# Distilled Lifelong Self-Adaptation for Configurable Systems

arXiv: https://arxiv.org/abs/2501.00840 · HTML: https://mlsanity.com/paper/2501.00840/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 70 references that are in the map.
- First posted on arXiv 1 Jan 2025.

## Builds on (most cited first, 23 of 23)

- [Finding Faster Configurations using FLASH](https://mlsanity.com/paper/1801.02175/) (2018)
- [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)
- [Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis](https://mlsanity.com/paper/1709.02280/) (2017)
- [MMO: Meta Multi-Objectivization for Software Configuration Tuning](https://mlsanity.com/paper/2112.07303/) (2021)
- [FEMOSAA: Feature Guided and Knee Driven Multi-Objective Optimization for Self-Adaptive Software](https://mlsanity.com/paper/1608.08933/) (2016)
- [MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements](https://mlsanity.com/paper/2012.03011/) (2020)
- [Self-Adaptive Trade-off Decision Making for Autoscaling Cloud-Based Services](https://mlsanity.com/paper/1608.05917/) (2016)
- [Predicting Configuration Performance in Multiple Environments with Sequential Meta-learning](https://mlsanity.com/paper/2402.03183/) (2024)
- [BiLO-CPDP: Bi-Level Programming for Automated Model Discovery in Cross-Project Defect Prediction](https://mlsanity.com/paper/2008.13489/) (2020)
- [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)
- [Transfer Learning for Improving Model Predictions in Highly Configurable Software](https://mlsanity.com/paper/1704.00234/) (2017)
- [Planning Landscape Analysis for Self-Adaptive Systems](https://mlsanity.com/paper/2203.12472/) (2022)
- [Synergizing Domain Expertise with Self-Awareness in Software Systems: A Patternized Architecture Guideline](https://mlsanity.com/paper/2001.07076/) (2020)
- [DATESSO: Self-Adapting Service Composition with Debt-Aware Two Levels Constraint Reasoning](https://mlsanity.com/paper/2003.14377/) (2020)
- [White-Box Performance-Influence Models: A Profiling and Learning Approach](https://mlsanity.com/paper/2102.06395/) (2021)
- [Deep Configuration Performance Learning: A Systematic Survey and Taxonomy](https://mlsanity.com/paper/2403.03322/) (2024)
- [Faster Discovery of Faster System Configurations with Spectral Learning](https://mlsanity.com/paper/1701.08106/) (2017)
- [Online QoS Modeling in the Cloud: A Hybrid and Adaptive Multi-Learners Approach](https://mlsanity.com/paper/1504.03961/) (2015)
- [The Vision of Self-Evolving Computing Systems](https://mlsanity.com/paper/2204.06825/) (2022)
- [Towards Adaptive Compliance](https://mlsanity.com/paper/1611.05626/) (2016)

## Built on by (most cited first, 12 of 12)

- [Faster Configuration Performance Bug Testing with Neural Dual-level Prioritization](https://mlsanity.com/paper/2501.15392/) (2025)
- [Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning](https://mlsanity.com/paper/2507.08730/) (2025)
- [Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes](https://mlsanity.com/paper/2603.19897/) (2026)
- [Learning Software Bug Reports: A Systematic Literature Review](https://mlsanity.com/paper/2507.04422/) (2025)
- [CoTune: Co-evolutionary Configuration Tuning](https://mlsanity.com/paper/2509.24694/) (2025)
- [Light over Heavy: Automated Performance Requirements Quantification with Linguistic Inducement](https://mlsanity.com/paper/2511.03421/) (2025)
- [Evaluating Useful Surrogate Models for Configuration Tuning Beyond Accuracy: A Fitness Landscape Analysis Perspective](https://mlsanity.com/paper/2509.21945/) (2025)
- [Conjecture and Inquiry: Quantifying Software Performance Requirements via Interactive Retrieval-Augmented Preference Elicitation](https://mlsanity.com/paper/2604.21380/) (2026)
- [CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG](https://mlsanity.com/paper/2605.08333/) (2026)
- [When to Use Which? Benchmarking Optimisers for Configurable Systems under Varying Budgets](https://mlsanity.com/paper/2607.16476/) (2026)
- [LLMSYS-HPOBench: Hyperparameter Optimization Benchmark Suite for Real-World LLM Systems](https://mlsanity.com/paper/2605.08305/) (2026)
- [Configuring RAG Systems: What Breaks, Where Originates, and How to Fix](https://mlsanity.com/paper/2610.18567/) (2026)

## Abstract (arXiv)

Modern configurable systems provide tremendous opportunities for engineering future intelligent software systems. A key difficulty thereof is how to effectively self-adapt the configuration of a running system such that its performance (e.g., runtime and throughput) can be optimized under time-varying workloads. This unfortunately remains unaddressed in existing approaches as they either overlook the available past knowledge or rely on static exploitation of past knowledge without reasoning the usefulness of information when planning for self-adaptation. In this paper, we tackle this challenging problem by proposing DLiSA, a framework that self-adapts configurable systems. DLiSA comes with two properties: firstly, it supports lifelong planning, and thereby the planning process runs continuously throughout the lifetime of the system, allowing dynamic exploitation of the accumulated knowledge for rapid adaptation. Secondly, the planning for a newly emerged workload is boosted via distilled knowledge seeding, in which the knowledge is dynamically purified such that only useful past configurations are seeded when necessary, mitigating misleading information. Extensive experiments suggest that the proposed DLiSA significantly outperforms state-of-the-art approaches, demonstrating a performance improvement of up to 229% and a resource acceleration of up to 2.22x on generating promising adaptation configurations. All data and sources can be found at our repository: https://github.com/ideas-labo/dlisa.