HPCDF: Optimal Service Provisioning in IoT Fog-based Environment for QoS-aware Delay-sensitive Application

Due to the explosive growth of smart devices, 5G, and the Internet of Things (IoT) applications in recent years, the volume and velocity of generated data, and consequently, delay-sensitive applications are increasing endlessly. This paper aims to improve the service delay and Quality of Service (QoS) by introducing HPCDF (Hybrid PSO-CRO Delay-improved for FogPlan) - an offline QoS-aware framework to deploy and release fog services dynamically. The proposed method provisions, i.e., deploy and release fog services to reduce service delay, based on the aggregated incoming traffic to each fog node. We formulate a cost function as an Integer Non-Linear Programming (INLP) problem by considering each service attributes, including required resources and associated traffic. This problem integrates storage, processing, deployment, communication costs, delay violation, high fog utilization reward, high traffic nodes cost, and service delay penalty. A hybrid binary PSO-CRO (Particle Swarm and Chemical Reaction Optimization) algorithm is proposed to achieve the lowest service delay and QoS loss to address this problem. The evaluation is performed on real-world traffic traces, provided by MAWI Working Group, under three different experiments to study the impact of various parameters of the hybrid binary PSO-CRO algorithm and the proposed framework on service delay. The evaluation results reveal that our proposed algorithm reduces service delay by 29.34%, service cost by 66.02%, and violates the delay 50.15% less in comparison to FogPlan framework.