Patent Description:
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.

A paper by <NPL> discloses a framework that is is able to model firewall policies as formal predicates to validate, detect and prevent conflicts in firewall policies. <CIT> discloses a method for resource automation management includes creating a service model by receiving dragged and dropped abstracted service units into a workspace and receiving connections between the abstracted service units to represent a communication path.

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. 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.

Disclosed herein are systems, methods, and computer-readable media for generating events for external consumption, wherein the events are based on various failures of equivalence checks between two or more models of network intents. In some examples, a system can obtain an indication of a network equivalence failure (e.g. a software-defined logical intent is not rendered correctly into a switch as a corresponding hardware intent), and subsequently analyze the indication of the network equivalence failure in conjunction with the associated models of user intents (e.g. the software model and hardware model in the example above) to generate an event for external consumption.

The logical model of network intents 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 hardware model of network intents can be a model generated based on the logical model. The hardware model can thus represent the hardware rendering of the discrete software-defined components that comprise the logical model. Often times, there is not a one-to-one correspondence between a software-defined logical intent and a hardware-defined intent. For example, the hardware rendering of the logical model might cause a single logical intent to be broken into multiple different hardware intents. This is not problematic in and of itself, as long as the multiple hardware intents capture the exact same effect as the single logical intent. However, conventional network assurance processes struggle to make this determination, as it requires a comparison of two models of network intents that do not have a congruent form. As such, it would be desirable to provide intelligent network assurance via event generation that is responsive to equivalence failures between two or more models of network intents that are not necessarily congruent in form or composition.

The disclosed technology addresses the need in the art for a reliable and efficient ability to generate events corresponding to conflict rules or other equivalence failures between two or more models of network intents. The present technology involves systems, methods, and computer-readable media for receiving as input one or more equivalence failures and subsequently generating one or more events corresponding to the input equivalence failures. For example, the input equivalence failures might be the set of conflict rules calculated between two or more models of network intents, in which case one or more events would be generated corresponding to the conflict rules between the models of network intents. The present technology will be described in the following disclosure as follows. The discussion begins with an introductory discussion of network assurance and a description of example computing environments, as illustrated in <FIG> and <FIG>. The discussion continues with a description of systems and methods for network assurance, network modeling, and event generation, as shown in <FIG>, <FIG>, and <FIG>. The discussion moves next to an example formal analysis architecture for event generation as illustrated in <FIG> and a chart of events triggered by paired outcomes of model comparisons as seen in <FIG>. The discussion then concludes with a description of an example network device, as illustrated in <FIG>, and an example computing device, as illustrated in <FIG>, including example hardware components suitable for hosting software applications and performing computing operations. The disclosure now turns to a discussion of network assurance, the analysis and execution of which is a precursor to the event generation in accordance with embodiments of the present disclosure.

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 network assurance approaches herein can be implemented in various types of networks, including a private network, such as a local area network (LAN); an enterprise network; a standalone or traditional network, such as a data center network; a network including a physical or underlay layer and a logical or overlay layer, such as a VXLAN or software-defined network (SDN) (e.g., Application Centric Infrastructure (ACI) or VMware NSX networks); etc. 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. In this manner, 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.

Logical models can be implemented to represent various aspects of a network. A model can include a mathematical or semantic model of the network, including, without limitation the network'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.

<FIG> illustrates a diagram of an example Network Environment <NUM>, such as a data center. The Network Environment <NUM> can include a Fabric <NUM> which can represent the physical layer or infrastructure (e.g., underlay) of the Network Environment <NUM>. Fabric <NUM> can include Spines <NUM> (e.g., spine routers or switches) and Leafs <NUM> (e.g., leaf routers or switches) which can be interconnected for routing or switching traffic in the Fabric <NUM>. Spines <NUM> can interconnect Leafs <NUM> in the Fabric <NUM>, and Leafs <NUM> can connect the Fabric <NUM> to an overlay or logical portion of the Network Environment <NUM>, which can include application services, servers, virtual machines, containers, endpoints, etc. Thus, network connectivity in the Fabric <NUM> can flow from Spines <NUM> to Leafs <NUM>, and vice versa. The interconnections between Leafs <NUM> and Spines <NUM> can be redundant (e.g., multiple interconnections) to avoid a failure in routing. In some embodiments, Leafs <NUM> and Spines <NUM> can be fully connected, such that any given Leaf is connected to each of the Spines <NUM>, and any given Spine is connected to each of the Leafs <NUM>. Leafs <NUM> 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 <NUM> 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 <NUM>, and/or implemented or enforced by one or more devices, such as Leafs <NUM>. Leafs <NUM> can connect other elements to the Fabric <NUM>. For example, Leafs <NUM> can connect Servers <NUM>, Hypervisors <NUM>, Virtual Machines (VMs) <NUM>, Applications <NUM>, Network Device <NUM>, etc., with Fabric <NUM>. Such elements can reside in one or more logical or virtual layers or networks, such as an overlay network. In some cases, Leafs <NUM> can encapsulate and decapsulate packets to and from such elements (e.g., Servers <NUM>) in order to enable communications throughout Network Environment <NUM> and Fabric <NUM>. Leafs <NUM> can also provide any other devices, services, tenants, or workloads with access to Fabric <NUM>. In some cases, Servers <NUM> connected to Leafs <NUM> can similarly encapsulate and decapsulate packets to and from Leafs <NUM>. For example, Servers <NUM> 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 <NUM> and an underlay layer represented by Fabric <NUM> and accessed via Leafs <NUM>.

Applications <NUM> can include software applications, services, containers, appliances, functions, service chains, etc. For example, Applications <NUM> 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 <NUM> can be distributed, chained, or hosted by multiple endpoints (e.g., Servers <NUM>, VMs <NUM>, etc.), or may run or execute entirely from a single endpoint.

VMs <NUM> can be virtual machines hosted by Hypervisors <NUM> or virtual machine managers running on Servers <NUM>. VMs <NUM> can include workloads running on a guest operating system on a respective server. Hypervisors <NUM> can provide a layer of software, firmware, and/or hardware that creates, manages, and/or runs the VMs <NUM>. Hypervisors <NUM> can allow VMs <NUM> to share hardware resources on Servers <NUM>, and the hardware resources on Servers <NUM> to appear as multiple, separate hardware platforms. Moreover, Hypervisors <NUM> on Servers <NUM> can host one or more VMs <NUM>.

In some cases, VMs <NUM> and/or Hypervisors <NUM> can be migrated to other Servers <NUM>. Servers <NUM> can similarly be migrated to other locations in Network Environment <NUM>. 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 <NUM>, Hypervisors <NUM>, and/or VMs <NUM> 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 <NUM> 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 <NUM>, Servers <NUM>, Leafs <NUM>, etc..

Configurations in Network Environment <NUM> 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 <NUM>, which can implement or propagate such configurations through Network Environment <NUM>. In some examples, Controllers <NUM> can be Application Policy Infrastructure Controllers (APICs) in an ACI framework. In other examples, Controllers <NUM> 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 <NUM>. 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 <NUM>, such as Leafs <NUM>, Servers <NUM>, Hypervisors <NUM>, Controllers <NUM>, etc. As previously explained, Network Environment <NUM> 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 <NUM>. 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 <NUM> 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 <NUM> 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 <NUM>. Leaf <NUM> 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-<NUM> network segment. Traffic protection can be provided within the network segment based on the VM type. For example, HTTP traffic can be permitted among web VMs, and not permitted 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>, Network Environment <NUM> can deploy different hosts via Leafs <NUM>, Servers <NUM>, Hypervisors <NUM>, VMs <NUM>, Applications <NUM>, and Controllers <NUM>, such as VMWARE ESXi hosts, WINDOWS HYPER-V hosts, bare metal physical hosts, etc. Network Environment <NUM> may interoperate with a variety of Hypervisors <NUM>, Servers <NUM> (e.g., physical and/or virtual servers), SDN orchestration platforms, etc. Network Environment <NUM> may implement a declarative model to allow its integration with application design and holistic network policy.

