Largest-job-first-scan-all scheduling policy for 2D mesh-connected systems
FRONTIERS '96 Proceedings of the 6th Symposium on the Frontiers of Massively Parallel Computation
Dynamic Scheduling of Parallel Jobs with QoS Demands in Multiclusters and Grids
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
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Efficient task management in a hypercube multi-processor becomes difficult due to system overflow, where an incoming job cannot be allocated in spite of a sufficient number of free processors. Overflow occurs either due to the inability of recognizing a free subcube or due to external fragmentation. In this paper, we propose an allocation strategy that tries to scale down an incoming job size if it cannot fit into a fragmented hypercube. We call it limit allocation. We discuss three simple schemes, Limit-k, Greedy and Average. We conduct both analysis and simulation to characterize and compare various allocation policies. An M/M/m queueing model is developed to predict the behavior of buddy, free list and limit-k policies. The simulation study shows that the two adaptive schemes, greedy and average, outperform all other schemes reported so far in the literature.