Date of Award
2026
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
Committee Chair
Tathagata Mukherjee
Committee Member
Letha Hughes Etzkorn
Committee Member
Jacob Hauenstein
Research Advisor
Tathagata Mukherjee
Subject(s)
Cloud computing, Database management, Multiagent systems, Artificial intelligence, Natural language processing (Computer science), Data privacy
Abstract
Cloud storage cost is an increasing concern for organizations, including the Commercial Satellite Data Acquisition (CSDA) program, which holds petabytes of satellite imagery in Amazon S3. How the data are stored and how often they are accessed determines the bill. At the petabyte scale, manually tracking the cloud state and arriving at valid cost-saving recommendations is a slow and non-scalable approach, and automating it with an agent could help. Given the privacy of these data, this thesis explores whether an Amazon-generated S3 inventory report, given as input to an agentic system built on a local large language model (LLM), can produce defensible, auditable cost-saving migration plans. The multi-agentic system is run on two real CSDA production buckets, SDX and Maxar, and produces recommendations that beat a status quo baseline, converge across model families, and improve in numerical accuracy through verifier-driven retries.
Recommended Citation
Niraula, Shrey, "Agentic framework for storage cost optimization for AWS" (2026). Theses. 846.
https://louis.uah.edu/uah-theses/846