New Machine Learning Approach Could Give a Big Boost to the Efficiency of Optical Networks

SAN DIEGO–(BUSINESS WIRE)–New work leveraging machine learning could increase the efficiency of
optical telecommunications networks. As our world becomes
increasingly interconnected, fiber optic cables offer the ability to
transmit more data over longer distances compared to traditional copper
wires. Optical Transport Networks (OTNs) have emerged as a solution for
packaging data in fiber optic cables, and improvements look to make them
more cost-effective.

A group of researchers from Universitat Politècnica de Catalunya in
Barcelona and the telecom company Huawei have retooled an artificial
intelligence technique used for chess and self-driving cars to make OTNs
run more efficiently. They will present their research at the upcoming
Optical Fiber Conference and Exposition, to be held 3-7 March in San
Diego, California, USA.

OTNs require rules for how to divvy up the high amounts of traffic they
manage and writing the rules for making those split-second decisions
becomes very complex. If the network gives more space than needed for a
voice call, for example, the unused space might have been better put to
use ensuring that an end user streaming a video doesn’t get “still
buffering” messages.

What OTNs need is a better traffic guard.

The researchers’ new approach to this problem combines two machine
learning techniques: The first, called reinforcement learning, creates a
virtual “agent” that learns through trial and error the particulars of a
system to optimize how resources are managed. The second, called deep
learning, adds an extra layer of sophistication to the
reinforcement-based approach by using so-called neural networks, which
are computer learning systems inspired by the human brain, to draw more
abstract conclusions from each round of trial and error.

“Deep reinforcement learning has been successfully applied to many
fields,” said one of the researchers, Albert Cabellos-Aparicio.
“However, its application to computer networks is very recent. We hope
that our paper helps kickstart deep-reinforcement learning in networking
and that other researchers propose different and even better approaches.”

So far, the most advanced deep reinforcement learning algorithms have
been able to optimize some resource allocation in OTNs, but they become
stuck when they run into novel scenarios. The researchers worked to
overcome this by varying the manner in which data are presented to the
agent.

After learning the OTNs through 5,000 rounds of simulations, the deep
reinforcement learning agent directed traffic with 30 percent greater
efficiency than the current state-of-the-art algorithm.

One thing that surprised Cabellos-Aparicio and his team was how easily
the new approach was able to learn about the networks after starting out
with a blank slate.

“This means that without prior knowledge, a deep reinforcement learning
agent can learn how to optimize a network autonomously,”
Cabellos-Aparicio said. “This results in optimization strategies that
outperform expert algorithms.”

With the enormous scale some optical transport networks already have,
Cabellos-Aparicio said, even small advances in efficiency can reap large
returns in reduced latency and operational costs.

Next, the group plans to apply their deep reinforcement strategies in
combination with graph networks, an emerging field within artificial
intelligence with the potential to transform scientific and industrial
fields, such as computer networks, chemistry and logistics.

Hear from the research team: “Routing Based On Deep Reinforcement
Learning In Optical Transport Networks,” by Jose Suarez-Varela, Albert
Mestres, Junlin Yu, Li Kuang, Haoyu Feng, Pere Barlet-Ros, Albert
Cabellos-Aparicio, will take place at 12:00 p.m. on Monday, 4 March in
Room 1 of the San Diego Convention Center.

MEDIA REGISTRATION: Media/analyst registration for OFC 2019 can
be accessed online. Further information is available on the event
website at OFC, including travel details.

About OFC

The Optical Fiber Conference and Exhibition (OFC) is
the largest global conference and exhibition for optical communications
and networking professionals. For more than 40 years, OFC has drawn
attendees from all corners of the globe to meet and greet, teach and
learn, make connections and move business forward.

OFC includes dynamic business programming, an exhibition of more than
700 companies, and high impact peer-reviewed research that, combined,
showcase the trends and pulse of the entire optical networking and
communications industry. OFC is managed by The Optical Society (OSA) and
co-sponsored by OSA, the IEEE Communications Society (IEEE/ComSoc), and
the IEEE Photonics Society. OFC 2019 will be held from 3-7 March 2019 at
the San Diego Convention Center, California, USA. Follow @OFCConference,
learn more at OFC
Community LinkedIn, and watch highlights on OFC
YouTube.

Authors: Jose Suarez-Varela, Albert Mestres, Junlin Yu, Li Kuang, Haoyu
Feng, Pere Barlet-Ros, Albert Cabellos-Aparicio
Author
Affiliations: Universitat Politècnica de Catalunya and Hauwei
Contact:
albert.cabellos@gmail.com

Caption: The architecture of a deep reinforcement agent operating an
Optical Transport Network.
Credit: José Suárez-Varela