Abstract
Mobile edge computing (MEC) provides user equipment (UE) with computing capability through wireless networks to improve the quality of experience (QoE). The scenario with multiple base stations and multiple mobile users is modeled and analyzed. The optimization strategy of task offloading with wireless and computing resource management (TOWCRM) in mobile edge computing is considered. A resource allocation algorithm based on an improved graph coloring method is used to allocate wireless resource blocks (RBs). The optimal solution of computing resource is obtained by using KKT conditions. To improve the system utility, a semi-distributed TOWCRM strategy is proposed to obtain the task offloading decision. Theoretical simulations under different system parameters are executed, and the proposed semi-distributed TOWCRM strategy can be completed with finite iterations. Simulation results have verified the effectiveness of the proposed algorithm.
Highlights
With the continuous development of the Internet of things and ubiquitous computing, mobile devices are increasingly running resource-intensive applications, such as interactive games and augmented reality [1, 2]
The optimization of the system utility is formulated by combining task offloading, wireless resource allocation, and computing resource allocation (iii) The optimal goal is decomposed into three subproblems including wireless resource block allocation (RBA), computing resource allocation (CRA), and task offloading decision
The RBA is solved by using a resource allocation algorithm based on an improved graph coloring method
Summary
With the continuous development of the Internet of things and ubiquitous computing, mobile devices are increasingly running resource-intensive applications, such as interactive games and augmented reality [1, 2]. MEC allows user equipment (UE) to offload computing tasks to network edge nodes through the wireless cellular network and performs the offloading tasks. This satisfies the expansion demand of users’ computing capabilities and compensates for the long delay of cloud computing [3]. Research works by combining task offloading and interference management are proposed to improve the system utility [9, 19,20,21]. The MEC server determines whether the task is processed locally or offloaded to the server according to the computing capacity of the mobile device, the size of data, the delay, and the energy consumption requirements.
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