WWW2026

Wiseswap: Elastic Datacenter Network-Aware Disaggregated Memory for Multi-Tenant Cloud

Mingxuan Liu, Jianhua Gu, Tianhai Zhao, Dong Sun

Abstract

Disaggregated Memory Systems (DMS) hold substantial potential for cloud datacenters but face critical deployment barriers in multi-tenant RDMA environments. Existing DMS designs rely on idealized assumptions-overlooking interference from co-located RDMA applications, oversimplifying fabric topology considerations, and lacking elastic service-level objectives (SLOs) guarantees-resulting in performance degradation and resource inefficiency. To bridge these gaps, we introduce Wiseswap, an elastic, datacenter network-aware DMS that delivers robust memory disaggregation for multi-tenant clouds through three key innovations: (1) Preemption-enabled isolation: A low-overhead kernel mechanism utilizes WAIT/ENABLE RDMA primitives to prioritize latency-critical swap operations over user-space RDMA flows, maintaining tenant fairness; (2) Adaptive fabric path selection: In-kernel telemetry dynamically probes latency and routes memory traffic through uncongested paths, mitigating interference from elephant flows; (3) Feedback-directed autoscaling: Fine-grained optimization of DMS-specific parameters-dynamically optimizes resource allocation under fluctuating workloads, guaranteeing stringent SLOs while minimizing resource overhead. Evaluations demonstrate that Wiseswap improves throughput by 1.3-2.4x and reduces tail latency by 57-73% under contention compared to state-of-the-art solutions, while consistently meeting strict SLO targets.