A simulation model of backfilling and I/O scheduling in a partitionable parallel system
Proceedings of the 32nd conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Scheduling Jobs on Parallel Systems Using a Relaxed Backfill Strategy
JSSPP '02 Revised Papers from the 8th International Workshop on Job Scheduling Strategies for Parallel Processing
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
Resource Allocation Strategies in a 2-Level Hierarchical Grid System
ANSS-41 '08 Proceedings of the 41st Annual Simulation Symposium (anss-41 2008)
Performance evaluation of gang scheduling in a two-cluster system with migrations
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Two level job-scheduling strategies for a computational grid
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
Multi-site scheduling with multiple job reservations and forecasting methods
ISPA'06 Proceedings of the 4th international conference on Parallel and Distributed Processing and Applications
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Efficient job scheduling in computational grids is a challenging task, especially when the workload consists of jobs submitted in a grid and local level. In this study, we consider such a grid system where both local jobs and grid jobs require service. The goal is to maintain a balance between the two competitive job types, in order for every job to be executed in a timely manner. However, local jobs are of higher importance compared to the grid jobs and it is imperative that their waiting time be minimized. Grid jobs are parallel jobs so gang scheduling is implemented, along with various other scheduling techniques in order to improve performance, such as backfilling. A simulation model is considered to evaluate system performance, and experiments are conducted to determine which proposed scheduling policy provides the best results.