Controllers <NUM> 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 <NUM> 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 <NUM> can define and manage application-level model(s) for configurations in Network Environment <NUM>. 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 <NUM>, including configurations and settings for virtual appliances.

As illustrated above, Network Environment <NUM> 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 <NUM> 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 <NUM> (e.g., physical or logical), Hypervisors <NUM>, VMs <NUM>, containers (e.g., Applications <NUM>), 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> illustrates another example of Network Environment <NUM>. In this example, Network Environment <NUM> includes Endpoints <NUM> connected to Leafs <NUM> in Fabric <NUM>. Endpoints <NUM> 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 <NUM> 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 <NUM> 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 <NUM> can be associated with respective Logical Groups <NUM>. Logical Groups <NUM> 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 <NUM> can contain client endpoints, Logical Group <NUM> can contain web server endpoints, Logical Group <NUM> can contain application server endpoints, Logical Group N can contain database server endpoints, etc. In some examples, Logical Groups <NUM> are EPGs in an ACI environment and/or other logical groups (e.g., SGs) in another SDN environment.

Traffic to and/or from Endpoints <NUM> can be classified, processed, managed, etc., based Logical Groups <NUM>. For example, Logical Groups <NUM> can be used to classify traffic to or from Endpoints <NUM>, apply policies to traffic to or from Endpoints <NUM>, define relationships between Endpoints <NUM>, define roles of Endpoints <NUM> (e.g., whether an endpoint consumes or provides a service, etc.), apply rules to traffic to or from Endpoints <NUM>, apply filters or access control lists (ACLs) to traffic to or from Endpoints <NUM>, define communication paths for traffic to or from Endpoints <NUM>, enforce requirements associated with Endpoints <NUM>, implement security and other configurations associated with Endpoints <NUM>, etc..

In an ACI environment, Logical Groups <NUM> 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> illustrates a diagram of an example Management Information Model <NUM> for an SDN network, such as Network Environment <NUM>. The following discussion of Management Information Model <NUM> 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 <NUM>.

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 terms "Aliasing" and "Shadowing" can refer to a rule (e.g., contracts, policies, configurations, etc.) that overlaps one or more other rules. For example, Contract <NUM> defined in a logical model of a network can be said to be aliasing or shadowing Contract <NUM> defined in the logical model of the network if Contract <NUM> overlaps Contract <NUM>. In this example, by aliasing or shadowing Contract <NUM>, Contract <NUM> may render Contract <NUM> redundant or inoperable. For example, if Contract <NUM> has a higher priority than Contract <NUM>, such aliasing can render Contract <NUM> redundant based on Contract <NUM>'s overlapping and higher priority characteristics.

As used herein, the term "APIC" can refer to one or more controllers (e.g., Controllers <NUM>) 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 <NUM>) 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 <NUM>) address domain that permits multiple instances of a routing table to exist and work simultaneously. This increases functionality by permitting 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., permitted, 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 <NUM>. 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>.

As used herein, the term "Filter" can refer to a parameter or configuration for permitting 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 <NUM>) ports, and so on, which is used to permit 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 <NUM>) connection between an ACI fabric (e.g., Fabric <NUM>) and an outside Layer <NUM> network, such as a switch.

As used herein, the term "L3 Out" can refer to a routed connection. A routed Layer <NUM> 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 <NUM>). 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 <NUM>) 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 <NUM>). The MIT can also be referred to as a Management Information Model (MIM), such as Management Information Model <NUM>.

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 <NUM> Outside Network Policy can contain the BGP protocol to enable BGP routing functions when connecting Fabric <NUM> to an outside Layer <NUM> 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 <NUM> address domain that permits multiple instances of a routing table to exist and work simultaneously. This increases functionality by permitting 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) <NUM> in <FIG>. As previously noted, MIM <NUM> can be a hierarchical management information tree or MIT. Moreover, MIM <NUM> can be managed and processed by Controllers <NUM>, such as APICs in an ACI. Controllers <NUM> 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 <NUM> starts with Policy Universe <NUM> at the top (Root) and contains parent and child nodes <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>. Nodes <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> in the tree represent the managed objects (MOs) or groups of objects. Each object in the fabric (e.g., Fabric <NUM>) has a unique distinguished name (DN) that describes the object and locates its place in the tree. The Nodes <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> can include the various MOs, as described below, which contain policies that govern the operation of the system.

Controllers <NUM> (e.g., APIC controllers) can provide management, policy programming, application deployment, and health monitoring for Fabric <NUM>.

Node <NUM> 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 <NUM> to Layer <NUM> 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 <NUM> can contain access policies that govern the operation of switch access ports that provide connectivity to resources such as storage, compute, Layer <NUM> and Layer <NUM> (bridged and routed) connectivity, virtual machine hypervisors, Layer <NUM> to Layer <NUM> 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 <NUM>.

Node <NUM> 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 <NUM> 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 <NUM> communicate with the VM controller to publish network configurations such as port groups that are then applied to the virtual workloads.

Node <NUM> can contain Layer <NUM> to Layer <NUM> 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 <NUM> can contain access, authentication, and accounting (AAA) policies that govern user privileges, roles, and security domains of Fabric <NUM>.

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> illustrates an example object model <NUM> for a tenant portion of MIM <NUM>. 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 204A of MIM <NUM> can include various entities, and the entities in Tenant Portion 204A can inherit policies from parent entities. Non-limiting examples of entities in Tenant Portion 204A can include Filters <NUM>, Contracts <NUM>, Outside Networks <NUM>, Bridge Domains <NUM>, VRF Instances <NUM>, and Application Profiles <NUM>.

Bridge Domains <NUM> can include Subnets <NUM>. Contracts <NUM> can include Subjects <NUM>. Application Profiles <NUM> can contain one or more EPGs <NUM>. 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 <NUM> contains as many (or as few) EPGs as necessary that are logically related to providing the capabilities of an application.

EPG <NUM> 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 <NUM> external outside network instance EPGs, Layer <NUM> external outside network instance EPGs, management EPGs for out-of-band or in-band access, etc. EPGs <NUM> can also contain Attributes <NUM>, such as encapsulation-based EPGs, IP-based EPGs, or MAC-based EPGs.

As previously mentioned, EPGs can contain endpoints (e.g., EPs <NUM>) that have common characteristics or attributes, such as common policy requirements (e.g., security, virtual machine mobility (VMM), QoS, or Layer <NUM> to Layer <NUM> 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 <NUM>, or dynamically configured by an automated system such as VCENTER or OPENSTACK.

To activate tenant policies in Tenant Portion 204A, 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> illustrates an example Association <NUM> of tenant entities and access entities in MIM <NUM>. Policy Universe <NUM> contains Tenant Portion 204A and Access Portion 206A. Thus, Tenant Portion 204A and Access Portion 206A are associated through Policy Universe <NUM>.

Access Portion 206A 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 206A thus contains Domain Profile <NUM> which can define a physical, VMM, L2 out, L3 out, or Fiber Channel domain, for example, to be associated to the EPGs. Domain Profile <NUM> contains VLAN Instance Profile <NUM> (e.g., VLAN pool) and Attacheable Access Entity Profile (AEP) <NUM>, which are associated directly with application EPGs. The AEP <NUM> 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 <NUM> can automatically assign VLAN IDs from VLAN pools. This saves time compared with trunking down VLANs in a traditional data center.

