Secure cross-site analytics on OpenShift logs

The project aims to explore whether cryptographically secure Multi-Party Computation, or MPC for short, can be used to perform secure cross-site analytics on OpenShift logs with minimum client participation.

Robust Data Systems Tuning

Note: Please see the Learned Cost-Models for Robust Tuning project page for research that has grown from this project. See the Robust LSM-Trees Under Workload Uncertainty project page for earlier results associated with this research. BU faculty members Manos...

Hema Veeradhi

Hema is a Senior Software Engineer working in the office of the CTO exploring and integrating open source AI operations.

Andy Huynh

Andy Huynh is an IBM Fellow and a graduate student at Boston University who is also working with Red Hat. His Red Hat advisor is Josh Berkus. His research interest are in machine learning, data mining, and high performance computing

Robust LSM-Trees Under Workload Uncertainty

We introduce a new robust tuning paradigm to aid in the design of data systems with uncertain assumptions by modeling the behavior of the system and then utilizing these models in conjunction with techniques in robust optimization. Our approach is demonstrated through tuning a popular log-structured merge-tree based storage engine, RocksDB