies, unused contracts, incomplete configurations, etc. Static Policy Analyzer  360  can check the specification of the user&#39;s intent or intents in L_Model  270 A to determine if any configurations in Controllers  116  are inconsistent with the specification of the user&#39;s intent or intents. 
     Static Policy Analyzer  360  can include one or more of the Operators  310  executed or hosted in Assurance Appliance  300 . However, in other configurations, Static Policy Analyzer  360  can run one or more operators or engines that are separate from Operators  310  and/or Assurance Appliance  300 . For example, Static Policy Analyzer  360  can be a VM, a cluster of VMs, or a collection of endpoints in a service function chain. 
     Static Policy Analyzer  360  can receive as input L_Model  270 A from Logical Model Collection Process  366  and Rules  368  defined for each feature (e.g., object) in L_Model  270 A. Rules  368  can be based on objects, relationships, definitions, configurations, and any other features in MIM  200 . Rules  368  can specify conditions, relationships, parameters, and/or any other information for identifying configuration violations or issues. 
     Moreover, Rules  368  can include information for identifying syntactic violations or issues. For example, Rules  368  can include one or more rules for performing syntactic checks. Syntactic checks can verify that the configuration of L_Model  270 A is complete, and can help identify configurations or rules that are not being used. Syntactic checks can also verify that the configurations in the hierarchical MIM  200  are complete (have been defined) and identify any configurations that are defined but not used. To illustrate, Rules  368  can specify that every tenant in L_Model  270 A should have a context configured configured; every contract in L_Model  270 A should specify a provider EPG and a consumer EPG; every contract in L_Model  270 A should specify a subject, filter, and/or port; etc. 
     Rules  368  can also include rules for performing semantic checks and identifying semantic violations or issues. Semantic checks can check conflicting rules or configurations. For example, Rule 1  and Rule 2  can have aliasing issues, Rule 1  can be more specific than Rule 2  and thereby create conflicts/issues, etc. Rules  368  can define conditions which may result in aliased rules, conflicting rules, etc. To illustrate, Rules  368  can specify that an allow policy for a specific communication between two objects can conflict with a deny policy for the same communication between two objects if the allow policy has a higher priority than the deny policy, or a rule for an object renders another rule unnecessary. 
     Static Policy Analyzer  360  can apply Rules  368  to L_Model  270 A to check configurations in L_Model  270 A and output Configuration Violation Events  370  (e.g., alerts, logs, notifications, etc.) based on any issues detected. Configuration Violation Events  370  can include semantic or semantic problems, such as incomplete configurations, conflicting configurations, aliased rules, unused configurations, errors, policy violations, misconfigured objects, incomplete configurations, incorrect contract scopes, improper object relationships, etc. 
     In some cases, Static Policy Analyzer  360  can iteratively traverse each node in a tree generated based on L_Model  270 A and/or MIM  200 , and apply Rules  368  at each node in the tree to determine if any nodes yield a violation (e.g., incomplete configuration, improper configuration, unused configuration, etc.). Static Policy Analyzer  360  can output Configuration Violation Events  370  when it detects any violations. 
       FIG.  4    illustrates an example configuration  400  for a network assurance platform  434 . The network assurance platform  434  can run the assurance operator  402  on each Leaf  104  to generate and emit fault codes from the Leafs  104 . In some cases, the assurance operator  402  can be, for example, one or more operators from the operators  310  illustrated in  FIG.  3 A . The fault codes can represent errors, such as hardware errors. The assurance operators  402  can send the raw faults  404  to the logical policy enrichers  406 . 
     The logical policy enrichers  406  can map the hardware identifiers (e.g., scope, pcTag, etc.) to the logical policy entity defined in the fabric configuration (e.g., ACI fabric configuration). For example, the logical policy enrichers  406  can map the hardware identifiers to particular tenants, EPGs, application profiles (APs), contracts, etc. The logical policy enrichers  406  can generate indexed faults  408  based on the mappings, and send the indexed faults  408  to tenant aggregators  418 ,  420 ,  422 ,  424 . In some cases, the indexed faults  408  can be transmitted to the aggregators as pairs such as key and tag pairs. Each key can represent a specific dimension, such as a tenant, a contract, an application profile, and EPG pair, etc. 