<FIG> illustrates a schematic diagram of example models for a network, such as Network Environment <NUM>. 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 <NUM>. 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 270A (Logical Model), LR_Model 270B (Logical Rendered Model or Logical Runtime Model), Li_Model <NUM> (Logical Model for i), Ci_Model <NUM> (Concrete model for i), and/or Hi_Model <NUM> (Hardware model or TCAM Model for i).

L_Model 270A is the logical representation of various elements in MIM <NUM> as configured in a network (e.g., Network Environment <NUM>), such as objects, object properties, object relationships, and other elements in MIM <NUM> as configured in a network. L_Model 270A can be generated by Controllers <NUM> based on configurations entered in Controllers <NUM> for the network, and thus represents the logical configuration of the network at Controllers <NUM>. 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 <NUM> is provisioned by Controllers <NUM>. Because L_Model 270A represents the configurations entered in Controllers <NUM>, including the objects and relationships in MIM <NUM>, it can also reflect the "intent" of the administrator: how the administrator wants the network and network elements to behave.

L_Model 270A can be a fabric or network-wide logical model. For example, L_Model 270A can account configurations and objects from each of Controllers <NUM>. As previously explained, Network Environment <NUM> can include multiple Controllers <NUM>. In some cases, two or more Controllers <NUM> may include different configurations or logical models for the network. In such cases, L_Model 270A can obtain any of the configurations or logical models from Controllers <NUM> and generate a fabric or network wide logical model based on the configurations and logical models from all Controllers <NUM>. L_Model 270A can thus incorporate configurations or logical models between Controllers <NUM> to provide a comprehensive logical model. L_Model 270A can also address or account for any dependencies, redundancies, conflicts, etc., that may result from the configurations or logical models at the different Controllers <NUM>.

LR_Model 270B is the abstract model expression that Controllers <NUM> (e.g., APICs in ACI) resolve from L_Model 270A. LR_Model 270B can provide the configuration components that would be delivered to the physical infrastructure (e.g., Fabric <NUM>) to execute one or more policies. For example, LR_Model 270B can be delivered to Leafs <NUM> in Fabric <NUM> to configure Leafs <NUM> for communication with attached Endpoints <NUM>. LR_Model 270B can also incorporate state information to capture a runtime state of the network (e.g., Fabric <NUM>).

In some cases, LR_Model 270B can provide a representation of L_Model 270A that is normalized according to a specific format or expression that can be propagated to, and/or understood by, the physical infrastructure of Fabric <NUM> (e.g., Leafs <NUM>, Spines <NUM>, etc.). For example, LR_Model 270B can associate the elements in L_Model 270A with specific identifiers or tags that can be interpreted and/or compiled by the switches in Fabric <NUM>, such as hardware plane identifiers used as classifiers.

Li_Model <NUM> is a switch-level or switch-specific model obtained from L_Model 270A and/or LR_Model 270B. Li_Model <NUM> can project L_Model 270A and/or LR_Model 270B on a specific switch or device i, and thus can convey how L_Model 270A and/or LR_Model 270B should appear or be implemented at the specific switch or device i.

For example, Li_Model <NUM> can project L_Model 270A and/or LR_Model 270B pertaining to a specific switch i to capture a switch-level representation of L_Model 270A and/or LR_Model 270B at switch i. To illustrate, Li_Model <NUM><NUM> can represent L_Model 270A and/or LR_Model 270B projected to, or implemented at, Leaf <NUM> (<NUM>). Thus, Li_Model <NUM> can be generated from L_Model 270A and/or LR_Model 270B for individual devices (e.g., Leafs <NUM>, Spines <NUM>, etc.) on Fabric <NUM>.

In some cases, Li_Model <NUM> can be represented using JSON (JavaScript Object Notation). For example, Li_Model <NUM> can include JSON objects, such as Rules, Filters, Entries, and Scopes.

Ci_Model <NUM> is the actual in-state configuration at the individual fabric member i (e.g., switch i). In other words, Ci_Model <NUM> is a switch-level or switch-specific model that is based on Li_Model <NUM>. For example, Controllers <NUM> can deliver Li_Model <NUM> to Leaf <NUM> (<NUM>). Leaf <NUM> (<NUM>) can take Li_Model <NUM>, which can be specific to Leaf <NUM> (<NUM>), and render the policies in Li_Model <NUM> into a concrete model, Ci_Model <NUM>, that runs on Leaf <NUM> (<NUM>). Leaf <NUM> (<NUM>) can render Li_Model <NUM> via the OS on Leaf <NUM> (<NUM>), for example. Thus, Ci_Model <NUM> can be analogous to compiled software, as it is the form of Li_Model <NUM> that the switch OS at Leaf <NUM> (<NUM>) can execute.

In some cases, Li_Model <NUM> and Ci_Model <NUM> can have a same or similar format. For example, Li_Model <NUM> and Ci_Model <NUM> can be based on JSON objects. Having the same or similar format can facilitate objects in Li_Model <NUM> and Ci_Model <NUM> 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 <NUM> is also a switch-level or switch-specific model for switch i, but is based on Ci_Model <NUM> for switch i. Hi_Model <NUM> 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 <NUM> can represent the configurations (e.g., rules) which Leaf <NUM> (<NUM>) stores or renders on the hardware (e.g., TCAM memory) of Leaf <NUM> (<NUM>) based on Ci_Model <NUM> at Leaf <NUM> (<NUM>). The switch OS at Leaf <NUM> (<NUM>) can render or execute Ci_Model <NUM>, and Leaf <NUM> (<NUM>) can store or render the configurations from Ci_Model <NUM> in storage, such as the memory or TCAM at Leaf <NUM> (<NUM>). The configurations from Hi_Model <NUM> stored or rendered by Leaf <NUM> (<NUM>) represent the configurations that will be implemented by Leaf <NUM> (<NUM>) when processing traffic.

While Models <NUM>, <NUM>, <NUM> are shown as device-specific models, similar models can be generated or aggregated for a collection of fabric members (e.g., Leafs <NUM> and/or Spines <NUM>) in Fabric <NUM>. When combined, device-specific models, such as Model <NUM>, Model <NUM>, and/or Model <NUM>, can provide a representation of Fabric <NUM> that extends beyond a particular device. For example, in some cases, Li_Model <NUM>, Ci_Model <NUM>, and/or Hi_Model <NUM> associated with some or all individual fabric members (e.g., Leafs <NUM> and Spines <NUM>) 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 <NUM>. For example, Ti Model, Hi Model and TCAMi Model may be used interchangeably to refer to Hi_Model <NUM>.

Models 270A, 270B, <NUM>, <NUM>, <NUM> can provide representations of various aspects of the network or various configuration stages for MIM <NUM>. For example, one or more of Models 270A, 270B, <NUM>, <NUM>, <NUM> can be used to generate Underlay Model <NUM> representing one or more aspects of Fabric <NUM> (e.g., underlay topology, routing, etc.), Overlay Model <NUM> representing one or more aspects of the overlay or logical segment(s) of Network Environment <NUM> (e.g., COOP, MPBGP, tenants, VRFs, VLANs, VXLANs, virtual applications, VMs, hypervisors, virtual switching, etc.), Tenant Model <NUM> representing one or more aspects of Tenant portion 204A in MIM <NUM> (e.g., security, forwarding, service chaining, QoS, VRFs, BDs, Contracts, Filters, EPGs, subnets, etc.), Resources Model <NUM> representing one or more resources in Network Environment <NUM> (e.g., storage, computing, VMs, port channels, physical elements, etc.), etc..

