内存交换：云规模内存交易
MemExchange: Utility-Driven Distributed Memory Reallocation for Multi-Tenant Datacenters
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中文总结 AI 辅助
研究云环境下内存管理问题，提出MemExchange系统，利用基于在线未命中率曲线估计的边际效用分配，通过RDMA重新分配空闲内存，设计MTC协议支持远程访问，经实验验证其能降低远程访问开销、提高内存利用率并降低租户未命中率。
中文摘要 AI 辅助
为应对不可预测的工作负载，云提供商通常过度配置内存以满足峰值需求，导致数据中心集群大量未充分利用。同时，内存受限租户可能缓存未命中率升高。MemExchange是一种集群范围的多租户内存管理系统，根据工作负载需求动态调整内存缓存租户。利用基于在线未命中率曲线估计的边际效用分配，通过RDMA在物理节点间的租户间重新分配空闲内存。设计了MemExchange跟踪器通信协议来支持高效远程访问。通过在Memcached中实现并在多达100台CloudLab服务器的微基准测试、中型和机架规模部署中进行评估，结果表明相比基于TCP的设计，远程访问开销降低达2.3倍，机架规模下集群内存利用率提高13%，在倾斜工作负载下内存受限租户的未命中率降低达63%。
英文摘要
To handle unpredictable workloads, cloud providers typically over-provision memory to meet peak demand, resulting in substantial underutilization across datacenter clusters. At the same time, memory-constrained tenants may suffer elevated cache miss rates, even when idle capacity remains stranded elsewhere in the infrastructure. MemExchange is a cluster-wide, multi-tenant memory management system that dynamically right-sizes in-memory caching tenants according to workload demand. Leveraging marginal-utility-based allocation derived from online Miss Ratio Curve (MRC) estimation, MemExchange redistributes idle memory between tenants across physical nodes using RDMA. This approach transforms the dedicated caching memory scattered across servers into a logically aggregated pool, enabling cross-node memory exchange without centralized coordination or forced tenant co-location. To support efficient remote access, we design the MemExchange Tracker Communication (MTC) protocol, an application-layer mechanism that coordinates memory reallocation and enables one-sided RDMA operations without involving remote CPUs. We implement MemExchange in Memcached and evaluate it through microbenchmarks, medium and rack-scale deployments of up to 100 CloudLab servers. Our results show up to 2.3x lower remote-access overhead compared to TCP-based designs, a 13% increase in cluster-wide memory utilization at rack scale, and up to 63% reduction in miss rate for memory-constrained tenants under skewed workloads.
