Computer Science – Distributed – Parallel – and Cluster Computing
Scientific paper
2006-08-08
Computer Science
Distributed, Parallel, and Cluster Computing
12 pages, 11 figures. To be published in the IEEE Transactions in Nuclear Science, IEEE Press. 2006
Scientific paper
10.1109/TNS.2006.886047
Results from the research and development of a Data Intensive and Network Aware (DIANA) scheduling engine, to be used primarily for data intensive sciences such as physics analysis, are described. In Grid analyses, tasks can involve thousands of computing, data handling, and network resources. The central problem in the scheduling of these resources is the coordinated management of computation and data at multiple locations and not just data replication or movement. However, this can prove to be a rather costly operation and efficient sing can be a challenge if compute and data resources are mapped without considering network costs. We have implemented an adaptive algorithm within the so-called DIANA Scheduler which takes into account data location and size, network performance and computation capability in order to enable efficient global scheduling. DIANA is a performance-aware and economy-guided Meta Scheduler. It iteratively allocates each job to the site that is most likely to produce the best performance as well as optimizing the global queue for any remaining jobs. Therefore it is equally suitable whether a single job is being submitted or bulk scheduling is being performed. Results indicate that considerable performance improvements can be gained by adopting the DIANA scheduling approach.
Ali Arshad
Anjum Ashiq
McClatchey Richard
Willers Ian
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