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Intel-backed startup Nyansa chases the total problem in the AI of network monitoring

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There are many opinions about what matters most in machine learning. Some would say it’s the data, some would say it is the algorithms and equations used to train computers on that data. Still, others would say it is the formulation of the question itself that is most important in machine learning.

The last point of view is representative of a startup called Nyansa, composed of networking veterans and big data specialists who believe they have a better approach to network management than, say, Cisco Systems or Hewlett-Packard Enterprise.

The reason, according to chief technologist and co-founder Anand Srinivas, is because Nyansa figures out all the different parts of a system — not just the switches and wireless access points, but the applications as well, that can affect what an end user experiences.

“Our innovation is not inventing new machine learning algorithms, it is in terms of bringing machine-learning algorithms to a use case like networking,” Srinivas said in an interview with ZDNet on Monday. Srinivas holds a PhD in wireless networks and algorithms from MIT, and has held a number of industry positions, especially for software-defined networking, at firms such as Overture Networks, Plexxi, and Airvana.

Also: Network technologies are changing faster than we can manage them

Based in Palo Alto, four-and-half-year-old Nyansa sells tools to IT to monitor the health of the network, explain degradations of performance when they happen, and then propose solutions. Its tools run on Amazon’s AWS, though they can also be installed on-premise, with hooks back to the public cloud. The company has over 100 customers, representing over 10 million devices “under observation,” it says, across 200,000 access points from different vendors, on hundreds of production networks. Clients include Uber, Tesla, and Lululemon. It has been bankrolled by chip giant Intel’s investment arm, and Formation8, to the tune of $27 million.

Its machine learning tools are very simple, far less sophisticated or adventuresome than today’s deep learning neural network approaches. They include things such as logistic regression analysis, random forest searches, nearest-neighbor searches, and “cluster” analysis. “A lot of this is off-the-shelf stuff,” confesses Srinivas.

“It is more straightforward than deep learning; deep learning is not the right approach for us, not yet, not until we grab all the low-hanging fruit which the simpler kinds of machine learning algorithms can give us.”

Nyansa gathers petabytes of data from those millions of client devices and thousands of access points, and first establishes a baseline. How well does the network perform, in terms of things such as what percentage of users have a wireless connection issue, on average, or what percentage have a Citrix application connectivity issue? Some of these devices have no users, they are Internet-of-Things gadgets, such as a General Electric wireless patient monitor, or the wrenches used by Tesla on the shop floor in its Fremont facility. Telemetry data must be gathered from those devices as a baseline for performance.


Nyansa, a network monitoring startup competing with Cisco and Hewlett-Packard Enterprise, believes it wins the day for clients such as Uber not by the complexity of its artificial intelligence tools, which are fairly routine, but by its understanding of the problem of network performance. (Image: Nyansa)

“One way you can think of us is as a vertically-integrated Splunk,” says Srinivas, referring to the Big Data monitoring system that ingests and mines customer data. “We can take any type of data and tie it into our system, but we go one step further to solve customer use cases.”

Also: Cisco launches UCS system for AI, machine learning, deep learning

By having data from multiple customers in the cloud, says Srinivas, a baseline can be set not just for a given customer but across an industry. “What’s good performance,” he asks rhetorically. For a given industry, “if the baseline is a 30 percent network performance connection failure, then 5 percent for a given customer may be fine for them.”

Once a baseline is established, deviations can be detected in order to determine if a problem is, say, a network problem per se, or rather an application problem. And once deviations from the baseline are established, predictions can be made. “Based on what other customers have done, what is our prediction for actions that have been most beneficial,” is how he describes it. By then observing how recommendations are carried out, and the effects, the system can move beyond mere correlation, the focus of a lot of machine learning, to a sense of causality.

“By automatically learning a baseline, and learning it everywhere, and doing it in the exact same way, we can give you a recommendation, and it will have an impact.”

“At first, perhaps it’s 80 percent correlation. But when a customer takes that action, that baseline will tell you the truth of whether the action made a difference; if it [performance] gets better, the recommendation by definition is correct. That feedback loop gets you back into causation.”

Also: Why you need to learn about application performance monitoring TechRepublic

As to how they differ from Cisco or Hewlett, Srinivas sees the Nyansa system as more comprehensive in what it looks at than either one. “We don’t care who the vendor is for wireless [access points] or RADIUS or DNS or DHCP, we will take whatever data from whatever source, that is a fundamental difference.”

“Their data sources are limited to their own stuff.”

Srinivas offers the example of GE bedside wireless patient monitors in a hospital run by client Mission Health, a healthcare system serving North Carolina. It is not enough to say that a network is or is not performing at baseline. “The final thing that matters is, are those waveforms [of patient data from the monitors] getting back to the nurse, and is that nurse able to see the oxygen level on the screen. It doesn’t matter if the wireless signal is good, you can’t just baseline wireless, because the particular data source you care about, the monitor, has nothing to do with switches.”

