# ENORM: A Framework For Edge NOde Resource Management

arXiv: https://arxiv.org/abs/1709.04061 · HTML: https://mlsanity.com/paper/1709.04061/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2017.

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

- [Management of Resource at the Network Edge for Federated Learning](https://mlsanity.com/paper/2107.03428/) (2021)
- [AI Back-End as a Service for Learning Switching of Mobile Apps between the Fog and the Cloud](https://mlsanity.com/paper/2110.00836/) (2021)
- [ScissionLite: Accelerating Distributed Deep Neural Networks Using Transfer Layer](https://mlsanity.com/paper/2105.02019/) (2021)
- [Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments](https://mlsanity.com/paper/2505.06268/) (2025)

## Abstract (arXiv)

Current computing techniques using the cloud as a centralised server will become untenable as billions of devices get connected to the Internet. This raises the need for fog computing, which leverages computing at the edge of the network on nodes, such as routers, base stations and switches, along with the cloud. However, to realise fog computing the challenge of managing edge nodes will need to be addressed. This paper is motivated to address the resource management challenge. We develop the first framework to manage edge nodes, namely the Edge NOde Resource Management (ENORM) framework. Mechanisms for provisioning and auto-scaling edge node resources are proposed. The feasibility of the framework is demonstrated on a PokeMon Go-like online game use-case. The benefits of using ENORM are observed by reduced application latency between 20% - 80% and reduced data transfer and communication frequency between the edge node and the cloud by up to 95%. These results highlight the potential of fog computing for improving the quality of service and experience.