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Prediction-based dynamic load-sharing heuristics

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3 Author(s)
Goswami, K.K. ; Center for Reliable & High Performance Comput., Illinois Univ., Urbana, IL, USA ; Murthy Devarakonda ; Iyer, R.K.

Presents dynamic load-sharing heuristics that use predicted resource requirements of processes to manage workloads in a distributed system. A previously developed statistical pattern-recognition method is employed for resource prediction. While nonprediction-based heuristics depend on a rapidly changing system status, the new heuristics depend on slowly changing program resource usage patterns. Furthermore, prediction-based heuristics can be more effective since they use future requirements rather than just the current system state. Four prediction-based heuristics, two centralized and two distributed, are presented. Using trace driven simulations, they are compared against random scheduling and two effective nonprediction based heuristics. Results show that the prediction-based centralized heuristics achieve up to 30% better response times than the nonprediction centralized heuristic, and that the prediction-based distributed heuristics achieve up to 50% improvements relative to their nonpredictive counterpart

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Parallel and Distributed Systems, IEEE Transactions on  (Volume:4 ,  Issue: 6 )