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Reinforcement learning–based QoS/QoE-aware service function chaining in software-driven 5G slices
Chen, X Li, Z Zhang, Y Long, R Yu, H Du, X Guizani, M
Chen, X
Li, Z
Zhang, Y
Long, R
Yu, H
Du, X
Guizani, M
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2018-11-01
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10.1002/ett.3477
Abstract
© 2018 John Wiley & Sons, Ltd. With the ever-growing diversity of devices and applications that will be connected to 5G networks, flexible and agile service orchestration with acknowledged quality of experience (QoE) that satisfies the end user's functional and quality-of-service (QoS) requirements is necessary. Software-defined networking (SDN) and network function virtualization (NFV) are considered key enabling technologies for 5G core networks. In this regard, this paper proposes a reinforcement learning–based QoS/QoE-aware service function chaining (SFC) scheme in SDN/NFV-enabled 5G slices. First, it implements a lightweight QoS information collector based on the Link Layer Discovery Protocol, which works in a piggyback fashion on the southbound interface of the SDN controller, to enable QoS-awareness. Then, a deep Q-network–based orchestration agent is designed to support SFC in the context of NFV. The agent takes into account the QoE and QoS as key aspects to formulate the reward so that it is expected to maximize QoE while respecting QoS constraints. The experiment results show that the proposed framework exhibits good performance in QoE provisioning and QoS requirements maintenance for SFC in dynamic network environments.
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Transactions on Emerging Telecommunications Technologies
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