{"title":"基于遗传算法的云计算能量优化调度策略","authors":"H. Jin, Lu Yang, Ouyang Hao","doi":"10.1109/ICCPS.2015.7454218","DOIUrl":null,"url":null,"abstract":"During the processing of Cloud platform it will generate a large amount of energy consumption. so how to improve energy efficiency become increasingly important. This paper presents a scheduling strategy which is based on the genetic algorithm for Cloud computing energy optimal. First, we adopt queuing network for system modeling and prove that the energy consumption of Cloud computing system is determined by the task scheduling probability. In order to obtain minimum energy consumption, genetic algorithms based on optimal reservation selection is use to optimize the dispatch probability. Simulation results show that this method is feasible to optimize energy consumption of cloud computing system.","PeriodicalId":319991,"journal":{"name":"2015 IEEE International Conference on Communication Problem-Solving (ICCP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Scheduling strategy based on genetic algorithm for Cloud computer energy optimization\",\"authors\":\"H. Jin, Lu Yang, Ouyang Hao\",\"doi\":\"10.1109/ICCPS.2015.7454218\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"During the processing of Cloud platform it will generate a large amount of energy consumption. so how to improve energy efficiency become increasingly important. This paper presents a scheduling strategy which is based on the genetic algorithm for Cloud computing energy optimal. First, we adopt queuing network for system modeling and prove that the energy consumption of Cloud computing system is determined by the task scheduling probability. In order to obtain minimum energy consumption, genetic algorithms based on optimal reservation selection is use to optimize the dispatch probability. Simulation results show that this method is feasible to optimize energy consumption of cloud computing system.\",\"PeriodicalId\":319991,\"journal\":{\"name\":\"2015 IEEE International Conference on Communication Problem-Solving (ICCP)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Communication Problem-Solving (ICCP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCPS.2015.7454218\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Communication Problem-Solving (ICCP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCPS.2015.7454218","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Scheduling strategy based on genetic algorithm for Cloud computer energy optimization
During the processing of Cloud platform it will generate a large amount of energy consumption. so how to improve energy efficiency become increasingly important. This paper presents a scheduling strategy which is based on the genetic algorithm for Cloud computing energy optimal. First, we adopt queuing network for system modeling and prove that the energy consumption of Cloud computing system is determined by the task scheduling probability. In order to obtain minimum energy consumption, genetic algorithms based on optimal reservation selection is use to optimize the dispatch probability. Simulation results show that this method is feasible to optimize energy consumption of cloud computing system.