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Reimagining Self-Adaptation in the Age of Large Language Models

Raghav Donakanti, Prakhar Jain, Shubham Kulkarni, Karthik Vaidhyanathan

TL;DR

Uncertainties in software QoS motivate the need for self-adaptation. The authors propose MSE-K, an LLM-enabled extension of the MAPE-K loop, to autonomously generate and apply context-aware architectural adaptations using Monitor, Synthesize, and Execute phases guided by prompts and knowledge memory. A SWIM exemplar demonstrates that LLM-driven adaptation can stabilize response times and deliver competitive utility under varying workloads, indicating promise for GenAI-assisted self-adaptation. This work lays a foundation for scalable, human-like adaptive reasoning in software systems and outlines rich directions for future research in GenAI-enhanced self-adaptation.

Abstract

Modern software systems are subjected to various types of uncertainties arising from context, environment, etc. To this end, self-adaptation techniques have been sought out as potential solutions. Although recent advances in self-adaptation through the use of ML techniques have demonstrated promising results, the capabilities are limited by constraints imposed by the ML techniques, such as the need for training samples, the ability to generalize, etc. Recent advancements in Generative AI (GenAI) open up new possibilities as it is trained on massive amounts of data, potentially enabling the interpretation of uncertainties and synthesis of adaptation strategies. In this context, this paper presents a vision for using GenAI, particularly Large Language Models (LLMs), to enhance the effectiveness and efficiency of architectural adaptation. Drawing parallels with human operators, we propose that LLMs can autonomously generate similar, context-sensitive adaptation strategies through its advanced natural language processing capabilities. This method allows software systems to understand their operational state and implement adaptations that align with their architectural requirements and environmental changes. By integrating LLMs into the self-adaptive system architecture, we facilitate nuanced decision-making that mirrors human-like adaptive reasoning. A case study with the SWIM exemplar system provides promising results, indicating that LLMs can potentially handle different adaptation scenarios. Our findings suggest that GenAI has significant potential to improve software systems' dynamic adaptability and resilience.

Reimagining Self-Adaptation in the Age of Large Language Models

TL;DR

Uncertainties in software QoS motivate the need for self-adaptation. The authors propose MSE-K, an LLM-enabled extension of the MAPE-K loop, to autonomously generate and apply context-aware architectural adaptations using Monitor, Synthesize, and Execute phases guided by prompts and knowledge memory. A SWIM exemplar demonstrates that LLM-driven adaptation can stabilize response times and deliver competitive utility under varying workloads, indicating promise for GenAI-assisted self-adaptation. This work lays a foundation for scalable, human-like adaptive reasoning in software systems and outlines rich directions for future research in GenAI-enhanced self-adaptation.

Abstract

Modern software systems are subjected to various types of uncertainties arising from context, environment, etc. To this end, self-adaptation techniques have been sought out as potential solutions. Although recent advances in self-adaptation through the use of ML techniques have demonstrated promising results, the capabilities are limited by constraints imposed by the ML techniques, such as the need for training samples, the ability to generalize, etc. Recent advancements in Generative AI (GenAI) open up new possibilities as it is trained on massive amounts of data, potentially enabling the interpretation of uncertainties and synthesis of adaptation strategies. In this context, this paper presents a vision for using GenAI, particularly Large Language Models (LLMs), to enhance the effectiveness and efficiency of architectural adaptation. Drawing parallels with human operators, we propose that LLMs can autonomously generate similar, context-sensitive adaptation strategies through its advanced natural language processing capabilities. This method allows software systems to understand their operational state and implement adaptations that align with their architectural requirements and environmental changes. By integrating LLMs into the self-adaptive system architecture, we facilitate nuanced decision-making that mirrors human-like adaptive reasoning. A case study with the SWIM exemplar system provides promising results, indicating that LLMs can potentially handle different adaptation scenarios. Our findings suggest that GenAI has significant potential to improve software systems' dynamic adaptability and resilience.
Paper Structure (6 sections, 4 figures)

This paper contains 6 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Related Works
  3. LLM-Based Architecture for Self-Adaptation
  4. Preliminary Results and Discussion
  5. Vision for the Future and Further Research
  6. Conclusion

Figures (4)

  • Figure 1: Approach
  • Figure 2: Example of $C$ and $AD$ in SWIM
  • Figure 3: Example of $P_{SWIM}$
  • Figure 4: Results from running the GPT-4 based adaptation manager on the world cup trace in SWIM. We observe that the average response time remains stable throughout the simulation.