Neuroadaptive DevOps: Real-Time ML-Driven Adaptation of Deployment Pipelines in Edge Environments

Abstract

The paradigm shift from centralized cloud architectures to decentralized edge computing has catalyzed a growing need for intelligent, adaptive software deployment processes. Traditional DevOps pipelines are predominantly static and centralized, assuming high availability of resources, low network latency, and predictable infrastructure assumptions that often fail in volatile, resource-constrained, and context-sensitive edge environments. In this paper, we propose Neuroadaptive DevOps, a novel framework that integrates real-time machine learning to autonomously reconfigure and optimize DevOps workflows in edge ecosystems. Drawing inspiration from neuroadaptive systems in human cognition, our framework is capable of learning from environmental feedback and telemetry to enable proactive decisions such as dynamic test selection, deployment delay, pipeline rollback, or configuration tuning. We present a modular architecture composed of telemetry sensors, prediction engines, adaptive policy modules, and execution agents. Our evaluation across 250 heterogeneous edge nodes demonstrates significant improvements in latency reduction, deployment success rates, and resource utilization, establishing Neuroadaptive DevOps as a promising approach for intelligent software operations in the edge computing era. This research contributes to the foundation of autonomic systems, offering critical insights for next-generation DevOps workflows that require real-time responsiveness and resilience under uncertainty.