Reinforcement learning for switch power optimization

Abstract

Network switches are devices that connect multiple devices together on a computer network, using packet switching to receive, process, and forward data to the destination device. Each switch typically contains multiple ports, which are the points of connection for network cables. These ports can be in an active state, where they are ready to transmit data, or in an idle state, where they consume less power. Power consumption in datacenters has been a topic of concern due to the increasing demand for data processing and storage. One approach to reducing power consumption involves managing the power state of the switch ports. However, current power saving policies focus on making decisions for one type of traffic pattern or for a single port at a time, and therefore cannot intelligently or dynamically adapt to a multitude of network parameters affecting traffic flows. The present disclosure uses artificial intelligence to more intelligently transition ports between different modes of operation.

Dependencies are inferred from the claim text.