Wi-Fi NOW Conference: Mojo, Mist, Google, Quantenna

We attended the Wi-Fi NOW conference in Redwood City, CA this week and attended some interesting presentations.  We are writing about our observations and notes from the Google, Quantenna, Mist Systems and Mojo Networks presentations.

Google Station presentation.  “GOOGLE STATION: PUBLIC WI-FI TO CONNECT THE NEXT BILLION INTERNET USERS.”  Monica Garde and Erika Wool made an interesting presentation.  The jist of the presentation, from our viewpoint, is that Google is partnering with service providers and enabling these service providers to monetize the Wi-Fi network through a revenue sharing system that is based primarily upon advertising.  The company shared some statistics, which we have in the accompanying slide.

Quantenna presentation.  James Chen, VP Product Line Management presented “GREAT INNOVATIONS PART ONE: MASSIVE MIMO & DUAL-BAND 802.11AX”.  Chen made the the case that 8×8 WiFi (that Quantenna calls Massive MIMO) outperforms 4×4 systems.  For instance, in its tests, at 85 RSSI and through a wall, performance was 1.6x greater using 8×8 compared to 4×4.  The company also made the case that Massive MIMO has greater throughput compared to non Massive MIMO, as well; the company has demonstrated >1 Gbps throughput in a typical home.  The company showed that Massive MIMO alleviates the “Sticky Client” using a 1×1 Samsung Galaxy Tab Active2 device.  The company did not talk about 802.11ax, unfortunately, other than to say that 8×8 is relevant for 802.11ax, as well.

Mojo Networks presentation.  Mojo CEO Rick Wilmer made the point that simply enabling Cloud-managed Wi-Fi has been done already, implying this is cloud 1.0, and that this message is boring.  The company explained that its cloud architecture is cloud 2.0 because it takes advantage of the capabilities in the cloud and enables – Cognitive Wi-Fi.  Cognitive Wi-Fi, as far as Mojo is concerned, has to do with big data (store key client parameters and run ML algorithms) and smart edge APs.  The company didn’t go into deep science of ML/AI, but explained the ML workflow: 1-data collection, 2-training the classifier model, 3-trained model in action, 4-result.

Mojo explained that it has lots of data to perform Machine Learning on.  It has 1/2M APs deployed.  The company shared that using 1 week of data of a subset across only 4 verticals (enterprise, education, mfg, retail & hospitality): 237K clients, 31M associations, 400+ applications.  Separately, the company said it has obtains 50M associations per week (in a press release).  A significant amount of the data that is delivered to the cloud has been pre-processed in the Mojo APs; the APs cache 2 days of data.  The point of these statistics, according to Mojo, is that it has more data than other Wi-Fi vendors to train its Machine Learning system on.

According to Mojo, using inference engine, automatically fixes everything possible.  Wilmer says that this makes interacting with the User Interface less necessary because it takes care of problems automatically.  Was Mojo serious or joking when it said, “the UI may disappear as we know it.”  Time will tell.

The company shared some other information that was interesting:

  • 130K APs deployed at a single customers – a very large customer.  Very scalable.
  • Many Mojo customers are decomissioning their EThernet infrastructure (including cables) in lieu of using Wi-Fi from Mojo.
  • Mojo deploys ‘a lot’ of 3 radio APs.  With third radio, a listening radio, it watches RF environment and does wireless intrusion scanning.  Every 5 seconds, it feeds critical information to the ML environment.  So, if users physically move, the AI system can make adjustments to the system so there are fewer performance issues.  The third radio can also be used as a client, to simulate performance.  This client capability can run the specific application that will used in an environment and simulate whether the Wi-Fi will perform adequately before an event (like a test at a university, for instance).
  • Open Standards.  Believe all APs from all vendors should interoperate like light bulbs.  That would accelerate the Wi-Fi market.  Wilmer highlighted that the OCP event, earlier this year, Mojo ‘lit up’ the entire trade show floor with non Mojo APs using the Mojo system.
  • Mojo will announce AP providers soon that are not Mojo APs that you can plug.  No margin stack from an OEM, purchased directly from an Asian ODM.

Mist Systems.  Bob Friday, of Mist made a presentation on May 17, 2018.  In addition to the content from his presentation, I interviewed other at Mist personnel at the show.  The company claims it is focusing and having success in selling to large enterprises.  We learned that Mist uses Broadcom WiFi chips and has a custom-designed Bluetooth antennae array (shown at the show).  The company highlights its location services as a unique capability, and it draws upon its Bluetooth capabilities to deliver location.  However, the company’s main message is its AI capabilities; in some ways, it has become the poster-child for AI amongst startups in the networking industry.  Mist’s presentation at the show reiterated the same point – that it is an AI company.

Stepping back, Mist has been shipping commercially for a year now.  In our observation and research, the company’s efforts to take share from competitors has landed it on the map – over the past two quarters, its larger competitors have taken notice of Mist and see it competing at large enterprise accounts.

During the Q&A part of the presentation by Bob Friday, Mist CTO and founder was asked something that we found very interesting; the question was what kinds of algorithms does Mist use in its system, and do they all need to learn?  The answer was to the effect that many different types of algorithms are used, linear optimization, decision tree analytics, neural networks, etc.  Friday made the case that there are just certain things you just know about how a WiFi network will and should work, so why go an have a machine learn about it when you already know it.  This begs the question – how necessary is AI in the first place, especially if the vendor and its IT workers or VARs have gobs of experience and can design and implement a Wi-Fi network right in the first place.  Looking at the problem differently, what this means is that some vendors may have had different backgrounds than competitors and can design Wi-Fi systems that know how to work under a variety of working conditions.  Friday was also asked another question – given that Mist is focusing so much on AI, does this mean that far fewer IT workers will become employed?  Bob’s answer was diplomatic, but probably true – he said that no, we’ll need the same number of workers in the near-term, and that AI Wi-Fi will simply allow the same number of IT workers to make better decisions.  Still, the question makes it clear – the audience is concerned about job loss as AI works its way into the IT industry.