A Formal Framework for Predicting Distributed System Performance under Faults

2026-02-22

AI Summary

Distributed systems operating in complex environments are highly susceptible to failures and adversarial behaviors, making their performance difficult to predict directly from formal designs. This work proposes the first framework that systematically enables performance prediction from formal models by integrating a reusable fault-injection library with a modeling methodology based on the Maude language. The authors develop an automated tool, PERF, which combines model composition and statistical analysis techniques to accurately estimate key performance metrics—such as throughput and latency—across diverse failure scenarios. Experimental evaluation demonstrates that PERF’s predictions align closely with measurements from real-world deployments on representative distributed systems, significantly enhancing the practical utility of formal methods in performance assessment.

Technology Category

• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Multiagent Systems: Other Foundations of Multi Agent Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Large-scale security measurements

Abstract

Today's distributed systems operate in complex environments that inevitably involve faults and even adversarial behaviors. Predicting their performance under such environments directly from formal designs remains a longstanding challenge. We present the first formal framework that systematically enables performance prediction of distributed systems across diverse faulty scenarios. Our framework features a fault injector together with a wide range of faults, reusable as a library, and model compositions that integrate the system and the fault injector into a unified model suitable for statistical analysis of performance properties such as throughput and latency. We formalize the framework in Maude and implement it as an automated tool, PERF. Applied to representative distributed systems, PERF accurately predicts system performance under varying fault settings, with estimations from formal designs consistent with evaluations on real deployments.

Problem

• distributed systems
• performance prediction
• faults
• formal methods
• adversarial behaviors

Innovation

• formal framework
• fault injection
• performance prediction
• distributed systems
• model composition