💰 Don't size caches for peak—let them stretch and shrink with demand to cut cost. Google applies the classic ski rental problem to a production database. Title: Optimizing cloud economics with linear elastic caching URL: 📦 Overview Linear elastic caching treats memory footprint as a variable cost that integrates over time, dynamically growing and shrinking cache size to match the workload. 🎯 The problem Cloud memory is expensive (serverless can charge up to $3/day per GiB). Fixed-size caches hit a "Goldilocks dilemma": too small hurts performance, too large wastes thousands on idle memory during low demand. 🎿 Method Each page faces a choice: "rent" (keep in RAM, paying continuous memory cost) or "buy the miss" (evict, risking latency/I/O later). A ski rental algorithm sets each page's TTL. The key result: eviction policy and rental duration can be optimized separately. 🌲 Implementation For Spanner, a lightweight shallow decision tree (compilable to C++) predicts the optimal TTL from data size, miss cost, and operation type—no heavy inference in the cache path. 📉 Results In production on Spanner: memory down 15.5%, misses up only 5.5%, TCO down ~5%, I/O impact a mere 0.5%. On public traces it consistently beat fixed-size (GDSF) baselines. #CloudComputing# #Algorithms#