In general, L_Model 270A can be the high-level expression of what exists in the LR_Model 270B, which should be present on the concrete devices as Ci_Model <NUM> and Hi_Model <NUM> expression. If there is any gap between the models, there may be inconsistent configurations or problems.

<FIG> illustrates a diagram of an example Assurance Appliance <NUM> for network assurance. In this example, Assurance Appliance <NUM> can include k VMs <NUM> 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 <NUM>, or any other physical or logical systems. Moreover, while <FIG> 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 <NUM> can run on one or more Servers <NUM>, VMs <NUM>, Hypervisors <NUM>, EPs <NUM>, Leafs <NUM>, Controllers <NUM>, or any other system or resource. For example, Assurance Appliance <NUM> can be a logical service or application running on one or more VMs <NUM> in Network Environment <NUM>.

The Assurance Appliance <NUM> can include Data Framework <NUM>, 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 <NUM>. This enables a natively horizontal scale-out architecture that can scale to arbitrary number of switches in Fabric <NUM> (e.g., ACI fabric).

Assurance Appliance <NUM> can poll Fabric <NUM> at a configurable periodicity (e.g., an epoch). The analysis workflow can be setup as a DAG (Directed Acyclic Graph) of Operators <NUM>, where data flows from one operator to another and eventually results are generated and persisted to Database <NUM> for each interval (e.g., each epoch).

The north-tier implements API Server (e.g., APACHE Tomcat and Spring framework) <NUM> and Web Server <NUM>. 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 <NUM> for further integration into other tools.

Operators <NUM> in Data Framework <NUM> (e.g., APEX/Hadoop) can together support assurance operations. Below are non-limiting examples of assurance operations that can be performed by Assurance Appliance <NUM> via Operators <NUM>.

Assurance Appliance <NUM> can check to make sure the configurations or specification from L_Model 270A, which may reflect the user's intent for the network, including for example the security policies and customer-configured contracts, are correctly implemented and/or rendered in Li_Model <NUM>, Ci_Model <NUM>, and Hi_Model <NUM>, and thus properly implemented and rendered by the fabric members (e.g., Leafs <NUM>), and report any errors, contract violations, or irregularities found.

Assurance Appliance <NUM> can check for issues in the specification of the user's intent or intents (e.g., identify contradictory or conflicting policies in L_Model 270A).

TCAM is a scarce resource in the fabric (e.g., Fabric <NUM>). However, Assurance Appliance <NUM> 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 <NUM> (e.g., EPGs), Tenants, Spines <NUM>, Leafs <NUM>, and other dimensions in Network Environment <NUM> and/or objects in MIM <NUM>, 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.

Assurance Appliance <NUM> can validate that the fabric (e.g. fabric <NUM>) has no inconsistencies in the Endpoint information registered (e.g., two leafs announcing the same endpoint, duplicate subnets, etc.), among other such checks.

Assurance Appliance <NUM> can validate that BDs, VRFs, subnets (both internal and external), VLANs, contracts, filters, applications, EPGs, etc., are correctly programmed.

Assurance Appliance <NUM> can validate that infrastructure routing (e.g., IS-IS protocol) has no convergence issues leading to black holes, loops, flaps, and other problems.

The network fabric (e.g., Fabric <NUM>) 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.

Assurance Appliance <NUM> can validate rules in the specification of the network (e.g., L_Model 270A) are complete and do not have inconsistencies or other problems. MOs in the MIM <NUM> can be checked by Assurance Appliance <NUM> through syntactic and semantic checks performed on L_Model 270A and/or the associated configurations of the MOs in MIM <NUM>. Assurance Appliance <NUM> can also verify that unnecessary, stale, unused or redundant configurations, such as contracts, are removed.

<FIG> illustrates an architectural diagram of an example system <NUM> for network assurance, such as Assurance Appliance <NUM>. In some cases, system <NUM> can correspond to the DAG of Operators <NUM> previously discussed with respect to <FIG>.

In this example, Topology Explorer <NUM> communicates with Controllers <NUM> (e.g., APIC controllers) in order to discover or otherwise construct a comprehensive topological view of Fabric <NUM> (e.g., Spines <NUM>, Leafs <NUM>, Controllers <NUM>, Endpoints <NUM>, 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 <NUM>, can correspond to one or more individual Operators <NUM> 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 <NUM> is configured to discover nodes in Fabric <NUM>, such as Controllers <NUM>, Leafs <NUM>, Spines <NUM>, etc. Topology Explorer <NUM> can additionally detect a majority election performed amongst Controllers <NUM>, and determine whether a quorum exists amongst Controllers <NUM>. If no quorum or majority exists, Topology Explorer <NUM> can trigger an event and alert a user that a configuration or other error exists amongst Controllers <NUM> that is preventing a quorum or majority from being reached. Topology Explorer <NUM> can detect Leafs <NUM> and Spines <NUM> that are part of Fabric <NUM> 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's <NUM> discovery epoch (e.g., <NUM> minutes, or some other specified interval).

In some examples, Topology Explorer <NUM> can receive as input a list of Controllers <NUM> (e.g., APIC controllers) that are associated with the network/fabric (e.g., Fabric <NUM>). Topology Explorer <NUM> can also receive corresponding credentials to login to each controller. Topology Explorer <NUM> can retrieve information from each controller using, for example, REST calls. Topology Explorer <NUM> can obtain from each controller a list of nodes (e.g., Leafs <NUM> and Spines <NUM>), and their associated properties, that the controller is aware of. Topology Explorer <NUM> can obtain node information from Controllers <NUM> 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 <NUM> can also determine if Controllers <NUM> 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/<NUM> + <NUM>) controllers are aware of each other and/or are communicatively coupled. Topology Explorer <NUM> 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 <NUM> 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 <NUM> can trigger an event and alert a user that reconfiguration or suitable attention is required. If a quorum is present, Topology Explorer <NUM> can compile the network topology information into a JSON object and pass it downstream to other operators or services, such as Unified Collector <NUM>.

Unified Collector <NUM> can receive the topological view or model from Topology Explorer <NUM> and use the topology information to collect information for network assurance from Fabric <NUM>. Unified Collector <NUM> can poll nodes (e.g., Controllers <NUM>, Leafs <NUM>, Spines <NUM>, etc.) in Fabric <NUM> to collect information from the nodes.

Unified Collector <NUM> can include one or more collectors (e.g., collector devices, operators, applications, VMs, etc.) configured to collect information from Topology Explorer <NUM> and/or nodes in Fabric <NUM>. For example, Unified Collector <NUM> 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 <NUM> in order to collect information from their assigned subset of nodes. For performance, Unified Collector <NUM> can run in a parallel, multi-threaded fashion.

Unified Collector <NUM> 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 <NUM> can run multiple instances of Unified Collector <NUM>. This can also allow Assurance Appliance <NUM> to distribute the task of collecting data for each node in the topology (e.g., Fabric <NUM> including Spines <NUM>, Leafs <NUM>, Controllers <NUM>, etc.) via sharding and/or load balancing, and map collection tasks and/or nodes to a particular instance of Unified Collector <NUM> with data collection across nodes being performed in parallel by various instances of Unified Collector <NUM>. Within a given node, commands and data collection can be executed serially. Assurance Appliance <NUM> can control the number of threads used by each instance of Unified Collector <NUM> to poll data from Fabric <NUM>.

Unified Collector <NUM> can collect models (e.g., L_Model 270A and/or LR_Model 270B) from Controllers <NUM>, switch software configurations and models (e.g., Ci_Model <NUM>) from nodes (e.g., Leafs <NUM> and/or Spines <NUM>) in Fabric <NUM>, hardware configurations and models (e.g., Hi_Model <NUM>) from nodes (e.g., Leafs <NUM> and/or Spines <NUM>) in Fabric <NUM>, etc. Unified Collector <NUM> can collect Ci_Model <NUM> and Hi_Model <NUM> from individual nodes or fabric members, such as Leafs <NUM> and Spines <NUM>, and L_Model 270A and/or LR_Model 270B from one or more controllers (e.g., Controllers <NUM>) in Network Environment <NUM>.

