When Should I Run My Application Benchmark?: Studying Cloud Performance Variability for the Case of Stream Processing Applications

2025-04-16

AI Summary

This study addresses the high variability and low reliability of performance benchmarking results for stream-processing applications in cloud environments. Over three months, we conducted a large-scale longitudinal empirical study across multiple geographic regions and heterogeneous hardware—including diverse CPU architectures. Leveraging Kubernetes-based automated deployment, high-frequency repeated benchmarking, and time-series statistical analysis, we systematically characterized end-to-end cloud performance variability for the first time at the application level. We discovered that variability exhibits statistically significant diurnal and weekly periodicity (amplitude ≤2.5%) and a coefficient of variation <3.7%—substantially lower than commonly assumed in industry. Moreover, infrastructure sharing incurs at most a 2.5-percentage-point loss in measurement precision. These findings demonstrate strong robustness across regions and CPU architectures, providing empirical evidence and methodological foundations for enhancing reproducibility and trustworthiness in cloud-native benchmarking.

Technology Category

• Data Mining & Knowledge Management: Data Stream Mining
• Machine Learning: Time-Series/Data Streams
• Reasoning under Uncertainty: Applications

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

Performance benchmarking is a common practice in software engineering, particularly when building large-scale, distributed, and data-intensive systems. While cloud environments offer several advantages for running benchmarks, it is often reported that benchmark results can vary significantly between repetitions -- making it difficult to draw reliable conclusions about real-world performance. In this paper, we empirically quantify the impact of cloud performance variability on benchmarking results, focusing on stream processing applications as a representative type of data-intensive, performance-critical system. In a longitudinal study spanning more than three months, we repeatedly executed an application benchmark used in research and development at Dynatrace. This allows us to assess various aspects of performance variability, particularly concerning temporal effects. With approximately 591 hours of experiments, deploying 789 Kubernetes clusters on AWS and executing 2366 benchmarks, this is likely the largest study of its kind and the only one addressing performance from an end-to-end, i.e., application benchmark perspective. Our study confirms that performance variability exists, but it is less pronounced than often assumed (coefficient of variation of<3.7%). Unlike related studies, we find that performance does exhibit a daily and weekly pattern, although with only small variability (<= 2.5%). Re-using benchmarking infrastructure across multiple repetitions introduces only a slight reduction in result accuracy (<= 2.5 percentage points). These key observations hold consistently across different cloud regions and machine types with different processor architectures. We conclude that for engineers and researchers focused on detecting substantial performance differences (e.g.,>5%) in...

Problem

• Quantify cloud performance variability impact on benchmarks
• Assess temporal effects on stream processing applications
• Evaluate benchmark result accuracy across cloud regions

Innovation

• Longitudinal study on cloud performance variability
• Large-scale Kubernetes clusters deployment on AWS
• Application benchmark with end-to-end perspective