Abstract: A network communication system and method provide a point-to-point connection (“PTP connection”) between a first site and a second site. To that end, the PTP connection includes an intermediate network device between the first and second sites, a first link from the first site to the intermediate network device, and a second link from the intermediate network device. The system and method then predict usage of the PTP connection, and dynamically change (e.g., in real-time or non-real time), as a function of predicting usage, the PTP connection by changing one or more of the first link, the intermediate network device, and the second link.

Abstract: An example method includes receiving, by a software-defined networking in a wide area network (SD-WAN) system having a first WAN link and a second WAN link for an SD-WAN service, WAN link characterization data for the first WAN link over a time period; determining, by the SD-WAN system based on processing the WAN link characterization data for the first WAN link using a machine learning model trained with historical WAN link characterization data for one or more WAN links, an indicator of a predicted performance metric of the first WAN link at a future time; and reassigning, by the SD-WAN system based on the indicator, an application from the first WAN link to the second WAN link.

Abstract: An example method includes receiving, by a software-defined networking in a wide area network (SD-WAN) system having a first WAN link and a second WAN link for an SD-WAN service, WAN link characterization data for the first WAN link over a time period; determining, by the SD-WAN system based on processing the WAN link characterization data for the first WAN link using a machine learning model trained with historical WAN link characterization data for one or more WAN links, an indicator of a predicted performance metric of the first WAN link at a future time; and reassigning, by the SD-WAN system based on the indicator, an application from the first WAN link to the second WAN link.

Abstract: Techniques are disclosed for the detection of different states of a session comprising a bidirectional flow of network traffic between client devices so as to enable a network device to apply different network policies to different states of the session. In one example, a computing device identifies multiple states of a session and defines a plurality of network policies. Each network policy defines performance requirements for network traffic during each state of the session. A network device receives the plurality of network policies and determines a state of the session. The network device selects a path based on the performance requirements of the network policy associated with the determined state of the session. The network device forwards traffic associated with the session along the selected path while the session is in the determined state.

Abstract: An example method includes receiving, by an SD-WAN system, WAN link characterization data for a plurality of WAN links of the SD-WAN system over a time period; and for each site of a plurality of sites of the SD-WAN system, generating, by the SD-WAN system, a local policy for the site, wherein generating the local policy is based on a machine learning model trained with the WAN link characterization data for the plurality of WAN links, and providing the local policy to an SD-WAN edge device of the site.

Abstract: Techniques are disclosed for the detection of different states of a session comprising a bidirectional flow of network traffic between client devices so as to enable a network device to apply different network policies to different states of the session. In one example, a computing device identifies multiple states of a session and defines a plurality of network policies. Each network policy defines performance requirements for network traffic during each state of the session. A network device receives the plurality of network policies and determines a state of the session. The network device selects a path based on the performance requirements of the network policy associated with the determined state of the session. The network device forwards traffic associated with the session along the selected path while the session is in the determined state.

Abstract: A network management system (NMS) generates a hierarchical attribution graph representing different scopes at different hierarchical levels of a wide area network (WAN); obtains logical path down data indicative of operational behavior including failure events associated with logical paths of network devices over the WAN; obtains total path data indicative of a historical number of active logical paths between the network devices; and determines a scope of a logical path down issue by, for a time period of the logical path down issue, determining a score for each scope of the different scopes based on the logical path down data aggregated across the respective scope and the total path data, and determining the scope of the logical path down issue as a particular scope of the different scopes having a highest score. The NMS may identify the particular scope as a root cause of the logical path down issue.

Abstract: A device may receive network traffic data that includes network traffic packet sizes, and may transform the network traffic data into transformed data. The device may process the transformed data, with a machine learning model, to generate an embedding, and may obtain a similarity metric for the embedding. The device may create a graph with nodes and edges based on the embedding and the similarity metric, and may process the graph, with a community detection model, to identify network traffic categories for the network traffic data. The device may perform one or more actions based on the network traffic categories.

Abstract: An example method includes receiving, by an SD-WAN system, WAN link characterization data for a plurality of WAN links of the SD-WAN system over a time period; and for each site of a plurality of sites of the SD-WAN system, generating, by the SD-WAN system, a local policy for the site, wherein generating the local policy is based on a machine learning model trained with the WAN link characterization data for the plurality of WAN links, and providing the local policy to an SD-WAN edge device of the site.

Abstract: A router advertises an aggregated service or route that can be evaluated by other routers as a unitary segment rather than as a group of individual links/paths associated with the aggregated service or route. The aggregated service or route can be based on service and topology state information received from one or more other routers and can be advertised with the router as the nexthop for the aggregated service or route. The router can advertise an aggregated metric for the aggregated service or route for use in such evaluation. An aggregated route can be associated with different aggregated metrics for different services.

