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.
翻译:网络负载平衡器是数据中心的核心组成部分,通过多个服务器分配工作量,从而有助于提供可扩缩的服务;然而,当负载平衡器在动态环境中运作,对应用程序服务器负荷的监测有限时,它们依赖需要人工配置的超自然算法,以公平性和性能为目的。为缓解这一点,本文件建议采用分散式非同步强化学习机制,不进行积极的负载平衡器国家监测和有限的网络观测,以增进负载平衡器实现的工作量分布的公平性。对拟议机制的性能进行了评估,并与模拟器中最先进的负载平衡算法相比较,这种模拟器的配置越来越复杂。初步结果显示基于RL的负载平衡算法的前景,并找出额外的挑战和未来研究方向,包括奖励功能的设计和模型的可扩展性。