Enhancing the Capability of Load Management Techniques in Cloud Using H_FAC Algorithm Optimization

Enhancing the Capability of Load Management Techniques in Cloud Using H_FAC Algorithm Optimization

Shadab Siddiqui (BBD University, Lucknow, India), Manuj Darbari (BBD University, Lucknow, India) and Diwakar Yagyasen (BBD University, Lucknow, India)
Copyright: © 2020 |Pages: 17
DOI: 10.4018/IJeC.2020040105


Load balancing is a major research discipline in Cloud computing. The services are provided to users on pay as you go manner. Although a lot of algorithms have been proposed for load balancing, but performance is still an issue. The authors have proposed a new hybrid algorithm H_FAC to optimize the performance in cloud computing. The hybrid technique combines cuckoo search along with the Firefly algorithm of swarm intelligence. The benefit of using hybridization technique is that strength of one algorithm will overcome the shortcomings of other algorithms. Blockchain ID based Signature technique is used to ensure the authenticity of cloud service provider. The experimental results of H_FAC minimize the standard deviation, execution time significantly and improved throughput thereby optimizing the performance. The hybrid algorithm is also compared with other algorithms like ant colony optimization, artificial bee colony, round robin, FCFS and modified throttled. This approach helps the users to get the resources from authentic resource providers with a reduced execution time.
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Load Balancing algorithms are of two types: ‘static’ and ‘dynamic’ algorithms. The ‘static’ algorithms are those in which host server does not have much variations in load.

Round Robin (Samal et al., 2013) is a ‘static’ load balancing algorithm. It takes into account the principle of ‘time quantum.’ Each user request is given a time quantum and has to complete within that period of time. First Come First Serve ‘FCFS’ (Samal et al., 2013) algorithm works on the principle of a ‘first-come-first-serve’ basis. The request which come first are allocated first to the server.

Modified throttled (Domanal et al., 2013) is dynamic load balancing algorithm which moves the user request to the available resource provider. These traditional algorithms have many limitations due to dynamic workload environment. Therefore, to overcome such limitations Swarm Intelligence SI algorithms are used.

Gerardo Beni and Jin Wang introduced the idea of swarm intelligence (SI) in 1989 (Yang et al., 2017). SI describes the collective behaviour of natural and self-organized systems. SI contains agents that communicate in natural environment. Algorithms of SI optimization (Yang et al., 2017) are ant colony optimization, artificial bee colony, Firefly algorithm, cuckoo search, etc. Metaheuristics define a procedure to follow a particular path that leads to best optimization problem.

The Ant Colony Optimization (Tawfeek et al., 2015) is based on the behavior of ants. The collective extensibility of these ants working in parallel manner helps in solving various difficult tasks. However, ‘ACO’ provides slower convergence rate and increases the network overhead with the increase in ants.

Artificial Bee Colony (Gamal et al., 2017) (Babu et al., 2016) is based on the foraging behavior of bees. These bees are divided into different categories like ‘Employer’, ‘Onlooker’ and ‘Scout’ bees. The limitation of the ABC algorithm is that it provides slow convergence rate and poor performance on smaller paths.

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