Latency-Optimal Task Offloading for Mobile-Edge Computing System in 5G Heterogeneous Networks

Mobile edge computing (MEC) is an emerging technology to improve the quality of computation experience for mobile devices. As a promising paradigm to deal with latency-sensitive and computation-intensive tasks, it provides cloud computing capabilities in close proximity to mobile devices in the fifth-generation (5G) networks. As the radio and computational resources are both limited in 5G networks, reducing system latency by task scheduling and resource allocation has gained renewed interests. To minimize the weighted-sum latency of all users in multi-user MEC system, we formulate an optimization problem based on partial offloading strategy. Since the optimization problem is NP-hard, we transform it into a piece-wise convex problem and get the latency-optimal offloading strategy using the sub- gradient method. We further put forward a simplified algorithm which can achieve close-to- optimal performance in linear time. Our proposed strategies are verified by numerical results, which indicate that our algorithms significantly reduce the weighted-sum latency compared with other baseline strategies.