Management of cloud resources and social change in a multi-tier environment: A novel finite automata using ant colony optimization with spanning tree

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The enormous demand for computational resources due to rapid Cloud growth has led to the creation of large-scale data centers (DC). Consequently, a tremendous amount of energy is consumed with high carbon dioxide emissions, which necessitates that cloud service providers (CSP) develop high quality of service (QoS) strategies to address such challenges. Proper management and utilization of resources ensure a well-balanced load distribution that makes energy consumption sufficient. Developing new algorithms and exploring efficient methods and techniques is highly desired for the management of virtualized DCs. This research proposes Finite Automata using Ant Colony Optimization with Spanning Tree (FAACOST), a machine-learning concept for managing cloud resources in a multi-tier environment. The overall objective is to minimize energy consumption through data placement leverage and virtual machines (VM) consolidation. The proposed technique's efficiency was benchmarked on four performance metrics (machine learning, dynamic nature, scalability, and QoS). Based on the extensive experiments conducted, FAACOST recorded an energy consumption of 132 w with 20 tasks compared to the benchmarked techniques that consumed 363 w. The experimental results show that FAACOST achieves the optimal number of physical machines (PMs) and is more energy-efficient.

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Technological Forecasting and Social Change



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