FAIR RESOURCE ALLOCATION FOR SYSTEM THROUGHPUT MAXIMIZATION IN MOBILE EDGE COMPUTING
Communication resource allocation is important for improving the performance of users in mobile edge computing (MEC) scenarios. In existing studies, the users in the MEC system typically suffer from the unfair resource allocation, which results in the inefficient resource utilization and degraded user performance. To address this challenge, in this paper, we propose a fair resource allocation approach to maximize the overall network throughput, under the constraint of each mobile user’s minimum transmission rate. We formulate the problem as a fair Nash bargaining resource allocation game, and the existence and uniqueness of the solution to this game model are analyzed. By adopting the time-sharing variable, we obtain the near optimal bargaining resource allocation strategy for the mixed integer nonlinear programming optimization. The user’s priority is further considered in the iterative implementation of the proposed algorithm by considering the time delay constraint of users. Simulation results show that the proposed scheme outperforms existing methods in terms of resource allocation fairness and overall system throughput. Index Terms—Mobile Edge Computing, resource allocation, fairness, system throughput maximization, minimum rate requirement.
mobile edge computing (MEC) has been considered as a promising technology to support the next generation Internet, such as Tactile Internet, Internet of Things (IoT), and Internet of Me, by migrating the mobile computing, network control and storage to the network edges. Therefore, it is possible to run the highly demanding applications at the user equipments while meeting strict delay requirements. With the development of mobile network and mobile devices, the number of mobile internet users has shown explosive growth, which results in the spectrum scarcity for mobile users. The spectrum scarcity has raised the necessity of a fair spectrum allocation for mobile users. Without fair resource allocation, the minimum rate constraint of some users may not be satisfied, which leads to the degradation of the user performance. Spectrum resource allocation has received considerable attentions in MEC systems. quantitative study on adaptive resource allocation by designing a frequency reuse scheme to mitigate interference and maintain high spectral efficiency. In order to improve the performance in terms of higher system capacity, a resource allocation scheme with differentiated QoS provisioning for cell-edge active users. Proposed a greedy heuristic method to achieve the optimal resource allocation for users. developed a piecewise resource allocation algorithm to allocate the communication and computation resources jointly. However, most of these approaches are centralized allocation
schemes without considering the channel diversity among users. The individual profit of each user may result in the deployment difficulty of the centralized allocation in MEC. Game theory is thus introduced to address the individual characteristics of mobile users in the spectrum allocation problem proposed a game-based distributed algorithm to solve the resource allocation problem among multiple mobile devices with limited communication resource. Taking energy consumption and transmission delay into account, developed a distributed algorithm to achieve joint radio and communication resource allocation with the game equilibrium. proposed an enhance adaptive video delivery scheme with joint cache and radio resource allocation in order to provide the low-latency and high quality services for mobile device users.
We consider an uplink transmission scenario in mobile networks with the MEC. As shown in Fig. 1, there is one base station (BS) that works in OFDMA mode with wireless channel set K = f1; 2; :::;Kg. The set of mobile users within BS coverage area is denoted by N = f1; 2; :::;Ng. In this scenario, one subchannel can only be used by one user at a time. It is assumed that each user has a delay-sensitive computation task to be completed on the mobile device or on the mobile edge cloud server via computation offloading. The tasks include interactive gaming, high-definition image processing, face recognition, virtual reality, and so on , . In general, each task to be processed can be described by a tuple as Ji = fdi; !ig; i 2 N, where di denotes the size
of computation input data, including the program codes and input parameters, and !i denotes the total number of CPU cycles required to accomplish this task. In this paper, it is assumed that the battery-powered mobile device has sufficient energy to support task offloading or local execution . Then, we discuss the computation overhead of time cost for both local execution and offloading approaches
In this paper, we investigate the overall network throughput maximization problem in MEC to meet the resource requirements of ever-increasing mobile devices. As a cooperative game theory, the Nash bargaining game is adopted to ensure the fairness of resource allocation. Furthermore, the existence and uniqueness of the Nash bargaining game based solution has been investigated. And an algorithm is developed to determine user priority by considering the users’ delay constraint. Evaluation results demonstrate that our scheme can improve the overall system rates considerably, and ensure the fairness among various users. In the future work, the coalition of MECs will be studied to improve the resources utilization further.
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