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About Deep Reinforcement Learning For Network Slicing With Heterogeneous Resource Requirements and Time Varying Traffic Dynamics - Koo Et Al. 2019
This document discusses using deep reinforcement learning for network slicing to efficiently allocate heterogeneous resources with dynamic traffic. Network slicing divides a single network infrastructure into virtual slices to meet varying requirements. Traditional optimization methods are intractable for dynamic environments. The authors propose using a deep reinforcement learning agent to learn slicing strategies by interacting with simulated and real workload data. This learned policy can then allocate resources like bandwidth and compute to satisfy slice requests while meeting service level agreements under varying conditions.
Detailed Information
| Author: | ['Evenso Ndlovu'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 9 |
| Language: | English |
| Format: | |
| Price: | FREE |
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