Causal Inference for Quantifying Noisy Neighbor Effects in Multi-Tenant Cloud Environments

TL;DR

This paper introduces an explainable causal inference methodology to quantify and diagnose the impact of noisy neighbors in multi-tenant cloud environments, validated through experiments and statistical analysis.

Contribution

It presents a novel analytical approach combining controlled experiments and causal inference to quantify and establish causality of noisy neighbor effects in cloud workloads.

Findings

Quantifies performance degradation up to 67% in I/O workloads.

Establishes causality with a 75% increase in causal links during noisy neighbor activation.

Identifies unique degradation signatures for different resource contentions.

Abstract

Resource sharing in multi-tenant cloud environments enables cost efficiency but introduces the Noisy Neighbor problem, i.e., co-located workloads that unpredictably degrade each other's performance. Despite extensive research on detecting such effects, there are no explainable methodologies for quantifying the severity of impact and establishing causal relationships among tenants. We propose an analytical that combines controlled experimentation with multi-stage causal inference and validates it across 10 independent rounds in a Kubernetes testbed. Our methodology not only quantifies severe performance degradations (e.g., up to 67\% in I/O-bound workloads under combined stress) but also statistically establishes causality through Granger causality analysis, revealing a 75\% increase in causal links when the noisy neighbor activates. Furthermore, we identify unique "degradation signatures" for each resource contention vector (i.e., CPU, memory, disk, network), enabling diagnostic capabilities that go beyond anomaly detection. This work transforms the Noisy Neighbor from an elusive problem into a quantifiable, diagnosable phenomenon, providing cloud operators with actionable insights for SLA management and smart resource allocation.