Systematic Reduction in the Cost of Big Data; The Human Cost of Big Data (Centers)

Executive Summary The global demand for cloud computing has reached unprecedented levels, bringing immense technological benefits but also raising critical environmental and social concerns. My Capstone project addresses the practical aspects of reducing carbon emissions associated with cloud data centers by developing an algorithm for sustainable workload allocation. Through this project I aimed to address the growing sustainability concerns through a technological lens. Concurrently, my STS research investigates broader sustainability strategies in cloud computing, exploring technological, regulatory, and community-based solutions. Together, these projects underscore the urgency of integrating environmental considerations into technological advancements, emphasizing their joint relevance in the rapidly evolving field of cloud computing. My Capstone project directly tackles the urgent issue of carbon emissions from cloud data centers, which currently consume nearly 1% of global electricity. I designed and implemented an innovative algorithm that prioritizes cloud compute instances based on their carbon footprint, alongside cost and performance metrics. This approach enables users to choose environmentally responsible computing resources without significant compromises in price or performance efficiency. Results from my Capstone project demonstrated significant potential for environmental impact. Testing showed that the carbon-footprint optimized algorithm consistently achieved meaningful reductions in emissions—up to a 12% decrease—while maintaining competitive pricing. The algorithm effectively balanced sustainability with economic viability, providing a practical method for cloud providers and users to reduce their environmental footprint. My STS research addresses the broader question: "How can the negative environmental and social footprints of data centers be reduced while maintaining performance and scalability?" This research employed a case-study methodology, analyzing sustainability practices of major cloud providers such as AWS, Google Cloud, and Microsoft Azure. Actor-Network Theory provided a framework to examine the interactions among technological innovations, regulatory policies, corporate responsibilities, and local communities. The STS research found that while technological advancements like renewable energy adoption and AI-driven cooling significantly reduce data center energy use, these innovations alone are insufficient to counteract rising demands, altogether proving the phenomenon known as Jevons Paradox. Effective strategies require comprehensive regulatory frameworks and increased corporate accountability to ensure meaningful sustainability outcomes. My findings highlight the necessity of a multifaceted approach, integrating technological solutions, policy interventions, and community engagement to achieve genuine environmental and social sustainability in cloud computing.