Optimal VNF Placement via Deep Reinforcement Learning in SDN/NFV-Enabled Networks

The emerging paradigm - Software-Defined Networking (SDN) and Network Function Virtualization (NFV) - makes it feasible and scalable to run Virtual Network Functions (VNFs) in commercial-off-the-shelf devices, which provides a variety of network services with reduced cost. Benefitting from centraliz...

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Bibliographic Details
Published in:IEEE journal on selected areas in communications 2020-02, Vol.38 (2), p.263-278
Main Authors: Pei, Jianing, Hong, Peilin, Pan, Miao, Liu, Jiangqing, Zhou, Jingsong
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
deep reinforcement learning
Delays
Electronic devices
Forecasting
Information management
Integer programming
Machine learning
Middleboxes
network function virtualization
Network topologies
Neural networks
Optimization
Placement
Reinforcement learning
Resource management
Software-defined networking
Solution space
Traffic information
Training
Virtual networks
VNF placement
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Summary:The emerging paradigm - Software-Defined Networking (SDN) and Network Function Virtualization (NFV) - makes it feasible and scalable to run Virtual Network Functions (VNFs) in commercial-off-the-shelf devices, which provides a variety of network services with reduced cost. Benefitting from centralized network management, lots of information about network devices, traffic and resources can be collected in SDN/NFV-enabled networks. Using powerful machine learning tools, algorithms can be designed in a customized way according to the collected information to efficiently optimize network performance. In this paper, we study the VNF placement problem in SDN/NFV-enabled networks, which is naturally formulated as a Binary Integer Programming (BIP) problem. Using deep reinforcement learning, we propose a Double Deep Q Network-based VNF Placement Algorithm (DDQN-VNFPA). Specifically, DDQN determines the optimal solution from a prohibitively large solution space and DDQN-VNFPA then places/releases VNF Instances (VNFIs) following a threshold-based policy. We evaluate DDQN-VNFPA with trace-driven simulations on a real-world network topology. Evaluation results show that DDQN-VNFPA can get improved network performance in terms of the reject number and reject ratio of Service Function Chain Requests (SFCRs), throughput, end-to-end delay, VNFI running time and load balancing compared with the algorithms in existing literatures.
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2019.2959181