Dedas: Online Task Dispatching and Scheduling with Bandwidth Constraint in Edge Computing

In this paper, we study online deadline-aware task dispatching and scheduling in edge computing. We jointly con skier management of the networking bandwidth and computing resources to meet the maximum number of deadlines. We propose an online algorithm Dedas, which greedily schedules newly arriving tasks and considers whether to replace some existing tasks in order to make the new deadlines satisfied. We derive a non-trivial competitive ratio theoretically, and our analysis is asymptotically tight. We then build DeEdge, an edge computing testbed installed with typical latency-sensitive applications such as loT sensor monitoring and face matching. Besides, we adopt a real-world data trace from the Google cluster for largescale emulations. Extensive testbed experiments and simulations demonstrate that the deadline miss ratio of Dedas is stable for online tasks, which is reduced by up to 60% compared with state-of-the-art methods. Moreover, Dedas performs well in minimizing the average task completion time.