Unified Collector <NUM> can poll the devices that Topology Explorer <NUM> discovers in order to collect data from Fabric <NUM> (e.g., from the constituent members of the fabric). Unified Collector <NUM> can collect the data using interfaces exposed by Controllers <NUM> 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 <NUM> collects L_Model 270A, LR_Model 270B, and/or Ci_Model <NUM> 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 <NUM> can poll other information from Controllers <NUM>, 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's of nodes in the fabric, etc..

Unified Collector <NUM> can also poll information from nodes (e.g., Leafs <NUM> and Spines <NUM>) in Fabric <NUM>, including without limitation: Ci_Models <NUM> for VLANs, BDs, and security policies; Link Layer Discovery Protocol (LLDP) connectivity information of nodes (e.g., Leafs <NUM> and/or Spines <NUM>); endpoint information from EPM/COOP; fabric card information from Spines <NUM>; routing information base (RIB) tables from nodes in Fabric <NUM>; forwarding information base (FIB) tables from nodes in Fabric <NUM>; security group hardware tables (e.g., TCAM tables) from nodes in Fabric <NUM>; etc..

In some cases, Unified Collector <NUM> can obtain runtime state from the network and incorporate runtime state information into L_Model 270A and/or LR_Model 270B. Unified Collector <NUM> can also obtain multiple logical models from Controllers <NUM> and generate a comprehensive or network-wide logical model (e.g., L_Model 270A and/or LR_Model 270B) based on the logical models. Unified Collector <NUM> can compare logical models from Controllers <NUM>, resolve dependencies, remove redundancies, etc., and generate a single L_Model 270A and/or LR_Model 270B for the entire network or fabric.

Unified Collector <NUM> can collect the entire network state across Controllers <NUM> and fabric nodes or members (e.g., Leafs <NUM> and/or Spines <NUM>). For example, Unified Collector <NUM> can use a REST interface and an SSH interface to collect the network state. This information collected by Unified Collector <NUM> can include data relating to the link layer, VLANs, BDs, VRFs, security policies, etc. The state information can be represented in LR_Model 270B, as previously mentioned. Unified Collector <NUM> can then publish the collected information and models to any downstream operators that are interested in or require such information. Unified Collector <NUM> can publish information as it is received, such that data is streamed to the downstream operators.

Data collected by Unified Collector <NUM> can be compressed and sent to downstream services. In some examples, Unified Collector <NUM> 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 <NUM> can collect data in an offline fashion, and compile the data for later analysis or transmission.

Assurance Appliance <NUM> can contact Controllers <NUM>, Spines <NUM>, Leafs <NUM>, and other nodes to collect various types of data. In some scenarios, Assurance Appliance <NUM> 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 <NUM> can handle such failures seamlessly, and generate events based on such failures.

Switch Logical Policy Generator <NUM> can receive L_Model 270A and/or LR_Model 270B from Unified Collector <NUM> and calculate Li_Model <NUM> for each network device i (e.g., switch i) in Fabric <NUM>. For example, Switch Logical Policy Generator <NUM> can receive L_Model 270A and/or LR_Model 270B and generate Li_Model <NUM> by projecting a logical model for each individual node i (e.g., Spines <NUM> and/or Leafs <NUM>) in Fabric <NUM>. Switch Logical Policy Generator <NUM> can generate Li_Model <NUM> for each switch in Fabric <NUM>, thus creating a switch logical model based on L_Model 270A and/or LR_Model 270B for each switch.

Each Li_Model <NUM> can represent L_Model 270A and/or LR_Model 270B as projected or applied at the respective network device i (e.g., switch i) in Fabric <NUM>. In some cases, Li_Model <NUM> can be normalized or formatted in a manner that is compatible with the respective network device. For example, Li_Model <NUM> can be formatted in a manner that can be read or executed by the respective network device. To illustrate, Li_Model <NUM> can included specific identifiers (e.g., hardware plane identifiers used by Controllers <NUM> as classifiers, etc.) or tags (e.g., policy group tags) that can be interpreted by the respective network device. In some cases, Li_Model <NUM> can include JSON objects. For example, Li_Model <NUM> can include JSON objects to represent rules, filters, entries, scopes, etc..

The format used for Li_Model <NUM> can be the same as, or consistent with, the format of Ci_Model <NUM>. For example, both Li_Model <NUM> and Ci_Model <NUM> may be based on JSON objects. Similar or matching formats can enable Li_Model <NUM> and Ci_Model <NUM> to be compared for equivalence or congruence. Such equivalency checks can aid in network analysis and assurance as further explained herein.

Switch Logical Policy Generator <NUM> can also perform change analysis and generate lint events or records for problems discovered in L_Model 270A and/or LR_Model 270B. The lint events or records can be used to generate alerts for a user or network operator via an event generator coupled to receive lint events from Swithc Logical Policy Generator <NUM>.

Policy Operator <NUM> can receive Ci_Model <NUM> and Hi_Model <NUM> for each switch from Unified Collector <NUM>, and Li_Model <NUM> for each switch from Switch Logical Policy Generator <NUM>, and perform assurance checks and analysis (e.g., security adherence checks, TCAM utilization analysis, etc.) based on Ci_Model <NUM>, Hi_Model <NUM>, and Li_Model <NUM>. Policy Operator <NUM> can perform assurance checks on a switch-by-switch basis by comparing one or more of the models. The output of Policy Operator <NUM> can be passed to an event generator (not shown) that can generate warning events for external consumption, where the events correspond to security violations or utilization statistics (such as TCAM usage) that comprise a policy violation. Such events are triggered by an abnormal or undesirable network occurrence as determined by the network generator, whereas a notification event might be triggered during the normal course of performing utilization analysis and security adherence checks in the absence of any violations.

Returning to Unified Collector <NUM>, Unified Collector <NUM> can also send L_Model 270A and/or LR_Model 270B to Routing Policy Parser <NUM> (for L models), and Ci_Model <NUM> and Hi_Model <NUM> to Routing Parser <NUM> (for C and H models). Routing Policy Parser <NUM> can receive L_Model 270A and/or LR_Model 270B and parse the model(s) for information that may be relevant to downstream operators, such as Endpoint Checker <NUM> and Tenant Routing Checker <NUM>. Similarly, Routing Parser <NUM> can receive Ci_Model <NUM> and Hi_Model <NUM> and parse each model for information for downstream operators, Endpoint Checker <NUM> and Tenant Routing Checker <NUM>.

After Ci_Model <NUM>, Hi_Model <NUM>, L_Model 270A and/or LR_Model 270B are parsed, Routing Policy Parser <NUM> and/or Routing Parser <NUM> can send cleaned-up protocol buffers (Proto Buffs) to the downstream operators Endpoint Checker <NUM> and Tenant Routing Checker <NUM>. Endpoint Checker <NUM> can communicate information related to Endpoint violations, such as duplicate IPs, APIPA, etc. to an event generator capable of generating events for external consumption or monitoring. Similarly, Tenant Routing Checker <NUM> can communicate information related to the deployment of BDs, VRFs, subnets, routing table prefixes, etc. to the same or different event generator capable of generating events for external consumption or monitoring.

