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Distributed Energy-Efficient Scheduling



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Proc 27th IEEE International Performance Computing and Communications Conference IPCCC Dec 2008 Distributed Energy Efficient Scheduling for Data Intensive Applications with Deadline Constraints on Data Grids Cong Liu1 Xiao Qin2 Santosh Kulkarni2 Chengjun Wang2 Shuang Li2 Adam Manzanares2 and Sanjeev Baskiyar2 University of North Carolina at Chapel Hill1 Auburn University2 technology bridge between the need to access extremely large data sets and the goal of achieving high data transfer rates by providing geographically distributed computing resources and large scale storage systems When it comes to distributed systems such as data grids it is the responsibility of schedulers to decide where to run applications the terms application and task are used interchangeably throughput this paper based on the applications specific requirements as well as system workload conditions Data resources are of paramount importance for many data intensive applications from long running simulations to remote sensing from biological sequence analysis to video ondemand systems 11 A key factor in the process of scheduling data intensive tasks is the location of input data required by the tasks A straightforward strategy to enhance performance of data intensive applications on data grids is to replicate popular data sets i e frequently accessed data sets to multiple resource sites thereby offering higher data access speeds compared to maintaining the data sets in a single site A wide range of data replication strategies which are practical and effective have been commonly applied in distributed data centers 15 12 However making too many replicas may ultimately lead to a number of drawbacks First it is challenging to maintain consistency among replicas in large scale distributed systems such as grids Second it is nontrivial to efficiently generate replicas of massive data sets on the fly in data grids Last but not least a large number of data replicas inevitably and dramatically increase



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