{"title":"云计算中独立任务调度的平衡启发式算法","authors":"Kadda Beghdad Bey, F. Benhammadi, R. Benaissa","doi":"10.1109/ISPS.2015.7244959","DOIUrl":null,"url":null,"abstract":"Distributed computing environment has become a new technology to execute large-scale applications and Cloud computing is one of these technologies. Resource allocation is one of the most important challenges in the Cloud Computing. The optimally assigning of the available resources to the needed cloud applications is known to be a NP complete problem. In this paper, we propose a new task scheduling strategy based on the total order for resource allocation to improve the Min-Min algorithm. We focus on minimizing the total executing time (makespan) of task scheduling and maximizing the use of resources. Experimental results demonstrate that the proposed approach permits more adaptive resources allocation for independent jobs scheduling in the cloud computing environment.","PeriodicalId":165465,"journal":{"name":"2015 12th International Symposium on Programming and Systems (ISPS)","volume":"2 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Balancing heuristic for independent task scheduling in cloud computing\",\"authors\":\"Kadda Beghdad Bey, F. Benhammadi, R. Benaissa\",\"doi\":\"10.1109/ISPS.2015.7244959\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Distributed computing environment has become a new technology to execute large-scale applications and Cloud computing is one of these technologies. Resource allocation is one of the most important challenges in the Cloud Computing. The optimally assigning of the available resources to the needed cloud applications is known to be a NP complete problem. In this paper, we propose a new task scheduling strategy based on the total order for resource allocation to improve the Min-Min algorithm. We focus on minimizing the total executing time (makespan) of task scheduling and maximizing the use of resources. Experimental results demonstrate that the proposed approach permits more adaptive resources allocation for independent jobs scheduling in the cloud computing environment.\",\"PeriodicalId\":165465,\"journal\":{\"name\":\"2015 12th International Symposium on Programming and Systems (ISPS)\",\"volume\":\"2 3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-04-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 12th International Symposium on Programming and Systems (ISPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISPS.2015.7244959\",\"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 12th International Symposium on Programming and Systems (ISPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPS.2015.7244959","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Balancing heuristic for independent task scheduling in cloud computing
Distributed computing environment has become a new technology to execute large-scale applications and Cloud computing is one of these technologies. Resource allocation is one of the most important challenges in the Cloud Computing. The optimally assigning of the available resources to the needed cloud applications is known to be a NP complete problem. In this paper, we propose a new task scheduling strategy based on the total order for resource allocation to improve the Min-Min algorithm. We focus on minimizing the total executing time (makespan) of task scheduling and maximizing the use of resources. Experimental results demonstrate that the proposed approach permits more adaptive resources allocation for independent jobs scheduling in the cloud computing environment.