The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Scheduling Divisible Loads in Parallel and Distributed Systems
Scheduling Divisible Loads in Parallel and Distributed Systems
Load Balancing Requirements in Parallel Implementations of Image Feature Extraction Tasks
IEEE Transactions on Parallel and Distributed Systems
Sharing Partitionable Workloads in Heterogeneous NOWs: Greedier Is Not Better
CLUSTER '01 Proceedings of the 3rd IEEE International Conference on Cluster Computing
UMR: A Multi-Round Algorithm for Scheduling Divisible Workloads
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Optimal Algorithms for Scheduling Divisible Workloads on Heterogeneous Systems
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
HPCC'06 Proceedings of the Second international conference on High Performance Computing and Communications
New method for scheduling heterogeneous multi-installment systems
Future Generation Computer Systems
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A parallel transferable uniform multi-round (PTUMR) scheduling algorithm is proposed for mitigating the adverse effect of the data transmission time by dividing workloads and allowing their parallel transmissions to distributed clients from the master in a network. The performance of parallel computing using the master/worker model for distributed grid computing tends to degrade when handling large data sets due to the impact of data transmission time. Multiple-round scheduling algorithms have therefore been proposed to mitigate the effects of the data transmission time by dividing the data into chunks that are sent in multiple rounds so as to overlap the time required for computation and communication. However, standard multiple-round algorithms assume a homogeneous network environment with uniform link transmission capacity, and as such cannot minimize the turnaround time effectively in real heterogeneous network environments. The proposed PTUMR algorithm optimizes the size of chunks, the number of rounds, and the number of workers to which data is to be transmitted in parallel, and is shown through performance evaluations to mitigate the adverse effects of data transmission time between the master and workers significantly, achieving turnaround times close to the theoretical lower limits.