# Cloud, Fog or Edge: Where to Compute?

arXiv: https://arxiv.org/abs/2101.10417 · HTML: https://mlsanity.com/paper/2101.10417/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2021.

## Built on by (most cited first, 4 of 4)

- [UniLCD: Unified Local-Cloud Decision-Making via Reinforcement Learning](https://mlsanity.com/paper/2409.11403/) (2024)
- [Multi-Dimensional Autoscaling of Stream Processing Services on Edge Devices](https://mlsanity.com/paper/2510.06882/) (2025)
- [A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remedies](https://mlsanity.com/paper/2102.07258/) (2021)
- [Decentralized Machine Learning for Intelligent Health Care Systems on the Computing Continuum](https://mlsanity.com/paper/2207.14584/) (2022)

## Abstract (arXiv)

The computing continuum extends the high-performance cloud data centers with energy-efficient and low-latency devices close to the data sources located at the edge of the network. However, the heterogeneity of the computing continuum raises multiple challenges related to application management. These include where to offload an application - from the cloud to the edge - to meet its computation and communication requirements. To support these decisions, we provide in this article a detailed performance and carbon footprint analysis of a selection of use case applications with complementary resource requirements across the computing continuum over a real-life evaluation testbed.