Rana Khudhair Abbas Ahmed

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: Quantum computing has brought in all new paradigm for computational processing providing unparallel ability for data analysis. Considering worldwide data production is expected to exceed 180 trillion zettabytes by 2025 the utilization of the conventional computing framework hampers the real-time processing of data. People consider quantum computing, which uses principles of quantum mechanics to solve problems 100 and 1,000 times faster than classical computing.   Objective: The article looks at quantum computing and its relevance to real time data analytics to determine its relevance, hence its impact, by the year 2025. It is worthwhile to emphasize the comparison of quantum algorithms with traditional approaches to dealing with extensive, data-centered workloads in various fields.   Methods: A comparison was made on quantum versus classical computing algorithms based on criteria such as, the flow rate, precision, and flexibility. Data sets provided by the finance stream, including real-time stock analysis, supply chain and logistics, genomic sequencing from the healthcare domain were used. Over 10 million simulation experiments were performed to gain trends and insights into the operational problems for quantum simulation.   Results: The study establishes differences in the efficiencies of these two approaches, with quantum algorithms speeding up particular tasks as much as a hundred times higher than classical algorithms and almost 15% of the error rate being decreased if quantum error correction modes were used. In scalability tests it was shown that quantum systems could process data sets larger than 10 terabytes with little slowdown, compared to a classical system, which reduced efficiency by as much as 30%. However, in present day quantum hardware, processing the capability is limited and problems arise with regards the error correction protocol.   Conclusion: Quantum computing, on the other hand, has an unconventional prospect of real-time data analytics to operate at high efficiency and big scale on data-bound concerns. However, much progress is required in the way of bettering coherence times and reducing exacting error rates, crucial advances for total realization of quantum potentialities by 2025