This summer, a team of students worked on an experiment that ultimately became part of the LinnOS paper that infers the SSD performance with the help of its built in light neural network architecture. The LinnOS paper, which utilizes Chameleon testbed to provide a public executable workflow, will be presented in OSDI ’20 and is available here.
Two of the students, Levent Toksoz and Mingzhe Hao, write about their experience in this Chameleon User Stories series. Toksoz is a recent graduate of the University of Chicago computer science masters program. He studied physics and math as an undergrad at the University of Michigan and is planning to apply to PhD programs in computer science. Hao is a Ph.D candidate of the UCARE group in the Department of Computer Science at the University of Chicago. His research interests include operating systems, storage systems, and distributed systems.