An iterative heuristic for scheduling grid workflows with budget constraints
Workflow scheduling which guarantees anticipated QoS (quality of service) is a complex problem in grids. In this paper, the budget-constrained scheduling of workflows represented by DAG (directed acyclic graph) with the objective of time optimization is considered. A new priority rule-based iterativ...
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creator | Ying-Chun Yuan Ke-Jian Wang Xin-Sheng Sun Tao Guo |
description | Workflow scheduling which guarantees anticipated QoS (quality of service) is a complex problem in grids. In this paper, the budget-constrained scheduling of workflows represented by DAG (directed acyclic graph) with the objective of time optimization is considered. A new priority rule-based iterative heuristic is proposed. According to the property of serial activities in DAG, the concept called SC (serial complexity) is defined. By incorporating it into priority rule MP (maximum profit), a novel priority rule, denoted as MPSC (maximum profit with serial complexity), is designed. It is implemented in the iterative heuristic to improve iteratively the initial feasible solution. Computational results show that MPSC can considerably improve the average performance of MP, MPBL and MPTL within a few iterations and a little computation time. As well, the impact of budget constraints on these heuristics is analyzed. |
doi_str_mv | 10.1109/ICMLC.2009.5212290 |
format | Conference Proceeding |
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In this paper, the budget-constrained scheduling of workflows represented by DAG (directed acyclic graph) with the objective of time optimization is considered. A new priority rule-based iterative heuristic is proposed. According to the property of serial activities in DAG, the concept called SC (serial complexity) is defined. By incorporating it into priority rule MP (maximum profit), a novel priority rule, denoted as MPSC (maximum profit with serial complexity), is designed. It is implemented in the iterative heuristic to improve iteratively the initial feasible solution. Computational results show that MPSC can considerably improve the average performance of MP, MPBL and MPTL within a few iterations and a little computation time. 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As well, the impact of budget constraints on these heuristics is analyzed.</description><subject>Agriculture</subject><subject>Computer applications</subject><subject>Constraint optimization</subject><subject>Costs</subject><subject>Cybernetics</subject><subject>Grid computing</subject><subject>Information science</subject><subject>Iterative heuristics</subject><subject>Machine learning</subject><subject>Priority rule</subject><subject>Processor scheduling</subject><subject>Quality of service</subject><subject>Serial complexity</subject><subject>Service grid</subject><subject>Workflow</subject><issn>2160-133X</issn><isbn>9781424437023</isbn><isbn>1424437024</isbn><isbn>1424437032</isbn><isbn>9781424437030</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1kM1OwzAQhI2gEm3pC8DFL5Cyu46T-FhF_FQK4tIDtyo_69ZQEmS7VLw9kShzGc1hRp9GiFuEJSKY-3X5UpVLAjBLTUhk4ELMMKU0VTkouhQLkxf_mdSVmBJmkKBSbxMxG3uFQTSarsUihHcYlWrKMzUV1aqXLrKvo_tmueejdyG6VtrBy9DuuTseXL-TO-86eRr8hz0MpyBPLu5lc-x2HGU79CH62vUx3IiJrQ-BF2efi83jw6Z8TqrXp3W5qhJnICbU8ojTKGACk2UAta5Tm2GhOwDknLDpmhFfM-XWMiJb1LpubJdmhTKk5uLub9Yx8_bLu8_a_2zPx6hfCORTKA</recordid><startdate>200907</startdate><enddate>200907</enddate><creator>Ying-Chun Yuan</creator><creator>Ke-Jian Wang</creator><creator>Xin-Sheng Sun</creator><creator>Tao Guo</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200907</creationdate><title>An iterative heuristic for scheduling grid workflows with budget constraints</title><author>Ying-Chun Yuan ; Ke-Jian Wang ; Xin-Sheng Sun ; Tao Guo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-2ce891b30e2096600a5a4f6185d001e721bdb1605e27ffe11ef155abfd4683923</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Agriculture</topic><topic>Computer applications</topic><topic>Constraint optimization</topic><topic>Costs</topic><topic>Cybernetics</topic><topic>Grid computing</topic><topic>Information science</topic><topic>Iterative heuristics</topic><topic>Machine learning</topic><topic>Priority rule</topic><topic>Processor scheduling</topic><topic>Quality of service</topic><topic>Serial complexity</topic><topic>Service grid</topic><topic>Workflow</topic><toplevel>online_resources</toplevel><creatorcontrib>Ying-Chun Yuan</creatorcontrib><creatorcontrib>Ke-Jian Wang</creatorcontrib><creatorcontrib>Xin-Sheng Sun</creatorcontrib><creatorcontrib>Tao Guo</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ying-Chun Yuan</au><au>Ke-Jian Wang</au><au>Xin-Sheng Sun</au><au>Tao Guo</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An iterative heuristic for scheduling grid workflows with budget constraints</atitle><btitle>2009 International Conference on Machine Learning and Cybernetics</btitle><stitle>ICMLC</stitle><date>2009-07</date><risdate>2009</risdate><volume>3</volume><spage>1700</spage><epage>1705</epage><pages>1700-1705</pages><issn>2160-133X</issn><isbn>9781424437023</isbn><isbn>1424437024</isbn><eisbn>1424437032</eisbn><eisbn>9781424437030</eisbn><abstract>Workflow scheduling which guarantees anticipated QoS (quality of service) is a complex problem in grids. In this paper, the budget-constrained scheduling of workflows represented by DAG (directed acyclic graph) with the objective of time optimization is considered. A new priority rule-based iterative heuristic is proposed. According to the property of serial activities in DAG, the concept called SC (serial complexity) is defined. By incorporating it into priority rule MP (maximum profit), a novel priority rule, denoted as MPSC (maximum profit with serial complexity), is designed. It is implemented in the iterative heuristic to improve iteratively the initial feasible solution. Computational results show that MPSC can considerably improve the average performance of MP, MPBL and MPTL within a few iterations and a little computation time. As well, the impact of budget constraints on these heuristics is analyzed.</abstract><pub>IEEE</pub><doi>10.1109/ICMLC.2009.5212290</doi><tpages>6</tpages></addata></record> |
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subjects | Agriculture Computer applications Constraint optimization Costs Cybernetics Grid computing Information science Iterative heuristics Machine learning Priority rule Processor scheduling Quality of service Serial complexity Service grid Workflow |
title | An iterative heuristic for scheduling grid workflows with budget constraints |
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