A Hybrid and Light Weight Metaheuristic Approach with Clustering for Multi-Objective Resource Scheduling and Application Placement in Fog Environment

Abstract

Fog computing is receiving considerable attention in the research community to deliver computing resources for Internet of Things (IoT) devices. With rapid advancements in IoT, it is necessary to cater to the applications closer to the user using fog which acts as a potential complement to the cloud. In practice, scheduling of fog devices and allocation of IoT applications to fog play a key role, if improperly done, IoT applications in a fog environment may not be deployed effectively. As a result, there is a wastage of resources, an increase in network latency, and a burden on Quality of Service (QoS) on the fog nodes, resulting in a multi-objective optimization problem. To analyze the scheduling and allocation problem of fog nodes this work proposes a combinatorial novel model based on a metaheuristic approach called Enhanced Multi-Objective Particle Swarm Optimization with Clustering (EMOPSOC) and Fog Picker. The proposed EMOPSOC is alightweight algorithmthat dynamically schedules the fog nodes based on a multi-objective optimization problem. The model usesthe application of machine learning techniques to enhance the evolutionary computation algorithmby minimizing the latency between the IoT and fog devices, resource wastage, and load imbalances, as well as making efficient use of the resources and reducing turnaround time. In addition, the Fog Picker deployed in the model allocates the IoT components to the fog nodes based on QoS attributes. The proposed approach is simulated and tested using iFogSim and jMetal. The performance of the proposed algorithm was tested on several benchmark test functions using Inverted Generational Distance (IGD) and Hypervolume (HV) indicators. The experimental results show that EMOPSOC in combination with Fog Picker has outperformed the variants of Multi-Objective Particle Swarm Optimization (MOPSO) algorithms and state-of-the-art algorithms such as Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), Strength Pareto Evolutionary Algorithm-II (SPEA-II) by maintaining meritoriously distributed populations, converging quickly, and maintaining steady performance without forfeiting steadiness. Finally, the numerical results verify, that the optimal solution set has improved convergence and divergence over the relative methods. The experimental results of the Fog Picker have increased by 80%, indicating that the efficiency is greatly improved even when the fog nodes and IoT application components are increased.