High-Performance Serverless Computing: A Systematic Literature Review on Serverless for HPC, AI, and Big Data

2026-01-14

IEEE Access

AI Summary

This study addresses the challenges serverless computing faces in supporting compute-intensive applications—such as high-performance computing, artificial intelligence, and big data—particularly concerning dynamism, parallelism, and distributed execution. Through a systematic literature review of 122 studies published between 2018 and 2025, the work constructs a taxonomy encompassing eight research directions and nine application domains, thereby revealing key technological trends and scholarly collaboration networks. The paper not only provides a comprehensive synthesis of deployment strategies, orchestration mechanisms, and optimization techniques for serverless architectures in compute-intensive scenarios but also offers a clear technical roadmap and theoretical foundation to guide future research and practical implementation.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

The widespread deployment of large-scale, compute-intensive applications such as high-performance computing, artificial intelligence, and big data is leading to convergence between cloud and high-performance computing infrastructures. Cloud providers are increasingly integrating high-performance computing capabilities in their infrastructures, such as hardware accelerators and high-speed interconnects, while researchers in the high-performance computing community are starting to explore cloud-native paradigms to improve scalability, elasticity, and resource utilization. In this context, serverless computing emerges as a promising execution model to efficiently handle highly dynamic, parallel, and distributed workloads. This paper presents a comprehensive systematic literature review of 122 research articles published between 2018 and early 2025, exploring the use of the serverless paradigm to develop, deploy, and orchestrate compute-intensive applications across cloud, high-performance computing, and hybrid environments. From these, a taxonomy comprising eight primary research directions and nine targeted use case domains is proposed, alongside an analysis of recent publication trends and collaboration networks among authors, highlighting the growing interest and interconnections within this emerging research field. Overall, this work aims to offer a valuable foundation for both new researchers and experienced practitioners, guiding the development of next-generation serverless solutions for parallel compute-intensive applications.

Problem

• serverless computing
• high-performance computing
• artificial intelligence
• big data
• compute-intensive applications

Innovation

• serverless computing
• high-performance computing
• systematic literature review
• compute-intensive applications
• cloud-native paradigms