2021
DOI: 10.1109/jsac.2020.3041385
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On Deep Reinforcement Learning for Traffic Engineering in SD-WAN

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Cited by 58 publications
(24 citation statements)
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References 27 publications
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“…SDN is expected to work at full capacity in 6G. Two future versions of SDN are Software-Defined Wide Area Network (SD-WAN) and Software-Defined Local Area Network (SD-LAN) [163], [164]. Like SDN, SD-WAN attempts to enhance network control performance and intelligence by separating the packet forwarding process (data plane) from the routing process (control plane).…”
Section: E Sd-wan Security: 6g Network Management Controlmentioning
confidence: 99%
“…SDN is expected to work at full capacity in 6G. Two future versions of SDN are Software-Defined Wide Area Network (SD-WAN) and Software-Defined Local Area Network (SD-LAN) [163], [164]. Like SDN, SD-WAN attempts to enhance network control performance and intelligence by separating the packet forwarding process (data plane) from the routing process (control plane).…”
Section: E Sd-wan Security: 6g Network Management Controlmentioning
confidence: 99%
“…We presented an active monitoring application, comprised of multiple software modules running on the CPEs, with the aim of measuring WAN performance, such as packet delay and loss, to route the network traffic efficiently. In [5], we proposed different kinds of traffic engineering applications, running over the SD-WAN controller, based on traditional and Machine Learning algorithms. We proved that Machine Learning is able to anticipate the WAN failures by proactively switching critical traffic flows on a different path.…”
Section: Introductionmentioning
confidence: 99%
“…The goal of this work is to build an experimental SD-WAN solution capable of running and optimizing delay-sensitive services, such as video streaming. Unlike our previous works, in this paper we present a monitoring system able to collect not only classical network statistics acquired from the CPEs, as in [2] and [5], but also real-time transport network statistics acquired directly from the servers (or hosts) that generate the network traffic. To do this, we exploit the extended Berkeley Packet Filter (eBPF) [6]: a revolutionary technology that can run programs into the Linux kernel without changing kernel source code or loading kernel modules.…”
Section: Introductionmentioning
confidence: 99%
“…We presented an active monitoring application, comprised of multiple software modules running on the CPEs, with the aim of measuring WAN performance, such as packet delay and loss, to route the network traffic efficiently. In [4], we proposed different kinds of traffic engineering applications, running over the SD-WAN controller, based on traditional and Machine Learning algorithms. We proved that Machine Learning is able to anticipate the WAN failures by proactively switching critical traffic flows on a different path.…”
Section: Introductionmentioning
confidence: 99%
“…We proved that Machine Learning is able to anticipate the WAN failures by proactively switching critical traffic flows on a different path. Unlike our previous works, in this demo we present a monitoring system no longer based on network statistics acquired from the CPEs, as in [1] and [4], but on transport network statistics acquired directly from the servers (or hosts) that generate the network traffic. To do this, we exploit the extended Berkeley Packet Filter (eBPF) [5]: a revolutionary technology that can run programs into the Linux kernel without changing kernel source code or loading kernel modules.…”
Section: Introductionmentioning
confidence: 99%