Lemonade: Learning-based Heterogeneous Metadata Offloading for Disaggregated Memory

Direct Access (DA) in Disaggregated Memory (DM) is a promising solution that meets the high-performance requirements of AI applications. However, it lacks effective support for metadata management, making metadata operations the major bottleneck. To address this, we propose Lemonade, a l earning-based h e terogeneous m etadata o ffloadi n g for dis a ggregate d m e mory. Lemonade splits the metadata into highly regular and irregular ones, thus offloading the former into the client to avoid remote queries and enabling request redirection in the SmartNIC for the latter to ensure cost-effective correction and updates. Evaluations under microbenchmark and YCSB workloads indicate that Lemonade reduces latency by 72.8% and achieves a 1.43× increase in throughput compared to the state-of-the-art systems.