Categories – User Experiments

Dr. Xiaoyi Lu is a research assistant professor at The Ohio State University focusing on High Performance Interconnects and Protocols, Big Data Computing, Deep Learning, Parallel Computing, Virtualization, and Cloud Computing. In this blog post, we explore his research and usage of Chameleon Cloud.

November’s Chameleon User Experiments blog features Nanqinqin Li, a first-year PhD student at Princeton University. Learn more about Li, his summer research on reproducibility and Solid-State Drive Simulators, and learn where to replicate his experiment on Trovi!

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.

This blog discusses a new experiment deployed on Chameleon called CIEF, a Cyber Infrastructure for Ecological Forecasting (Dietz & Matta, 2018). CIEF supports data-driven research in ecological forecasting to understand our ecosystem and drive policy. Examples include predicting environmental changes, corn production in the near to medium term, types of disease-carrying mosquitos, based on data related to air, land, and water.

Modern computational science depends on many complex, compute, and data-intensive applications operating on distributed datasets that originate from a variety of scientific instruments and data repositories. Two major challenges for these applications are: (1) the provisioning of compute resources and (2) the integration of data into the scientists’ workflow.

One challenge for power budgeting systems is how to power cap dependent applications for high performance. Existing approaches, however, have major limitations. Our work proposes a hierarchical, distributed, dynamic power management system for dependent applications.

Motivated by the opportunity to optimize the architecture of data transfer infrastructure, we recently prototyped an elastic architecture for data transfer on Chameleon Cloud in which the DTNs expand and shrink based on the demand...

Exploring custom high-performance drivers in specialized operating systems with an aim to scale HPC applications in order to meet the future needs of exascale computing has motivated us to build a high-performance InfiniBand driver for Nautilus (Aero-Kernel) and evaluate its performance. In this blog, we would like to share our experimental framework and results achieved.

Ready-to-use Data Transfer Node (DTN) is provided, and it can be used to provide efficient network data transfer over a long fat network. In addition, a Chameleon Complex Appliance is publish for easy spawning a set of DTNs in Chameleon Cloud.

This blog describes a prototype of a system that leverages the capabilities of flexible switches that incorporate protocol-independent packet processing in order to intelligently route traffic based on application headers.