Task offloading and resource allocation in mobile-edge computing system

Mobile-edge computing (MEC) system is a new paradigm to provide cloud computing capacities at the edge of radio access network (RAN) which is close to mobile users. In this paper, we aim to promote QoS by offloading the computationally intensive tasks to the MEC server. There are many papers discuss this issue. Nevertheless, most of them just think over one-dimension resource allocation, radio resources or computation resources, and make the MEC system less effective. Hence, we consider the allocation of both radio resources and computation resources of the MEC server to increase system effectiveness. Apart from this, we take the variety of tasks' requirements into account. That is, we assume that different tasks may have different delay requirements. We formulate this problem as a cost minimization problem and design a heuristic algorithm to address it. Numerical results show that our algorithm can greatly promote QoS.