Riyam M. Alsammarraie

Background: The exponential growth of data centers has significantly increased their carbon footprint, raising concerns about their environmental impact. As the demand for digital services and cloud computing intensifies, sustainable computing practices have become crucial for mitigating climate change. Objective: This paper aims to explore strategies for reducing the carbon footprint of data centers by integrating sustainable computing practices, including energy-efficient hardware, renewable energy sources, and optimized cooling technologies. Methods: A comprehensive review of existing literature was conducted, along with an analysis of case studies from major technology firms employing green computing strategies. Data center energy consumption patterns and carbon emissions were evaluated using energy efficiency metrics such as Power Usage Effectiveness (PUE) and Carbon Usage Effectiveness (CUE). Results: Findings indicate that adopting energy-efficient hardware, coupled with renewable energy sources, can significantly reduce energy consumption and carbon emissions. Optimized cooling techniques, such as liquid cooling and free-air cooling, further contribute to energy savings. Companies employing these practices reported a reduction in carbon emissions by up to 30%. Conclusion: Sustainable computing practices offer a viable path for reducing the environmental impact of data centers. By prioritizing energy efficiency and renewable energy integration, data centers can minimize their carbon footprint while maintaining operational efficiency, thus contributing to global sustainability goals.

Background : Security has been a major issue of discussion due to increase in the number and sophistication of Cyber threats in the modern era. Conventional approaches to threat identification might face difficulties in a number of things, namely the relevancy and the ability to process new and constantly evolving threats. Machine learning (ML) and deep learning (DL) based Approaches present AI as a potential solution to the problem of efficient threat detection.   Objective : The article aims to compare the RF, SVM, CNNs, and RNNs models’ performance, computational time, and resilience in identifying potential cyber threats, such as malware, phishing, and DoS attacks.   Methods : The proposed models were trained as well as evaluated on the NSL-KDD and CICIDS 2017 datasets. This was done based on common scheme indicators including accuracy, precision, recollection, F1 measure, detection rate of efficiency, AUC-ROC, False Alarm Rate (FAR), and the stability to adversaries. Rating of computational efficiency was defined by training time and memory consumption.   Results : The findings indicate that the CNNs gave the best accuracy (96%) and resisted perturbation better, and the RF showed good performance with little computational load. RNNs have been proved effective in sequential data analysis and SVM also performed fairly well on binary data classification although there is a problem of scalability.   Conclusion : CNNs used in AI models are the best solutions to protection from the threats in the cybersecurity space. Nevertheless, some of them still require computational optimization in order to make those beneficial in scenarios with a limited usage of computational resources. It is suggested that these findings can be used in the context of subsequent research and practical applications.