Computer Science Assistant Professor Lewis Tseng awarded $242K NSF research grant

BC Computer Science Assistant Professor Lewis Tseng has received funding from the National Science Foundation for a research project entitled Improving Latency in Geo-Replicated Storage by Relaxing Consistency Requirements. This award will provide a total of approximately $242K in funding over a period three years.

More information below (from the project's abstract):

Recent years have seen a tremendous growth in the popularity of cloud services. Distributed storage systems are a key component of the cloud computing revolution. Distributed storage systems are designed to achieve a suitable tradeoff between latency, data consistency and dependability. This project addresses development of new consistency models that take into account application characteristics and hybrid fault models. The ultimate goal is to improve our understanding of the trade-offs between consistency, latency, and dependability in distributed storage systems.

The project includes two synergistic thrusts. First, the project explores consistency models that consider graph-based application characteristics. For example, social and trust graphs model user relation and interaction for many applications. The first thrust explores graph-based consistency models that are acceptable for the applications, and yet improve latency of client operations. Second, prior storage systems typically either only tolerate benign failures, or tolerate worst-case faults with high overhead. This project considers non-colluding faults as well as Byzantine faults, and investigates approaches to reduce overhead by relaxing some of the requirements imposed by prior systems.

Proposed research is expected to improve the understanding of the impact of application characteristics and different types of faults on consistency-latency trade-off in distributed storage systems. Tolerance of more severe faults is likely to become important for future critical web-based services and applications. Additionally, with the tremendous growth in social and trust networks, mechanisms to improve their performance are of interest. This project helps achieve such improvements through the development of new consistency models.