MEDO: Adaptive Multi-Stream Data Offloading for Efficient Management of Disaggregated Memory Pool

Abstract

The scale of data-intensive workloads in intelligent datacenters has grown rapidly in recent years, intensifying the need for efficient data movement within disaggregated memory pools across memory and storage subsystems. However, most existing solutions focus primarily on optimizing communication between compute nodes and data nodes, overlooking fine-grained management of in-pool data flows. Furthermore, prevailing data swap strategies often suffer from high overhead, limited parallelism, and a lack of adaptivity to dynamic workloads, especially for bursty data access patterns in heterogeneous memory pools. To address these issues, this paper introduces MEDO, a high-parallelism data offloading system for disaggregated memory pools. MEDO leverages a novel multi-stream data offloading architecture, featuring parallel data streams and approximate LRU queues, to maximize throughput and efficiently handle diverse workloads. Additionally, MEDO incorporates a lightweight, adaptive offloading agent that dynamically optimizes data placement decisions and fine-grained system configurations. Our prototype achieves up to 3.6 × latency reduction on real-world data services compared with baselines and can reduce in-pool memory usage by up to 50% on state-of-the-art disaggregated memory systems.