Aethon: Performance-aware Memory Offloading for Co-running Applications in Public Clouds

Abstract

As applications demand increasing memory capacity in clouds, memory pooling provides a cost-effective way to improve utilization and expand capacity. Compute Express Link (CXL), which enables high-performance direct access to remote memory, makes this approach increasingly practical. However, existing studies of memory offloading fall short when multiple applications share a memory pool, as they overlook application heterogeneity and cross-application interference. We present Aethon, a memory offloading system for public clouds that maximizes offloaded data while ensuring each application satisfies the service-level agreement (SLA). Aethon integrates an application-transparent predictor to estimate offloading-induced performance degradation, and adaptively determines cold- and hot-data offloading volumes for each application. Compared with representative work, Aethon offloads 14.2% more data on average (up to 27.4%), while satisfying SLA requirements.