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New Heuristic Function in Ant Colony System for Job Scheduling in Grid Computing

机译：蚁群系统中启发式函数在网格计算中的工作调度

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摘要

Job scheduling is one of the main factors affecting grid computing performance. Job scheduling problem classified as an NP-hard problem. Such a problem can be solved only by using approximate algorithms such as heuristic and meta-heuristic algorithms. Ant colony system algorithm is a meta-heuristic algorithm which has the ability to solve differenttypes of NP-hard problems.However, ant colony system algorithm has a deficiency in its heuristic function which affects the algorithm behavior in terms of finding the shortest connection between edges. Thispaper focuses on enhancing the heuristicfunction where information about recent ants' discoveries will be taken into account. Experiments were conducted using a simulator with dynamic environment features to mimicthe grid environment.Results show that the proposed enhanced algorithm produce better output in term of utilization and makespan.
机译：作业调度是影响网格计算性能的主要因素之一。作业调度问题被归类为NP难题。仅通过使用近似算法（例如启发式算法和元启发式算法）才能解决此问题。蚁群系统算法是一种能够解决不同类型的NP难题的元启发式算法，但是，蚁群系统算法的启发式功能存在缺陷，这在寻找边缘之间的最短连接方面会影响算法的行为。本文着重于增强启发式功能，其中将考虑有关最新蚂蚁发现的信息。使用具有动态环境特征的仿真器模拟网格环境进行了实验。结果表明，该改进算法在利用率和延展性方面产生了更好的输出。

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