# An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing Systems

arXiv: https://arxiv.org/abs/1606.06543 · HTML: https://mlsanity.com/paper/1606.06543/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2016.

## Built on by (most cited first, 17 of 17)

- [Multi-Objectivizing Software Configuration Tuning (for a single performance concern)](https://mlsanity.com/paper/2106.01331/) (2021)
- [Do Performance Aspirations Matter for Guiding Software Configuration Tuning?](https://mlsanity.com/paper/2301.03290/) (2023)
- [MMO: Meta Multi-Objectivization for Software Configuration Tuning](https://mlsanity.com/paper/2112.07303/) (2021)
- [The Weights can be Harmful: Pareto Search versus Weighted Search in Multi-Objective Search-Based Software Engineering](https://mlsanity.com/paper/2202.03728/) (2022)
- [Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?](https://mlsanity.com/paper/2201.07096/) (2022)
- [Predicting Software Performance with Divide-and-Learn](https://mlsanity.com/paper/2306.06651/) (2023)
- [Adapting Multi-objectivized Software Configuration Tuning](https://mlsanity.com/paper/2404.04744/) (2024)
- [Unicorn: Reasoning about Configurable System Performance through the lens of Causality](https://mlsanity.com/paper/2201.08413/) (2022)
- [Planning Landscape Analysis for Self-Adaptive Systems](https://mlsanity.com/paper/2203.12472/) (2022)
- [Does Configuration Encoding Matter in Learning Software Performance? An Empirical Study on Encoding Schemes](https://mlsanity.com/paper/2203.15988/) (2022)
- [Faster Configuration Performance Bug Testing with Neural Dual-level Prioritization](https://mlsanity.com/paper/2501.15392/) (2025)
- [GOSH: Task Scheduling Using Deep Surrogate Models in Fog Computing Environments](https://mlsanity.com/paper/2112.08916/) (2021)
- [Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes](https://mlsanity.com/paper/2603.19897/) (2026)
- [Dividable Configuration Performance Learning](https://mlsanity.com/paper/2409.07629/) (2024)
- [CoTune: Co-evolutionary Configuration Tuning](https://mlsanity.com/paper/2509.24694/) (2025)
- [Evaluating Useful Surrogate Models for Configuration Tuning Beyond Accuracy: A Fitness Landscape Analysis Perspective](https://mlsanity.com/paper/2509.21945/) (2025)
- [When to Use Which? Benchmarking Optimisers for Configurable Systems under Varying Budgets](https://mlsanity.com/paper/2607.16476/) (2026)

## Abstract (arXiv)

Finding optimal configurations for Stream Processing Systems (SPS) is a challenging problem due to the large number of parameters that can influence their performance and the lack of analytical models to anticipate the effect of a change. To tackle this issue, we consider tuning methods where an experimenter is given a limited budget of experiments and needs to carefully allocate this budget to find optimal configurations. We propose in this setting Bayesian Optimization for Configuration Optimization (BO4CO), an auto-tuning algorithm that leverages Gaussian Processes (GPs) to iteratively capture posterior distributions of the configuration spaces and sequentially drive the experimentation. Validation based on Apache Storm demonstrates that our approach locates optimal configurations within a limited experimental budget, with an improvement of SPS performance typically of at least an order of magnitude compared to existing configuration algorithms.