A hybrid approach for fault-tolerance aware load balancing in fog computing

Abstract

Fog computing has grown in popularity in recent years because to its potential to deliver real-time processing, low latency, and reduce network congestion. However, the implementation of Internet of Things (IoT) enabled smart devices in environments using fog computing may lead to resource limitations and higher computational demands. Load balancing and fault tolerance strategies are necessary to tackle these difficulties for optimal resource usage and system availability. In order to accomplish fault tolerance aware load balancing in fog computing, a hybrid meta-heuristic approach in this study that combines the Modified Harris-Hawks Optimization (MHHO) and Ant Colony Optimization (ACO) is proposed. The MHHO algorithm is utilized for load balancing, whereas the ACO algorithm is used for fault tolerance. By employing the proposed technique, the load on fog nodes is balanced, the makespan time is minimized, energy consumption and execution costs are minimized, and fault tolerance in fog computing environments is ensured. It is evaluated using a simulation framework built on the iFogSim toolkit. In terms of load balancing, fault tolerance, and other factors, the results of the experiments show that the suggested hybrid algorithm performs better than earlier state-of-the-art methods.

Optimized intrusion detection in IoT and fog computing using ensemble learning and advanced feature selection

“…The distributed architecture of fog computing requires collaborative threat detection and response across various heterogeneous fog nodes [ 21 ]. Reliability and fault tolerance are also critical considerations for ensuring resilient operation during intermittent connectivity between fog nodes at various locations [ 22 ]. Security issues in the real world, including DDoS attacks and other malware infections, have exposed the practical vulnerabilities of critical IoT infrastructures reliant on fog computing [ 23 ].…”