AdaptiFlow: An Extensible Framework for Event-Driven Autonomy in Cloud Microservices

2025-12-29

AI Summary

Cloud-native microservices lack lightweight, decentralized adaptive capabilities in dynamic environments. Method: This paper proposes an event-driven, rule-engine-based local autonomy framework that decouples monitoring, decision-making, and execution. It introduces a service-instrumentation workflow grounded in a simplified MAPE-K model—emphasizing Monitor and Execute phases—supports declarative adaptation actions, unifies metric collection, and employs a lightweight rule engine. Contribution/Results: The framework innovatively enables decentralized self-healing, self-protection, and self-optimization, demonstrating that localized decisions can collectively emerge into system-wide adaptability. Evaluated on an enhanced TeaStore benchmark, it achieves database auto-recovery, DDoS auto-mitigation, and traffic auto-optimization with minimal code modifications, significantly improving system resilience and architectural independence.

Technology Category

• Machine Learning: Hardware-aware ML
• Multiagent Systems: Adversarial Agents
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Modern cloud architectures demand self-adaptive capabilities to manage dynamic operational conditions. Yet, existing solutions often impose centralized control models ill-suited to microservices decentralized nature. This paper presents AdaptiFlow, a framework that leverages well-established principles of autonomous computing to provide abstraction layers focused on the Monitor and Execute phases of the MAPE-K loop. By decoupling metrics collection and action execution from adaptation logic, AdaptiFlow enables microservices to evolve into autonomous elements through standardized interfaces, preserving their architectural independence while enabling system-wide adaptability. The framework introduces: (1) Metrics Collectors for unified infrastructure/business metric gathering, (2) Adaptation Actions as declarative actuators for runtime adjustments, and (3) a lightweight Event-Driven and rule-based mechanism for adaptation logic specification. Validation through the enhanced Adaptable TeaStore benchmark demonstrates practical implementation of three adaptation scenarios targeting three levels of autonomy self-healing (database recovery), self-protection (DDoS mitigation), and self-optimization (traffic management) with minimal code modification per service. Key innovations include a workflow for service instrumentation and evidence that decentralized adaptation can emerge from localized decisions without global coordination. The work bridges autonomic computing theory with cloud-native practice, providing both a conceptual framework and concrete tools for building resilient distributed systems. Future work includes integration with formal coordination models and application of adaptation techniques relying on AI agents for proactive adaptation to address complex adaptation scenarios.

Problem

• Centralized control models are unsuitable for decentralized microservices architectures.
• Microservices lack standardized interfaces for autonomous adaptation while preserving independence.
• Existing solutions fail to bridge autonomic computing theory with cloud-native practice.

Innovation

• Decouples metrics collection and action execution from adaptation logic
• Introduces lightweight event-driven rule-based adaptation mechanism
• Enables decentralized adaptation through standardized interfaces preserving independence