Hierarchical Fog-Cloud Computing for IoT Systems: A Computation Offloading Game

TL;DR

This paper models hierarchical fog-cloud resource allocation for IoT systems as a game, proving equilibrium existence and proposing a near-optimal mechanism that improves user QoE and reduces delays.

Contribution

It introduces a novel game-theoretic framework for resource allocation in fog-cloud IoT systems and proposes an efficient mechanism for near-optimal offloading.

Findings

Proves existence of pure Nash equilibrium in the game.

Proposes an $ ext{ extepsilon}$-Nash equilibrium mechanism with $O(N/ ext{ extepsilon})$ complexity.

Demonstrates improved QoE and reduced delay with the proposed mechanism.

Abstract

Fog computing, which provides low-latency computing services at the network edge, is an enabler for the emerging Internet of Things (IoT) systems. In this paper, we study the allocation of fog computing resources to the IoT users in a hierarchical computing paradigm including fog and remote cloud computing services. We formulate a computation offloading game to model the competition between IoT users and allocate the limited processing power of fog nodes efficiently. Each user aims to maximize its own quality of experience (QoE), which reflects its satisfaction of using computing services in terms of the reduction in computation energy and delay. Utilizing a potential game approach, we prove the existence of a pure Nash equilibrium and provide an upper bound for the price of anarchy. Since the time complexity to reach the equilibrium increases exponentially in the number of users, we further propose a near-optimal resource allocation mechanism and prove that in a system with $N$ IoT users, it can achieve an $\epsilon$-Nash equilibrium in $O(N/\epsilon)$ time. Through numerical studies, we evaluate the users' QoE as well as the equilibrium efficiency. Our results reveal that by utilizing the proposed mechanism, more users benefit from computing services in comparison to an existing offloading mechanism. We further show that our proposed mechanism significantly reduces the computation delay and enables low-latency fog computing services for delay-sensitive IoT applications.