Joint Task Offloading and Resource Allocation for Multi-Task Multi-Server NOMA-MEC Networks

Abstract

By offloading computationally intensive tasks of smart end devices to edge servers deployed at the edge of the network, mobile edge computing (MEC) has become a promising technology to provide computing services for Internet of Things (IoT) devices. In order to further improve the access capability of MEC and increase the spectrum utilization efficiency, in this paper, Non-Orthogonal Multiple Access (NOMA) technology is introduced into MEC systems and we study the computing offloading problem of multi-user, multi-task and multi-server through joint optimization of task offloading and resource allocation, we intend to maximize the system's processing capability as an optimization goal. To solve the proposed mixed integer nonlinear programming (MINLP) problem, the objective optimization problem is firstly decoupled into two sub-problems of resource allocation and task allocation. Secondly the resource allocation problem is further decomposed into computation resource optimization and communication resource allocation. For the communication resource allocation, it first fixed power allocation, then the subchannel allocation problem is regarded as a many-to-one matching problem between sub-channels and users. In addition, we propose a low-complexity sub-optimal matching algorithm for sub-channel allocation to maximize the offloading efficiency. Based on our proposed sub-channel allocation scheme, the transmission power allocation is regarded as a convex optimization problem, which is tackled by Lagrangian multiplier method. Finally, under the condition of resource allocation, the tasks of all end devices (EDs) are allocated. Experimental numerical results show that the proposed scheme can effectively decrease latency and energy consumption of networks, improve system processing capability, and further improve MEC system performance.INDEX TERMS Mobile edge computing, multi-task multi-server, non-orthogonal multiple access, processing capability, resource allocation, task offloading

Multi-Access Edge Computing-Based Vehicle-Vehicle-RSU Data Offloading Over the Multi-RSU-Overlapped Environment

“…In order to improve the access capability of MEC and increase the spectrum utilization efficiency, the authors of [19] studied the offloading problem of task offloading and resource allocation. The key idea is to maximize the MEC's processing capability as the optimization goal for the multi-user, multi-task and multi-server scenario.…”

Task Allocation Among Connected Devices: Requirements, Approaches, and Challenges

“…Other less common TA approaches, reported in Table III under the heading "Others", are present in the literature. It encompasses some well-known approaches, which however are applied in less than 5% cases, among which fuzzy logic [95], nonlinear programming [96], evolutionary algorithms [97], and Hungarian algorithms [98].…”

An Effective Task Offloading Method for Separable Complex Mobile Terminal Tasks

“…Considering the task completion time and the mobile device energy consumption, the authors in [14] proposed a heuristic offloading decision algorithm (HODA), which jointly optimized the offloading decisions, communication, and computing resources to maximize the system utility. In order to reduce the complexity of the joint optimization problem, the original problem was decomposed into two subproblems in [15][16][17][18][19][20][21]. In [15], the authors addressed the resource allocation problem using the convex and quasi-convex optimization techniques and solved the problem of task assignment by a heuristic algorithm.…”

“…In [16], the task partitioning subproblem was taken as a set of univariate optimization problems, which can be easily solved, and the task scheduling subproblem was solved through a heuristic algorithm. In [17], the problem of resource allocation was further decomposed into two stages: the computing resource optimization and the communication resource allocation.…”