Energy-Latency-Aware Task Offloading and Approximate Computing at the Mobile Edge

Task offloading with Mobile-Edge Computing (MEC) is envisioned as a promising technique for prolonging battery lifetime and enhancing the computation capacity of mobile devices. In this paper, we consider a multi-user MEC system with a Base Station (BS) equipped with a computation server assisting mobile users in executing computation-intensive real-time tasks via offloading technique. We formulate the Energy-Latency-aware Task Offloading and Approximate Computing (ETORS) problem, which aims at optimizing the trade-off between energy consumption and application completion time. Due to the centralized and mixed-integer natures of this problem, it is very challenging to derive the optimal solution in practical time. This motivates us to employ the Dual-Decomposition Method (DDM) to decompose the original problem into three subproblems—namely the Task-Offloading Decision (TOD), the CPU Frequency Scaling (CFS), and the Quality of Computation Control (QoCC). Our approach consists of two iterative layers: in the outer layer, we adopt the duality technique to find the optimal value of Lagrangian multiplier associated prime problem; and in the inner layer, we formulate the subproblems that can be solved efficiently using convex optimization techniques. We show that the computation offloading selection depends not only on the computing workload of a task, but also on the maximum completion time of its immediate predecessors and on the clock frequency as well as on the transmission power of the mobile device. Simulation results coupled with real-time experiments on a small-scale MEC testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.