Multiobjective Optimization for Computation Offloading in Fog Computing

TLDR: In this article, the authors utilized queuing theory to bring a thorough study on the energy consumption, execution delay, and payment cost of offloading processes in a fog computing system, where three queuing models were applied, respectively, to the MD, fog, and cloud centers, and the data rate and power consumption of the wireless link were explicitly considered.

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.