工作流调度与数据传输优化和增强云数据中心的可靠性

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Karima Oukfif, Fatima Oulebsir-Boumghar, S. Bouzefrane, S. Banerjee
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引用次数: 3

摘要

基础设施即服务(IaaS)云为解决大规模科学问题提供了巨大的机会。如果没有正确调度,在这样的环境中执行工作流的时间开销会很大。尽管对云中的工作流调度进行了广泛的研究,但大多数方法都关注两个用户的服务质量需求,即makespan(即完成时间)和成本。必须考虑云计算的其他重要特性,例如资源的异构性和可靠性。在本文中,我们提出了一种基于离散粒子群优化(RDPSO)的可靠性感知方法,用于多云和异构数据中心的工作流调度。我们的目标是优化数据传输时间,同时最小化完工时间并提高可靠性。基于仿真,我们的结果显示,对于现实世界的工作流,相对于可靠性感知HEFT方法(异构最早完成时间),在makespan、传输数据和可靠性方面有了显著的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Workflow scheduling with data transfer optimisation and enhancement of reliability in cloud data centres
Infrastructure as a service (IaaS) clouds offer huge opportunities to solve large-scale scientific problems. Executing workflows in such environments can be expensive in time if not scheduled rightly. Although scheduling workflows in the cloud is widely studied, most approaches focus on two user's quality of service requirements namely makespan (i.e., completion time) and costs. Other important features of cloud computing such as the heterogeneity of resources and reliability must be considered. In this paper, we present a reliability-aware method based on discrete particle swarm optimisation (RDPSO) for workflow scheduling in multiple and heterogeneous cloud data centres. Our aim is to optimise data transfer time while minimising makespan and enhancing reliability. Based on simulation, our results show a significant improvement in terms of makespan, transferred data and reliability relative to reliability-aware HEFT method (heterogeneous earliest finish time), for the real-world workflows.
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来源期刊
CiteScore
2.50
自引率
46.20%
发文量
57
期刊介绍: IJCNDS aims to improve the state-of-the-art of worldwide research in communication networks and distributed systems and to address the various methodologies, tools, techniques, algorithms and results. It is not limited to networking issues in telecommunications; network problems in other application domains such as biological networks, social networks, and chemical networks will also be considered. This feature helps in promoting interdisciplinary research in these areas.
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