基于多目标进化强化学习的负载均衡器减少金融云服务中的空闲
Reducing Idleness in Financial Cloud Services via Multi-objective Evolutionary Reinforcement Learning based Load Balancer
AI总结：
本文针对金融服务中减少保留少量连接的空闲服务器的需求，将其建模为双目标在线负载均衡问题，提出基于神经网络的可扩展策略与进化多目标训练框架优化权重，使空闲率较传统方案降低超130%且原负载均衡目标略有改善。
摘要：
近年来，各公司开始将数据服务从传统数据中心迁移至云端。主要动机之一是借助云弹性节省运营成本。本文探讨了金融服务中的一项新兴需求：减少仅保留极少用户连接的空闲服务器的出现频率，且不从服务器端断开这些连接。本文将此需求视为一个双目标在线负载均衡问题。设计了一种基于神经网络的可扩展策略，用于将用户请求路由到不同数量的服务器以实现所需的弹性。提出了一种进化多目标训练框架来优化该策略的权重。新设定的空闲目标相比传统工业解决方案降低了超过130%，同时原有的负载均衡目标本身也得到了轻微改善。使用合成数据和真实世界数据进行的广泛仿真，有助于揭示所提方法对金融服务中减少空闲这一突发问题的详细适用性。
英文摘要：
In recent years, various companies have started to shift their data services from traditional data centers to the cloud. One of the major motivations is to save on operational costs with the aid of cloud elasticity. This paper discusses an emerging need from financial services to reduce the incidence of idle servers retaining very few user connections, without disconnecting them from the server side. This paper considers this need as a bi-objective online load balancing problem. A neural network based scalable policy is designed to route user requests to varied numbers of servers for the required elasticity. An evolutionary multi-objective training framework is proposed to optimize the weights of the policy. Not only is the new objective of idleness is reduced by over 130% more than traditional industrial solutions, but the original load balancing objective itself is also slightly improved. Extensive simulations with both synthetic and real-world data help reveal the detailed applicability of the proposed method to the emergent problem of reducing idleness in financial services.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
