{"title":"云协同调度反负载均衡算法:CSAAC","authors":"Cheikhou Thiam, Georges Da Costa, J. Pierson","doi":"10.1109/CloudCom.2013.63","DOIUrl":null,"url":null,"abstract":"In the past decade, more and more attention focuses on job scheduling strategies in a variety of scenarios. Due to the characteristics of clouds, meta-scheduling turns out to be an important scheduling pattern because it is responsible for orchestrating resources managed by independent local schedulers and bridges the gap between participating nodes. Likewise, to overcome issues such as bottleneck, overloading, under loading and impractical unique administrative management, which are normally led by conventional centralized or hierarchical schemes, the distributed scheduling scheme is emerging as a promising approach because of its capability with regards to scalability and flexibility. In this paper, we introduce a decentralized dynamic scheduling approach entitled Cooperative scheduling Anti-load balancing Algorithm for cloud (CSAAC). To validate CSAAC we used a simulator which extends the MaGateSim simulator and provides better support to energy aware scheduling algorithms. CSAAC goal is to achieve optimized scheduling performance and energy gain over the scope of overall cloud, instead of individual participating nodes. The extensive experimental evaluation with a real workload dataset shows that, when compared to the centralized scheduling scheme with Best Fit as the meta-scheduling policy, the use of CSAAC can lead to a 30%61% energy gain, and a 20%30% shorter average job execution time in a decentralized scheduling manner without requiring detailed real-time processing information from participating nodes.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"232 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Cooperative Scheduling Anti-load Balancing Algorithm for Cloud: CSAAC\",\"authors\":\"Cheikhou Thiam, Georges Da Costa, J. 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To validate CSAAC we used a simulator which extends the MaGateSim simulator and provides better support to energy aware scheduling algorithms. CSAAC goal is to achieve optimized scheduling performance and energy gain over the scope of overall cloud, instead of individual participating nodes. The extensive experimental evaluation with a real workload dataset shows that, when compared to the centralized scheduling scheme with Best Fit as the meta-scheduling policy, the use of CSAAC can lead to a 30%61% energy gain, and a 20%30% shorter average job execution time in a decentralized scheduling manner without requiring detailed real-time processing information from participating nodes.\",\"PeriodicalId\":198053,\"journal\":{\"name\":\"2013 IEEE 5th International Conference on Cloud Computing Technology and Science\",\"volume\":\"232 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE 5th International Conference on Cloud Computing Technology and Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CloudCom.2013.63\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CloudCom.2013.63","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cooperative Scheduling Anti-load Balancing Algorithm for Cloud: CSAAC
In the past decade, more and more attention focuses on job scheduling strategies in a variety of scenarios. Due to the characteristics of clouds, meta-scheduling turns out to be an important scheduling pattern because it is responsible for orchestrating resources managed by independent local schedulers and bridges the gap between participating nodes. Likewise, to overcome issues such as bottleneck, overloading, under loading and impractical unique administrative management, which are normally led by conventional centralized or hierarchical schemes, the distributed scheduling scheme is emerging as a promising approach because of its capability with regards to scalability and flexibility. In this paper, we introduce a decentralized dynamic scheduling approach entitled Cooperative scheduling Anti-load balancing Algorithm for cloud (CSAAC). To validate CSAAC we used a simulator which extends the MaGateSim simulator and provides better support to energy aware scheduling algorithms. CSAAC goal is to achieve optimized scheduling performance and energy gain over the scope of overall cloud, instead of individual participating nodes. The extensive experimental evaluation with a real workload dataset shows that, when compared to the centralized scheduling scheme with Best Fit as the meta-scheduling policy, the use of CSAAC can lead to a 30%61% energy gain, and a 20%30% shorter average job execution time in a decentralized scheduling manner without requiring detailed real-time processing information from participating nodes.