     The aggregators  418 ,  420 ,  422 ,  424  can represent an aggregation layer. In some cases, the aggregators  418 ,  420 ,  422 ,  424  can be specifically set to aggregate along a pre-determined dimension, such as tenant (e.g., aggregator  418 ), contract (e.g., aggregator  420 ), application profile (e.g., aggregator  422 ), EPG pair (e.g., aggregator  424 ), etc. The aggregators  418 ,  420 ,  422 ,  424  can generate faults along a specific dimension, such as faults by tenant  410 , faults by contract  412 , faults by application profile  414 , faults by EPG pair  416 , etc. 
     The network assurance platform  434  can then generate and/or store visualization data for specific dimensions. For example, the network assurance platform  434  can maintain tenant error visualization  426 , contract error visualization  428 , application profile error visualization  430 , EPG pair error visualization  432 , and so forth. The visualizations can provide hardware-level visibility of errors along specific dimensions in an SDN, such as an ACI network. Moreover, the tenant error visualization  426 , contract error visualization  428 , application profile error visualization  430 , EPG pair error visualization  432  can be stored in one or more respective storage locations, such as databases or storage servers. 
       FIG.  5 A  illustrates an example flowchart for a network assurance model. At step  500 , the method involves data collection. Data collection can include collection of data for operator intent, such as fabric data (e.g., topology, switch, interface policies, application policies, endpoint groups, etc.), network policies (e.g., BDs, VRFs, L2Outs, L3Outs, protocol configurations, etc.), security policies (e.g., contracts, filters, etc.), service chaining policies, and so forth. Data collection can also include data for the concrete, hardware model, such as network configuration (e.g., RIB/FIB, VLAN, MAC, ISIS, DB, BGP, OSPF, ARP, VPC, LLDP, MTU, QoS, etc.), security policies (e.g., TCAM, ECMP tables, etc.), endpoint dynamics (e.g., EPM, COOP EP DB, etc.), statistics (e.g., TCAM rule hits, interface counters, bandwidth, etc.). 
     At step  502 , the method can involve formal modeling and analysis. Formal modeling and analysis can involve determining equivalency between logical and hardware models, such as security policies between models, etc. 
     At step  504 , the method can involve smart event generation. Smart events can be generated using deep object hierarchy for detailed analysis, such as: Tenant, Leaf, VRFs, Rules; Filters, Routes, Prefixes, Port Numbers. 
     At step  506 , the method can involve visualization. Formal models can be used to identify problems for analysis and debugging, in a user-friendly GUI. 
       FIG.  5 B  illustrates an example method for fault code aggregation. At step  520 , the assurance operators  402  obtain respective fault codes corresponding to one or more network devices in a network (e.g., Leafs  104 ). At step  522 , the logical policy enrichers  406  map the one or more network devices and/or the respective fault codes to respective logical policy entities defined in a logical policy model of the network, to yield fault code mappings. The logical policy model can be a model of the fabric and/or network generated based on the SDN or ACI configurations. 
     At step  524 , the aggregators  418 ,  420 ,  422 ,  424  aggregate one or more of the fault code mappings along respective logical policy dimensions in the network to yield an aggregation of fault codes across respective logical policy dimensions. At step  526 , the network assurance platform  434  presents, for each of the respective logical policy dimensions, one or more hardware-level errors along the respective logical policy dimension. In some cases, the network assurance platform  434  can generate visualization data or interface data for presenting the one or more hardware-level errors along the respective logical policy dimension. Such data can be based on the aggregation of fault codes across the respective logical policy dimensions. 
     The disclosure now turns to  FIGS.  6  and  7   , which illustrate example network devices and computing devices, such as switches, routers, load balancers, client devices, and so forth. 
       FIG.  6    illustrates an example network device  600  suitable for performing switching, routing, load balancing, and other networking operations. Network device  600  includes a central processing unit (CPU)  604 , interfaces  602 , and a bus  610  (e.g., a PCI bus). When acting under the control of appropriate software or firmware, the CPU  604  is responsible for executing packet management, error detection, and/or routing functions. The CPU  604  preferably accomplishes all these functions under the control of software including an operating system and any appropriate applications software. CPU  604  may include one or more processors  608 , such as a processor from the INTEL X86 family of microprocessors. In some cases, processor  608  can be specially designed hardware for controlling the operations of network device  600 . In some cases, a memory  606  (e.g., non-volatile RAM, ROM, etc.) also forms part of CPU  604 . However, there are many different ways in which memory could be coupled to the system. 
