# DRL-based Slice Placement under Realistic Network Load Conditions

arXiv: https://arxiv.org/abs/2109.12857 · HTML: https://mlsanity.com/paper/2109.12857/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 27 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [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)
- [DRL-based Slice Placement Under Non-Stationary Conditions](https://mlsanity.com/paper/2108.02495/) (2021)

## Abstract (arXiv)

We propose to demonstrate a network slice placement optimization solution based on Deep Reinforcement Learning (DRL), referred to as Heuristically-controlled DRL, which uses a heuristic to control the DRL algorithm convergence. The solution is adapted to realistic networks with large scale and under non-stationary traffic conditions (namely, the network load). We demonstrate the applicability of the proposed solution and its higher and stable performance over a non-controlled DRL-based solution. Demonstration scenarios include full online learning with multiple volatile network slice placement request arrivals.