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ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

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Systematic Review Article

Emerging Paradigms in Distributed Computing: A Systematic Review of Serverless Edge Technologies

Author(s): Sana Bharti*, Rupali Gill, Shilpi Harnal and Salil Bharany

Volume 20, Issue 8, 2026

Published on: 31 March, 2026

Article ID: e18722121424408

Pages: 16

DOI: 10.2174/0118722121424408251204210812

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Abstract

Introduction: Both serverless and edge computing represent major technological shifts that can significantly reduce response times for latency-sensitive applications such as IoT and smart city solutions. Their integration, termed serverless edge computing, enables very low latency, scalable, and cost-effective solutions, transitioning from monolithic to event-driven architectures for improved modularity and deployment.

Methods: A bibliometric and systematic review was conducted on 11,604 articles from databases including IEEE Xplore, Web of Science, ScienceDirect, and ACM, published between 2014 and May 2024. After applying inclusion and exclusion criteria, 2,019 relevant articles were analysed using Biblioshiny for keyword co-occurrence, institutional and geographical contributions, author metrics, publication venues, and research focus.

Results: The analysis indicates a growing research interest in serverless edge computing, with China leading in the number of publications, followed by India, Italy, and Germany. Top contributing universities included Shanghai Jiao Tong University and Huazhong University. The most commonly used platforms and tools were AWS Lambda, OpenFaaS, Kubernetes, and Docker. Key limitations identified were cold start latency, task offloading, AI-based scheduling, and microservice coordination. The rapid growth of the field was reflected in the evolving word cloud, increasing number of references, and a high compound annual growth rate of 57.6%.

Discussion: Despite rapid progress, the field faces significant challenges, including limited edge resources, security vulnerabilities, insufficient automation, and complexity in function orchestration. The increasing application of AI methods for workload scheduling and resource optimisation points to the development of smart, self-adjusting serverless edge environments. However, aspects such as security, quality-of-service guarantees, and multi-tenancy remain underexplored. While there is substantial academic research, patent data could also provide insights into innovation and industrial uptake trends.

Conclusion: Serverless edge computing has transformed distributed systems by enabling low-latency, resource-efficient deployments for real-time applications. To support its mainstream adoption, future research must address orchestration, dynamic scheduling, privacy, and interoperability. Integrating 5G, blockchain, and quantum computing with AI-driven models can further enhance resilience, performance, and scalability across critical sectors such as healthcare, industrial automation, and smart cities.

Keywords: Serverless computing, edge computing, microservices, FaaS, smart city solutions, internet of things.

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