{"title":"VMR:志愿者在大规模互联网上使用MapReduce","authors":"Fernando Costa, L. Veiga, P. Ferreira","doi":"10.1145/2405136.2405137","DOIUrl":null,"url":null,"abstract":"Volunteer Computing systems (VC) harness computing resources of machines from around the world to perform distributed independent tasks. Existing infrastructures follow a master/worker model, with a centralized architecture, which limits the scalability of the solution given its dependence on the server. We intend to create a distributed model, in order to improve performance and reduce the burden on the server.\n In this paper we present VMR, a VC system able to run MapReduce applications on top of volunteer resources, over the large scale Internet. We describe VMR's architecture and evaluate its performance by executing several MapReduce applications on a wide area testbed.\n Our results show that VMR successfully runs MapReduce tasks over the Internet. When compared to an unmodified VC system, VMR obtains a performance increase of over 60% in application turnaround time, while reducing the bandwidth use by an order of magnitude.","PeriodicalId":313448,"journal":{"name":"Middleware for Grid Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"VMR: volunteer MapReduce over the large scale internet\",\"authors\":\"Fernando Costa, L. Veiga, P. Ferreira\",\"doi\":\"10.1145/2405136.2405137\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Volunteer Computing systems (VC) harness computing resources of machines from around the world to perform distributed independent tasks. Existing infrastructures follow a master/worker model, with a centralized architecture, which limits the scalability of the solution given its dependence on the server. We intend to create a distributed model, in order to improve performance and reduce the burden on the server.\\n In this paper we present VMR, a VC system able to run MapReduce applications on top of volunteer resources, over the large scale Internet. We describe VMR's architecture and evaluate its performance by executing several MapReduce applications on a wide area testbed.\\n Our results show that VMR successfully runs MapReduce tasks over the Internet. When compared to an unmodified VC system, VMR obtains a performance increase of over 60% in application turnaround time, while reducing the bandwidth use by an order of magnitude.\",\"PeriodicalId\":313448,\"journal\":{\"name\":\"Middleware for Grid Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Middleware for Grid Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2405136.2405137\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Middleware for Grid Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2405136.2405137","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
VMR: volunteer MapReduce over the large scale internet
Volunteer Computing systems (VC) harness computing resources of machines from around the world to perform distributed independent tasks. Existing infrastructures follow a master/worker model, with a centralized architecture, which limits the scalability of the solution given its dependence on the server. We intend to create a distributed model, in order to improve performance and reduce the burden on the server.
In this paper we present VMR, a VC system able to run MapReduce applications on top of volunteer resources, over the large scale Internet. We describe VMR's architecture and evaluate its performance by executing several MapReduce applications on a wide area testbed.
Our results show that VMR successfully runs MapReduce tasks over the Internet. When compared to an unmodified VC system, VMR obtains a performance increase of over 60% in application turnaround time, while reducing the bandwidth use by an order of magnitude.