Late Breaking Results: Source-Aware Adaptive Cache Management for CXL-enabled Disaggregated Memory Sharing

Dynamic workloads running on multiple hosts will bring changing access patterns on CXL-enabled shared disaggregated memory. Existing works often un-traceably cache multi-source accesses, making it hard to exploit each host’s access behavior and assure service quality. Our solution Alchemy jointly optimizes cache replacement and bypassing and runs as an online reinforcement learning agent with source-aware adaptivity. It gives rewards derived from sampling-based action effectiveness and per-host macro performance. The multi-host prototype-based results on FPGAs show $8.71 \%-14.56 \%$ reduction in average access latency over LRU policy and 44x faster than the hardware-efficient ICGMM method in decision-making with comparable overhead.