Cisco Technology, Inc.

Manish Chandra Agrawal

Patnala Debashis Rao

Lukas Krattiger

Samar Sharma

Yibin Yang

Liqin Dong

Chiajen Tsai

Jason David Notari

Rick G Chang

Shyam N Kapadia

Deepak Cherian

The present technology provides a system, method and computer-readable medium for configuration pattern recognition and inference, directed to a device with an existing configuration, through an extensible policy framework. The policy framework uses a mixture of python template logic and CLI micro-templates as a mask to infer the intent behind an existing device configuration in a bottom-up learning inference process. Unique values for device/network identifiers and addresses as well as other resources are extracted and accounted for. The consistency of devices within the fabric is checked based on the specific policies built into the extensible framework definition. Any inconsistencies found are flagged for user correction or automatically remedied by a network controller. This dynamic configuration pattern recognition ability allows a fabric to grow without being destroyed and re-created, thus new devices with existing configurations may be added and automatically configured to grow a Brownfield fabric.

Learning by inference from previous deployments

The disclosed technology relates to a load balancing system. A load balancing system is configured to receive health monitoring metrics, at a controller, from a plurality of leaf switches. The load balancing system is further configured to determine, based on the health monitoring metrics, that a server has failed and modify a load balancing configuration for the network fabric. The load balancing system is further configured to transmit the load balancing configuration to each leaf switch in the network fabric and update the tables in each leaf switch to reflect an available server.

Distributed load balancer health management using data center network manager

Learning by inference from brownfield deployments

A switch/switching fabric is configured to load balance traffic. The switch fabric includes a plurality of switches. A packet is received at a first switch of the plurality of switches. The first switch load balances the packet to a particular entity among a plurality of entities. Each of the entities is connected to one of the plurality of switches. The first switch determines a particular switch of the plurality of switches to which the packet should be directed, the particular entity being connected to the particular switch of the plurality of switches. The particular switch receives the packet, and determines which interface of the particular switch to direct the packet to the particular entity. The plurality of entities include servers and network appliances as physical devices or virtual machines.

Switch fabric based load balancing

An example method for facilitating multiple mobility domains with VLAN translation in a multi-tenant network environment is provided and includes detecting attachment of a first virtual machine on a first port and a second virtual machine on a second port of a network element, the first port and the second port being configured with a first mobility domain and a second mobility domain, respectively, and the first and second virtual machines being configured on a same original VLAN, determining whether the original VLAN falls within a pre-configured VLAN range, translating the original VLAN to a first VLAN on the first port corresponding to the first mobility domain and to a second VLAN on the second port corresponding to the second mobility domain, and segregating traffic on the original VLAN into the first VLAN and the second VLAN according to the respective mobility domains for per-port VLAN significance.

Multiple mobility domains with VLAN translation in a multi-tenant network environment

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San Jose, CA, United States of America

Manish Chandra Agrawal