Caching to Improve Latency and Efficiency at Scale w/ CacheLib

CORGi is part of the Parallel Data Lab’s ongoing collaboration with Facebook’s CacheLib team on improving the latency, efficiency, and cost of cache at datacenter-scale. Our OSDI’20 paper introduced and characterized the CacheLib system, which was developed and widely deployed at Facebook to aggregate caching optimizations in a single system. We are now actively working with other PDLers and Facebook on caching systems that improve cache latency, efficiency, and cost even further. These projects are being prototyped and tested within CacheLib. More information can be found on the PDL caching page.

Sponsor

Facebook

Projects

Smart Caching at Datacenter Scale

Publications

Baleen: ML Admission & Prefetching for Flash Caches [pdf]

Daniel Lin-Kit Wong, Hao Wu, Carson Molder, Sathya Gunasekar, Jimmy Lu, Snehal Khandkar, Abhinav Sharma, Daniel S. Berger, Nathan Beckmann, Greg Ganger. FAST 2024.

Kangaroo: Theory and Practice of Caching Billions of Tiny Objects on Flash [pdf]

Sara McAllister, Benjamin Berg, Julian Tutuncu-Macias, Juncheng Yang, Sathya Gunasekar, Jimmy Lu, Daniel Berger, Nathan Beckmann, Gregory R. Ganger. ACM Transactions on Storage 2022.

Kangaroo: Caching Billions of Tiny Objects on Flash [pdf]

Sara McAllister, Benjamin Berg, Julian Tutuncu-Macias, Juncheng Yang, Sathya Gunasekar, Jimmy Lu, Daniel Berger, Nathan Beckmann, Gregory R. Ganger. SOSP 2021.

The CacheLib Caching Engine: Design and Experiences at Scale [pdf]

Benjamin Berg, Daniel Berger, Sara McAllister, Isaac Grosof, Sathya Gunasekar, Jimmy Lu, Michael Uhlar, Jim Carrig, Nathan Beckmann, Mor Harchol-Balter, Gregory R. Ganger. OSDI 2020.