Reinforced Workload Distribution Fairness
This addresses fairness and performance issues in data center load balancing for scalable services, but appears incremental as it builds on existing RL approaches with specific limitations.
The paper tackles the problem of workload distribution fairness in network load balancers operating in dynamic environments with limited monitoring, proposing a distributed asynchronous reinforcement learning mechanism that improves fairness without active load balancer state monitoring. Preliminary results in a simulator show promise compared to state-of-the-art algorithms, though challenges like reward function design and scalability remain.
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.