Abstract: A routing system for implementing a service and topology exchange protocol (STEP) includes a primary STEP server configured to maintain a STEP repository and a plurality of routers, with each router including a STEP client in communication with the primary STEP server. The STEP client of each router is configured to transmit, using STEP, STEP documents containing service and topology state information for at least one route or service available through the router to the primary STEP server for storage in the STEP repository. The primary STEP server is configured to transmit to the STEP client of each router, using STEP, service and topology state information from the STEP repository for at least one other router based on configured relationships between routers. Each router is configured to make routing decisions based at least in part on the service and topology state information from the at least one other router.

Abstract: An example method includes receiving, by a software-defined networking in a wide area network (SD-WAN) system having a first WAN link and a second WAN link for an SD-WAN service, WAN link characterization data for the first WAN link over a time period; determining, by the SD-WAN system based on processing the WAN link characterization data for the first WAN link using a machine learning model trained with historical WAN link characterization data for one or more WAN links, an indicator of a predicted performance metric of the first WAN link at a future time; and reassigning, by the SD-WAN system based on the indicator, an application from the first WAN link to the second WAN link.

Abstract: A device may receive network traffic data that includes network traffic packet sizes, and may transform the network traffic data into transformed data. The device may process the transformed data, with a machine learning model, to generate an embedding, and may obtain a similarity metric for the embedding. The device may create a graph with nodes and edges based on the embedding and the similarity metric, and may process the graph, with a community detection model, to identify network traffic categories for the network traffic data. The device may perform one or more actions based on the network traffic categories.

Abstract: A method and apparatus form and/or define a network topology in a Layer 3 network with a plurality of nodes, where each node has at least one interface. To that end, the method defines a plurality of neighborhoods, and assigns at least one interface of each node to at least one of the neighborhoods. The method also assigns a communication role to each interface so that each communication role is effective relative to one of the plurality of neighborhoods. The method then enables communication between the interfaces of the plurality of nodes as a function of the neighborhoods and the communication roles.

Abstract: An example method includes receiving, by a software-defined networking in a wide area network (SD-WAN) system having a first WAN link and a second WAN link for an SD-WAN service, WAN link characterization data for the first WAN link over a time period; determining, by the SD-WAN system based on processing the WAN link characterization data for the first WAN link using a machine learning model trained with historical WAN link characterization data for one or more WAN links, an indicator of a predicted performance metric of the first WAN link at a future time; and reassigning, by the SD-WAN system based on the indicator, an application from the first WAN link to the second WAN link.

Abstract: An example method includes receiving, by an SD-WAN system, WAN link characterization data for a plurality of WAN links of the SD-WAN system over a time period; and for each site of a plurality of sites of the SD-WAN system, generating, by the SD-WAN system, a local policy for the site, wherein generating the local policy is based on a machine learning model trained with the WAN link characterization data for the plurality of WAN links, and providing the local policy to an SD-WAN edge device of the site.

Abstract: An example method includes receiving, by an SD-WAN system, WAN link characterization data for a plurality of WAN links of the SD-WAN system over a time period; and for each site of a plurality of sites of the SD-WAN system, generating, by the SD-WAN system, a local policy for the site, wherein generating the local policy is based on a machine learning model trained with the WAN link characterization data for the plurality of WAN links, and providing the local policy to an SD-WAN edge device of the site.

Abstract: A router advertises an aggregated service or route that can be evaluated by other routers as a unitary segment rather than as a group of individual links/paths associated with the aggregated service or route. The aggregated service or route can be based on service and topology state information received from one or more other routers and can be advertised with the router as the nexthop for the aggregated service or route. The router can advertise an aggregated metric for the aggregated service or route for use in such evaluation. An aggregated route can be associated with different aggregated metrics for different services.

Abstract: A method and apparatus for routing a plurality of session packets across a network toward a destination modifies each packet to include a sequence number that is different from the sequence number of other packets in the plurality of packets. Accordingly, at this point, each of the plurality of packets is transformed into a corresponding plurality of processed packets. The method also duplicates the plurality of processed packets to produce a corresponding plurality of duplicated packets. Next, the method forwards the plurality of processed packets toward the destination using a first stateful path through the network, and correspondingly forwards the plurality of duplicated packets toward the destination using a second stateful path through the network. In preferred embodiments, the first stateful path is different from the second stateful path. For example, the two paths may be entirely distinct in that they share no common intermediary elements.

Abstract: A router advertises an aggregated service or route that can be evaluated by other routers as a unitary segment rather than as a group of individual links/paths associated with the aggregated service or route. The aggregated service or route can be based on service and topology state information received from one or more other routers and can be advertised with the router as the nexthop for the aggregated service or route. The router can advertise an aggregated metric for the aggregated service or route for use in such evaluation. An aggregated route can be associated with different aggregated metrics for different services.