Deep Reinforcement Learning based Load Balancing Policy for balancing network traffic in datacenter environment

Abstract

Load balancer plays important role in handling a huge amount of network traffic by routing the request/traffic in such a way that clients get immediate response to their requests. But traffic management in this era of bigdata is becoming a challenging task and to maintain them with human support is becoming more expensive. We can address this challenge by applying Deep reinforcement learning for a network load balancer which will be both time and cost effective. Deep reinforcement learning understands and adjusts continuously with dynamic environment. Which can be used to optimize the performance of load balancer.