Modeling Anomaly Detection in Cloud Services: Analysis of the Properties that Impact Latency and Resource Consumption

2025-11-21

AI Summary

This paper addresses the latency–resource trade-off in performance anomaly detection for cloud services. We propose a modeling and quantitative analysis framework based on Stochastic Reward Nets (SRNs) to formally characterize how detection accuracy, recall, and inspection frequency jointly influence service latency and computational overhead. Our analysis reveals a dynamic trade-off: accuracy dominates the performance–cost balance under high-frequency inspection, whereas recall becomes the critical bottleneck under low-frequency inspection. Experimental evaluation demonstrates that the model accurately quantifies the aggregate cost of diverse detection strategies and provides a theoretical foundation for adaptive parameter tuning. The primary contribution is the first systematic formal modeling and empirical validation of the nonlinear trade-offs among anomaly detection parameters in cloud environments—enabling cost-effective, intelligent monitoring strategy design.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Evaluation and Analysis
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Detecting and resolving performance anomalies in Cloud services is crucial for maintaining desired performance objectives. Scaling actions triggered by an anomaly detector help achieve target latency at the cost of extra resource consumption. However, performance anomaly detectors make mistakes. This paper studies which characteristics of performance anomaly detection are important to optimize the trade-off between performance and cost. Using Stochastic Reward Nets, we model a Cloud service monitored by a performance anomaly detector. Using our model, we study the impact of detector characteristics, namely precision, recall and inspection frequency, on the average latency and resource consumption of the monitored service. Our results show that achieving a high precision and a high recall is not always necessary. If detection can be run frequently, a high precision is enough to obtain a good performance-to-cost trade-off, but if the detector is run infrequently, recall becomes the most important.

Problem

• Optimizing trade-off between cloud performance and cost
• Analyzing impact of detector precision and recall
• Determining optimal anomaly detection frequency for efficiency

Innovation

• Uses Stochastic Reward Nets for cloud modeling
• Analyzes precision recall frequency impact on performance
• Shows high precision sufficient with frequent detection