{"title":"面向 hadoop 框架的成本感知优化资源配置 Map-Reduce 调度器","authors":"Archana Bhaskar, Rajeev Ranjan","doi":"10.11591/ijai.v13.i2.pp1262-1271","DOIUrl":null,"url":null,"abstract":"Distributed data processing model has been one of the primary components in the case of data-intensive applications; furthermore, due to advancements in technologies, there has been a huge volume of data generation of diverse nature. Hadoop map reduce framework is responsible for adopting the ease of deployment mechanism in an open-source framework. The existing Hadoop MapReduce framework possesses high makespan time and high Input/Output overhead and it mainly affects the cost of a model. Thus, this research work presents an optimized cost aware resource provisioning MapReduce model also known as the cost-effective resource provisioning MapReduce (CRP-MR) model. CRP-MR model introduces the two integrated approaches to minimize the cost; at first, this model presents the optimal resource optimization and optimal Input/Output optimization cleansing in the Hadoop MapReduce (HMR) scheduler. CRP-MR is evaluated considering the bioinformatics dataset and CRP-MR performs better than the existing model. ","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cost-aware optimal resource provisioning Map-Reduce scheduler for hadoop framework\",\"authors\":\"Archana Bhaskar, Rajeev Ranjan\",\"doi\":\"10.11591/ijai.v13.i2.pp1262-1271\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Distributed data processing model has been one of the primary components in the case of data-intensive applications; furthermore, due to advancements in technologies, there has been a huge volume of data generation of diverse nature. Hadoop map reduce framework is responsible for adopting the ease of deployment mechanism in an open-source framework. The existing Hadoop MapReduce framework possesses high makespan time and high Input/Output overhead and it mainly affects the cost of a model. Thus, this research work presents an optimized cost aware resource provisioning MapReduce model also known as the cost-effective resource provisioning MapReduce (CRP-MR) model. CRP-MR model introduces the two integrated approaches to minimize the cost; at first, this model presents the optimal resource optimization and optimal Input/Output optimization cleansing in the Hadoop MapReduce (HMR) scheduler. CRP-MR is evaluated considering the bioinformatics dataset and CRP-MR performs better than the existing model. \",\"PeriodicalId\":507934,\"journal\":{\"name\":\"IAES International Journal of Artificial Intelligence (IJ-AI)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IAES International Journal of Artificial Intelligence (IJ-AI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.11591/ijai.v13.i2.pp1262-1271\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IAES International Journal of Artificial Intelligence (IJ-AI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11591/ijai.v13.i2.pp1262-1271","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cost-aware optimal resource provisioning Map-Reduce scheduler for hadoop framework
Distributed data processing model has been one of the primary components in the case of data-intensive applications; furthermore, due to advancements in technologies, there has been a huge volume of data generation of diverse nature. Hadoop map reduce framework is responsible for adopting the ease of deployment mechanism in an open-source framework. The existing Hadoop MapReduce framework possesses high makespan time and high Input/Output overhead and it mainly affects the cost of a model. Thus, this research work presents an optimized cost aware resource provisioning MapReduce model also known as the cost-effective resource provisioning MapReduce (CRP-MR) model. CRP-MR model introduces the two integrated approaches to minimize the cost; at first, this model presents the optimal resource optimization and optimal Input/Output optimization cleansing in the Hadoop MapReduce (HMR) scheduler. CRP-MR is evaluated considering the bioinformatics dataset and CRP-MR performs better than the existing model.