{"title":"使用基于网格的点对点并行I/O进行负载平衡","authors":"Yijian Wang, D. Kaeli","doi":"10.1109/CLUSTR.2005.347040","DOIUrl":null,"url":null,"abstract":"In the area of grid computing, there is a growing need to process large amounts of data. To support this trend, we need to develop efficient parallel storage systems that can provide for high performance for data-intensive applications. In order to overcome I/O bottlenecks and to increase I/O parallelism, data streams need to be parallelized at both the application level and the storage device level. In this paper, we propose a novel peer-to-peer (P2P) storage architecture for MPI applications on grid systems. We first present an analytic model of our P2P storage architecture. Next, we describe a profile-guided data allocation algorithm that can increase the degree of I/O parallelism present in the system, as well as to balance I/O in a heterogeneous system. We present results on an actual implementation. Our experimental results show that by partitioning data across all available storage devices and carefully tuning I/O workloads in the grid system, our peer-to-peer scheme can deliver scalable high performance I/O that can address I/O-intensive workloads","PeriodicalId":255312,"journal":{"name":"2005 IEEE International Conference on Cluster Computing","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Load Balancing using Grid-based Peer-to-Peer Parallel I/O\",\"authors\":\"Yijian Wang, D. Kaeli\",\"doi\":\"10.1109/CLUSTR.2005.347040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the area of grid computing, there is a growing need to process large amounts of data. To support this trend, we need to develop efficient parallel storage systems that can provide for high performance for data-intensive applications. In order to overcome I/O bottlenecks and to increase I/O parallelism, data streams need to be parallelized at both the application level and the storage device level. In this paper, we propose a novel peer-to-peer (P2P) storage architecture for MPI applications on grid systems. We first present an analytic model of our P2P storage architecture. Next, we describe a profile-guided data allocation algorithm that can increase the degree of I/O parallelism present in the system, as well as to balance I/O in a heterogeneous system. We present results on an actual implementation. Our experimental results show that by partitioning data across all available storage devices and carefully tuning I/O workloads in the grid system, our peer-to-peer scheme can deliver scalable high performance I/O that can address I/O-intensive workloads\",\"PeriodicalId\":255312,\"journal\":{\"name\":\"2005 IEEE International Conference on Cluster Computing\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2005 IEEE International Conference on Cluster Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CLUSTR.2005.347040\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 IEEE International Conference on Cluster Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CLUSTR.2005.347040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Load Balancing using Grid-based Peer-to-Peer Parallel I/O
In the area of grid computing, there is a growing need to process large amounts of data. To support this trend, we need to develop efficient parallel storage systems that can provide for high performance for data-intensive applications. In order to overcome I/O bottlenecks and to increase I/O parallelism, data streams need to be parallelized at both the application level and the storage device level. In this paper, we propose a novel peer-to-peer (P2P) storage architecture for MPI applications on grid systems. We first present an analytic model of our P2P storage architecture. Next, we describe a profile-guided data allocation algorithm that can increase the degree of I/O parallelism present in the system, as well as to balance I/O in a heterogeneous system. We present results on an actual implementation. Our experimental results show that by partitioning data across all available storage devices and carefully tuning I/O workloads in the grid system, our peer-to-peer scheme can deliver scalable high performance I/O that can address I/O-intensive workloads