Reinforced Workload Distribution Fairness

TL;DR

This paper introduces a distributed reinforcement learning approach to improve workload distribution fairness in data center load balancers operating with limited monitoring, showing promising results over traditional algorithms.

Contribution

It proposes a novel asynchronous reinforcement learning mechanism for load balancing without active state monitoring, addressing fairness in dynamic environments.

Findings

RL-based load balancing outperforms traditional heuristics in fairness.

Preliminary results demonstrate the potential of RL in complex, limited-information scenarios.

Challenges include reward function design and scalability issues.

Abstract

Network load balancers are central components in data centers, that distributes workloads across multiple servers and thereby contribute to offering scalable services. However, when load balancers operate in dynamic environments with limited monitoring of application server loads, they rely on heuristic algorithms that require manual configurations for fairness and performance. To alleviate that, this paper proposes a distributed asynchronous reinforcement learning mechanism to-with no active load balancer state monitoring and limited network observations-improve the fairness of the workload distribution achieved by a load balancer. The performance of proposed mechanism is evaluated and compared with stateof-the-art load balancing algorithms in a simulator, under configurations with progressively increasing complexities. Preliminary results show promise in RLbased load balancing algorithms, and identify additional challenges and future research directions, including reward function design and model scalability.