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首页> 外文期刊>International journal of systems assurance engineering and management >Meta-heuristic based reliable and green workflow scheduling in cloud computing
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Meta-heuristic based reliable and green workflow scheduling in cloud computing

机译：云计算中基于元启发式的可靠绿色工作流调度

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摘要

Efficient workflow scheduling in modern cloud environment involves optimization of various conflictive objectives like execution performance (time), reliability, energy consumption etc. Despite this trend, numerous heuristics have been devoted to workflow scheduling mainly focused on the optimization of makespan (execution time) only without giving much attention on other important objectives. Reducing energy consumption is the major concern as it brings several important benefits like reduction in the operating costs, increase in the system reliability and environmental protection. Moreover, the compute processors in cloud are not failure free. Any kind of failure can be critical for an application. Hence in this paper, we proposed the multi-objective NSGA-II based scheduling algorithm for workflow applications with the aim to optimize three conflicting criterion simultaneously: makespanexecution time, reliability and energy consumption for executing the workflow application in cloud environment. In order to reduce the computation complexity of the algorithm, we used the efficient non-domination level update mechanism rather than applying the non-domination sorting from the scratch each time. The simulation analysis of the proposed algorithm on CloudSim toolkit shows that the Pareto optimal solutions obtained have good convergence, uniform diversity and computational efficiency.
机译：在现代云环境中，高效的工作流调度涉及优化各种冲突目标，例如执行性能（时间），可靠性，能耗等。尽管有这种趋势，但许多启发式方法已专门用于工作流调度，主要侧重于优化制造时间（执行时间）并没有过多关注其他重要目标。降低能耗是主要的关注点，因为它带来了许多重要的好处，例如降低了运营成本，提高了系统可靠性和环境保护。此外，云中的计算处理器并非没有故障。任何类型的故障对于应用程序而言都是至关重要的。因此，本文提出了一种基于多目标NSGA-II的工作流应用调度算法，旨在同时优化三个冲突标准：在云环境下执行工作流应用的执行时间，可靠性和能耗。为了降低算法的计算复杂度，我们使用了有效的非支配级别更新机制，而不是每次都从头开始应用非支配排序。在CloudSim工具箱上对该算法的仿真分析表明，所获得的Pareto最优解具有良好的收敛性，均一的分集性和计算效率。

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