Evaluating Dispatching and Scheduling Strategies for Firm Real-Time Jobs in Edge Computing

We consider the problem of on-arrival dispatching and scheduling jobs with stochastic execution times, inter-arrival times, and deadlines in multi-server fog and edge computing platforms. In terms of mean response times, it has been shown that size-based scheduling policies, when combined with dispatching policies such as join-shortest-queue, provide better performance over policies such as first-in-first-out. Since job sizes may not always be known apriori, prediction-based policies have been shown to perform reasonably well. However, little is known about the performance of prediction-based policies for jobs with firm deadlines. In this paper, we address this issue by considering the number of jobs that complete within their deadlines as a performance metric and investigate, using simulations, the performance of a prediction-based shortest-job-first scheduling policy for the considered metric and compare it against scheduling policies that prioritize based on deadlines (EDF) and arrival times (FIFO). The evaluation indicates that in under-loaded conditions, the prediction-based policy is outperformed by both FIFO and EDF policies. However, in overloaded scenarios, the prediction-based policy offers slightly better performance.