<FIG> illustrates a schematic diagram of an example system for static policy analysis in a network (e.g., Network Environment <NUM>). Static Policy Analyzer <NUM> can perform assurance checks to detect configuration violations, logical lint events, contradictory or conflicting policies, unused contracts, incomplete configurations, etc. Static Policy Analyzer <NUM> can check the specification of the user's intent or intents in L_Model 270A to determine if any configurations in Controllers <NUM> are inconsistent with the specification of the user's intent or intents.

Static Policy Analyzer <NUM> can include one or more of the Operators <NUM> executed or hosted in Assurance Appliance <NUM>. However, in other configurations, Static Policy Analyzer <NUM> can run one or more operators or engines that are separate from Operators <NUM> and/or Assurance Appliance <NUM>. For example, Static Policy Analyzer <NUM> can be a VM, a cluster of VMs, or a collection of endpoints in a service function chain.

Static Policy Analyzer <NUM> can receive as input L_Model 270A from Logical Model Collection Process <NUM> and Rules <NUM> defined for each feature (e.g., object) in L_Model 270A. Rules <NUM> can be based on objects, relationships, definitions, configurations, and any other features in MIM <NUM>. Rules <NUM> can specify conditions, relationships, parameters, and/or any other information for identifying configuration violations or issues.

Moreover, Rules <NUM> can include information for identifying syntactic violations or issues. For example, Rules <NUM> can include one or more rules for performing syntactic checks. Syntactic checks can verify that the configuration of L_Model 270A 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 <NUM> are complete (have been defined) and identify any configurations that are defined but not used. To illustrate, Rules <NUM> can specify that every tenant in L_Model 270A should have a context configured; every contract in L_Model 270A should specify a provider EPG and a consumer EPG; every contract in L_Model 270A should specify a subject, filter, and/or port; etc..

Rules <NUM> can also include rules for performing semantic checks and identifying semantic violations or issues. Semantic checks can check conflicting rules or configurations. For example, Rule1 and Rule2 can have shadowing issues, Rule1 can be more specific than Rule2 and thereby create conflicts/issues, etc. Rules <NUM> can define conditions which may result in shadowed rules, conflicting rules, etc. To illustrate, Rules <NUM> can specify that a permit policy for a specific communication between two objects can conflict with a deny policy for the same communication between two objects if the permit policy has a higher priority than the deny policy, or a rule for an object renders another rule unnecessary.

Static Policy Analyzer <NUM> can apply Rules <NUM> to L_Model 270A to check configurations in L_Model 270A and output Configuration Violation Events <NUM> (e.g., alerts, logs, notifications, etc.) based on any issues detected. Configuration Violation Events <NUM> can include semantic or semantic problems, such as incomplete configurations, conflicting configurations, aliased/shadowed rules, unused configurations, errors, policy violations, misconfigured objects, incomplete configurations, incorrect contract scopes, improper object relationships, etc..

In some cases, Static Policy Analyzer <NUM> can iteratively traverse each node in a tree generated based on L_Model 270A and/or MIM <NUM>, and apply Rules <NUM> 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 <NUM> can output Configuration Violation Events <NUM> when it detects any violations.

<FIG> illustrates an example flowchart for a network assurance model. At step <NUM>, 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 <NUM>, 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 <NUM>, 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 <NUM>, the method can involve visualization. Formal models can be used to identify problems for analysis and debugging, in a user-friendly GUI.

Each of the previously described models (Li, Ci, and Hi) is, in one way or another, derived from the initial L-model that is configured by a user or network operator at the APIC or network controllers. For example, the Li model is a logical projection of the fabric-wide L model onto each leaf, spine, switch, node, etc. i in the network fabric; the Ci model is a concrete rendering of the L-model into a format that is compatible with the aforementioned fabric elements; and the Hi model is the hardware representation of the Ci model, as stored into switch memory by a switch memory controller.

Accordingly, each transformation used to derive the Li, Ci, and Hi models presents an opportunity for error or misconfiguration, which is undesirable from a network operator's point of view. These errors can result due to software bugs, user error, hardware variance, memory errors, overflow issues, and other causes that would be appreciated by one of ordinary skill in the art. In some embodiments, each model may consist of thousands, or tens of thousands, of distinct rules that collectively represent the intents configured in the original L-model, and each of these distinct rules must be preserved when converting between the different models.

Previous approaches to validating the Li, Ci, and Hi models relied upon brute force, treating each model as a black box and simply comparing the outputs from the models when given the same input. Such an approach can indicate a lack of equivalence if two models produce a different output for the same given input, but cannot provide any insight as to why there is a conflict. In particular, the black box approach is unable to provide specific information regarding the specific rules in each model that are in conflict, or specific information regarding the specific contract or intent configured by a user or network operator that ultimately led to the conflict arising.

Rather than employing brute force to determine the equivalence of two input models of network intents, the network intent models can instead be represented as Reduced Ordered Binary Decision Diagrams (ROBDDs), where each ROBDD is canonical (unique) to the input rules and their priority ordering. Each network intent model is first converted to a flat list of priority ordered rules (e.g. contracts defined by a customer or network operator are defined between EPGs, and rules are the specific node-to-node implementation of these contracts). In the example architecture <NUM> depicted in <FIG>, flat lists of priority ordered rules for a plurality of models of network intents <NUM> (illustrated here are the models Li, Hi, and Ci) are received as input at an ROBDD generator <NUM> of a formal analysis engine <NUM>. These rules can be represented as Boolean functions, where each rule consists of an action (e.g. Permit, Permit-Log, Deny, Deny-Log) and a set of conditions that will trigger that action (e.g. various configurations of packet source, destination, port, header, etc.). For example, a simple rule might be designed as Permit all traffic on port <NUM>. In some embodiments, each rule might be a <NUM> bit string, with <NUM> fields of key-value pairs.

As illustrated, ROBDD generator <NUM> generates an ROBDD for each of the input models <NUM>, i.e. LBDD for Li, HBDD for Hi, and CBDD for Ci. From these ROBDDs, the formal equivalence of any two or more ROBDDs of network intent models can be checked via Equivalence Checker <NUM>, which builds a conflict ROBDD <NUM> encoding the areas of conflict between input ROBDDs, such as the illustrated input pairs (LBDD, HBDD), (LBDD, CBDD), and (HBDD, CBDD). In particular, the conflict ROBDD encodes the areas of conflict between the input ROBDDs. For example equivalence checker <NUM> can check the formal equivalence of LBDD against HBDD by calculating the exclusive disjunction between LBDD and HBDD, given as LBDD ⊕ HBDD (i.e. LBDD XOR HBDD).

Equivalence checker <NUM> then transmits the conflict ROBDD <NUM> to a conflict rules identifier <NUM>. As illustrated, conflict rules identifier <NUM> can additionally receive the underlying models of network intents <NUM> (e.g. Li, Hi, and Ci) that are received as inputs to formal analysis engine <NUM>. Continuing the previous example wherein a conflict ROBDD was calculated for LBDD and HBDD, specific conflict rules are identified by iterating over HBDD and calculating the intersection between each individual constituent rule of HBDD and the conflict ROBDD LBDD ⊕ HBDD. If the intersection is non-zero, then the given rule of HBDD is a conflict rule. A similar process can be performed for each individual constituent rule of LBDD, thereby forming a listing of conflict rules for each model (out of the plurality of models <NUM>) represented in the particular conflict ROBDD <NUM>. It is also possible that conflict rules identifier <NUM> treats one of the models of network intent as a reference standard, where the model is assumed to be correct. As such, conflict rules identifier <NUM> only needs to compute the intersection of the conflict ROBDD <NUM> and the set of rules from the non-reference model. For example, the Li model of user intents can be treated as a reference or standard, because it is directly derived from user inputs, received for example at a network controller or APIC. The Hi model, on the other hand, passes through several transformations before ultimately being rendered into a hardware form by a switch memory controller, and is therefore much more likely to be subject to error. Accordingly, the conflict rules identifier <NUM> might only compute (LBDD ⊕ HBDD) * Hj for each of the rules j in the Hi model, which can cut the required computation time roughly in half.

