{"title":"网格工作流调度的MOPSO方法","authors":"Yunxia Pei","doi":"10.1109/APWCS.2010.108","DOIUrl":null,"url":null,"abstract":"In order to meet user’s QoS requirement for the implementation of grid workflow, the key tasks of the workflow were analyzed firstly, and the QoS of the whole workflow was divided into segments which are the QoS-constrained of a single task. Then, a grid workflow scheduling algorithm based on multi-objective particle swarm optimization (MOPSO) was proposed. Through both positive layering and reverse layering, this algorithm can find the parallel relation between tasks easily and accurately. The algorithm can relax the task execution time, increase flexibility scheduling and meet user QoS requirements. Simulation results show that algorithm has a shorted execution time and a less execution cost compared with NSGA-¿ algorithm when the two algorithms have the same deadline.","PeriodicalId":354322,"journal":{"name":"2010 Asia-Pacific Conference on Wearable Computing Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A MOPSO Approach to Grid Workflow Scheduling\",\"authors\":\"Yunxia Pei\",\"doi\":\"10.1109/APWCS.2010.108\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to meet user’s QoS requirement for the implementation of grid workflow, the key tasks of the workflow were analyzed firstly, and the QoS of the whole workflow was divided into segments which are the QoS-constrained of a single task. Then, a grid workflow scheduling algorithm based on multi-objective particle swarm optimization (MOPSO) was proposed. Through both positive layering and reverse layering, this algorithm can find the parallel relation between tasks easily and accurately. The algorithm can relax the task execution time, increase flexibility scheduling and meet user QoS requirements. Simulation results show that algorithm has a shorted execution time and a less execution cost compared with NSGA-¿ algorithm when the two algorithms have the same deadline.\",\"PeriodicalId\":354322,\"journal\":{\"name\":\"2010 Asia-Pacific Conference on Wearable Computing Systems\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Asia-Pacific Conference on Wearable Computing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APWCS.2010.108\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Asia-Pacific Conference on Wearable Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APWCS.2010.108","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In order to meet user’s QoS requirement for the implementation of grid workflow, the key tasks of the workflow were analyzed firstly, and the QoS of the whole workflow was divided into segments which are the QoS-constrained of a single task. Then, a grid workflow scheduling algorithm based on multi-objective particle swarm optimization (MOPSO) was proposed. Through both positive layering and reverse layering, this algorithm can find the parallel relation between tasks easily and accurately. The algorithm can relax the task execution time, increase flexibility scheduling and meet user QoS requirements. Simulation results show that algorithm has a shorted execution time and a less execution cost compared with NSGA-¿ algorithm when the two algorithms have the same deadline.