Topological Analysis for Identifying Anomalies in Serverless Platforms

2026-03-11

AI Summary

This work addresses the challenge of identifying structural anomalies in serverless architectures, where information flows are complex and non-conservative. The authors propose an analytical framework based on topological modeling and Hodge decomposition to decouple execution flows into locally correctable components and global harmonic modes. Notably, harmonic flows are reinterpreted as intrinsic structural properties of the system rather than mere misconfigurations. Building on this insight, the paper introduces a “damping effect” strategy to mitigate the inefficiencies induced by harmonic flows without requiring global topological reconfiguration. Through iterative flow analysis, the approach effectively uncovers latent structural issues and offers actionable mitigation strategies. Experimental results demonstrate the method’s efficacy in both detecting and optimizing structural anomalies in serverless environments.

Technology Category

• Machine Learning: Structured Learning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

The information flows in serverless platforms are complex and non-conservative. This is a direct result of how independently deployed functions interact under the platform coarse-grained control mechanisms. To manage this complexity, we introduce a topological model for serverless services. Using Hodge decomposition, we can separate observed operational flows into two distinct categories. They include components that can be corrected locally and harmonic modes that persist at any scale. Our analysis reveals that these harmonic flows emerge naturally from different types of inter-function interactions. They should be understood as structural properties of serverless systems, not as configuration errors. Building on this insight, we present an iterative method for analyzing inter-function flows. This method helps deriving practical remediation strategies. One such strategy is the introduction of "dumping effects" to contain harmonic inefficiencies, offering an alternative to completely restructuring the service's topological model. Our experimental results confirm that this approach can uncover latent architectural structures.

Problem

• serverless platforms
• anomaly identification
• information flows
• topological analysis
• inter-function interactions

Innovation

• topological modeling
• Hodge decomposition
• harmonic flows
• serverless platforms
• anomaly detection