{"title":"基于遗传算法的tag参数设置与探索","authors":"Hagit Sarfati, E. Bachmat, Sagit Kedem-Yemini","doi":"10.1109/SCIS.2007.367702","DOIUrl":null,"url":null,"abstract":"We consider the performance of TAGS, a multi-host job assignment policy. We use a genetic algorithm to compute the optimal parameter settings for the policy. We then explore the performance of the policy using the optimal parameters, when the job size distribution is a heavy-tailed bounded Pareto distribution with parameter alpha. We show that TAGS only operates at low inter-arrival rates. At low rates it is very efficient in comparison with other standard policies. At high rates TAGS has to be combined with other policies to achieve good performance. We also show that the performance is nearly symmetrical around the value alpha = 1, with the best performance when alpha = 1","PeriodicalId":184726,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Scheduling","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Parameter setting and exploration of TAGS using a genetic algorithm\",\"authors\":\"Hagit Sarfati, E. Bachmat, Sagit Kedem-Yemini\",\"doi\":\"10.1109/SCIS.2007.367702\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider the performance of TAGS, a multi-host job assignment policy. We use a genetic algorithm to compute the optimal parameter settings for the policy. We then explore the performance of the policy using the optimal parameters, when the job size distribution is a heavy-tailed bounded Pareto distribution with parameter alpha. We show that TAGS only operates at low inter-arrival rates. At low rates it is very efficient in comparison with other standard policies. At high rates TAGS has to be combined with other policies to achieve good performance. We also show that the performance is nearly symmetrical around the value alpha = 1, with the best performance when alpha = 1\",\"PeriodicalId\":184726,\"journal\":{\"name\":\"2007 IEEE Symposium on Computational Intelligence in Scheduling\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE Symposium on Computational Intelligence in Scheduling\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SCIS.2007.367702\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Symposium on Computational Intelligence in Scheduling","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCIS.2007.367702","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parameter setting and exploration of TAGS using a genetic algorithm
We consider the performance of TAGS, a multi-host job assignment policy. We use a genetic algorithm to compute the optimal parameter settings for the policy. We then explore the performance of the policy using the optimal parameters, when the job size distribution is a heavy-tailed bounded Pareto distribution with parameter alpha. We show that TAGS only operates at low inter-arrival rates. At low rates it is very efficient in comparison with other standard policies. At high rates TAGS has to be combined with other policies to achieve good performance. We also show that the performance is nearly symmetrical around the value alpha = 1, with the best performance when alpha = 1