Quantitative system performance: computer system analysis using queueing network models
Quantitative system performance: computer system analysis using queueing network models
An evaluation of parallel job scheduling for ASCI Blue-Pacific
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Impact of Workload and System Parameters on Next Generation Cluster Scheduling Mechanisms
IEEE Transactions on Parallel and Distributed Systems
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Improving Parallel Job Scheduling by Combining Gang Scheduling and Backfilling Techniques
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Libra: a computational economy-based job scheduling system for clusters
Software—Practice & Experience
A performance study of job management systems: Research Articles
Concurrency and Computation: Practice & Experience - Systems Performance Evaluation
A runtime resolution scheme for priority boost conflict in implicit coscheduling
The Journal of Supercomputing
The portable batch scheduler and the maui scheduler on linux clusters
ALS'00 Proceedings of the 4th annual Linux Showcase & Conference - Volume 4
Task partitioning, scheduling and load balancing strategy for mixed nature of tasks
The Journal of Supercomputing
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Cluster computing is receiving exponential popularity as a choice for high performance computing. This is mainly due to its effective cost performance ratio. Resource management systems (RMS) are the key component to manage the resources of clusters efficiently and have a very vital role in the performance of distributed parallel systems especially a job scheduling module. In this paper, we have empirically evaluated four resource management systems (SGE, TORQUE, and MAUI Scheduler and SLURM) with special focus on job scheduler component. These schedulers have been evaluated on a more comprehensive set of metrics such as throughput, CPU, memory and network utilization. Experiments were carried out on three different size testbeds with a range of scheduler configurations such as FCFS, Backfilling, Fair share and SJF scheduling techniques.A head-to-head comparison of different scheduling techniques has also been presented which highlights the effect of RMS on the performance of scheduling techniques. It has been observed from results that relative difference among the performance of scheduling techniques reached up to 63%. We conclude from the experiments that there is no single choice of RMS which can be identified as the best but SLURM performs better than others in most of the cases.