# Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing

arXiv: https://arxiv.org/abs/2203.07998 · HTML: https://mlsanity.com/paper/2203.07998/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Reinforcement Learning for Combinatorial Optimization: A Survey](https://mlsanity.com/paper/2003.03600/) (2020)
- [Device Placement Optimization with Reinforcement Learning](https://mlsanity.com/paper/1706.04972/) (2017)
- [Chip Placement with Deep Reinforcement Learning](https://mlsanity.com/paper/2004.10746/) (2020)
- [Planning and Learning with Stochastic Action Sets](https://mlsanity.com/paper/1805.02363/) (2018)
- [Placement Optimization with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.08445/) (2020)
- [Reinforcement Learning When All Actions are Not Always Available](https://mlsanity.com/paper/1906.01772/) (2019)
- [Deep Reinforcement Learning Based Mobile Edge Computing for Intelligent Internet of Things](https://mlsanity.com/paper/2008.00250/) (2020)

## Built on by (most cited first, 1 of 1)

- [Clustered Edge Intelligence: Beyond Just Convergence of Edge Computing and AI](https://mlsanity.com/paper/2607.20937/) (2026)

## Abstract (arXiv)

Cloud computing is a reliable solution to provide distributed computation power. However, real-time response is still challenging regarding the enormous amount of data generated by the IoT devices in 5G and 6G networks. Thus, multi-access edge computing (MEC), which consists of distributing the edge servers in the proximity of end-users to have low latency besides the higher processing power, is increasingly becoming a vital factor for the success of modern applications. This paper addresses the problem of minimizing both, the network delay, which is the main objective of MEC, and the number of edge servers to provide a MEC design with minimum cost. This MEC design consists of edge servers placement and base stations allocation, which makes it a joint combinatorial optimization problem (COP). Recently, reinforcement learning (RL) has shown promising results for COPs. However, modeling real-world problems using RL when the state and action spaces are large still needs investigation. We propose a novel RL framework with an efficient representation and modeling of the state space, action space and the penalty function in the design of the underlying Markov Decision Process (MDP) for solving our problem.