Multi-user Multi-task Offloading and Resource Allocation in Mobile Cloud Systems
Meng-Hsi Chen, Ben Liang, Min Dong

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…
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