Performance characteristics of gang scheduling in multiprogrammed environments
SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
Gang Scheduling with a Queue for Large Jobs
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Gang scheduling for highly efficient, distributed multiprocessor systems
FRONTIERS '96 Proceedings of the 6th Symposium on the Frontiers of Massively Parallel Computation
Gang Scheduling with Memory Considerations
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Improving Throughput and Utilization in Parallel Machines through Concurrent Gang
IPDPS '00 Proceedings of the 14th 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
Adaptive Parallel Job Scheduling with Flexible Coscheduling
IEEE Transactions on Parallel and Distributed Systems
A comparison of local and gang scheduling on a Beowulf cluster
CLUSTER '04 Proceedings of the 2004 IEEE International Conference on Cluster Computing
HASS: a scheduler for heterogeneous multicore systems
ACM SIGOPS Operating Systems Review
Energy-Conscious Co-scheduling of Tasks and Packets in Wireless Real-Time Environments
RTAS '09 Proceedings of the 2009 15th IEEE Symposium on Real-Time and Embedded Technology and Applications
New challenges of parallel job scheduling
JSSPP'07 Proceedings of the 13th international conference on Job scheduling strategies for parallel processing
Computers and Electrical Engineering
An efficient scheduler of RTOS for multi/many-core system
Computers and Electrical Engineering
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Parallel jobs submitted to processors should be efficiently scheduled to achieve high performance. Early scheduling strategies for parallel jobs make use of either space-sharing approach or time-sharing approach. The scheduling strategy proposed in this work, makes use of both the policies for parallel jobs while scheduling under clusters. Static and dynamic scheduling algorithms were developed for communication intensive jobs. The algorithms are used to handle different types of jobs such as serial, parallel and mixed jobs. For performance evaluation, the workload from Grid5000 platform is considered. The main objective is to achieve performance and power improvement. The dynamic scheduling algorithm with communication aware policy gives better performance when compared to static scheduling algorithm that is tested under the given workload.