# DRL-based Slice Placement Under Non-Stationary Conditions

arXiv: https://arxiv.org/abs/2108.02495 · HTML: https://mlsanity.com/paper/2108.02495/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Heuristic for Edge-enabled Network Slicing Optimization using the Power of Two Choices](https://mlsanity.com/paper/2010.08287/) (2020)
- [A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement](https://mlsanity.com/paper/2105.06741/) (2021)

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

- [DRL-based Slice Placement under Realistic Network Load Conditions](https://mlsanity.com/paper/2109.12857/) (2021)

## Abstract (arXiv)

We consider online learning for optimal network slice placement under the assumption that slice requests arrive according to a non-stationary Poisson process. We propose a framework based on Deep Reinforcement Learning (DRL) combined with a heuristic to design algorithms. We specifically design two pure-DRL algorithms and two families of hybrid DRL-heuristic algorithms. To validate their performance, we perform extensive simulations in the context of a large-scale operator infrastructure. The evaluation results show that the proposed hybrid DRL-heuristic algorithms require three orders of magnitude of learning episodes less than pure-DRL to achieve convergence. This result indicates that the proposed hybrid DRL-heuristic approach is more reliable than pure-DRL in a real non-stationary network scenario.