Abstract
Energy-efficient computing and strategies for environmentally sustainable computing have become indispensable in the contemporary world, given the escalating demand for computing power and the growing emphasis on ecological conservation. This summary delves into diverse facets of energy efficient computing and green computing methodologies, elucidating their significance, hurdles, and potential remedies. The primary objective of energy-efficient computing is to curtail the energy consumption of computing systems while either maintaining or enhancing their performance. This proves pivotal in alleviating the ecological ramifications of computing, especially as data centers and similar facilities consume substantial amounts of energy. Green computing techniques encompass a broad spectrum of practices geared towards diminishing the environmental footprint of computing. These encompass the utilization of renewable energy sources, the crafting of energy-efficient hardware, the optimization of software algorithms, and the cultivation of energy consciousness among users. Despite the
advantages associated with energy-efficient computing and green computing methodologies, various challenges necessitate attention. These encompass the delicate balance between energy efficiency and performance, the intricacies involved in designing energy-efficient systems, and the absence of standardized metrics for gauging and comparing energy efficiency. Exploration in this domain propels technological progress, resulting in the creation of more effective hardware, software, and systems. These advancements not only positively impact the environment but also propel the overall evolution of computing technology. The Information and Communication Technology (ICT) sector substantially adds to worldwide carbon emissions. Energy-efficient computing and environmentally conscious computing
approaches can play a crucial role in mitigating this impact, decreasing energy usage, and advocating for the adoption of renewable energy sources. The aim of this research is to investigate the complexities associated with multiple attribute decision-making when confronted with intuitionist fuzzy information. In this context, the weights of attributes are not entirely known, and the attribute values are expressed using
intuitionist fuzzy numbers. To ascertain the attribute weights, an optimization model is formulated based on the foundational principles of traditional grey relational analysis (GRA). The proposed approach entails computing the grey relation degree between each alternative and the positive-ideal solution as well as the negative-ideal solution. This degree is subsequently utilized to establish a relative relational degree, facilitating the simultaneous ranking of all alternatives concerning both the positive-ideal solution (PIS)
and negative-ideal solution (NIS). From the result Efficient Algorithms is ranked at first position and Green Data Centers is ranked at fifth position.