Alan Weissberger Cloud Computing

2015 Hot Interconnects Summary Part II – Huawei, Brocade/Deep Machine Learning, SD-WAN


In this second of a multi-part series on the Hot Interconnects 2015 conference, held in Santa Clara, CA August 26-28, 2015, we cover: Huawei’s open source activities, Deep Machine Learning and its application to networking (from Brocade’s Research Dept), and Software Defined WANs (which is a much broader topic than SDN for WANs). The first article in the series may be accessed here.

1. Huawei on the move:

Huawei will host the 2016 Hot Interconnects at their Santa Clara, CA campus. The company is either #1 or #2 in the worldwide market for telecom/networking equipment, even though they don’t sell much gear in the U.S.

During his opening remarks, Dan Pitt, PhD & ONF Executive Director said that “Huawei has been a prolific contributor to and leader in the ONF.” Mike McBride, Senior Director of Innovation & Technology Strategy within Huawei’s IP Lab CTO group, said his company is participating in several Open Networking/Open Source projects. Those include: Open Daylight (ODL)ONOS, and the Open Compute Project (OCP). Huawei announced at the 2015 Open Networking Summit that it planned to be a commercial open source vendor of software for Open Networking/ SDN.

The company is also participating in:

  1. CORD (Central Office Rearchitected as a Data center) project (PDF) to disaggregate telecom/network equipment into functions, many of which might be implemented in software/firmware.
  2. Apache Spark –  a fast and general engine for large-scale data processing aimed at “lightening fast cluster computing.”

2. Recent Advances in Machine Learning and their Application to Networking, David Meyer of Brocade:


This excellent keynote speech by David Meyer, CTO & Chief Scientist at Brocade, was very refreshing. It demonstrated that real research is being done by a Silicon Valley company other than Google!

Machine learning currently spans a wide variety of applications, including perceptual tasks such as image search, object and scene recognition and captioning, voice and natural language (speech) recognition and generation, self-driving cars and automated assistants such as Siri, as well as various engineering, financial, medical and scientific applications. However, almost none of this applied research has spilled over into the networking space. David believes there’s a huge opportunity there, especially in predicting incipient network node/link failures. He also talked about Machine Learning (ML) tools for DevOps/ network operations (see below).

Key points made by Meyer:

  • ML is needed to realize intelligent machines.
  • Examples of new ML applications: Auto captioning for speech, real time speech translation, object recognition (e.g. in self driving cars), use of facial key points (pattern recognition) to assess a person’s emotional state, pattern generation, anomaly detection, predictions (financial market prices, machine failures, etc).
  • Before 2006, researchers thought deep neural nets couldn’t be trained.
  • Theoretical breakthroughs in 2006 showed how to train deep neural nets. The “vanishing/ exploding gradient” problems were solved.
  • Faster processors came together with new algorithms to advance ML.
  • Massive Graphic Processor Units (GPUs) also helped.
  • Next generation telemetry system that feeds ML network analytics was described at NANOG64 this June in SF.
  • OpenConfig (started by Google) aims to specify a vendor neutral/independent configuration management system. That management system has a big ML component from a telemetry configuration model.
  • OPNFV consortium  is specifying Operating System components to realize a Network Function Virtualization (NFV) system. There’s a Predictor module that includes an intelligence training system.
  • One can envision a network as a huge collection of sensors that form a multi-dimensional vector space. The data collected is ideal for analysis/learning via deep neural networks.
  • There are predictive and reactive roles for ML in network management and control.
  • “We are at the beginning of a network intelligence revolution,” David said.
  • ML tools for DevOps: domain knowledge is needed from an analytics platform, which should include a recommendation system.
  • Application profiling was cited as an example to build tools for a DevOps environment: 1] Predict congestion for a given application. 2] Correlate with queue length to avoid dropped packets. 3] Anomaly detection of a pattern that doesn’t conform to expected behavior (if that behavior can be defined?)

Future of ML – What’s Next:

  • Deep neural nets that learn computation functions.
  • More emphasis on control- analyze sophisticated time series.
  • Long range dependencies via reinforcement learning.
  • Will apply to compute, storage, network, sensors, and energy management.
  • Huge application in networking will be predictive failure analysis (and re-route BEFORE the failure actually occurs).

