| Summary: | As a new computing paradigm, mobile edge computing (MEC) enables users to execute computation-intensive tasks at the network edge nodes (ENs) through computation offloading. Energy consumption of computation offloading is envisioned as a significant metric to satisfy the high quality of experience (QoE). In multi-ENs MEC networks, computation scheduling and power control of each user is tightly coupled with task offloading. Moreover, due to the stochastic task arrivals and unknown wireless channel conditions, it is challenging to allocate resources for efficient offloading without prior information of tasks and channels. In this paper, we propose an individualized utility metric of each user. We formulate the problem of computation scheduling and power control of each user as a stochastic optimization problem. We aim to maximize the long-term averaged utility quality of all users by jointly optimizing the computation scheduling, task-partition factor and power control. We use Lyapunov optimization technique to convert the long-term stochastic problem into a series of deterministic sub-problems in each time slot. We propose an online algorithm for utility quality maximization (OAUQM). The asymptotic optimality and queue stability of our algorithm are analyzed. Experimental simulations are conducted to evaluate the performance of the proposed algorithm against the benchmark offloading algorithms in terms of utility quality and energy consumption.
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