Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks

Abstract

Mobile-Edge Computing (MEC) is an emerging paradigm that provides a capillary distribution of cloud computing capabilities to the edge of the wireless access network, enabling rich services and applications in close proximity to the end users. In this article, a MEC enabled multi-cell wireless network is considered where each Base Station (BS) is equipped with a MEC server that can assist mobile users in executing computation-intensive tasks via task offloading. The problem of Joint Task Offloading and Resource Allocation (JTORA) is studied in order to maximize the users' task offloading gains, which is measured by the reduction in task completion time and energy consumption. The considered problem is formulated as a Mixed Integer Non-linear Program (MINLP) that involves jointly optimizing the task offloading decision, uplink transmission power of mobile users, and computing resource allocation at the MEC servers. Due to the NP-hardness of this problem, solving for optimal solution is difficult and impractical for a large-scale network. To overcome this drawback, our approach is to decompose the original problem into (i) a Resource Allocation (RA) problem with fixed task offloading decision and (ii) a Task Offloading (TO) problem that optimizes the optimal-value function corresponding to the RA problem. We address the RA problem using convex and quasi-convex optimization techniques, and propose a novel heuristic algorithm to the TO problem that achieves a suboptimal solution in polynomial time. Numerical simulation results show that our algorithm performs closely to the optimal solution and that it significantly improves the users' offloading utility over traditional approaches. Index TermsMobile edge computing; computation offloading; multi-server resource allocation; distributed systems.The authors are with the

Service-Oriented Computing

“…The authors of [13] also made an assumption that each small geographical area will only receive coverage from only a single edge server, which will be unlikely to happen in real-world scenarios. In [11], the authors formulated a problem similar to the EUA problem but with different objectives, which are to reduce task completion time and energy consumption. Yin et al [18] addressed the edge server placement and provisioning problem with the objective of maximizing users coverage and minimizing network latency.…”

Deep-Learning-Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks

“…For instance, [9] proposed a coordinate descent (CD) method that searches along one binary variable at a time. A similar heuristic search method for multi-server MEC networks was studied in [10], which iteratively adjusts binary offloading decisions. Another widely adopted heuristic method is through convex relaxation, e.g., by relaxing integer variables to be continuous between 0 and 1 [11] or by approximating the binary constraints with quadratic constraints [12].…”

Computation Offloading and Resource Allocation for Backhaul Limited Cooperative MEC Systems

“…From the MU's perspective, CO can reduce the local CL but it also consumes extra energy for the data transmission. Therefore, joint CO decision and resource allocation (RA) becomes a major challenge in MEC systems which has been studied in several recent works [3]- [10]. In particular, the papers [3]- [6] consider MEC systems in which one FS serves multiple MUs.…”