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

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.

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