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

TL;DR

This paper proposes a hybrid PSO and CRO optimization method to improve fog service deployment in IoT environments, significantly reducing delay and costs for delay-sensitive applications.

Contribution

It introduces a novel hybrid optimization algorithm for fog service allocation, enhancing QoS and reducing delay and costs in IoT fog computing.

Findings

29.34% reduction in service delay

66.02% decrease in service costs

50.15% reduction in delay violations

Abstract

This paper addresses the escalating challenges posed by the ever-increasing data volume, velocity, and the demand for low-latency applications, driven by the proliferation of smart devices and Internet of Things (IoT) applications. To mitigate service delay and enhance Quality of Service (QoS), we introduce a hybrid optimization of Particle Swarm (PSO) and Chemical Reaction (CRO) to improve service delay in FogPlan, an offline framework that prioritizes QoS and enables dynamic fog service deployment. The method optimizes fog service allocation based on incoming traffic to each fog node, formulating it as an Integer Non-Linear Programming (INLP) problem, considering various service attributes and costs. Our proposed algorithm aims to minimize service delay and QoS degradation. The evaluation using real MAWI Working Group traffic data demonstrates a substantial 29.34% reduction in service delay, a 66.02% decrease in service costs, and a noteworthy 50.15% reduction in delay violations compared to the FogPlan framework.