The field of Green Computing research, which aims to make computing more sustainable and
environmentally friendly, has been driven by the increasing integration of large-scale data processing and storage technologies. The growing complexity and massive volume of data from
various sources has challenged traditional infrastructures, leading to the exploration of multi-core
platforms. Despite the significant increase in performance and energy efficiency with the use
of multi-core machines, nevertheless, due to the growing demand for consumption, energy
expenditure has reached high figures. In this study, we found that polynomial regression models
are more effective than linear ones for predicting energy consumption, especially in complex
data. In addition, CPU is the largest energy consumer, suggesting the need for optimization or the
use of GPUs. We did not find a direct correlation between execution time and energy consumption, suggesting that time-consuming applications may have lower costs due to optimizations.
Cluster analysis of the benchmarks indicated similar consumption patterns, useful for future
optimizations. Degree 3 polynomial regression was efficient in many cases, but effectiveness
varies with the amount of data, and personalized data models proved more efficient than unified
approaches.
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