Grid task scheduling based on constraint satisfaction neural network
Task scheduling is of great significance to shorten performing time and minimize the cost for computational Grid. A grid task schedule algorithm is presented in this paper, which is based on a constraint satisfaction neural network. The constraint satisfaction means to remove the violations for sequ...
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| creator | Dong Yueli Guo Quan |
| description | Task scheduling is of great significance to shorten performing time and minimize the cost for computational Grid. A grid task schedule algorithm is presented in this paper, which is based on a constraint satisfaction neural network. The constraint satisfaction means to remove the violations for sequence and resource constraints during scheduling subtasks for grid environment. The data-transferring costs among subtasks are also considered in our task scheduling. The simulation in this paper has shown that the task schedule algorithm is efficient with respect to the quality of solutions and the solving speed. |
| doi_str_mv | 10.1109/ICACC.2010.5487161 |
| format | Conference Proceeding |
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A grid task schedule algorithm is presented in this paper, which is based on a constraint satisfaction neural network. The constraint satisfaction means to remove the violations for sequence and resource constraints during scheduling subtasks for grid environment. The data-transferring costs among subtasks are also considered in our task scheduling. The simulation in this paper has shown that the task schedule algorithm is efficient with respect to the quality of solutions and the solving speed.</description><identifier>ISBN: 1424458455</identifier><identifier>ISBN: 9781424458455</identifier><identifier>EISBN: 9781424458486</identifier><identifier>EISBN: 1424458471</identifier><identifier>EISBN: 9781424458479</identifier><identifier>EISBN: 142445848X</identifier><identifier>DOI: 10.1109/ICACC.2010.5487161</identifier><language>eng</language><publisher>IEEE</publisher><subject>Computational efficiency ; Computer networks ; Computer science ; constraint ; Costs ; grid ; Grid computing ; neural network ; Neural networks ; Neurons ; Processor scheduling ; Scheduling algorithm ; System recovery ; task scheduling</subject><ispartof>2010 2nd International Conference on Advanced Computer Control, 2010, Vol.1, p.513-515</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5487161$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5487161$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Dong Yueli</creatorcontrib><creatorcontrib>Guo Quan</creatorcontrib><title>Grid task scheduling based on constraint satisfaction neural network</title><title>2010 2nd International Conference on Advanced Computer Control</title><addtitle>ICACC</addtitle><description>Task scheduling is of great significance to shorten performing time and minimize the cost for computational Grid. A grid task schedule algorithm is presented in this paper, which is based on a constraint satisfaction neural network. The constraint satisfaction means to remove the violations for sequence and resource constraints during scheduling subtasks for grid environment. The data-transferring costs among subtasks are also considered in our task scheduling. The simulation in this paper has shown that the task schedule algorithm is efficient with respect to the quality of solutions and the solving speed.</description><subject>Computational efficiency</subject><subject>Computer networks</subject><subject>Computer science</subject><subject>constraint</subject><subject>Costs</subject><subject>grid</subject><subject>Grid computing</subject><subject>neural network</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Processor scheduling</subject><subject>Scheduling algorithm</subject><subject>System recovery</subject><subject>task scheduling</subject><isbn>1424458455</isbn><isbn>9781424458455</isbn><isbn>9781424458486</isbn><isbn>1424458471</isbn><isbn>9781424458479</isbn><isbn>142445848X</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1T8FKxDAUjIigrv0BveQHuua1SV56lKqrsOBFz0tMXjVubSXJIv69kV3nMswMDDOMXYJYAoju-rG_6ftlI4pW0iBoOGJVhwZkI6Uy0uhjdv4vlDplVUofokCqRhs4Y7erGDzPNm15cu_kd2OY3virTeT5PHE3TylHG6bMk80hDdblUPyJdtGOhfL3HLcX7GSwY6LqwAv2cn_33D_U66dVWbiuA6DKtUfUnbeuzMRWGCVF54RXGqF1jSYCcv4vIi8RvBbKDQ4lEnppG-GwXbCrfW8gos1XDJ82_mwOx9tfhkNMQA</recordid><startdate>201003</startdate><enddate>201003</enddate><creator>Dong Yueli</creator><creator>Guo Quan</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201003</creationdate><title>Grid task scheduling based on constraint satisfaction neural network</title><author>Dong Yueli ; Guo Quan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-d7769dac71673085409c0d56713c26ee1ecd1673ed471d605cfc747e7d4a20c73</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Computational efficiency</topic><topic>Computer networks</topic><topic>Computer science</topic><topic>constraint</topic><topic>Costs</topic><topic>grid</topic><topic>Grid computing</topic><topic>neural network</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Processor scheduling</topic><topic>Scheduling algorithm</topic><topic>System recovery</topic><topic>task scheduling</topic><toplevel>online_resources</toplevel><creatorcontrib>Dong Yueli</creatorcontrib><creatorcontrib>Guo Quan</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 Electronic Library (IEL)</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>Dong Yueli</au><au>Guo Quan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Grid task scheduling based on constraint satisfaction neural network</atitle><btitle>2010 2nd International Conference on Advanced Computer Control</btitle><stitle>ICACC</stitle><date>2010-03</date><risdate>2010</risdate><volume>1</volume><spage>513</spage><epage>515</epage><pages>513-515</pages><isbn>1424458455</isbn><isbn>9781424458455</isbn><eisbn>9781424458486</eisbn><eisbn>1424458471</eisbn><eisbn>9781424458479</eisbn><eisbn>142445848X</eisbn><abstract>Task scheduling is of great significance to shorten performing time and minimize the cost for computational Grid. A grid task schedule algorithm is presented in this paper, which is based on a constraint satisfaction neural network. The constraint satisfaction means to remove the violations for sequence and resource constraints during scheduling subtasks for grid environment. The data-transferring costs among subtasks are also considered in our task scheduling. The simulation in this paper has shown that the task schedule algorithm is efficient with respect to the quality of solutions and the solving speed.</abstract><pub>IEEE</pub><doi>10.1109/ICACC.2010.5487161</doi><tpages>3</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Computational efficiency Computer networks Computer science constraint Costs grid Grid computing neural network Neural networks Neurons Processor scheduling Scheduling algorithm System recovery task scheduling |
| title | Grid task scheduling based on constraint satisfaction neural network |
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