Fuzzy Firefly Based Intelligent Algorithm for Load Balancing in Mobile Cloud Computing

Abstract

This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan. The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly. It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function. It works for 3-tier architecture, including cloudlet and public cloud. As cloudlets have limited resources, fuzzy logic is used for cloudlet selection using capacity and waiting time as input. Fuzzy provides human-like decisions without using any mathematical model. Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed. It balances the load on cloud and cloudlet while minimizing makespan and execution time. However, it may trap in local optimum; levy flight can handle it. Hybridization of fuzzy firefly with levy flight is a novel technique that provides reduced makespan, execution time, and Degree of imbalance while balancing the load. Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration (NASA) and Clarknet datasets. Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker (ACOQDM), Distributed Scheduling Optimization Algorithm (DSOA), and Utility-based Firefly Algorithm (UFA) when compared in terms of makespan, Degree of imbalance, and Figure of Merit.