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In the last few decades, data center architecture evolved from thetraditional client-server to access-aggregation-core architectures. Recentlythere is a new shift in the data center architecture due to the increasing needfor low latency and high throughput between server-to-server communications,load balancing and, loop-free environment. This new architecture, known as leafand spine architecture, provides low latency and minimum packet loss byenabling the addition and deletion of network nodes on demand. Network nodescan be added or deleted from the network based on network statistics like linkspeed, packet loss, latency, and throughput.  With the maturity of Open Virtual Switch (OvS) and OpenFlow based SoftwareDefined Network (SDN) controllers, network automation through programmaticextensions has become possible based on network statistics. The separation ofthe control plane and data plane has enabled automated management of networkand Machine Learning (ML) can be applied to learn and optimize the network.  In this publication, we propose the design of an ML-based approach to gathernetwork statistics and build a knowledge plane. We demonstrate that thisknowledge plane enables data center optimization using southbound APIs and SDNcontrollers. We describe the design components of this approach - using anetwork simulator and show that it can maintain the historical patterns ofnetwork statistics to predict future growth or decline. We also provide anopen-source software that can be utilized in a leaf and spine data center toprovide elastic capacity based on load forecasts.

Designing knowledge plane to optimize leaf and spine data center