Serverless Architectures for Real-Time Fleet and Edge Services: A Theoretical and Empirical Synthesis

Abstract

This article presents a comprehensive, publication-ready examination of serverless computing architectures applied to real-time fleet management, edge-enabled Internet of Things (IoT) systems, and distributed stateful applications. Building strictly on the references provided, the work synthesizes existing empirical findings, theoretical frameworks, design patterns, and open problems into a cohesive narrative that frames the current state of knowledge and proposes directions for future research. The abstract summarizes the research aim, methodology, primary findings, and conclusions. The aim is to clarify how serverless paradigms—function-as-a-service (FaaS), event-driven pipelines, and serverless-enabled edge computing—can be structured to meet the latency, statefulness, scalability, and sustainability requirements of modern fleet services and IoT applications. Methodologically, the paper performs an exhaustive conceptual analysis of the literature, extracts recurring architectural motifs, and constructs a descriptive model of how these motifs interact in practice. Results reveal that serverless approaches can deliver substantial operational simplicity and cost-efficiency while exposing specific challenges: state management, cold-start latency, and coordination across heterogeneous devices. The discussion interprets these findings in light of system-design trade-offs and explores emergent patterns such as serverless-enabled fleet-as-a-service (FaaS) and the role of ARM-based low-power processors in enabling edge serverless deployments. Limitations focus on the need for rigorous empirical benchmarking across diverse deployments, and future scope outlines experimental agendas, operational guidelines, and theoretical extensions to address identified gaps. This synthesis is relevant to researchers, platform architects, and practitioners designing the next generation of distributed, event-driven, and sustainable real-time systems.