P. Tamilarasu, G. Singaravel, Premkumar Manoharan, Shitharth Selvarajan
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引用次数: 0
Abstract
Cloud computing (CC) has emerged as a transformative technology, offering customers unprecedented access to extensive computing resources and the diverse services for hosting various applications. However, this environment comes with several challenges. While cloud users seek optimal resources to cater to their specific requirements, the prevalent scenario often involves trading more monetary resources for less computational time. Existing algorithms, mostly focused on optimizing individual variables, lack a holistic approach. Addressing these issues necessitates a new approach to combine these conflicting objectives. This research focuses on developing and improving a dynamic task-processing framework that can find and use the optimal resources in real-time. The focus extends to running applications of different types and levels of complexity on virtual machines (VMs) using the multi-objective adaptive particle swarm optimization (MAPSO) algorithm. The MAPSO handles the multi-objective problem using the weighted-sum approach. The system operates within predefined constraints to meet users' specific time limitations. Through comprehensive simulations on a wide range of datasets, the proposed methodology yields a set of non-dominated optimal solutions. This outcome is instrumental in improving critical quality of service (QoS) metrics, including processing time, execution costs, throughput, and task rejection ratios. The effectiveness of the MAPSO-based approach are evident in its capacity to improve these numerous QoS aspects, including processing time, execution cost, throughput, and task rejection ratio compared and clearly shows that it is superior to the existing algorithms, such as ant colony optimization (ACO), hybrid version of bat optimization algorithm and particle swarm optimization (BOA+PSO), and hybrid grey wolf optimization and artificial bee colony (GWO+ABC). The time complexity for completing the tasks of the MAPSO algorithm is reduced by 5%, executes each schedule's tasks faster by 5% to 13%, and calculated execution costs also get reduced when compared to ACO, BOA+PSO, and GWO+ABC. Moreover, the suggested methodology convincingly outperforms existing state-of-the-art methods in terms of computational performance. This study pioneers a unique solution in cloud service provisioning by integrating multi-objective optimization within a real-time resource allocation framework. The resulting combination of intelligent resource allocation and enhanced QoS metrics promises to change the way cloud-based application deployment is done. Ultimately, this work establishes a paradigm shift in balancing resource allocation and user-centric QoS optimization in cloud computing environments.
期刊介绍:
IET Communications covers the fundamental and generic research for a better understanding of communication technologies to harness the signals for better performing communication systems using various wired and/or wireless media. This Journal is particularly interested in research papers reporting novel solutions to the dominating problems of noise, interference, timing and errors for reduction systems deficiencies such as wasting scarce resources such as spectra, energy and bandwidth.
Topics include, but are not limited to:
Coding and Communication Theory;
Modulation and Signal Design;
Wired, Wireless and Optical Communication;
Communication System
Special Issues. Current Call for Papers:
Cognitive and AI-enabled Wireless and Mobile - https://digital-library.theiet.org/files/IET_COM_CFP_CAWM.pdf
UAV-Enabled Mobile Edge Computing - https://digital-library.theiet.org/files/IET_COM_CFP_UAV.pdf