Distributed job scheduling based on Swarm Intelligence: A survey
Distributed job scheduling based on Swarm Intelligence: A survey
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DOI：
10.1016/j.compeleceng.2013.11.023
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Elina Pacini;C. Mateos;C. Garino
中科院分区：
文献类型：
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作者：
Elina Pacini;C. Mateos;C. Garino
科学家和工程师需要计算能力来满足他们的模拟日益增长的资源密集型特性。例如，运行参数扫描实验(PSE)涉及处理多个独立作业，这些作业由针对相同程序代码的多个初始配置(输入参数值)给出。因此，为了获得可伸缩性，采用了网格计算和云计算等范例。然而，网格和云环境中的作业调度是一个困难的问题，因为它基本上是NP-完全的。因此，已经提出了许多基于逼近技术的变体，特别是来自群智能(SI)的那些变体。这些技术具有以非常有效的方式搜索问题解决方案的能力。针对分布式计算环境下的任务袋应用(如PSES)，研究了基于SI的作业调度算法，并基于导出的比较框架对它们进行了统一的比较。我们还讨论了该领域存在的问题和未来的研究方向。
Scientists and engineers need computational power to satisfy the increasing resource intensive nature of their simulations. For example, running Parameter Sweep Experiments (PSE) involve processing many independent jobs, given by multiple initial configurations (input parameter values) against the same program code. Hence, paradigms like Grid Computing and Cloud Computing are employed for gaining scalability. However, job scheduling in Grid and Cloud environments represents a difficult issue since it is basically NP-complete. Thus, many variants based on approximation techniques, specially those from Swarm Intelligence (SI), have been proposed. These techniques have the ability of searching for problem solutions in a very efficient way. This paper surveys SI-based job scheduling algorithms for bag-of-tasks applications(such as PSEs) on distributed computing environments, and uniformly compares them based on a derived comparison framework. We also discuss open problems and future research in the area.