# Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics

arXiv: https://arxiv.org/abs/1908.03242 · HTML: https://mlsanity.com/paper/1908.03242/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories](https://mlsanity.com/paper/2404.14238/) (2024)
- [Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicing](https://mlsanity.com/paper/2101.08724/) (2021)
- [Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey](https://mlsanity.com/paper/2108.04087/) (2021)
- [Federated learning and next generation wireless communications: A survey on bidirectional relationship](https://mlsanity.com/paper/2110.07649/) (2021)
- [Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning](https://mlsanity.com/paper/2101.00687/) (2021)
- [Adaptive Services Function Chain Orchestration For Digital Health Twin Use Cases: Heuristic-boosted Q-Learning Approach](https://mlsanity.com/paper/2304.12853/) (2023)

## Abstract (arXiv)

Efficient network slicing is vital to deal with the highly variable and dynamic characteristics of network traffic generated by a varied range of applications. The problem is made more challenging with the advent of new technologies such as 5G and new architectures such as SDN and NFV. Network slicing addresses a challenging dynamic network resource allocation problem where a single network infrastructure is divided into (virtual) multiple slices to meet the demands of different users with varying requirements, the main challenges being --- the traffic arrival characteristics and the job resource requirements (e.g., compute, memory and bandwidth resources) for each slice can be highly dynamic. Traditional model-based optimization or queueing theoretic modeling becomes intractable with the high reliability, and stringent bandwidth and latency requirements imposed by 5G technologies. In addition these approaches lack adaptivity in dynamic environments. We propose a deep reinforcement learning approach to address this dynamic coupled resource allocation problem. Model evaluation using both synthetic simulation data and real workload driven traces demonstrates that our deep reinforcement learning solution improves overall resource utilization, latency performance, and demands satisfied as compared to a baseline equal-slicing strategy.