Heuristic for Edge-enabled Network Slicing Optimization using the Power of Two Choices
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Oct 2020.
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Citing papers per year: 2021: 5.
5 papers.
- 2021A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placementcited by 4
- 2021DRL-based Slice Placement Under Non-Stationary Conditionscited by 1
- 2021On the Robustness of Controlled Deep Reinforcement Learning for Slice Placementcited by 1
- 2021Controlled Deep Reinforcement Learning for Optimized Slice Placementcited by 0
- 2021DRL-based Slice Placement under Realistic Network Load Conditionscited by 0
- 2021DRL-based Slice Placement under Realistic Network Load Conditions27 Sep 2021
- 2021On the Robustness of Controlled Deep Reinforcement Learning for Slice Placement5 Aug 2021
- 2021DRL-based Slice Placement Under Non-Stationary Conditions5 Aug 2021
- 2021Controlled Deep Reinforcement Learning for Optimized Slice Placement3 Aug 2021
- 2021A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement14 May 2021