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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 fireflywith 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), andUtility-based Firefly Algorithm (UFA) when compared in terms of makespan, Degree of imbalance, and Figure of Merit. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this era, fog computing is a horizontal, physical, or virtual resource emerging paradigm that extends cloud computing to the edge of the network. Now a day, there are many resources hungry applications i.e. face recognition, augmentation reality, video surveillance, speech recognition, virtual reality that requires high computation capabilities and large memory, which are not available at the end devices. Moreover the distance between the cloud and the end devices may also be an issue for delay sensitive applications. This paper present a comprehensive survey of computation offloading in fog computing. It reviews different techniques used to implement computation offloading along with variation parameters. This study also represents that there are various use cases of computation offloading in real world environment. Finally, open research issues related to computation offloading are also investigated to point out future research directions for the efficient computation offloading in the fog computing environment.. [ABSTRACT FROM AUTHOR]

In today's era, distributed computing is rising in the field of data processing. It gives a broad measure for the services to be processed for clients through the web. Everyone's data is on cloud. There resides many problems when we process data on cloud like as security, privacy etc. And load balancing is one of the problem in cloud computing. There are many requests generating on a single virtual machine and these requests are to be processed on various machines. This is controlled by load balancer. Load balancing is the technique used for assigning different- different tasks on different- different virtual machines by using various algorithms. Load balancing plays a vital role in the era of cloud computing which is assumed to circulate on various machines. This paper presents the study of various load balancing techniques. [ABSTRACT FROM AUTHOR]