# Edge to Cloud Tools: A Multivocal Literature Review

arXiv: https://arxiv.org/abs/2305.17464 · HTML: https://mlsanity.com/paper/2305.17464/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2023.

## Built on by (most cited first, 3 of 3)

- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [6GSoft: Software for Edge-to-Cloud Continuum](https://mlsanity.com/paper/2407.05963/) (2024)
- [Hardware optimization on Android for inference of AI models](https://mlsanity.com/paper/2511.13453/) (2025)

## Abstract (arXiv)

Edge-to-cloud computing is an emerging paradigm for distributing computational tasks between edge devices and cloud resources. Different approaches for orchestration, offloading, and many more purposes have been introduced in research. However, it is still not clear what has been implemented in the industry. This work aims to merge this gap by mapping the existing knowledge on edge-to-cloud tools by providing an overview of the current state of research in this area and identifying research gaps and challenges. For this purpose, we conducted a Multivocal Literature Review (MLR) by analyzing 40 tools from 1073 primary studies (220 PS from the white literature and 853 PS from the gray literature). We categorized the tools based on their characteristics and targeted environments. Overall, this systematic mapping study provides a comprehensive overview of edge-to-cloud tools and highlights several opportunities for researchers and practitioners for future research in this area.