Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments

详细信息	查看全文 | 推荐本文 |

• 作者：Hongbo Liu^a ; ^b ; ^c ; ^{lhb@dlut.edu.cn} ; Ajith Abraham^c ; ^d ; ^{ajith.abraham@ieee.org} ; Vá ; clav Sná ; &scaron ; el^c ; ^d ; ^{vaclav.snasel@vsb.cz} ; Seá ; n McLoone^e ; ^{sean.mcloone@eeng.nuim.ie}
• 关键词：Swarm intelligence ; Particle swarm ; Scheduling problem ; Work-flow ; Security constraints ; Distributed data-intensive computing environments
• 刊名：Information Sciences
• 出版年：2012
• 期刊代码：62_00200255
• 类别：cp
• 出版时间：1 June, 2012
• 卷：192
• 期：Complete
• 页码：228-243
• 文件大小：645 K

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.