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Adaptive intelligence job online scheduling within dynamic grid environment based on gridsim

机译:基于gridsim的动态网格环境下的自适应智能作业在线调度

摘要

This paper concentrates on the design and implement of the grid system for study of adaptive job scheduling algorithm based on GridSim. The common problems of job scheduling in grid system like heterogeneous of jobs, resources and dynamic an arrival time of new jobs significantly changes, can be deal with this solution. The idea behind the adaptive job scheduling algorithm is the hybrid algorithms that consist of Ant Colony Optimization (ACO) and Tabu algorithms. Additionally, the provided common information from Grid Information Service (GIS) and an arrival new job are calculated by Fuzzy C-Means (FCM) algorithm in order· to evaluate the current status of resources and groups of arrival jobs. Moreover, both dynamic and static information are handled by the solution. In static case, the resource information such as a number of CPUs of a machine, CPU speed, a number machine in the grid system is significantly known in advance while dynamic information like the arrival jobs that are submitted to the system any time during simulation. In the results, this paper shows the comparison results between the adaptive job scheduling algorithms and the traditional algorithms.
机译:本文重点研究基于GridSim的自适应作业调度算法的网格系统的设计与实现。网格系统中作业调度的常见问题,例如作业的异构性,资源和新作业的动态到达时间会发生重大变化,可以使用此解决方案来解决。自适应作业调度算法背后的思想是由蚁群优化(ACO)和禁忌算法组成的混合算法。此外,通过模糊C均值(FCM)算法计算了来自网格信息服务(GIS)的公共信息和到达新作业,以便评估资源和到达作业组的当前状态。此外,解决方案可以处理动态和静态信息。在静态情况下,资源信息(例如机器的CPU数量,CPU速度,网格系统中的数字机器)是事先已知的,而动态信息(例如到达作业)则在仿真过程中随时提交给系统。结果显示了自适应作业调度算法与传统算法的比较结果。

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