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.

翻译：近年来，各企业纷纷将数据服务从传统数据中心迁移至云端。其主要动因之一在于借助云弹性降低运营成本。本文探讨金融服务领域的新兴需求：在维持服务器端用户连接不断开的前提下，减少承载极少用户连接的闲置服务器数量。本文将这一需求建模为双目标在线负载均衡问题：设计基于神经网络的可扩展策略，将用户请求路由至不同数量服务器以实现所需弹性；提出进化多目标训练框架优化策略权重。实验表明，该方案不仅将闲置率这一新目标较传统工业方案降低超过130%，同时原负载均衡目标本身亦略有改进。基于合成数据与真实数据的广泛仿真揭示了所提方法在解决金融服务领域闲置率降低这一新兴问题时的详细适用性。