Enhancing Efficiency in Cloud Computing Entails Optimizing Resource Apportionment Through the Utilization of the Shuffled Frog-Leaping Algorithm (SFLA) and Firefly Algorithm
Keywords:
Cloud Computing, Shuffled frog leaping Algorithm, Firefly Algorithm, Resource apportionmentAbstract
The imperative role of 'cloud computing' in modern technology brings attention to Resource apportionment as a pivotal facet. This paper introduces a Hybridized Optimization algorithm that combines the effectiveness of the 'Shuffled Frog Leaping Algorithm' (SFLA) and the 'Firefly Algorithm.' This innovative approach overcomes limitations seen in current works like the HABCCS algorithm, GTS algorithm task, and the krill herd algorithm, while amalgamating the unique features of both SFLA and the Firefly Algorithm. Within this methodology, the SFLA section oversees initial steps, encompassing the initialization of request size, request generation, estimation of SFLA's fitness value, sorting, division, and evaluation of user requests. SFLA is recognized for its rapid convergence and straightforward implementation, boasting the capability for global optimization and widespread utilization across diverse domains. Concurrently, the Firefly Algorithm takes on pivotal operations such as initialization, request generation, fitness function evaluation, modification, and the assessment of new solutions. The Firefly Algorithm is characterized by its ease of evaluation and suitability for complex situations, providing a notable advantage. In this system, the evaluation of request speed and sizes plays a critical role in Resource apportionment on the server side, contributing to reduced computation times. Experimental results substantiate the efficacy of this hybrid approach, illustrating its superior performance in comparison to additional similar technique.
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