ML-Aided Adaptation in Self-Adaptive Systems

• Machine Learning–Aided Adaptation is a paradigm that integrates ML models into self-adaptive systems to reconfigure operations under uncertain, dynamic conditions.
• Techniques such as surrogate modeling, online incremental learning, and deep neural reduction reliably prune vast adaptation spaces, achieving over 90% reduction in some case studies.
• Emerging approaches employ lifelong learning and large language models to detect drift and enable human-level reasoning for complex system trade-offs.

Machine Learning–Aided Adaptation

Machine learning–aided adaptation is an overarching paradigm in which learning-based models and algorithms are integrated into adaptive software and cyber-physical systems to enable context-, environment-, or workload-dependent reconfiguration and control. By leveraging statistical inference, predictive modeling, and, more recently, LLMs, ML approaches address key challenges of modern self-adaptive systems: handling multidimensional uncertainty, scaling decision-making in large adaptation spaces, reducing development overhead, coping with drift in system behavior, and supporting human-level reasoning for complex trade-offs. This article details the foundational concepts, principal methodologies, illustrative case studies, and active challenges associated with machine learning–aided adaptation, with reference to current research frontiers.

1. Foundational Architecture and Problem Setting

Self-adaptive systems implement closed-loop adaptation by monitoring execution and environment, analyzing data, planning and executing adaptation actions, and maintaining a knowledge base (the MAPE-K loop). The core objective is to dynamically adjust configuration or behavior to maintain or optimize non-functional properties (e.g., performance, reliability, energy) under uncertainty. Uncertainties are typically classified as:

• Contextual uncertainty: variations in internal components or resources (e.g., failures, resource changes).
• Environmental uncertainty: changes in ambient infrastructure or network conditions.
• Workload uncertainty: fluctuations in demand or traffic patterns (Donakanti et al., 2024).

In this setting, the decision space—possible adaptation options—often scales combinatorially, making exhaustive evaluation intractable. Classical ML approaches (e.g., regression, classification, reinforcement learning) have been employed to predict or rank candidate adaptations, prune infeasible options, and generalize from prior experience. However, standard methods have two principal limitations: (1) high data requirements (data hunger), and (2) limited ability to extrapolate to novel or out-of-distribution scenarios, sometimes necessitating costly retraining (Donakanti et al., 2024, Quin et al., 2023).

2. Machine Learning Approaches for Adaptation Space Reduction

A primary application of ML in adaptive systems is adaptation space reduction: efficiently selecting a subset of promising configuration options from a large combinatorial adaptation space, thereby enabling tractable formal verification or detailed simulation.

Representative Techniques

• Surrogate Modeling for Pruning: Linear regression or more sophisticated predictive models estimate quality-of-service (QoS) attributes for configurations; options unlikely to satisfy system goals are pruned prior to expensive analysis. Tight theoretical error bounds on regression performance can be derived under Vapnik–Chervonenkis theory, ensuring that the residual selection error is controlled when composed with statistical model checking (Gheibi et al., 2021).
• ML2ASR⁺ Approach: Combines feature extraction, online incremental learners (classifiers for constraint goals; regressors for optimization goals), and runtime subspace filtering to achieve >90% reduction in adaptation space with negligible utility loss across both IoT and service-oriented domains (Quin et al., 2023).
• End-to-End Deep Learning Reduction (DLASeR⁺): Employs a deep network with goal-specific heads (classification for thresholds/setpoints, regression for optimization) directly on raw configuration/context features, supporting three types of goals and eliminating the need for manual feature engineering. Online training delivers >90% reduction in space and time across thousands of options (Weyns et al., 2022).

Empirical Impact

In case studies (e.g., DeltaIoT with 4 096 configurations), ML-based reducers maintained utility penalties below 0.05% for threshold goals, with analysis time reduced by 90–99% (Quin et al., 2023, Weyns et al., 2022).

3. Handling Drift and Lifelong Adaptation

Static ML models, once deployed for adaptation, can become obsolete as the distributions of system/environmental variables or quality metrics drift. Drift of adaptation spaces (i.e., changes in which configurations yield high quality) leads to the appearance of novel classes of options or regions in the quality space that are not covered by the original model (Gheibi et al., 2022).

Lifelong Self-Adaptation Layer

To address this, a lifelong machine learning meta-layer is integrated atop the MAPE-K loop:

• Task Detection and Labeling: Tracks recent adaptation decisions and detects distributional shifts via statistical tests (e.g., Mahalanobis distance, sliding-window GMM, BIC “elbow” method).
• Model Evolution: On detecting a novel region (class), structurally updates the classifier (typically a GMM-based model), with option for human-in-the-loop refinement of class boundaries and preference ranking.
• Meta-Update Protocol: Automatically or semi-automatically retrains or extends the adaptation classifier to incorporate new knowledge, ensuring robust operation under unforeseen drift (Gheibi et al., 2022).

In validation (DeltaIoT), lifelong adaptation with operator feedback preserved near-optimal utility and qualitative behaviors even under adversarial drift scenarios, maintaining performance indistinguishable from drift-free baselines (utility ≈0.80 vs. ≈0.84, RSM ≈0.06 vs ≈0.002) (Gheibi et al., 2022).

4. LLMs for Adaptive Reasoning

Recent advances in generative AI, notably LLMs, have opened new capabilities for adaptation, enabling contextual, human-like reasoning over raw system logs and metrics without the need for extensive labeled training for every possible scenario.

