Clustering of Stochastic Processes from Request Streams for Workload Modeling and Synthetic Generation

The goal is to improve storage workload modeling via unsupervised clustering of stochastic processes, with the goal of synthetic workload generation to improve the state-of-the-art in benchmarking and simulation based evaluations. This project is funded through a Google Faculty Research Award
Team: Cristina Abad, Edwin Boza, José Viteri, Jorge Cedeño, Sixto Castro, César San Lucas