Robustness of a distributed computing system is defined as the ability to maintain its performance in the presence of uncertain parameters. Uncertainty is a key problem in heterogeneous (and even homogeneous) distributed computing systems that perturbs system robustness. Notably, the performance of these systems is perturbed by uncertainty in both task execution time and arrival. Accordingly, our goal is to make the system robust against these uncertainties. Considering task execution time as a random variable, we use probabilistic analysis to develop an autonomous proactive task dropping mechanism to attain our robustness goal. Specifically, we provide a mathematical model that identifies the optimality of a task dropping decision, so that the system robustness is maximized. Then, we leverage the mathematical model to develop a task dropping heuristic that achieves the system robustness within a feasible time complexity. Although the proposed model is generic and can be applied to any distributed system, we concentrate on heterogeneous computing (HC) systems that have a higher degree of exposure to uncertainty than homogeneous systems. Experimental results demonstrate that the autonomous proactive dropping mechanism can improve the system robustness by up to 20%.

中文翻译：

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在异构计算系统中实现鲁棒性的自主任务丢弃机制

分布式计算系统的鲁棒性被定义为在存在不确定参数的情况下保持其性能的能力。不确定性是异构（甚至是同构）分布式计算系统中的一个关键问题，它会扰乱系统的鲁棒性。值得注意的是，这些系统的性能受到任务执行时间和到达时间的不确定性的影响。因此，我们的目标是使系统能够抵御这些不确定性。将任务执行时间视为随机变量，我们使用概率分析来开发自主主动任务丢弃机制，以实现我们的鲁棒性目标。具体来说，我们提供了一个数学模型来识别任务丢弃决策的最优性，从而最大限度地提高系统的鲁棒性。然后，我们利用数学模型来开发任务丢弃启发式方法，在可行的时间复杂度内实现系统的鲁棒性。尽管所提出的模型是通用的并且可以应用于任何分布式系统，但我们专注于异构计算 (HC) 系统，这些系统比同类系统具有更高程度的不确定性。实验结果表明，自主主动丢弃机制可以将系统鲁棒性提高高达 20%。