COMPARISON AND EVALUATION OF ENERGY-EFFICIENT CLOUD COMPUTING TECHNIQUES WITH LOAD BALANCING APPROACHES (ASEAN Engineering Journal, 2024)
The advent of Cloud Computing has revolutionized the IT landscape by offering computing resources as a service, similar to conventional utilities like electricity. This paradigm shift has made cloud computing a cornerstone of the contemporary digital economy, attracting substantial focus from both academic and industrial sectors. Its unique pay-as-you-go model provides customers with on-demand resource availability, enhancing operational flexibility. However, this convenience is offset by the growing energy demands of cloud data centers, which not only escalate operational expenses but also contribute to environmental degradation through increased carbon footprints. To combat these issues, Green cloud computing has been introduced, striving for energy-efficient and sustainable operations. This involves employing strategies that minimize energy consumption and resource utilization through the application of energy-conscious algorithms. Although numerous algorithms based on server consolidation have been proposed to optimize energy use in cloud environments, they often lack uniform evaluative comparisons and vary in performance due to differing experimental conditions. This variance presents a challenge in selecting the most effective algorithm tailored to specific needs. This study aims to provide a nuanced analysis of existing energy-efficient algorithms, assisting researchers in identifying the algorithm that best suits their requirements. We undertake an exhaustive comparison of various algorithms, examining their architecture, modelling approaches, and performance metrics. These algorithms are then implemented and tested under uniform conditions using the CloudSim toolkit. Our findings offer an in-depth comparative analysis of these algorithms, shedding light on their respective advantages and shortcomings. Additionally, we delve into a thorough discussion of each algorithm's features and their implications for cloud computing environments.