High-Performance Serverless Computing: A Systematic Literature Review on Serverless for HPC, AI, and Big Data
Valerio Besozzi, Matteo Della Bartola, Patrizio Dazzi, Marco Danelutto
TL;DR
The paper tackles the problem of understanding how serverless computing can power compute-intensive HPC, AI, and big data workloads. It systematically synthesizes 122 studies from 2018 to early 2025, introducing a taxonomy of eight research directions and nine use-case domains, and it couples this with a bibliometric analysis of collaboration patterns. The main contributions include a comprehensive taxonomy, an integrated view of accelerators, architectures, programming models, platforms, and workflows, plus insights into trends and gaps that shape future work. The findings highlight progress in platform and scheduling innovations and identify open challenges in cold starts, data movement, and hardware heterogeneity, underscoring opportunities to advance sustainable, secure, and benchmarked high-performance serverless systems with real-world impact.
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.