A Particle Swarm Optimization-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments

被引:476
作者
Pandey, Suraj [1 ]
Wu, Linlin [1 ]
Guru, Siddeswara Mayura [2 ]
Buyya, Rajkumar [1 ]
机构
[1] Univ Melbourne, Dept Comp Sci & Software Engn, Cloud Comp & Distributed Syst Lab, Melbourne, Vic 3010, Australia
[2] CSIRO, Tasmanian ICT Ctr, Hobart, Tas, Australia
来源
2010 24TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION NETWORKING AND APPLICATIONS (AINA) | 2010年
基金
澳大利亚研究理事会;
关键词
TASK ASSIGNMENT; ALGORITHM; TOOL;
D O I
10.1109/AINA.2010.31
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data retrieval and execution costs when they are scheduled taking into account only the 'execution time'. In addition to optimizing execution time, the cost arising from data transfers between resources as well as execution costs must also be taken into account. In this paper, we present a particle swarm optimization (PSO) based heuristic to schedule applications to cloud resources that takes into account both computation cost and data transmission cost. We experiment with a workflow application by varying its computation and communication costs. We compare the cost savings when using PSO and existing 'Best Resource Selection' (BRS) algorithm. Our results show that PSO can achieve: a) as much as 3 times cost savings as compared to BRS, and b) good distribution of workload onto resources.
引用
收藏
页码:400 / 407
页数:8
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