Deadline Based Resource Provisioningand Scheduling Algorithm for Scientific Workflows on Clouds
Cloud computing is the latest distributed computing paradigm and it offers tremendous opportunities to solve large-scale scientific problems. However, it presents various challenges that need to be addressed in order to be efficiently utilized for workflow applications. Although the workflow schedul...
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| Published in | IEEE transactions on cloud computing Vol. 2; no. 2; pp. 222 - 235 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE Computer Society
01.04.2014
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Cloud computing is the latest distributed computing paradigm and it offers tremendous opportunities to solve large-scale scientific problems. However, it presents various challenges that need to be addressed in order to be efficiently utilized for workflow applications. Although the workflow scheduling problem has been widely studied, there are very few initiatives tailored for cloud environments. Furthermore, the existing works fail to either meet the user's quality of service (QoS) requirements or to incorporate some basic principles of cloud computing such as the elasticity and heterogeneity of the computing resources. This paper proposes a resource provisioning and scheduling strategy for scientific workflows on Infrastructure as a Service (IaaS) clouds. We present an algorithm based on the meta-heuristic optimization technique, particle swarm optimization (PSO), which aims to minimize the overall workflow execution cost while meeting deadline constraints. Our heuristic is evaluated using CloudSim and various well-known scientific workflows of different sizes. The results show that our approach performs better than the current state-of-the-art algorithms. |
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| AbstractList | Cloud computing is the latest distributed computing paradigm and it offers tremendous opportunities to solve large-scale scientific problems. However, it presents various challenges that need to be addressed in order to be efficiently utilized for workflow applications. Although the workflow scheduling problem has been widely studied, there are very few initiatives tailored for cloud environments. Furthermore, the existing works fail to either meet the user's quality of service (QoS) requirements or to incorporate some basic principles of cloud computing such as the elasticity and heterogeneity of the computing resources. This paper proposes a resource provisioning and scheduling strategy for scientific workflows on Infrastructure as a Service (IaaS) clouds. We present an algorithm based on the meta-heuristic optimization technique, particle swarm optimization (PSO), which aims to minimize the overall workflow execution cost while meeting deadline constraints. Our heuristic is evaluated using CloudSim and various well-known scientific workflows of different sizes. The results show that our approach performs better than the current state-of-the-art algorithms. |
| Author | Buyya, Rajkumar Rodriguez, Maria Alejandra |
| Author_xml | – sequence: 1 givenname: Maria Alejandra surname: Rodriguez fullname: Rodriguez, Maria Alejandra email: mariaars@student.unimelb.edu.au organization: Dept. of Comput. & Inf. Syst., Univ. of Melbourne, Melbourne, VIC, Australia – sequence: 2 givenname: Rajkumar surname: Buyya fullname: Buyya, Rajkumar email: rbuyya@unimelb.edu.au organization: Dept. of Comput. & Inf. Syst., Univ. of Melbourne, Melbourne, VIC, Australia |
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| CODEN | ITCCF6 |
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| References_xml | – ident: ref22 doi: 10.1109/CLOUD.2012.103 – ident: ref20 doi: 10.1109/4235.996017 – start-page: 1942 year: 0 ident: ref4 article-title: Particle swarm optimization publication-title: Proc IEEE Int Conf Neural Netw contributor: fullname: kennedy – ident: ref7 doi: 10.1016/j.parco.2003.12.015 – ident: ref1 doi: 10.1016/j.future.2012.08.015 – ident: ref15 doi: 10.1109/AINA.2010.31 – start-page: 140 year: 0 ident: ref24 article-title: A cost based scheduling of scientific workflow applications on utility grids publication-title: Proc 1st IEEE Int Conf E-Sci Grid Comput contributor: fullname: yu – ident: ref10 doi: 10.1109/TSMCC.2008.2001722 – volume: 238 year: 1979 ident: ref8 publication-title: Computers and Intractability A Guide to the Theory of NP-Completeness contributor: fullname: garey – start-page: 115 year: 2010 ident: ref17 article-title: A performance analysis of EC2 cloud computing services for scientific computing publication-title: Cloud Computing contributor: fullname: ostermann – volume: 87 year: 2010 ident: ref3 publication-title: Cloud Computing Principles and Paradigms contributor: fullname: buyya – ident: ref6 doi: 10.1016/S0098-1354(02)00153-9 – start-page: 117 year: 0 ident: ref5 article-title: A particle swarm optimization for reactive power and voltage control considering voltage stability publication-title: Proc IEEE Int Conf Intell Syst Appl Power Syst contributor: fullname: fukuyama – volume: 22 start-page: 1 year: 0 ident: ref13 article-title: Cost-and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds publication-title: Proc Int Conf High Perform Comput Netw Storage Anal contributor: fullname: malawski – ident: ref9 doi: 10.1109/E-SCIENCE.2007.3 – ident: ref19 doi: 10.1002/spe.995 – volume: 23 start-page: 1400 year: 2012 ident: ref14 article-title: Deadline-constrained workflow scheduling algorithms for IaaS clouds publication-title: Future Generation Comput Syst contributor: fullname: abrishami – start-page: 1 year: 0 ident: ref12 article-title: Auto-scaling to minimize cost and meet application deadlines in cloud workflows publication-title: Proc Int Conf High Perform Comput Netw Storage Anal contributor: fullname: mao – ident: ref21 doi: 10.14778/1920841.1920902 – ident: ref18 doi: 10.5772/109 – year: 0 ident: ref2 – ident: ref11 doi: 10.1109/WORKS.2006.5282330 – ident: ref16 doi: 10.1109/CIS.2010.46 – ident: ref23 doi: 10.1016/j.future.2011.05.001 |
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| Snippet | Cloud computing is the latest distributed computing paradigm and it offers tremendous opportunities to solve large-scale scientific problems. However, it... |
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| SubjectTerms | Cloud computing Computational modeling Computer applications Distributed processing Mathematical model Processor scheduling Quality of service |
| Title | Deadline Based Resource Provisioningand Scheduling Algorithm for Scientific Workflows on Clouds |
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