Abstract
Cloud computing is a model of IT infrastructure allocation that has rapidly developed in the past decade in the tech sphere and is lauded for its flexibility of use, scalability in processing and storage capability, and durability in the face of high internet traffic and hardware failures. All of these qualities are what makes cloud computing an infrastructural model with many advantages over traditional IT infrastructure, as businesses can outsource their IT resources to a cloud provider, who charges them on a pay-as-you-go basis, minimizing overhead costs relating to slowdowns in traffic, or underutilized resources. This is especially enticing to companies that experience variable demand for their web services such as Airbnb, which experiences more web traffic in the spring and summer when people travel in greater numbers, as they can easily expand the capacity of their cloud resources in times of high demand, and then contract after peak usage, without needing to pay for the upkeep of an underutilized server for the rest of the year. My technical thesis discusses my internship experience for Fannie Mae, where I worked on a software application that had just been migrated to cloud infrastructure, but lacked certain UI features that would result in users misusing the application and creating slowdowns that would increase processing time and generate unnecessary costs to the company. I detail my implementation of UI features using a combination of Python, SQL, HTML, and Javascript to create a feedback system for users that would report the status of their requests. These implementations provided a much-needed window of transparency to the user experience that cuts down on erroneous or duplicate requests.
The graphics processing unit, or GPU is a component in a computing system that is responsible for rendering computer graphics. Thanks to the popularization of mobile devices and video gaming, GPUs have become ubiquitous. During the COVID-19 pandemic, lockdowns and border closures resulted in supply shocks that created shortages in many goods, including the GPU, leading to exorbitantly high prices, as well as erratic behaviors such as scamming and price gouging. The GPU market during the pandemic was shaped by various forces, including increased demand for entertainment due to quarantine, as well as developments in the cryptocurrency market in a period of economic turmoil. My STS thesis draws upon various news sources and econometric data to tell the story of the GPU shortage and employs economic and STS theory to guide my analysis of the state of the market and its network of participants, and what we can learn about how resources and technology are allocated.
