VisSched: An Auction-Based Scheduler for Vision Workloads on Heterogeneous Processors

With the growth of edge computing, application-specific workloads based on computer vision are steadily migrating to edge cloudlets. Scheduling has been identified to be a major problem in these cloudlets. In this article, we propose a generic architectural solution, VisSched, that leverages the fact that most vision workloads share similar code kernels (such as library code for linear algebra), and as a result, they tend to exhibit similar phase behavior. This allows us to create an auction theory-based scheduling mechanism, where we give each thread a replenishable virtual wallet, and threads are scheduled based on the amounts that they bid for executing on a free core. We show that in 20%-40% of the cases, our scheduling algorithm is theoretically optimal, and in the remaining cases, it reaches a global optimum obtained using Monte Carlo simulations 90%-95% of the time. Our results for the MEVBench vision workloads show a 17% higher performance and a 14% lower ED2 as compared to the nearest competing algorithm in the literature.