AdaptiFlow: Framework for Autonomous Cloud Microservices
Research Paper#Cloud Computing, Microservices, Autonomic Computing🔬 Research|Analyzed: Jan 3, 2026 16:05•
Published: Dec 29, 2025 14:35
•1 min read
•ArXivAnalysis
This paper introduces AdaptiFlow, a framework designed to enable self-adaptive capabilities in cloud microservices. It addresses the limitations of centralized control models by promoting a decentralized approach based on the MAPE-K loop (Monitor, Analyze, Plan, Execute, Knowledge). The framework's key contributions are its modular design, decoupling metrics collection and action execution from adaptation logic, and its event-driven, rule-based mechanism. The validation using the TeaStore benchmark demonstrates practical application in self-healing, self-protection, and self-optimization scenarios. The paper's significance lies in bridging autonomic computing theory with cloud-native practice, offering a concrete solution for building resilient distributed systems.
Key Takeaways
- •AdaptiFlow provides a framework for building self-adaptive cloud microservices.
- •It uses a decentralized approach based on the MAPE-K loop.
- •Key components include Metrics Collectors, Adaptation Actions, and an event-driven adaptation mechanism.
- •Validation demonstrates practical application in self-healing, self-protection, and self-optimization.
- •The framework bridges autonomic computing theory with cloud-native practice.
Reference / Citation
View Original"AdaptiFlow enables microservices to evolve into autonomous elements through standardized interfaces, preserving their architectural independence while enabling system-wide adaptability."