Selfish Computation Offloading for Mobile Cloud Computing in Dense Wireless Networks

TL;DR

This paper analyzes selfish computation offloading in dense wireless networks using game theory, demonstrating the existence of Nash equilibria and evaluating their efficiency through simulations.

Contribution

It provides a game theoretical framework for offloading decisions, proving equilibrium existence and offering algorithms for both elastic and non-elastic cloud scenarios.

Findings

Pure Nash equilibria exist in both elastic and non-elastic cloud cases.

Equilibrium costs are close to optimal in realistic scenarios.

The price of anarchy is bounded, indicating near-optimal efficiency of equilibria.

Abstract

Offloading computation to a mobile cloud is a promising solution to augment the computation capabilities of mobile devices. In this paper we consider selfish mobile devices in a dense wireless network, in which individual mobile devices can offload computations via multiple access points (APs) to a mobile cloud so as to minimize their computation costs, and we provide a game theoretical analysis of the problem. We show that in the case of an elastic cloud, all improvement paths are finite, and thus a pure strategy Nash equilibrium exists and can be computed easily. In the case of a non-elastic cloud we show that improvement paths may cycle, yet we show that a pure Nash equilibrium exists and we provide an efficient algorithm for computing one. Furthermore, we provide an upper bound on the price of anarchy (PoA) of the game. We use simulations to evaluate the time complexity of computing Nash equilibria and to provide insights into the PoA under realistic scenarios. Our results show that the equilibrium cost may be close to optimal, and the cost difference is due to too many mobile users offloading simultaneously.