QoS-aware and energy-efficient metaheuristic optimization based service placement strategy for fog-based IoT applications
Optimal placement of services is a major challenge in the context of fog computing due to the resource-constrained and heterogeneous nature of nodes. Services must be placed optimally without overloading the nodes to fulfill the increasing demand of Internet of Things (IoT) applications. Energy consumption should also be minimized, along with the provision of Quality of Service (QoS). Typical problems of existing metaheuristic approaches, such as premature convergence, limited exploration capability, and high computational cost, result in suboptimal service placement. To solve this problem, we propose a QoS-aware and energy-efficient metaheuristic optimization-based service placement strategy for an integrated IoT and fog computing environment to improve QoS. In this metaheuristic optimization-based strategy, Differential Evolution (DE) is integrated with JAYA-based exploitation, which explores the search space, which in turn accelerates convergence toward optimum service placement in fog computing environments. This proposed strategy accounts for both communication and computational energy consumption when placing service modules, thereby enhancing execution efficiency and optimizing service delay and network usage in constrained fog computing settings. The experimental results demonstrate that the developed hybrid algorithm reduces energy consumption by 3.09% compared to the existing baseline approaches, while optimizing delay and minimizing network usage. Additionally, the proposed approach has been found to minimize network usage significantly compared with baselines.