Energy-Efficient Resource Allocation in Fog Computing Networks With the Candidate Mechanism

Abstract

Recently, a fog computing network that widely deploys fog nodes (FNs) at the edge of the network has been able to provide better communication performance and powerful computation support to the resource-limited Internet-of-Things (IoT) devices. In this article, we analyze the energy-efficient (EE) resource allocation problem in fog computing networks with the candidate FNs mechanism to ensure the network loading balance under the transmission performance constraints. In the scenario, the associated computation capability allocated to IoT devices from FNs is related to the historical energy consumption and the current energy consumption. The FN that reports nonzero computation capability is considered as the candidate FN and included in the candidate set. Moreover, a candidate FN-based EE resource allocation (CF-EE) algorithm is proposed to maximize network EE, which is converted into the Lyapunov optimization for each time slot. The optimal resource allocation can be obtained by minimizing the upper bound of the Lyapunov drift function and the penalty term to guarantee the loading balance and network stability. Finally, the optimization problem is decomposed into two suboptimization problems: 1) transmission resource allocation optimization and 2) power allocation optimization, and solved separately. The simulation results demonstrate that the proposed CF-EE algorithm can achieve a considerable performance improvement compared with algorithms in the literature.