The state-of-the-art scheduler of containerized cloud services considers load balance as the only criterion; many other important properties, including application performance, are overlooked. In the era of Big Data, however, applications evolve to be increasingly more data-intensive thus perform poorly when deployed on containerized cloud services. To that end, this paper aims to improve today's cloud service by taking application performance into account for the next-generation container schedulers. More specifically, in this work we build and analyze a new model that respects both load balance and application performance. Unlike prior studies, our model abstracts the dilemma between load balance and application performance into a unified optimization problem and then employs a statistical method to efficiently solve it. The most challenging part is that some sub-problems are extremely complex (for example, NP-hard), and heuristic algorithms have to be devised. Last but not least, we implement a system prototype of the proposed scheduling strategy for containerized cloud services. Experimental results show that our system can significantly boost application performance while preserving high load balance.

中文翻译：

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云计算中容器的位置感知调度

最先进的容器化云服务调度器以负载均衡为唯一标准；许多其他重要属性，包括应用程序性能，都被忽略了。然而，在大数据时代，应用程序演变为越来越多的数据密集型，因此在部署在容器化云服务上时表现不佳。为此，本文旨在通过考虑下一代容器调度程序的应用程序性能来改进当今的云服务。更具体地说，在这项工作中，我们构建并分析了一个兼顾负载平衡和应用程序性能的新模型。与之前的研究不同，我们的模型将负载平衡和应用程序性能之间的困境抽象为一个统一的优化问题，然后采用统计方法有效地解决它。最具挑战性的部分是一些子问题非常复杂（例如，NP-hard），必须设计启发式算法。最后但并非最不重要的一点是，我们为容器化云服务实现了所提出的调度策略的系统原型。实验结果表明，我们的系统可以在保持高负载平衡的同时显着提高应用程序性能。