Robust Dynamic Resource Allocation via Probabilistic Task Pruning in Heterogeneous Computing Systems
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In heterogeneous distributed computing (HC) systems, diversity can exist in both computational resources and arriving tasks. In an inconsistently heterogeneous computing system, task types have different execution times on heterogeneous machines. A method is required to map arriving tasks to machines based on machine availability and performance, maximizing the number of tasks meeting deadlines (defined as robustness). For tasks with hard deadlines (eg those in live video streaming), tasks that miss their deadlines are dropped. The problem investigated in this research is maximizing the robustness of an oversubscribed HC system. A way to maximize this robustness is to prune (ie defer or drop) tasks with low probability of meeting their deadlines to increase the probability of other tasks meeting their deadlines. In this paper, we first provide a mathematical model to estimate a task's probability of meeting its deadline in the presence of task dropping. We then investigate methods for engaging probabilistic dropping and we find thresholds for dropping and deferring. Next, we develop a pruning-aware mapping heuristic and extend it to engender fairness across various task types. We show the cost benefit of using probabilistic pruning in an HC system. Simulation results, harnessing a selection of mapping heuristics, show efficacy of the pruning mechanism in improving robustness (on average by 25 system by up to 40
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