A causal inference methodology quantifies noisy neighbor effects in multi-tenant clouds, reporting up to 67% performance degradation and a 75% increase in causal links via Granger analysis, plus resource-specific degradation signatures.
Root cause analysis of failures in microservices through causal discovery
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Praxium detects anomalies in cloud microservices via telemetry and infers root causes from dependency installations using causal impact analysis, reporting >0.97 macro-F1 on synthetic data.
citing papers explorer
-
Causal Inference for Quantifying Noisy Neighbor Effects in Multi-Tenant Cloud Environments
A causal inference methodology quantifies noisy neighbor effects in multi-tenant clouds, reporting up to 67% performance degradation and a 75% increase in causal links via Granger analysis, plus resource-specific degradation signatures.
-
Praxium: Diagnosing Cloud Anomalies with AI-based Telemetry and Dependency Analysis
Praxium detects anomalies in cloud microservices via telemetry and infers root causes from dependency installations using causal impact analysis, reporting >0.97 macro-F1 on synthetic data.