Autopoiesis Consensus Edge Cloud Orchestration for Distributed AI Inference Services

Abstract

Edge computing infrastructures face four orchestration challenges: centralized control architectures introduce single points of failure, Byzantine consensus mechanisms bound fault tolerance under node failures, static resource allocation policies fail to accommodate dynamic workload variations and heterogeneous environments, and management frameworks agnostic to artificial intelligence (AI) workloads degrade performance in inference-intensive applications. This paper presents the Autopoiesis Consensus Edge Cloud (ACEC), an orchestration framework that integrates established distributed-systems techniques within a novel, formally defined autopoietic architecture inspired by biological self-organization. ACEC integrates four core mechanisms: a multi-master architecture enabling autonomous recursive self-orchestration at each edge node; a quality-weighted distributed consensus protocol that adjusts node influence according to operational health and AI performance metrics; an embedded federated learning scheme that jointly optimizes service placement and model evolution; and a predictive autopoiesis learning engine that drives continuous system adaptation without external intervention. The paper provides the framework’s mathematical formulation and a convergence analysis under stated assumptions. ACEC is evaluated in simulation, including a smart traffic monitoring scenario parameterized by published benchmark datasets and real detection model architectures, with a single run per configuration and no physical hardware or live camera feeds. ACEC attains a resilience index of 0.847 against 0.634–0.763 for four baselines under node failure, model accuracy of 92.3% against 85.7–88.1%, 67 ms average and 95 ms 99th-percentile end-to-end latency, and inter-zone consensus quality of 0.72–0.94, mean 0.8353. These results position ACEC as a candidate architecture for fault-tolerant edge AI orchestration, pending hardware validation and repeated trials.