SOSP ’24, November 4–6, 2024, Austin, TX, USA.
Hojin Park^, Ziyue Qiu^*, Gregory R. Ganger^, George Amvrosiadis^
^ Carnegie Mellon University
* Uber
An increasing demand for cross-cloud and cross-region data access is bringing forth challenges related to high data transfer costs and latency. In response, we introduce Macaron, an auto-configuring cache system designed to minimize cost for remote data access. A key insight behind Macaron is that cloud cache size is tied to cost, not hardware limits, shifting the way we think about cache design and eviction policies. Macaron dynamically configures cache size and utilizes a mix of cloud storage types, in order to adapt to workload changes and reduce cloud costs. We demonstrate that Macaron can reduce cross-cloud workload costs by 65% and cross-region costs by 67%, mainly by reducing outgoing data transfer and by leveraging object storage alongside DRAM to reduce cache capacity cost.
FULL PAPER: pdf