Round Trip Time: A Benign Signal or an Indirect Window into Datacenter Workloads?

2026-07-28

AI Summary

This study addresses a critical security limitation in logically isolated leaf-spine networks within multi-tenant data centers, where shared congestion dynamics can lead to indirect information leakage despite logical separation. The work demonstrates for the first time that round-trip time (RTT) serves as an effective side channel capable of revealing co-located tenants’ workload characteristics. By analyzing RTT variations over overlapping network paths and combining congestion signature extraction with machine learning–driven classification, the authors achieve highly accurate workload inference. Experimental results show a 97.3% accuracy in cross-path inference scenarios, challenging the conventional assumption that logical isolation alone suffices for security and highlighting a novel privacy risk inherent in shared network architectures.

Abstract

Multi-tenant datacenter networks increasingly rely on shared leaf-spine fabrics, where traffic from multiple tenants traverses common network resources. While logical isolation mechanisms prevent direct access between tenants, shared congestion dynamics may still expose indirect information about co-located workloads through observable latency variations. In this paper, we investigate a network side-channel vulnerability arising from shared congestion behavior in multi-tenant datacenter fabrics using RTT observations collected along overlapping network paths. We develop a framework to explore how workload-induced latency variations contain sufficiently distinguishable signatures to enable workload inference under realistic deployment conditions. Our evaluations show that indirect RTT observations can reveal meaningful workload information, achieving up to 97.3\% run-level accuracy under cross-path evaluation when workload-induced congestion is sufficiently observable. The findings suggest that logical network isolation alone may be insufficient to prevent information leakage through shared congestion dynamics in modern datacenter infrastructures.

Problem

• multi-tenant datacenter
• network side-channel
• congestion dynamics
• RTT
• workload inference

Innovation

• RTT side-channel
• multi-tenant datacenter
• workload inference
• shared congestion
• network isolation