Multi-User Multi-Task Offloading and Resource Allocation in Mobile Cloud Systems

Abstract

We consider a general multi-user Mobile Cloud Computing (MCC) system where each mobile user has multiple independent tasks. These mobile users share the computation and communication resources while offloading tasks to the cloud. We study both the conventional MCC where tasks are offloaded to the cloud through a wireless access point, and MCC with a computing access point (CAP), where the CAP serves both as the network access gateway and a computation service provider to the mobile users. We aim to jointly optimize the offloading decisions of all users as well as the allocation of computation and communication resources, to minimize the overall cost of energy, computation, and delay for all users. The optimization problem is formulated as a non-convex quadratically constrained quadratic program, which is NP-hard in general. For the case without a CAP, an efficient approximate solution named MUMTO is proposed by using separable semidefinite relaxation (SDR), followed by recovery of the binary offloading decision and optimal allocation of the communication resource. To solve the more complicated problem with a CAP, we further propose an efficient three-step algorithm named MUMTO-C comprising of generalized MUMTO SDR with CAP, alternating optimization, and sequential tuning, which always computes a locally optimal solution. For performance benchmarking, we further present numerical lower bounds of the minimum system cost with and without the CAP. By comparison with this lower bound, our simulation results show that the proposed solutions for both scenarios give nearly optimal performance under various parameter settings, and the resultant efficient utilization of a CAP can bring substantial cost benefit.

Optimal Energy Allocation and Task Offloading Policy for Wireless Powered Mobile Edge Computing Systems

“…In this paper, we are interested in minimizing the transmission energy consumption for WPT at the ET while achieving sustainable operation for the user's communication and computation 3 . In particular, our objective is to minimize the ET's transmission energy consumption (i.e., N i=1 τ p i ), subject to the user's task causality constraints in (1), task completion constraint in (2), and energy causality constraints in (7).…”

Cross-Server Computation Offloading for Multi-Task Mobile Edge Computing

“…In multi-task single server offloading scenario, the authors in [22,23] proposed a three-step algorithm for jointly optimizing the resources allocation of both computation and communication in the case with and without computing access point, respectively. Considering multiple users may usually offload their tasks to one MECS simultaneously, Chen et al [24] used Lyaponuv Optimization Approach to determine the energy harvesting policy, and presented greedy maximal scheduling algorithms to solve the multi-task offloading problem for multiple users.…”

Delay-Aware and User-Adaptive Offloading of Computation-Intensive Applications with Per-Task Delay in Mobile Edge Computing Networks

“…is defined by the following mapping list [(j, g 1 (j))] j∈[ [1;6]] . This list contains three groups where the first g 1 = {τ 2 1 , τ 3 1 , τ 5 1 } is associated to server s 1 ; the second group g 2 = {τ 4 1 , τ 6 1 } is associated to server s 2 , and the third g 3 = {τ 1 1 } is associated to server s 3 . Thus, the g 1 function is given by the mapping list [(2, 1), (3, 1), (5, 1), (4, 2), (6, 2), (1, 3)].…”

“…Alike, in [5] an optimization problem is derived to select the best offloading policy while saving the energy consumption. The next work [6] present, in our knowledge, the first try to enhance the energy consumption while considering devices with multiple independent tasks. But, the authors impractically consider mobile devices with the same tasks' number.…”