Utility Aware Task Offloading for Mobile Edge Computing

Mobile edge computing (MEC) casts the computation-intensive and delay-sensitive applications of mobiles on the network edges. Task offloading incurs extra communication latency and energy cost, and extensive efforts have been focused on the offloading scheme. To achieve satisfactory quality of experience, many metrics of the system utility are defined. However, most existing works overlook the balancing between the throughput and fairness. This paper investigates the problem of seeking optimal offloading scheme and the objective of the optimization is to maximize the system utility for leveraging between throughput and fairness. Based on KKT condition, we analyze the expectation of time complexity for deriving the optimal scheme. We provide an increment based greedy approximation algorithm with \(1 + \frac{1}{{e - 1}}\) ratio. Experimental results show that the proposed algorithm has better performance.