Supporting Enterprise Cloud Enablement: AWS Solutions for Migration and Automation; Data Center Clusters and their Consequences: Local Community Impact and Global Environmental Costs 126 views

Capstone Research: My capstone project talks about my internship at the National Rural Electric Cooperative Association (NRECA) with a focus on supporting their long-term initiative to migrate on-premises systems to Amazon Web Services (AWS) to reduce infrastructure costs. At NRECA, the Cloud Delivery and Enablement Team had been working closely with internal teams to construct scalable tools that would support their workflows. To accelerate this transition, I developed cloud-based automation applications and scalable infrastructure templates primarily using the AWS Cloud Development Kit (AWS CDK). A large portion of this work was done by transitioning legacy CloudFormation templates into reusable CDK constructs written in C#. These constructs included flexible API Gateway and Cloudfront constructs that other teams could adopt and customize to support their deployment rather than building these structures from scratch. In addition to the CDK constructs, I built a Python automation script for the Containerization Team that had simplified secure SSH key management across multiple AWS accounts by automating the creation of EC2 key pairs and integrating this execution into a GitHub Actions pipeline. The project supported NRECA’s long-term migration towards AWS and directly accelerated the onboarding and deployment speeds for teams like Form7 which ended up using these new infrastructural components. STS Research: My STS research paper presents an analysis on the rapid expansion of data centers and their reasoning as to why they form in clusters. My research aims to answer the question: how and why are more data centers being built in data hubs or clusters, and how does this shape communities on a local scale while impacting the environment on a global scale? While public discourse often views this expansion as the inevitable byproduct of technological advancements like AI and Cloud services, I argue that these clusters are actually heavily engineered by regional governments that compete to establish these data centers. This is done through financial incentives, tax exemptions, and specialized land rezoning. On a global scale, we see these data centers draining regional watersheds to support their cooling systems, with even some facilities capable of consuming up to 5 million gallons of water daily. The need for immense cooling systems is due to the overwhelming concentrated power that these data centers demand. These data centers utilize a tremendous amount of electricity which ultimately increases globally. On a local scale, when clustered, these data centers put a lot of stress on their local power grids, which even forces some operations to extend their operations to fossil fuel plants to prevent outages. This leads to increased electricity bills from local neighborhoods that either live close to the data centers or use the same electricity provider as these data centers. These local neighborhoods have also seen increased levels of noise pollution through continuous low-frequency noises. As a result, my research aims to argue that without stricter environmental regulations and policies that give local communities a say in many decisions, the public will continue to take on detrimental costs that affect people all around the world.

BS (Bachelor of Science)

Datacenter; AWS; Cloud

School of Engineering and Applied Science Bachelor of Science in Computer Science Technical Advisor: Rosanne Vrugtman STS Advisor: Karina Ripley Technical Team Members: Aaron Park

English

2026-05-06