# Big Data Caching for Networking: Moving from Cloud to Edge

arXiv: https://arxiv.org/abs/1606.01581 · HTML: https://mlsanity.com/paper/1606.01581/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2016.

## Built on by (most cited first, 8 of 8)

- [Decentralized Inference with Graph Neural Networks in Wireless Communication Systems](https://mlsanity.com/paper/2104.09027/) (2021)
- [TrimCaching: Parameter-sharing AI Model Caching in Wireless Edge Networks](https://mlsanity.com/paper/2405.03990/) (2024)
- [Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks](https://mlsanity.com/paper/2208.06963/) (2022)
- [Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile Edge Computing Networks](https://mlsanity.com/paper/2207.00755/) (2022)
- [Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization](https://mlsanity.com/paper/2305.13503/) (2023)
- [DRAGON: Decentralized Fault Tolerance in Edge Federations](https://mlsanity.com/paper/2208.07658/) (2022)
- [Baechi: Fast Device Placement of Machine Learning Graphs](https://mlsanity.com/paper/2301.08695/) (2023)
- [Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach](https://mlsanity.com/paper/2206.05894/) (2022)

## Abstract (arXiv)

In order to cope with the relentless data tsunami in 5G wireless networks, current approaches such as acquiring new spectrum, deploying more base stations (BSs) and increasing nodes in mobile packet core networks are becoming ineffective in terms of scalability, cost and flexibility. In this regard, context-aware 5G networks with edge/cloud computing and exploitation of big data analytics can yield significant gains to mobile operators. In this article, proactive content caching in 5G wireless networks is investigated in which a big data-enabled architecture is proposed. In this practical architecture, vast amount of data is harnessed for content popularity estimation and strategic contents are cached at the BSs to achieve higher users' satisfaction and backhaul offloading. To validate the proposed solution, we consider a real-world case study where several hours of mobile data traffic is collected from a major telecom operator in Turkey and a big data-enabled analysis is carried out leveraging tools from machine learning. Based on the available information and storage capacity, numerical studies show that several gains are achieved both in terms of users' satisfaction and backhaul offloading. For example, in the case of 16 BSs with 30% of content ratings and 13 Gbyte of storage size (78% of total library size), proactive caching yields 100% of users' satisfaction and offloads 98% of the backhaul.