     The interfaces  602  are typically provided as modular interface cards (sometimes referred to as “line cards”). Generally, they control the sending and receiving of data packets over the network and sometimes support other peripherals used with the network device  600 . Among the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, and the like. In addition, various very high-speed interfaces may be provided such as fast token ring interfaces, wireless interfaces, Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces, HSSI interfaces, POS interfaces, FDDI interfaces, WIFI interfaces, 3G/4G/5G cellular interfaces, CAN BUS, LoRA, and the like. Generally, these interfaces may include ports appropriate for communication with the appropriate media. In some cases, they may also include an independent processor and, in some instances, volatile RAM. The independent processors may control such communications intensive tasks as packet switching, media control, signal processing, crypto processing, and management. By providing separate processors for the communications intensive tasks, these interfaces allow the master microprocessor  604  to efficiently perform routing computations, network diagnostics, security functions, etc. 
     Although the system shown in  FIG.  6    is one specific network device of the present invention, it is by no means the only network device architecture on which the present invention can be implemented. For example, an architecture having a single processor that handles communications as well as routing computations, etc., is often used. Further, other types of interfaces and media could also be used with the network device  600 . 
     Regardless of the network device&#39;s configuration, it may employ one or more memories or memory modules (including memory  606 ) configured to store program instructions for the general-purpose network operations and mechanisms for roaming, route optimization and routing functions described herein. The program instructions may control the operation of an operating system and/or one or more applications, for example. The memory or memories may also be configured to store tables such as mobility binding, registration, and association tables, etc. Memory  606  could also hold various software containers and virtualized execution environments and data. 
     The network device  600  can also include an application-specific integrated circuit (ASIC), which can be configured to perform routing and/or switching operations. The ASIC can communicate with other components in the network device  600  via the bus  610 , to exchange data and signals and coordinate various types of operations by the network device  600 , such as routing, switching, and/or data storage operations, for example. 
       FIG.  7    illustrates a computing system architecture  700  wherein the components of the system are in electrical communication with each other using a connection  705 , such as a bus. Exemplary system  700  includes a processing unit (CPU or processor)  710  and a system connection  705  that couples various system components including the system memory  715 , such as read only memory (ROM)  720  and random access memory (RAM)  725 , to the processor  710 . The system  700  can include a cache of high-speed memory connected directly with, in close proximity to, or integrated as part of the processor  710 . The system  700  can copy data from the memory  715  and/or the storage device  730  to the cache  712  for quick access by the processor  710 . In this way, the cache can provide a performance boost that avoids processor  710  delays while waiting for data. These and other modules can control or be configured to control the processor  710  to perform various actions. Other system memory  715  may be available for use as well. The memory  715  can include multiple different types of memory with different performance characteristics. The processor  710  can include any general purpose processor and a hardware or software service, such as service  1   732 , service  2   734 , and service  3   736  stored in storage device  730 , configured to control the processor  710  as well as a special-purpose processor where software instructions are incorporated into the actual processor design. The processor  710  may be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric. 
     To enable user interaction with the computing device  700 , an input device  745  can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. An output device  735  can also be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems can enable a user to provide multiple types of input to communicate with the computing device  700 . The communications interface  740  can generally govern and manage the user input and system output. There is no restriction on operating on any particular hardware arrangement and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed. 
     Storage device  730  is a non-volatile memory and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, random access memories (RAMs)  725 , read only memory (ROM)  720 , and hybrids thereof. 
     The storage device  730  can include services  732 ,  734 ,  736  for controlling the processor  710 . Other hardware or software modules are contemplated. The storage device  730  can be connected to the system connection  705 . In one aspect, a hardware module that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as the processor  710 , connection  705 , output device  735 , and so forth, to carry out the function. 
     For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software. 
     In some embodiments the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se. 
     Methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer readable media. Such instructions can comprise, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, or source code. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during methods according to described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on. 
     Devices implementing methods according to these disclosures can comprise hardware, firmware and/or software, and can take any of a variety of form factors. Typical examples of such form factors include laptops, smart phones, small form factor personal computers, personal digital assistants, rackmount devices, standalone devices, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example. 
     The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are means for providing the functions described in these disclosures. 
     Although a variety of examples and other information was used to explain aspects within the scope of the appended claims, no limitation of the claims should be implied based on particular features or arrangements in such examples, as one of ordinary skill would be able to use these examples to derive a wide variety of implementations. Further and although some subject matter may have been described in language specific to examples of structural features and/or method steps, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to these described features or acts. For example, such functionality can be distributed differently or performed in components other than those identified herein. Rather, the described features and steps are disclosed as examples of components of systems and methods within the scope of the appended claims. 
     Claim language reciting “at least one of” refers to at least one of a set and indicates that one member of the set or multiple members of the set satisfy the claim. For example, claim language reciting “at least one of A and B” means A, B, or A and B.