3. Software Defined WANs- a tutorial by Inder Monga of ESnet & Srini Seetharaman of Infinera

This was a terrific “tag team” lecture/discussion by Inder & Srini who alternated describing each slide/diagram. We present selected highlights below.

Inder summarized many fundamental problems in all facets of WANs:

  • Agility requirements are not met for WAN provisioning (sometimes takes days or weeks to provision a new circuit or IP-MPLS VPN)
  • Traditional wide-area networking is inflexible, opaque and expensive
  • WAN resources are not efficiently utilized (over-provisioning prevails)
  • Interoperability issues across vendors, layers and domains reduces chance of automation
  • Hard to support new value propositions, like: Route selection at enterprises, Dynamic peering at exchanges, Auto bandwidth and bandwidth calendaring, Mapping elephant (very large) data flows to different Flexi-Grid channels

Srini commented that the Network Virtualization (NV)/ overlay model has more market traction than the pure SDN/Open Flow model.

Overlay networks run as independent virtual networks on top of a (real) physical network infrastructure. These virtual network overlays allow cloud service and DC providers to provision and orchestrate networks alongside other virtual resources (like compute servers). They also offer a new path to converged networks and programability. However, network overlays shouldn’t be confused with “pure SDN” which doesn’t permit overlays or network virtualization. [We’ve previously described both of these “SDN” approaches in multiple articles at and]

Several vendors provide NV software on compute servers running in DCs (e.g. VMWare, Nuage Networks, Juniper, etc). They support VxLAN for tunneling L2 frames withing a DC network (in lieu of VLANs) and then map VxLAN frames to IP-MPLS packets for inter DC transport. However, none of those NV software vendor’s inter-operate with other vendors on an end to end basis. That confirms again that at least the NV version of SDN is not really “open,” as the same vendor’s NV software must be used on the compute servers.

Gartner Group finds that SDN in general (including all the myriad versions, twists and tweaks), is approaching the bottom of the “trough of disillusionment” after falling hard from the peak of inflated expectations that was built up due to all the hype and BS. This is illustrated in the graph below:

Expectations for various technologies.
Image courtesy of Gartner

It’s interesting to note that SD- WANs, which have a much broader connotation than SDN for WANs, continue to ramp up the innovation trigger curve. They’ve yet to reach their peak of excitement and/or hype. White box switches, which we think is the future of true open networking, is on the downward path towards disillusionment, according to Gartner.

We totally disagree as we see years of tremendous potential ahead for open networking software running on bare metal switches (made by ODMs in China and Taiwan).

In closing, we note that National Research & Education Networks (NRENs) have deployed an East-West interface for multi-domain SDN – something we’ve screamed was missing from ONF specified SDN specs for a long time! Please refer to Dan Pitt’s remarks on that topic during my interview with him at the 2015 Open Networking Summit

The NREN East-West/multi-domain interface is evidently based on a Network Services Interface (NSI) spec from the Open Grid Forum

The OGF- NSI document Introduction states:

NSI is designed to support the creation of circuits (called Connections in NSI) that transit several networks managed by different providers. Traditional models of circuit services and control planes adopt a single very tightly defined data plane technology, and then hard code these service attributes into the control plane protocols. Multi-domain services need to be employed over heterogeneous data plane technologies.”

Kuddos to Inder and Srini for looking through all the marketing hype, identifying WAN problems and some potential solutions that might be solved by new software. The one that I’m most enthusiastic about is the OpenConfig project (described above in the Machine Learning section) for vendor neutral configuration. It’s purpose and functions are described in this tutorial article

End Note: A comprehensive summary of this superb 3+ hour tutorial is beyond the scope of this article. For those interested in the contents of this tutorial as well as more in depth coverage of networking/telecom please inquire about consulting arrangements by emailing the author.

Author Alan Weissberger

By Alan Weissberger

Alan Weissberger is a renowned researcher in the telecommunications field. Having consulted for telcos, equipment manufacturers, semiconductor companies, large end users, venture capitalists and market research firms, we are fortunate to have his critical eye examining new technologies.

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