One of the first motivations of using grids comes from applications managing large data sets in field such as high energy physics or life sciences. To improve the global through-put of software environments, replicas are usually put at wisely selected sites. Moreover, computation requests have to be scheduled among the available resources. To get the best performance, scheduling and data replication have to be tightly coupled. However, there are few approaches that provide this coupling. This paper presents an algorithm that combines data management and scheduling using a steady-state approach. Our theoretical results are validated using simulation an
Grid is a distributed system that enables dynamic aggregation of geographically dislocated computing...
Grid computing is emerging as a new paradigm for solving large-scale problems and is becoming an est...
enable a group of collaborating but geographically separated researchers to effectively investigate ...
(eng) One of the first motivations of using grids comes from applications managing large data sets l...
One of the first motivations of using grids comes from applications managing large data sets like fo...
Abstract — Managing large datasets has become one major application of grids. Life science applicati...
International audienceThis paper focuses on simultaneous scheduling of computation and data replicat...
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and...
There are many challenges in Data Grids, and especially the data replication and the job scheduling ...
Many current international scientific projects are based on large scale applications that are both c...
Many current international scientific projects are based on large scale applications that are both c...
In data grids huge amount of data are generated and processed by users around the world. Objective o...
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and...
Grid computing is fast emerging as the solution to the problems posed by the massive computational a...
Computational grids process large, computationally intensive problems on small data sets. In contras...
Grid is a distributed system that enables dynamic aggregation of geographically dislocated computing...
Grid computing is emerging as a new paradigm for solving large-scale problems and is becoming an est...
enable a group of collaborating but geographically separated researchers to effectively investigate ...
(eng) One of the first motivations of using grids comes from applications managing large data sets l...
One of the first motivations of using grids comes from applications managing large data sets like fo...
Abstract — Managing large datasets has become one major application of grids. Life science applicati...
International audienceThis paper focuses on simultaneous scheduling of computation and data replicat...
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and...
There are many challenges in Data Grids, and especially the data replication and the job scheduling ...
Many current international scientific projects are based on large scale applications that are both c...
Many current international scientific projects are based on large scale applications that are both c...
In data grids huge amount of data are generated and processed by users around the world. Objective o...
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and...
Grid computing is fast emerging as the solution to the problems posed by the massive computational a...
Computational grids process large, computationally intensive problems on small data sets. In contras...
Grid is a distributed system that enables dynamic aggregation of geographically dislocated computing...
Grid computing is emerging as a new paradigm for solving large-scale problems and is becoming an est...
enable a group of collaborating but geographically separated researchers to effectively investigate ...