# Fair Resource Allocation in Virtualized O-RAN Platforms

arXiv: https://arxiv.org/abs/2402.11285 · HTML: https://mlsanity.com/paper/2402.11285/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 92 references that are in the map.
- First posted on arXiv 17 Feb 2024.

## Builds on (most cited first, 23 of 23)

- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [Online Learning: A Modern Introduction Using Convex Optimization](https://mlsanity.com/paper/1912.13213/) (2019)
- [Optimization, Learning, and Games with Predictable Sequences](https://mlsanity.com/paper/1311.1869/) (2013)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Bandits with concave rewards and convex knapsacks](https://mlsanity.com/paper/1402.5758/) (2014)
- [Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks](https://mlsanity.com/paper/2012.01263/) (2020)
- [Individual Fairness in Hindsight](https://mlsanity.com/paper/1812.04069/) (2018)
- [Fair Exploration via Axiomatic Bargaining](https://mlsanity.com/paper/2106.02553/) (2021)
- [dApps: Distributed Applications for Real-time Inference and Control in O-RAN](https://mlsanity.com/paper/2203.02370/) (2022)
- [Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve](https://mlsanity.com/paper/2105.05308/) (2021)
- [MISO: Exploiting Multi-Instance GPU Capability on Multi-Tenant Systems for Machine Learning](https://mlsanity.com/paper/2207.11428/) (2022)
- [Enabling Long-term Fairness in Dynamic Resource Allocation](https://mlsanity.com/paper/2208.05898/) (2022)
- [Optimistic No-regret Algorithms for Discrete Caching](https://mlsanity.com/paper/2208.06414/) (2022)
- [Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing](https://mlsanity.com/paper/1703.06060/) (2017)
- [Group-Fair Online Allocation in Continuous Time](https://mlsanity.com/paper/2006.06852/) (2020)
- [Energy-aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning](https://mlsanity.com/paper/2208.09956/) (2022)
- [Nonstationary Dual Averaging and Online Fair Allocation](https://mlsanity.com/paper/2202.11614/) (2022)
- [Computationally Aware Sum-Rate Optimal Scheduling for Centralized Radio Access Networks](https://mlsanity.com/paper/1507.01971/) (2015)
- [A Market-Based Framework for Multi-Resource Allocation in Fog Computing](https://mlsanity.com/paper/1807.09756/) (2018)
- [FALCON: An accurate real-time monitor for client-based mobile network data analytics](https://mlsanity.com/paper/1907.10110/) (2019)
- [Dominant Resource Fairness in Cloud Computing Systems with Heterogeneous Servers](https://mlsanity.com/paper/1308.0083/) (2013)
- [Multi-resource fairness: Objectives, algorithms and performance](https://mlsanity.com/paper/1410.0782/) (2014)
- [Fairness Guaranteed and Auction-based x-haul and Cloud Resource Allocation in Multi-tenant O-RANs](https://mlsanity.com/paper/2301.00597/) (2023)

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

- [On the Dynamic Regret of Following the Regularized Leader: Optimism with History Pruning](https://mlsanity.com/paper/2505.22899/) (2025)
- [Optimistic Online Non-stochastic Control via FTRL](https://mlsanity.com/paper/2404.03309/) (2024)
- [Optimistic Learning for Communication Networks](https://mlsanity.com/paper/2504.03499/) (2025)
- [Equitable Multi-Task Learning for AI-RANs](https://mlsanity.com/paper/2603.08717/) (2026)

## Abstract (arXiv)

O-RAN systems and their deployment in virtualized general-purpose computing platforms (O-Cloud) constitute a paradigm shift expected to bring unprecedented performance gains. However, these architectures raise new implementation challenges and threaten to worsen the already-high energy consumption of mobile networks. This paper presents first a series of experiments which assess the O-Cloud's energy costs and their dependency on the servers' hardware, capacity and data traffic properties which, typically, change over time. Next, it proposes a compute policy for assigning the base station data loads to O-Cloud servers in an energy-efficient fashion; and a radio policy that determines at near-real-time the minimum transmission block size for each user so as to avoid unnecessary energy costs. The policies balance energy savings with performance, and ensure that both of them are dispersed fairly across the servers and users, respectively. To cater for the unknown and time-varying parameters affecting the policies, we develop a novel online learning framework with fairness guarantees that apply to the entire operation horizon of the system (long-term fairness). The policies are evaluated using trace-driven simulations and are fully implemented in an O-RAN compatible system where we measure the energy costs and throughput in realistic scenarios.