Abstract
Cooperation between the mobile edge computing (MEC) and the mobile cloud computing (MCC) in offloading computing could improve quality of service (QoS) of user equipments (UEs) with computation-intensive tasks. In this paper, in order to minimize the expect charge, we focus on the problem of how to offload the computation-intensive task from the resource-scarce UE to access point’s (AP) and the cloud, and the density allocation of APs’ at mobile edge. We consider three offloading computing modes and focus on the coverage probability of each mode and corresponding ergodic rates. The resulting optimization problem is a mixed-integer and non-convex problem in the objective function and constraints. We propose a low-complexity suboptimal algorithm called Iteration of Convex Optimization and Nonlinear Programming (ICONP) to solve it. Numerical results verify the better performance of our proposed algorithm. Optimal computing ratios and APs’ density allocation contribute to the charge saving.
Highlights
With the rapid development of smart mobile user equipments (UEs), many applications with advanced features have emerged, such as augmented reality, facial recognition, and online games
Different from the above approaches, in this paper, we study the joint optimization of offloading decision making and access mode selection for a mixed mobile edge computing (MEC)/Mobile cloud computing (MCC) system to minimize the expect charge
7 Conclusion In this paper, a mixed MEC/MCC system based on offloading computing was investigated, which joint optimized the computing ratios at each layer and distribution density of F-access point (AP) to minimize the expect charge
Summary
With the rapid development of smart mobile user equipments (UEs), many applications with advanced features have emerged, such as augmented reality, facial recognition, and online games. The UEs who have computation-intensive applications to compute may demand powerful computing capacity and huge amounts of energy [1]. The demands lead to contradictions in UEs which are resource-scarce. Mobile cloud computing (MCC) [2,3,4] has been proposed as a promising way to address challenges by offloading computing tasks to the cloud which has abundant computing resources and energy. For delay sensitive applications, the delay of cloud computing is noneligible because of the long distance between the terminal device and the cloud [5]. The burden on fronthaul is huge, which may lead to heavy jam in data transmission and computing
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