Multiobjective Optimization for Computation Offloading in Fog Computing

TLDR

Fog computing provides distributed computing, storage, control, and networking for IoT, allowing mobile devices to offload tasks to nearby fog nodes instead of distant clouds, which can reduce device energy but may increase overall execution delay due to transmission and server processing. The study aims to balance energy consumption and delay while developing a cost model that lets mobile devices benefit from fog and cloud services. By applying queuing theory to models of the mobile device, fog, and cloud, and incorporating wireless link data rate and power, the authors formulate a multi‑objective optimization that jointly minimizes energy, delay, and payment cost through optimal offloading probability and transmit power. Simulation results demonstrate the proposed scheme’s effectiveness and superior performance compared to several existing approaches.

Abstract

Fog computing system is an emergent architecture for providing computing, storage, control, and networking capabilities for realizing Internet of Things. In the fog computing system, the mobile devices (MDs) can offload its data or computational expensive tasks to the fog node within its proximity, instead of distant cloud. Although offloading can reduce energy consumption at the MDs, it may also incur a larger execution delay including transmission time between the MDs and the fog/cloud servers, and waiting and execution time at the servers. Therefore, how to balance the energy consumption and delay performance is of research importance. Moreover, based on the energy consumption and delay, how to design a cost model for the MDs to enjoy the fog and cloud services is also important. In this paper, we utilize queuing theory to bring a thorough study on the energy consumption, execution delay, and payment cost of offloading processes in a fog computing system. Specifically, three queuing models are applied, respectively, to the MD, fog, and cloud centers, and the data rate and power consumption of the wireless link are explicitly considered. Based on the theoretical analysis, a multiobjective optimization problem is formulated with a joint objective to minimize the energy consumption, execution delay, and payment cost by finding the optimal offloading probability and transmit power for each MD. Extensive simulation studies are conducted to demonstrate the effectiveness of the proposed scheme and the superior performance over several existed schemes are observed.