Online computation offloading for deadline-aware tasks in edge computing

Xin He, Jiaqi Zheng, Qiang He, Haipeng Dai, Bowen Liu, Wanchun Dou, Guihai Chen

Abstract: Edge computing provides task offloading services to extend the computational capacity of mobile users and reduce task latency. Prior studies mainly focus on tasks with strict deadlines. However, in the real world, some tasks may not always have to be finished before hard deadlines, e.g., multimedia tasks. Tasks with soft deadlines can miss their primary deadlines, but not by too much, and still be timely. This has not been properly considered by existing offloading approaches. In this paper, we propose CONFECT, a novel offloading approach that handles tasks with mixtures of hard deadlines and soft deadlines. Specifically, we first formulate the problem as an integer linear programming and prove its hardness. Then, we propose two online algorithms with proven competitive ratios to solve the problem collectively, including an algorithm that assigns tasks to edge servers to maximize the task completion ratio and an algorithm that adjusts the task execution order to maximize the task completion revenue. Moreover, to balance the fairness among tasks and system revenue elastically, we extend CONFECT by using a tunable fairness knob. Finally, extensive experiments show that CONFECT outperforms five baseline algorithms in terms of task completion ratio and completion revenue.