MSE-K (Monitor, Synthesize, Execute – Knowledge) Loop

The MSE-K architecture reframes self-adaptation as a natural-language reasoning problem:

• Monitor: Aggregates logs and QoS metrics into context vectors, appended to the knowledge base.
• Knowledge: Stores adaptation history, prompt repositories with objectives, and manages a pool of fine-tuned LLM agents for specialized roles.
• Synthesize: Constructs prompts from context, history, and objectives, invokes the LLM to generate adaptation decisions in natural language, and parses responses into concrete adaptation actions.
• Execute: Optionally verifies quantitative constraints before applying the adaptation to the system.

The decision function at time t is given by:

$$\text{AD}_t = \mathrm{LLM}(P_t), \quad P_t = \mathrm{Prompt}(C_t, H_{t-1}, O, \text{few-shot}),$$

with system utility guided by a cumulative function

$U = \sum_{t=1}^T u(C_t, \text{AD}_t),$

where $u$ incorporates response time, resource usage, and quality factors (Donakanti et al., 2024).

Empirical Results

In a SWIM web-infrastructure case study, a GPT-4 adaptation manager maintained response times $<0.1$ s with smooth performance, achieving 71% of the baseline’s overall utility but with significantly fewer latency spikes. Notably, this was achieved without scenario-specific retraining, demonstrating the capability of LLMs to generalize adaptive strategies using their pre-trained knowledge (Donakanti et al., 2024).

5. Practical Adaptation Scenarios and Subdomains

Machine learning–aided adaptation applies across diverse domains and application settings:

Domain	ML-Aided Adaptation Role	Key Reference
IoT Networks, Cyber-Physical	Large-space reduction, drift detection, robust decision making	(Quin et al., 2023Weyns et al., 2022Gheibi et al., 2022)
Edge/Cloud Computing	Rapid sub-model selection, block-level decomposition, efficient aggregation	(Zhuang et al., 2023)
Green AI/ML Systems	Energy-accuracy trade-off via real-time model balancing	(Tedla et al., 2024)
Mobile Devices	On-device drift detection, root-cause analysis, cause-specific adaptation	(Hao et al., 2023)
High-Performance Computing	Code variant selection, autotuning via ML-predictors	(Georgakoudis et al., 2023)
Automated ML Pipelines	Online drift adaptation strategies, evolving data stream handling	(Celik et al., 2020Bakirov et al., 2018)
Autonomous Robotics	ML-based Pareto-front synthesis for tractable MDP planning	(Jamshidi et al., 2019)

For example, in edge–cloud collaborative ML, ECLM assigns block-level modules to devices and aggregates updated parameters using resource-aware local sub-models, achieving up to 18.89% accuracy improvement and 7.12× communication reduction in highly non-IID, dynamic settings (Zhuang et al., 2023). Energy-aware architectures such as EcoMLS dynamically switch among ML models (e.g., YOLOv5 variants) to optimize the energy-confidence trade-off at runtime, reducing energy costs by up to 84% with minimal accuracy loss (Tedla et al., 2024).

6. Limitations, Open Challenges, and Prospects

Acknowledged Limitations

• Quantitative Reasoning Limitations: LLM-based methods may struggle with complex quantitative trade-offs requiring explicit numeric modeling; hybridizing with domain-specific submodels or verification is an open avenue (Donakanti et al., 2024).
• Hallucination Risks and Verification: Natural-language adaptation proposals from generative models may not always satisfy quantitative constraints, necessitating pre-execution verification (Donakanti et al., 2024).
• Drift-Adaptation Boundaries: Even lifelong learning approaches require operator input to maintain alignment of adaptation goals as fundamentally novel classes or objectives emerge (Gheibi et al., 2022).
• Context-Length and Scalability: Memory and token limitations hinder LLM-based control loops for long histories; research into extended memory architectures is ongoing (e.g., MemGPT, StreamingLLM) (Donakanti et al., 2024).
• Domain Generalization: While deep learning can obviate feature engineering, empirical validation across more domains and larger adaptation spaces is still needed (Weyns et al., 2022).

Future Directions

• Multi-agent architectures, with LLMs assigned to specialized roles (log analysis, metric forecasting), to enhance scalability and specialization.
• Integration of reinforcement learning using system feedback (RLSF) for continual adaptation and policy improvement.
• Hybrid learning–verification pipelines for formal guarantees on adaptive decisions.
• Automated adjustment of exploration/exploitation parameters (e.g., $\epsilon$ in EcoMLS, adaptive module selection in ECLM) for optimal trade-offs in online learning.
• Privacy-preserving federated adaptation and differentially private module updates, especially in edge and mobile scenarios (Zhuang et al., 2023, Hao et al., 2023).

7. Synthesis and Outlook

Machine learning–aided adaptation constitutes a central research thrust for enabling agile, resilient, and efficient self-adaptive systems across computing, cyber-physical, and software domains. By strategically integrating statistical and neural inference, lifelong learning, and generative models within closed-loop architectures, these systems achieve scalable and context-sensitive decision making under multidimensional uncertainty. Continued progress towards hybrid ML–formal reasoning pipelines, scalable lifelong learning, and robust handling of drift and distributional shift will further expand the operational boundaries and reliability guarantees attainable through machine learning–aided adaptation (Donakanti et al., 2024, Quin et al., 2023, Gheibi et al., 2022).