In this article, we study online deadline-aware task dispatching and scheduling in edge computing. We jointly considerthe management of the networking and computing resources to meet the maximum number of deadlines. We propose an online algorithm, named 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 of Dedas theoretically, and our analysis is asymptotically tight. Besides, we implement a distributed approximation D - Dedas with a better scalability and less than 10 percent performance loss compared with the centralized algorithm Dedas. We then build DeEdge, an edge computing testbed installed with typical latency-sensitive applications such as IoT sensor monitoring and face matching. We adopt a real-world data trace from the Google cluster for large-scale 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 percent compared with state-of-the-art methods. Moreover, Dedas performs well in minimizing the average task completion time.

The rapid development of the Internet of Things (IoT) has brought profound changes in the cloud computing paradigm. One promising computing model in IoT-related applications is edge computing, which can decrease the request response time by deploying edge servers close to IoT devices. Two fundamental problems in edge computing include how to place a limited number of edge servers on the candidate locations (e.g., the Access Points) and how to configure the applications on each server. In this paper, we jointly study the edge server placement and the application configuration to minimize the weighted sum of the service cost and edge server opening cost. We propose a local-search based algorithm, named SPAC, to solve the problem efficiently with its approximation ratio analyzed. Extensive simulations on Google data traces demonstrate SPAC reduces the total cost by up to 60% compared with state-of-the-art methods, and outperforms the baselines consistently in different parameter settings.

In this paper, we study online deadline-aware task dispatching and scheduling in edge computing. We jointly consider 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 IoT sensor monitoring and face matching. Besides, we adopt a real-world data trace from the Google cluster for large-scale 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.