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Deep Reinforcement Learning for NFV-based Service Function Chaining in Multi-Service Networks : Invited Paper

Ning, Zili, Wang, Ning and Tafazolli, Rahim (2020) Deep Reinforcement Learning for NFV-based Service Function Chaining in Multi-Service Networks : Invited Paper In: 2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR), 11-14 May 2020, Newark, NJ, USA.

Deep Reinforcement Learning - AAM.pdf - Accepted version Manuscript

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With the advent of Network Function Virtualization (NFV) techniques, a subset of the Internet traffic will be treated by a chain of virtual network functions (VNFs) during their journeys while the rest of the background traffic will still be carried based on traditional routing protocols. Under such a multi-service network environment, we consider the co-existence of heterogeneous traffic control mechanisms, including flexible, dynamic service function chaining (SFC) traffic control and static, dummy IP routing for the aforementioned two types of traffic that share common network resources. Depending on the traffic patterns of the background traffic which is statically routed through the traditional IP routing platform, we aim to perform dynamic service function chaining for the foreground traffic requiring VNF treatments, so that both the end-to-end SFC performance and the overall network resource utilization can be optimized. Towards this end, we propose a deep reinforcement learning based scheme to enable intelligent SFC routing decision-making in dynamic network conditions. The proposed scheme is ready to be deployed on both hybrid SDN/IP platforms and future advanced IP environments. Based on the real GEANT network topology and its one-week traffic traces, our experiments show that the proposed scheme is able to significantly improve from the traditional routing paradigm and achieve close-to-optimal performances very fast while satisfying the end-to-end SFC requirements.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Ning, Zili
Date : 22 May 2020
Funders : Engineering and Physical Sciences Research Council (EPSRC)
DOI : 10.1109/HPSR48589.2020.9098994
Copyright Disclaimer : © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : SFC; NFV; Routing; Reinforcement Learning
Related URLs :
Depositing User : Clive Harris
Date Deposited : 27 May 2020 14:24
Last Modified : 27 May 2020 14:34

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