  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%.
