# Service Placement in Small Cell Networks Using Distributed Best Arm Identification in Linear Bandits

arXiv: https://arxiv.org/abs/2506.22480 · HTML: https://mlsanity.com/paper/2506.22480/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 43 references that are in the map.
- First posted on arXiv 22 Jun 2025.

## Builds on (most cited first, 19 of 19)

- [On the Complexity of Best Arm Identification in Multi-Armed Bandit Models](https://mlsanity.com/paper/1407.4443/) (2014)
- [Best-Arm Identification in Linear Bandits](https://mlsanity.com/paper/1409.6110/) (2014)
- [Optimal Best-arm Identification in Linear Bandits](https://mlsanity.com/paper/2006.16073/) (2020)
- [Federated Linear Contextual Bandits](https://mlsanity.com/paper/2110.14177/) (2021)
- [Federated Multi-Armed Bandits](https://mlsanity.com/paper/2101.12204/) (2021)
- [Fully adaptive algorithm for pure exploration in linear bandits](https://mlsanity.com/paper/1710.05552/) (2017)
- [Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Bandits](https://mlsanity.com/paper/1904.03293/) (2019)
- [A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits](https://mlsanity.com/paper/2207.03106/) (2022)
- [Exploiting Heterogeneity in Robust Federated Best-Arm Identification](https://mlsanity.com/paper/2109.05700/) (2021)
- [Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost](https://mlsanity.com/paper/2205.13170/) (2022)
- [Meta-Learning for Simple Regret Minimization](https://mlsanity.com/paper/2202.12888/) (2022)
- [On Elimination Strategies for Bandit Fixed-Confidence Identification](https://mlsanity.com/paper/2205.10936/) (2022)
- [Federated Best Arm Identification with Heterogeneous Clients](https://mlsanity.com/paper/2210.07780/) (2022)
- [Budget-constrained Edge Service Provisioning with Demand Estimation via Bandit Learning](https://mlsanity.com/paper/1903.09080/) (2019)
- [A Survey on Mobile Edge Computing for Video Streaming: Opportunities and Challenges](https://mlsanity.com/paper/2209.05761/) (2022)
- [Content Popularity Prediction Towards Location-Aware Mobile Edge Caching](https://mlsanity.com/paper/1809.00232/) (2018)
- [Spatio-temporal Edge Service Placement: A Bandit Learning Approach](https://mlsanity.com/paper/1810.03069/) (2018)
- [Decentralized Task Offloading and Load-Balancing for Mobile Edge Computing in Dense Networks](https://mlsanity.com/paper/2407.00080/) (2024)
- [Multi-Time Scale Service Caching and Pricing in MEC Systems with Dynamic Program Popularity](https://mlsanity.com/paper/2407.03804/) (2024)

## Abstract (arXiv)

As users in small cell networks increasingly rely on computation-intensive services, cloud-based access often results in high latency. Multi-access edge computing (MEC) mitigates this by bringing computational resources closer to end users, with small base stations (SBSs) serving as edge servers to enable low-latency service delivery. However, limited edge capacity makes it challenging to decide which services to deploy locally versus in the cloud, especially under unknown service demand and dynamic network conditions. To tackle this problem, we model service demand as a linear function of service attributes and formulate the service placement task as a linear bandit problem, where SBSs act as agents and services as arms. The goal is to identify the service that, when placed at the edge, offers the greatest reduction in total user delay compared to cloud deployment. We propose a distributed and adaptive multi-agent best-arm identification (BAI) algorithm under a fixed-confidence setting, where SBSs collaborate to accelerate learning. Simulations show that our algorithm identifies the optimal service with the desired confidence and achieves near-optimal speedup, as the number of learning rounds decreases proportionally with the number of SBSs. We also provide theoretical analysis of the algorithm's sample complexity and communication overhead.