Classification of Request-Based Mobility Load Balancing in Fog Computing

Abstract

Every day, more and more data is being produced by the Internet of Things (IoT) applications. IoT data differ in amount, diversity, veracity, and velocity. Because of latency, various types of data handling in cloud computing are not suitable for many time-sensitive applications. When users move from one site to another, mobility also adds to the latency. By placing computing close to IoT devices with mobility support, fog computing addresses these problems. An efficient Load Balancing Algorithm (LBA) improves user experience and Quality of Service (QoS). Classification of Request (CoR) based Resource Adaptive LBA is suggested in this research. This technique clusters fog nodes using an efficient K-means clustering algorithm and then uses a Decision Tree approach to categorize the request. The decision-making process for time-sensitive and delay-tolerable requests is facilitated by the classification of requests. LBA does the operation based on these classifications. The MobFogSim simulation program is utilized to assess how well the algorithm with mobility features performs. The outcome demonstrates that the LBA algorithm’s performance enhances the total system performance, which was attained by (90.8%). Using LBA, several metrics may be examined, including Response Time (RT), delay (d), Energy Consumption (EC), and latency. Through the on-demand provisioning of necessary resources to IoT users, our suggested LBA assures effective resource usage.