{"title":"云中的超启发式MapReduce工作流调度","authors":"Arunkumar Panneerselvam, B. Subbaraman","doi":"10.1109/I-SMAC.2018.8653677","DOIUrl":null,"url":null,"abstract":"The Advancement in the field of computing requires new technologies and algorithms for efficient processing of large scale data such as Big Data. Distributed environments such as Cloud are prominent in storing and processing Big Data. Hadoop is a framework for processing Big Data. Hadoop follows MapReduce technique to process data in parallel. Today MapReduce workflows are extensively used in large scale scientific applications which are executed in cloud. Cloud offers rented resources for scheduling MapReduce workflows. Hyper Heuristic technique can be efficiently used for efficient scheduling of MapReduce task to the cloud resources. This paper explores the basis of MapReduce workflow execution in IaaS cloud and application of Hyper Heuristic technique in resource provisioning.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"32 1","pages":"691-693"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Hyper Heuristic MapReduce Workflow Scheduling in Cloud\",\"authors\":\"Arunkumar Panneerselvam, B. Subbaraman\",\"doi\":\"10.1109/I-SMAC.2018.8653677\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Advancement in the field of computing requires new technologies and algorithms for efficient processing of large scale data such as Big Data. Distributed environments such as Cloud are prominent in storing and processing Big Data. Hadoop is a framework for processing Big Data. Hadoop follows MapReduce technique to process data in parallel. Today MapReduce workflows are extensively used in large scale scientific applications which are executed in cloud. Cloud offers rented resources for scheduling MapReduce workflows. Hyper Heuristic technique can be efficiently used for efficient scheduling of MapReduce task to the cloud resources. This paper explores the basis of MapReduce workflow execution in IaaS cloud and application of Hyper Heuristic technique in resource provisioning.\",\"PeriodicalId\":53631,\"journal\":{\"name\":\"Koomesh\",\"volume\":\"32 1\",\"pages\":\"691-693\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Koomesh\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/I-SMAC.2018.8653677\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Koomesh","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I-SMAC.2018.8653677","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
Hyper Heuristic MapReduce Workflow Scheduling in Cloud
The Advancement in the field of computing requires new technologies and algorithms for efficient processing of large scale data such as Big Data. Distributed environments such as Cloud are prominent in storing and processing Big Data. Hadoop is a framework for processing Big Data. Hadoop follows MapReduce technique to process data in parallel. Today MapReduce workflows are extensively used in large scale scientific applications which are executed in cloud. Cloud offers rented resources for scheduling MapReduce workflows. Hyper Heuristic technique can be efficiently used for efficient scheduling of MapReduce task to the cloud resources. This paper explores the basis of MapReduce workflow execution in IaaS cloud and application of Hyper Heuristic technique in resource provisioning.