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首页> 外文期刊>Information Sciences: An International Journal >Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments
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Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments

机译：在分布式数据密集型计算环境中具有安全约束的工作流应用程序的群调度方法

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摘要

The scheduling problem in distributed data-intensive computing environments has become an active research topic due to the tremendous growth in grid and cloud computing environments. As an innovative distributed intelligent paradigm, swarm intelligence provides a novel approach to solving these potentially intractable problems. In this paper, we formulate the scheduling problem for work-flow applications with security constraints in distributed data-intensive computing environments and present a novel security constraint model. Several meta-heuristic adaptations to the particle swarm optimization algorithm are introduced to deal with the formulation of efficient schedules. A variable neighborhood particle swarm optimization algorithm is compared with a multi-start particle swarm optimization and multi-start genetic algorithm. Experimental results illustrate that population based meta-heuristics approaches usually provide a good balance between global exploration and local exploitation and their feasibility and effectiveness for scheduling work-flow applications.
机译：由于网格和云计算环境的迅猛发展，分布式数据密集型计算环境中的调度问题已成为活跃的研究主题。群体智能作为一种创新的分布式智能范例，为解决这些潜在的棘手问题提供了一种新颖的方法。在本文中，我们为分布式数据密集型计算环境中具有安全性约束的工作流应用程序制定了调度问题，并提出了一种新颖的安全性约束模型。介绍了对粒子群优化算法的几种元启发式适应方法，以处理有效调度的制定。将可变邻域粒子群算法与多起点粒子群算法和多起点遗传算法进行了比较。实验结果表明，基于种群的元启发式方法通常可以在全球勘探与本地开发及其安排工作流程应用程序的可行性和有效性之间取得良好的平衡。

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