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.

提出了一种分布式异步强化学习机制，用于提高负载均衡器实现的工作负载分配的公平性，评估并将其与最新的负载平衡算法在模拟器中进行比较，并指出今后的研究方向。