# Heuristic for Edge-enabled Network Slicing Optimization using the Power of Two Choices

arXiv: https://arxiv.org/abs/2010.08287 · HTML: https://mlsanity.com/paper/2010.08287/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [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)
- [On the Robustness of Controlled Deep Reinforcement Learning for Slice Placement](https://mlsanity.com/paper/2108.02505/) (2021)
- [Controlled Deep Reinforcement Learning for Optimized Slice Placement](https://mlsanity.com/paper/2108.01544/) (2021)
- [DRL-based Slice Placement under Realistic Network Load Conditions](https://mlsanity.com/paper/2109.12857/) (2021)

## Abstract (arXiv)

We propose an online heuristic algorithm for the problem of network slice placement optimization. The solution is adapted to support placement on large scale networks and integrates Edge-specific and URLLC constraints. We rely on an approach called the Power of Two Choices to build the heuristic. The evaluation results show the good performance of the heuristic that solves the problem in few seconds under a large scale scenario. The heuristic also improves the acceptance ratio of network slice placement requests when compared against a deterministic online Integer Linear Programming (ILP) solution.