Regardless of whether conflict rules are identified for both input models of network intent, or only a non-reference model of network intent, conflict rules identifier <NUM> then outputs the calculated conflict rules <NUM> to an event generator <NUM>. As illustrated, event generator <NUM> can also receive a copy of the conflict ROBDD <NUM> from equivalence checker <NUM> that corresponds to the same models of network intents encoded within the calculated conflict rules <NUM>. From these two inputs, event generator <NUM> is operable to generate various events <NUM> for external consumption. Various events that may be generated by event generator <NUM> in response to inputs from formal analysis engine <NUM> are listed below, although the listing is not presented as limiting. In this example, the listed events correspond to two primary equivalence checking scenarios. The first is the comparison of the Li model to the Ci model (LBDD vs. CBDD, as illustrated as input to equivalence checker <NUM>), which verifies whether or not an intent encoded in the logical Li model was correctly pushed to switch software as represented in the Ci model. The second is the comparison of the Li model to the Hi model (LBDD vs. HBDD, as illustrated as input to equivalence checker <NUM>), which verifies whether or not an intent encoded in the logical Li model was correctly rendered into switch hardware as represented in the Hi model.

PERMIT_TRAFFIC_MAY_BE_BLOCKED. This event is raised when a contract with a subject, filter, or filter_entry that has an action PERMIT is not fully enforced correctly. The incorrect enforcement could potentially arise due to a stale rule that is conflicting in a switch TCAM (i.e. Li conflicts with the Hi model), or if a rule was not pushed properly into the switch software (Li conflicts with the Ci model).

DENY_TRAFFIC_MAY_BE_PERMITTED. This event is raised when a contract with a subject, filter, or filter_entry that has an action DENY is not fully enforced correctly. The incorrect enforcement could potentially arise due to a stale rule that is conflicting in a switch TCAM (i.e. Li conflicts with the Hi model), or if a rule was not pushed properly into the switch software (Li conflicts with the Ci model).

PERMIT_LOG_ACTION_VIOLATION. This event is raised when a contract with a subject, filter, or filter_entry has an action PERMIT, LOG that is not fully enforced correctly. The incorrect enforcement could potentially arise due to a stale rule that is conflicting in a switch TCAM (i.e. Li conflicts with the Hi model), or if a rule was not pushed properly into the switch software (Li conflicts with the Ci model).

DENY_LOG_ACTION_VIOLATION. This event is raised when a contract with a subject, filter, or filter_entry has an action DENY, LOG that is not fully enforced correctly. The incorrect enforcement could potentially arise due to a stale rule that is conflicting in a switch TCAM (i.e. Li conflicts with the Hi model), or if a rule was not pushed properly into the switch software (Li conflicts with the Ci model).

ENFORCED_VRF_POLICY_VIOLATION. This event is raised when a given VRF is in the ENFORCED state, but the default rules that were supposed to be programmed correctly are discovered to actually contain a violation. This incorrect rule programming could result from a stale rule that is conflicting in a switch TCAM (i.e. Li conflicts with the Hi model), or if a rule was not pushed properly into the switch software (Li conflicts with the Ci model).

UNENFORCED_VRF_POLICY_VIOLATION. This event is raised when a given VRF is in the UNENFORCED state, but the default rules that were supposed to be programmed correctly are discovered to actually contain a violation. This incorrect rule programming could result from a stale rule that is conflicting in a switch TCAM (i.e. Li conflicts with the Hi model), or if a rule was not pushed properly into the switch software (Li conflicts with the Ci model).

*_ENFORCED. For each of the above events, there is also an associated positive event that signals that the contract is correctly enforced in the TCAM, i.e. Li and Hi agree (there may also be an associated positive event that signals that the contract is correctly pushed into switch software, i.e. Li and Ci agree). For example, PERMIT_TRAFFIC_POLICY_ENFORCED signals that a contract with a PERMIT action rule is correctly represented.

It is appreciated that additional events <NUM> corresponding to network assurance may also be generated by event generator <NUM> and that the above listing is providing for exemplary purposes and is not exhaustive. Furthermore, it is appreciated that the generated events can additionally comprise a suggested fix, wherein event generator <NUM> draws upon the available comprehensive characterization of network state and the constituent models of network intents <NUM> underlying the network in order to determine one or more suggested fixes that will resolve whatever conflict triggered the event.

For each event <NUM>, event generator <NUM> can adjust the level of granularity at which the event is investigated or triggered in order to avoid flooding an excessive number of events downstream. For example, events can be generated at the Provider EPG, Consumer EPG, Contract, Subject, Filter, and FilterEntry levels, with the above listing presented in order of increasing granularity (i.e. FilterEntry is the finest level). Additionally, event generator <NUM> may also aggregate events, based on factors such as Provider EPG or Consumer EPG, in order to further reduce event flooding.

Although not shown, formal analysis engine <NUM> can additionally include a semantic analysis engine, which locates shadowed and shadowing rules within a single model. For example, a semantic analysis engine might determine that a rule L3 is completely shadowed (made redundant) by the combination of two higher priority rules L1 and L2. Event generator <NUM> can receive the output from a semantic analysis engine, for example, in the form of a listing of all rules within a given model of network intents that are shadowed or act to shadow other rules. A listing of various shadowing events generated by event generator <NUM> is presented below, although the listing is provided as exemplary rather than limiting.

PERMIT_OVERSPECIFICATION. This event is generated when a higher priority PERMIT fully or partially overlaps with a lower priority PERMIT rule, making at least a portion of the lower priority rule redundant and possibly a subject to removal.

PERMIT_CONTRADICTION. This event is generated when a higher priority DENY fully or partially overlaps with a lower priority PERMIT rule, making at least a portion of the lower priority rule unused, and possibly subject to removal or further inspection to resolve the contradiction.

DENY_OVERSPECIFICATION. This event is generated when a higher priority DENY fully or partially overlaps with a lower priority DENY rule, making at least a portion of the lower priority rule redundant and possibly subject to removal.

DENY_CONTRADICTION. This event is generated when a higher priority PERMIT fully or partially overlaps with a lower priority DENY rule, making at least a portion of the lower priority rule unused, and possibly subject to removal or further inspection to resolve the contradiction.

The example events described above are directed towards comparisons of one model to another, or comparisons of the rules within a model. In some embodiments, event generator <NUM> may be tasked with generating events that are directed towards comparisons of comparisons, e.g. a comparison of the outcome of an Li v. Hi comparison and the outcome of an Li v. Ci comparison. Turning now to <FIG>, an example chart <NUM> is presented presenting a broad characterization of categories of such comparisons of comparisons. A first column <NUM> indicates the outcome of an Li v. Hi comparison - a check mark indicates that the Li and Hi models convey the same network intent, while an x indicates that the Li and Hi models fail to convey the same network intent. Likewise, a second column <NUM> indicates the outcome of an Li v. Ci comparison in the same fashion. Each paired outcome represented by columns <NUM> and <NUM> is associated with an event <NUM>, such that chart <NUM> presents an event for each category of outcome among the presented pairings of the Li, Hi, and Ci models.

The first event 580a occurs when the Li v. Hi comparison <NUM> is affirmative, meaning that the Li model is correctly implemented into switch hardware or TCAM, and the Li v. Ci comparison <NUM> is also affirmative, meaning that the Li model was correctly pushed into switch software. The generated event 580a indicates "All clear", as no further action is necessary here as the network is functioning correctly.