Also: Facebook is using AI to curb exploitative and naked images of kids CNET

The answer, then, to machine learning, in its simpler form and perhaps even its more complex incarnations, is how engaging with the complexity of situations builds understanding. “Even with deep learning, the magic is in tuning the deep learning network, knowing how to turn the knobs,” observes Srinivas. “The crux of it is experience, over time knowing exactly how to tune things.”

Previous and related coverage:

What is AI? Everything you need to know

An executive guide to artificial intelligence, from machine learning and general AI to neural networks.

What is deep learning? Everything you need to know

The lowdown on deep learning: from how it relates to the wider field of machine learning through to how to get started with it.

What is machine learning? Everything you need to know

This guide explains what machine learning is, how it is related to artificial intelligence, how it works and why it matters.

What is cloud computing? Everything you need to know about

An introduction to cloud computing right from the basics up to IaaS and PaaS, hybrid, public, and private cloud.

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How To Build Your Own Retro Gaming Console With A Raspberry Pi

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Once your micro SD card is mounted with RetroPie, you can plug it into your fully assembled Raspberry Pi 4 and begin the setup process in the software menu that appears. Make sure your controller is nearby, as you’ll need it during the first boot process. If you’re using a USB controller, make sure it’s connected physically, then follow the instructions on-screen.

If you’re using a Bluetooth controller, tap F4 on your USB keyboard to exit back into the Linux command prompt screen, then type and execute the command “sudo ~/RetroPie-S etup/retropie-setup.sh” that loads you into a backend RetroPie menu. Navigate to the Bluetooth option and then open it to begin searching for a controller. Set your Bluetooth controller to sync mode, then pair it in the menu. Return to the Linux command prompt and type the command “sudo shutdown -r now”. Upon loading back into RetroPie, you should be able to use your Bluetooth controller by simply turning it on and following the on-screen menu. Once everything is complete, you’ll end up on another menu with the option RASPI-CONFIG, which you should now select.

Upon tapping that option, you’ll be taken to the main configuration menu for RetroPie, which includes all sorts of different settings. Go ahead and configure whichever settings you need. It’s also a good idea to navigate to Advanced Settings and disable Overscan if you’re using an HDTV. From here, you should be able to load your ROMs (stored on your SD card) and play them from the menu that appears when you boot up RetroPie. Check out the RetroPie documentation for troubleshooting any issues you may encounter, and happy gaming!

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Today’s Wordle Answer #377 – July 1, 2022 Word Solution And Hints

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The solution for the July 1, 2022, edition of Wordle is pinto. It made its way to the English vocabulary from the Spanish word pinto, which refers to a subject that is spotted or mottled. Horses with a patchy coloration, especially those rocking white patches, are affectionately known as pinto.

The word traces its etymological roots to the Latin term pinctus, which is used to describe something that has been painted over. The pinto bean, which is a staple in Mexican, Spanish, and Brazilian cuisines, also gets its name from the patchy color profile of its outer skin. According to Ancestry data, Pinto is a popular Catalan name that eventually made its way to the Indian subcontinent with the advent of Portuguese invaders.

Interestingly, it is also used to describe a person with greying hair, something pop culture describes as a salt-and-pepper look. You can trace the history of Pinto family migration across the U.S. and Canada in the 19th century here. As for famous personalities with that surname, the actress described above is Freida Pinto, while the footballer in question is José Manuel Pinto. Meanwhile, Fernão Mendes Pinto was a renowned Portuguese explorer and writer who also has a crater on the planet Mercury named after him.

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This New $6 Raspberry Pi Is The Computer The DIY Smart Home Needs

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In terms of hardware, the Raspberry Pi Pico W is identical to its predecessor; it sports the same RP2040 Arm Cortex M0+ Dual-Core SoC, which is based on TSMC’s 40nm low power manufacturing process. This chip clocks up to 133MHz and also packs in 264KB of SRAM. There is 2MB of onboard flash storage thrown in, as well. Additionally, the machine features a 40-pin GPIO just like the original Pico from 2021. The onboard micro USB controller can be used for data transfer and receiving power.

The Wi-Fi module on the Raspberry Pi Pico is the Infineon CYW43439 wireless that, apart from supporting 2.4GHz Wi-Fi networks, also adds Bluetooth Classic and Bluetooth Low-Energy support. However, as of now, Raspberry has chosen not to enable Bluetooth capability in the machine. The company does not rule out the possibility of enabling Bluetooth further down the line, though.

With over 2 million Raspberry Pi Pico boards in the hands of consumers, the company expects its new model to enjoy similar success. The company also believes that the ongoing chip shortage has been among the prime reasons for the popularity of the RP2040-based Raspberry Pi Pico. The Pico W, thanks to its newfound wireless capability, will continue to be a great product that can power many IoT-based applications and DIY smart home needs. With a price tag of $6, the Raspberry Pi Pico W costs just $2 more than its predecessor. As the ecosystem for starter microcontrollers evolves, the $6 you spend on the Pico W will definitely be a worthwhile investment.

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