Cisco Technology, Inc.

Nataraj Batchu

Ayan Banerjee

Raghava Kodigenahalli Sivaramu

Varun A Shah

Srinivasan Ramabadran

Mehak Mahajan

Roshan Lal

Rishi Chhibber

Anand Kumar Singh

Padmanab Pathikonda

Ankan Ghosh

Ann Roshini Paul

This disclosure describes techniques for collecting network parameter data for network switches and/or physical servers and provisioning virtual resources of a service on physical servers based on network resource availability. The network parameter data may include network resource availability data, diagnostic constraint data, traffic flow data, etc. The techniques include determining network switches that have an availability of network resources to support a virtual resource on a connected physical server. A scheduler may deploy virtual machines to particular servers based on the network parameter data in lieu of, or in addition to, the server utilization data of the physical servers (e.g., CPU usage, memory usage, etc.). In this way, a virtual resource may be deployed to a physical server that has an availability of the server resources, but also is connected to a network switch with the availability of network resources to support the virtual resource.

Utilizing network analytics for service provisioning

In accordance with one example embodiment, a system configured for programming a network layer multicast address entry in a routing table of an ingress line card module is disclosed. The network layer multicast address entry includes a network layer address associated with at least one egress line card. The system is further configured for programming a data link layer multicast routing address entry in a routing table of a fabric card module in which the data link layer multicast routing address entry corresponds to the network layer multicast address entry.

Multicast routing

Customized feature vectors are used to train a machine learning algorithm to automatically identify a network component where a network fault has occurred. A database comprising network components and associated network faults is analyzed to select a set of network components associated with the largest quantity of network faults. Customized features associated with the network faults are identified and selected for use in a feature vector as input to a machine learning algorithm. The features are selected based upon analysis of consistency checks, component configuration limits, and network wide configurations.

Identifying locations and causes of network faults

In one illustrative example, an IP network media data router includes a spine and leaf switch architecture operative to provide IP multicast delivery of media data from source devices to receiver devices without the overhead communication with a controller. The architecture can include K spine switches, K sets of L leaf switches, M data links between each leaf switch, and a plurality of bidirectional data ports connected to each leaf switch for a guaranteed non-blocking IP multicast delivery of data. A deterministic hash function a used on both the first hop router and the last hop router to ensure the same spine node is selected for flow stitching. Accordingly, without the extra communication with a centralized controller, the right spine for establishing a multicast flow can be chosen using the deterministic hash function and the distributed resource information stored on each node.

Methods and apparatuses for non-blocking IP multicast delivery of media data in a multi-spine network

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Sunnyvale, CA, United States of America

Nataraj Batchu