In recent years, various companies started to shift their data services from traditional data centers onto cloud. One of the major motivations is to save operation costs with the aid of cloud elasticity. This paper discusses an emerging need from financial services to reduce 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 elasticity. An evolutionary multi-objective training framework is proposed to optimize the weights of the policy. Not only the new objective of idleness is reduced by over 130% more than traditional industrial solutions, but the original load balancing objective is slightly improved. Extensive simulations help reveal the detailed applicability of the proposed method to the emerging problem of reducing idleness in financial services.

翻译：近年来，各类企业纷纷将数据服务从传统数据中心迁移至云端，其主要动机之一是利用云的弹性来降低运营成本。本文针对金融服务中一项新兴需求展开讨论：在不强行断开用户与服务器连接的前提下，减少仅维持极少数用户连接的闲置服务器。本文将这一需求建模为双目标在线负载均衡问题。我们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至数量可变的服务器以实现弹性扩展，并提出了一个进化多目标训练框架来优化该策略的权重。实验表明，与传统工业解决方案相比，新引入的闲置率目标降低了130%以上，同时原始负载均衡目标略有改善。大量仿真揭示了该方法在解决金融服务中减少闲置这一新兴问题上的详细适用性。