Dynamic Job Scheduling in the Cloud Using Slowdown Optimization and Sandpile Cellular Automata Model

Jakub Gasior, F. Seredyński
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引用次数: 1

Abstract

We present in this paper a general framework to study issues of effective load balancing and scheduling in highly parallel and distributed environments such as currently built Cloud computing systems. We propose a novel approach based on the concept of the Sandpile cellular automaton: a decentralized multi-agent system working in a critical state at the edge of chaos. Our goal is providing fairness between concurrent job submissions by minimizing slowdown of individual applications and dynamically rescheduling them to the best suited resources. The algorithm design is experimentally validated by a number of numerical experiments showing the effectiveness and scalability of the scheme in the presence of a large number of jobs and resources and its ability to react to dynamic changes in real time.
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基于减速优化和沙堆元胞自动机模型的云中动态作业调度
在本文中,我们提出了一个通用框架来研究高度并行和分布式环境(如当前构建的云计算系统)中有效的负载平衡和调度问题。我们提出了一种基于Sandpile元胞自动机概念的新方法:在混沌边缘的临界状态下工作的分散多智能体系统。我们的目标是通过最小化单个应用程序的减速并动态地将它们重新调度到最适合的资源,从而在并发作业提交之间提供公平性。通过大量的数值实验验证了算法设计的有效性和可扩展性,证明了该方案在大量作业和资源存在下的有效性和可扩展性,以及对动态变化的实时反应能力。
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