Abstract: Techniques for defining a service flow definition among container pods that provide services in a network. The techniques may include an orchestrator of a computer network platform of the network determining which container pods need to communicate with which container pods. The service flow definition thus indicates needed paths between the container pods. In configurations, a cluster topology may be determined where the cluster topology indicates corresponding nodes of the network in which each container pod is located, as well as end points of the network with which the corresponding nodes communicate. Based at least in part on the service flow definition and the cluster topology, corresponding route distribution policies may be determined for the end points. The corresponding route distribution policies may be applied to the end points.

Abstract: Techniques for defining a service flow definition among container pods that provide services in a network. The techniques may include an orchestrator of a computer network platform of the network determining which container pods need to communicate with which container pods. The service flow definition thus indicates needed paths between the container pods. In configurations, a cluster topology may be determined where the cluster topology indicates corresponding nodes of the network in which each container pod is located, as well as end points of the network with which the corresponding nodes communicate. Based at least in part on the service flow definition and the cluster topology, corresponding route distribution policies may be determined for the end points. The corresponding route distribution policies may be applied to the end points.

Abstract: In one embodiment, a device in a network monitors Ethernet virtual private network (EVPN) traffic in the network for a plurality of media access control (MAC) addresses. The device generates a machine learning-based traffic model for the MAC addresses based on the monitored EVPN traffic. The device determines a timeout for a particular one of the MAC addresses based on traffic predicted by the machine learning-based traffic model for the particular MAC address. The device causes the particular MAC address to be timed out from one or more forwarding tables in the network based on the determined timeout.

Abstract: In one embodiment, a device in a network monitors Ethernet virtual private network (EVPN) traffic in the network for a plurality of media access control (MAC) addresses. The device generates a machine learning-based traffic model for the MAC addresses based on the monitored EVPN traffic. The device determines a timeout for a particular one of the MAC addresses based on traffic predicted by the machine learning-based traffic model for the particular MAC address. The device causes the particular MAC address to be timed out from one or more forwarding tables in the network based on the determined timeout.