Resource Sharing of a Computing Access Point for Multi-user Mobile Cloud Offloading with Delay Constraints

TL;DR

This paper proposes an optimized resource sharing framework for a mobile cloud system with a computing access point, improving multi-user task offloading efficiency under delay constraints through advanced optimization techniques.

Contribution

It introduces a novel joint optimization approach for offloading and resource allocation in a multi-user mobile cloud system with a CAP, including solutions for delay constraints.

Findings

Proposed solutions achieve near-optimal performance.

Adding a CAP significantly reduces offloading costs.

The approach effectively manages delay constraints in multi-user scenarios.

Abstract

We consider a mobile cloud computing system with multiple users, a remote cloud server, and a computing access point (CAP). The CAP serves both as the network access gateway and a computation service provider to the mobile users. It can either process the received tasks from mobile users or offload them to the cloud. We jointly optimize the offloading decisions of all users, together with the allocation of computation and communication resources, to minimize the overall cost of energy consumption, computation, and maximum delay among users. The joint optimization problem is formulated as a mixed-integer program. We show that the problem can be reformulated and transformed into a non-convex quadratically constrained quadratic program, which is NP-hard in general. We then propose an efficient solution to this problem by semidefinite relaxation and a novel randomization mapping method. Furthermore, when there is a strict delay constraint for processing each user's task, we further propose a three-step algorithm to guarantee the feasibility and local optimality of the obtained solution. Our simulation results show that the proposed solutions give nearly optimal performance under a wide range of parameter settings, and the addition of a CAP can significantly reduce the cost of multi-user task offloading compared with conventional mobile cloud computing where only the remote cloud server is available.