  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.