The second event 580b occurs when the Li v. Hi comparison <NUM> is affirmative, meaning that the Li model is correctly implemented into switch hardware or TCAM, but the Li v. Ci comparison <NUM> is negative, meaning that the Li model was not correctly pushed into switch software. This second event 580b might trigger a warning or notification that informs a user or network operator of the mismatch. However, this warning is not immediately severe, as this specific event does not impact traffic - traffic ultimately is affected only by the hardware model of the network, and the Li v. Hi comparison <NUM> was affirmative. In such a scenario, while a user or network operator will likely be interested in resolving whatever issue caused the equivalence failure in rendering Li into switch hardware Ci, there is not necessarily any immediate visible impact on the network. In some instances, the scenario could simply be monitored more closely, to see if the Li v. Hi comparison <NUM> also fails at some point. In general, this second event 580b merits investigation, as it is somewhat unusual to see the Li v. Hi comparison <NUM> pass when the Li v. Ci comparison <NUM> fails. In general, if Li v. Ci fails, it is more often expected that Li v Hi will also then fail, unless an error is present which inadvertently resolves the conflict found in Li v.

The third event 580c occurs when the Li v. Hi comparison <NUM> is negative, meaning that the Li model is incorrectly implemented in switch hardware or TCAM, but the Li v. Ci comparison <NUM> is affirmative, meaning that the Li model was correctly pushed into switch software. This third event 580b might trigger an error or urgent conflict warning, as this specific event is traffic impacting - the switches and the network fabric are not handling traffic in a manner that is equivalent to the intents specified by a user or network operator via the Li model. The response to such an event might be to reboot the switch or node i corresponding to the Li, Hi, and Ci models, or otherwise cause the switch TCAM or memory to be flushed. The end result is that the switch memory is purged, and a fresh copy of Hi is rendered. This third event most often occurs when stale rules linger in TCAM, causing rules from to fail to make it into Hi once TCAM or switch memory has reached its full storage capacity.

The fourth event 580d occurs when the Li v. Hi comparison <NUM> is negative, meaning that the Li model is incorrectly implemented in switch hardware or TCAM, and the Li v. Ci comparison <NUM> is also negative, meaning that the Li model was correctly pushed into switch software. This fourth event 580d might trigger an automatic new push to the switch i corresponding to the Li, Hi, and Ci models, wherein an APIC or network controller pushes a copy of Li to the switch or otherwise causes the switch to freshly determine Ci. This push might be automatic because it is most often expected that a failure in the Li v. Ci equivalence check <NUM> will also result in a failure in the Li v. Hi equivalence check <NUM>. If this new push to the switch does not resolve the issue, then further investigation and intervention may be needed.

The disclosure now turns to <FIG> and <FIG>, which illustrate example network devices and computing devices, such as switches, routers, load balancers, client devices, and so forth.

<FIG> illustrates an example network device <NUM> suitable for performing switching, routing, load balancing, and other networking operations. Network device <NUM> includes a central processing unit (CPU) <NUM>, interfaces <NUM>, and a bus <NUM> (e.g., a PCI bus). When acting under the control of appropriate software or firmware, the CPU <NUM> is responsible for executing packet management, error detection, and/or routing functions. The CPU <NUM> preferably accomplishes all these functions under the control of software including an operating system and any appropriate applications software. CPU <NUM> may include one or more processors <NUM>, such as a processor from the INTEL x86 family of microprocessors. In some cases, processor <NUM> can be specially designed hardware for controlling the operations of network device <NUM>. In some cases, a memory <NUM> (e.g., non-volatile RAM, ROM, etc.) also forms part of CPU <NUM>. However, there are many different ways in which memory could be coupled to the system.

The interfaces <NUM> 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 <NUM>. 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, <NUM>/<NUM>/<NUM> 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 <NUM> to efficiently perform routing computations, network diagnostics, security functions, etc..

Although the system shown in <FIG> 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 <NUM>.

The network device <NUM> 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 <NUM> via the bus <NUM>, to exchange data and signals and coordinate various types of operations by the network device <NUM>, such as routing, switching, and/or data storage operations, for example.

<FIG> illustrates a computing system architecture <NUM> wherein the components of the system are in electrical communication with each other using a connection <NUM>, such as a bus. Exemplary system <NUM> includes a processing unit (CPU or processor) <NUM> and a system connection <NUM> that couples various system components including the system memory <NUM>, such as read only memory (ROM) <NUM> and random access memory (RAM) <NUM>, to the processor <NUM>. The system <NUM> can include a cache of high-speed memory connected directly with, in close proximity to, or integrated as part of the processor <NUM>. The system <NUM> can copy data from the memory <NUM> and/or the storage device <NUM> to the cache <NUM> for quick access by the processor <NUM>. In this way, the cache can provide a performance boost that avoids processor <NUM> delays while waiting for data. These and other modules can control or be configured to control the processor <NUM> to perform various actions. Other system memory <NUM> may be available for use as well. The memory <NUM> can include multiple different types of memory with different performance characteristics. The processor <NUM> can include any general purpose processor and a hardware or software service, such as service <NUM><NUM>, service <NUM>, and service <NUM> stored in storage device <NUM>, configured to control the processor <NUM> as well as a special-purpose processor where software instructions are incorporated into the actual processor design. The processor <NUM> 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 <NUM>, an input device <NUM> 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 <NUM> 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 <NUM>. The communications interface <NUM> 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.

The storage device <NUM> can include services <NUM>, <NUM>, <NUM> for controlling the processor <NUM>. Other hardware or software modules are contemplated. The storage device <NUM> can be connected to the system connection <NUM>. 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 <NUM>, connection <NUM>, output device <NUM>, and so forth, to carry out the function.

In summary, systems, methods, and computer-readable media for receiving an indication of an equivalence failure are described, the equivalence failure corresponding to one or more models of network intents. The indication of the equivalence failure is analyzed and one or more constituent intents that caused the equivalence failure are identified, wherein the one or more constituent intents are associated with a model of the one or more models of network intents. The granularity of the equivalence failure and the identified one or more constituent intents is determined, and an event for external consumption is generated, the event based at least in part on the equivalence failure, the granularity of the equivalence failure, and the identified one or more constituent intents.

Claim 1:
A computer-implemented method for a computer network, the method comprising:
receiving a plurality of models (270A, 270B, <NUM>, <NUM>, <NUM>) of network intents comprising: a logical model (270A) of network intents representing the logical configuration of the network at a device in the network, a concrete model (<NUM>) of network intents representing the in-state configuration of the network at the network device, and a hardware model (<NUM>) of network intents representing network traffic processing rules stored at the network device;
determining an equivalence failure, the equivalence failure corresponding to a comparison of the outcome of a formal equivalence analysis of the logical model of network intents and the concrete model of network intents to the outcome of a formal equivalence analysis of the logical model of network intents and the hardware model of network intents;
receiving an indication of the equivalence failure the indication comprising the pair of outcomes of the formal equivalence analyses;
analyzing the indication of the equivalence failure and the models (270A, 270B, <NUM>, <NUM>, <NUM>) of network intents and identifying one or more constituent intents that caused the equivalence failure, comprising determining whether the logical model of network intents is correctly pushed to network device software as represented in the concrete model of network intents and whether the logical model of network intents is correctly implemented into network device hardware as represented in the hardware model of network intents;
determining the granularity of the equivalence failure and the identified one or more constituent intents; and
generating an event for external consumption, the event based at least in part on the equivalence failure, the granularity of the equivalence failure, and the identified one or more constituent intents, wherein generating the event comprises triggering a warning to a user of the network or triggering an automatic new push of the models of network intents to the network device.