[2607.11579] MemExchange: Utility-Driven Distributed Memory Reallocation for Multi-Tenant Datacenters

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arXiv:2607.11579 (cs)

[Submitted on 13 Jul 2026 (v1), last revised 14 Jul 2026 (this version, v2)]

Title:MemExchange: Utility-Driven Distributed Memory Reallocation for Multi-Tenant Datacenters

Authors:AmirHossein Seyri, Abhisek Pan, Balajee Vamanan<br>View a PDF of the paper titled MemExchange: Utility-Driven Distributed Memory Reallocation for Multi-Tenant Datacenters, by AmirHossein Seyri and 2 other authors

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Abstract: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.

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Distributed, Parallel, and Cluster Computing (cs.DC)

Cite as:<br>arXiv:2607.11579 [cs.DC]

(or<br>arXiv:2607.11579v2 [cs.DC] for this version)

https://doi.org/10.48550/arXiv.2607.11579

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arXiv-issued DOI via DataCite

Submission history<br>From: Balajee Vamanan [view email]<br>[v1]<br>Mon, 13 Jul 2026 13:59:59 UTC (245 KB)

[v2]<br>Tue, 14 Jul 2026 02:05:25 UTC (245 KB)

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