Google Research· Research

Optimizing cloud economics with linear elastic caching

Todd Lipcon, Distinguished Engineer, Google Cloud, and Manish Purohit, Research Scientist, Google Research Linear elastic caching minimizes total cache cost by framing page eviction as a ski rental problem, using lightweight machine learning to optimize the trade-off between memory footprint and cache misses. Quick links Paper Share Copy link × Modern high-performance database systems and cloud services rely on in-memory caching to keep frequently accessed data in RAM to bypass slow disk operations and deliver the lightning-fast response times users expect. But this performance comes with a cost (literally): high-speed memory is expensive, and some serverless cloud providers charge up to $3 per day for just 1 GiB of memory. Historically, cache management has been treated as a fixed-resource problem. With regular, fixed-sized caching , engineers allocate a specific amount of memory for the cache and the system uses eviction policies like least recently used (LRU) replacement …

Announced

25 Jun 2026 10:03 UTC