Multi-user Multi-task Offloading and Resource Allocation in Mobile Cloud Systems

TL;DR

This paper develops and evaluates algorithms for multi-user, multi-task offloading and resource allocation in mobile cloud systems, optimizing energy, computation, and delay costs with or without a computing access point.

Contribution

It introduces the MUMTO and MUMTO-C algorithms for joint offloading and resource allocation, addressing complex non-convex optimization problems in MCC systems.

Findings

Proposed algorithms achieve near-optimal performance compared to lower bounds.

Utilization of a computing access point significantly reduces system costs.

Algorithms are efficient and adaptable to various system parameters.

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.