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首页> 外文期刊>Procedia Computer Science >Genetic-based Multi-criteria Workflow Scheduling with Dynamic Resource Provisioning in Hybrid Large Scale Distributed Systems
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Genetic-based Multi-criteria Workflow Scheduling with Dynamic Resource Provisioning in Hybrid Large Scale Distributed Systems

机译：混合大型分布式系统中基于遗传和动态资源配置的多准则工作流调度

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摘要

Nowadays, scientific progress in multiple disciplines gave rise to conducting large scale scientific experiments and applications known as High Performance Computing (HPC). These HPC applications are commonly structured as workflows of heavy tasks with large data size and intricate dependencies which are typically performed in large-scale distributed systems (LSDS) such as clusters, Grids and recently Cloud infrastructures. In fact, workflow scheduling in distributed systems, especially in Clouds, is proved to be an NP hard problem. Our main target in this paper is to design a workflow scheduling approach based on the Non-dominated Sorting Genetic Algorithm version 2 (NSGA-II) in hybrid distributed systems by optimizing the Makespan and cost. In this work, we also studied the improvement of the Makespan-Cost trade-off with the scalability concept in the Cloud with our designed approach. For that, we proposed different scenarios dealing with the provisioning strategy of processing nodes alongside an existing resource pool. Conducted experiments show the advantage of Cloud infrastructures against other distributed systems and allow investigating the different factors in correlation with resources provisioning process.
机译：如今，多个学科的科学进步催生了进行大规模科学实验和应用的能力，即高性能计算（HPC）。这些HPC应用程序通常被构造为具有大数据量和复杂依赖性的繁重任务的工作流，通常在大型分布式系统（LSDS）（例如集群，网格和最近的云基础架构）中执行。实际上，事实证明，分布式系统（特别是在Clouds中）的工作流调度是一个NP难题。本文的主要目标是通过优化Makespan和成本，设计一种基于混合分布式系统中非支配排序遗传算法版本2（NSGA-II）的工作流调度方法。在这项工作中，我们还使用我们设计的方法研究了在云中具有可伸缩性概念的Makespan-Cost权衡的改进。为此，我们提出了不同的方案来处理现有资源池旁边的处理节点的供应策略。进行的实验证明了云基础架构相对于其他分布式系统的优势，并允许调查与资源供应过程相关的不同因素。

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