Towards an Optimized Benchmarking Platform for CI/CD Pipelines

2025-10-21

AI Summary

Performance regression detection in large-scale software systems is hindered by the high overhead and low frequency of traditional benchmarking, limiting its integration into CI/CD pipelines. This paper introduces CloudBench, an efficient performance benchmarking platform designed for cloud-native CI/CD. Its core contributions are: (1) composable lightweight optimizations—including sampling, differential execution, and cache reuse—that drastically reduce benchmarking overhead; (2) an automated regression detection mechanism combining statistical hypothesis testing with SLA-aware thresholds; and (3) a highly available, declarative architecture enabling seamless integration with mainstream CI/CD toolchains. Experimental evaluation demonstrates that CloudBench achieves 99% detection accuracy while reducing average benchmark execution time by 7.3×, thereby enabling per-commit performance validation. To our knowledge, CloudBench provides the first production-ready, systematic solution for continuous performance engineering.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Evaluation and Analysis
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

Performance regressions in large-scale software systems can lead to substantial resource inefficiencies, making their early detection critical. Frequent benchmarking is essential for identifying these regressions and maintaining service-level agreements (SLAs). Performance benchmarks, however, are resource-intensive and time-consuming, which is a major challenge for integration into Continuous Integration / Continuous Deployment (CI/CD) pipelines. Although numerous benchmark optimization techniques have been proposed to accelerate benchmark execution, there is currently no practical system that integrates these optimizations seamlessly into real-world CI/CD pipelines. In this vision paper, we argue that the field of benchmark optimization remains under-explored in key areas that hinder its broader adoption. We identify three central challenges to enabling frequent and efficient benchmarking: (a) the composability of benchmark optimization strategies, (b) automated evaluation of benchmarking results, and (c) the usability and complexity of applying these strategies as part of CI/CD systems in practice. We also introduce a conceptual cloud-based benchmarking framework handling these challenges transparently. By presenting these open problems, we aim to stimulate research toward making performance regression detection in CI/CD systems more practical and effective.

Problem

• Detecting performance regressions in CI/CD pipelines early
• Optimizing resource-intensive benchmarks for efficient execution
• Integrating benchmark optimizations into practical CI/CD systems

Innovation

• Cloud-based framework for transparent benchmark optimization
• Composable strategies for accelerated CI/CD benchmarking
• Automated evaluation of performance regression results