Blended Storage

In recent years, emerging storage hardware technologies have focused on divergent goals: better performance or lower cost-per-bit:

Correspondingly, data systems that employ these technologies are typically optimized either to be fast (but expensive) or cheap (but slow). We take a different approach: by architecting a storage system to natively utilize two tiers of fast and low-cost storage technologies, we can achieve a Pareto-efficient balance between performance and cost-per-bit.

​This project sets to fundamentally rethink data structures, and algorithms for caching, prefetching and migration for multi-tiered storage.

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Publications

Efficient Migrations Between Storage Tiers with PrismDB, Ashwini Raina, Jianan Lu, Asaf Cidon, Michael J. Freedman, under preparation.

Artifacts

Coming soon

Team

Faculty / senior researchers: Asaf Cidon, Michael J. Freedman
PhD students: Ashwini Raina, Jianan Lu, Haoyu Li

Teaching

Fall 2021: class project in EECS 6897, distributed storage systems

Funding