Personal blog of Yzmir Ramirez

Applications networking company F5 announced today that it is acquiring Volterra, a multi-cloud management startup, for $500 million. That breaks down to $440 million in cash and $60 million in deferred and unvested incentive compensation.

Volterra emerged in 2019 with a $50 million investment from multiple sources, including Khosla Ventures and Mayfield, along with strategic investors like M12 (Microsoft’s venture arm) and Samsung Ventures. As the company described it to me at the time of the funding:

Volterra has innovated a consistent, cloud-native environment that can be deployed across multiple public clouds and edge sites — a distributed cloud platform. Within this SaaS-based offering, Volterra integrates a broad range of services that have normally been siloed across many point products and network or cloud providers.

The solution is designed to provide a single way to view security, operations and management components.

F5 president and CEO François Locoh-Donou sees Volterra’s edge solution integrating across its product line. “With Volterra, we advance our Adaptive Applications vision with an Edge 2.0 platform that solves the complex multi-cloud reality enterprise customers confront. Our platform will create a SaaS solution that solves our customers’ biggest pain points,” he said in a statement.

Volterra founder and CEO Ankur Singla, writing in a company blog post announcing the deal, says the need for this solution only accelerated during 2020 when companies were shifting rapidly to the cloud due to the pandemic. “When we started Volterra, multi-cloud and edge were still buzzwords and venture funding was still searching for tangible use cases. Fast forward three years and COVID-19 has dramatically changed the landscape — it has accelerated digitization of physical experiences and moved more of our day-to-day activities online. This is causing massive spikes in global Internet traffic while creating new attack vectors that impact the security and availability of our increasing set of daily apps,” he wrote.

He sees Volterra’s capabilities fitting in well with the F5 family of products to help solve these issues. While F5 had a quiet 2020 on the M&A front, today’s purchase comes on top of a couple of major acquisitions in 2019, including Shape Security for $1 billion and NGINX for $670 million.

The deal has been approved by both companies’ boards, and is expected to close before the end of March, subject to regulatory approvals.

One of the areas that is often left behind when it comes to cloud computing is the industrial sector. That’s because these facilities often have older equipment or proprietary systems that aren’t well suited to the cloud. Amazon wants to change that, and today the company announced a slew of new services at AWS re:Invent aimed at helping the industrial sector understand their equipment and environments better.

For starters, the company announced Amazon Monitron, which is designed to monitor equipment and send signals to the engineering team when the equipment could be breaking down. If industrial companies can know when their equipment is breaking, it allows them to repair on it their own terms, rather than waiting until after it breaks down and having the equipment down at what could be an inopportune time.

As AWS CEO Andy Jassy says, an experienced engineer will know when equipment is breaking down by a certain change in sound or a vibration, but if the machine could tell you even before it got that far, it would be a huge boost to these teams.

“…a lot of companies either don’t have sensors, they’re not modern powerful sensors, or they are not consistent and they don’t know how to take that data from the sensors and send it to the cloud, and they don’t know how to build machine learning models, and our manufacturing companies we work with are asking [us] just solve this [and] build an end-to-end solution. So I’m excited to announce today the launch of Amazon Monotron, which is an end-to-end solution for equipment monitoring,” Jassy said.

The company builds a machine learning model that understands what a normal state looks like, then uses that information to find anomalies and send back information to the team in a mobile app about equipment that needs maintenance now based on the data the model is seeing.

For those companies who may have a more modern system and don’t need the complete package that Monotron offers, Amazon has something for these customers as well. If you have modern sensors, but you don’t have a sophisticated machine learning model, Amazon can ingest this data and apply its machine learning algorithms to find anomalies just as it can with Monotron.

“So we have something for this group of customers as well to announce today, which is the launch of Amazon Lookout for Equipment, which does anomaly detection for industrial machinery,” he said.

In addition, the company announced the Panorama Appliance for companies using cameras at the edge who want to use more sophisticated computer vision, but might not have the most modern equipment to do that. “I’m excited to announce today the launch of the AWS Panorama Appliance which is a new hardware appliance [that allows] organizations to add computer vision to existing on premises smart cameras,” Jassy told AWS re:Invent today.

In addition, it also announced a Panorama SDK to help hardware vendors build smarter cameras based on Panorama.

All of these services are designed to give industrial companies access to sophisticated cloud and machine learning technology at whatever level they may require depending on where they are on the technology journey.

Latent AI, a startup that was spun out of SRI International, makes it easier to run AI workloads at the edge by dynamically managing workloads as necessary.

Using its proprietary compression and compilation process, Latent AI promises to compress library files by 10x and run them with 5x lower latency than other systems, all while using less power thanks to its new adaptive AI technology, which the company is launching as part of its appearance in the TechCrunch Disrupt Battlefield competition today.

Founded by CEO Jags Kandasamy and CTO Sek Chai, the company has already raised a $6.5 million seed round led by Steve Jurvetson of Future Ventures and followed by Autotech Ventures .

Before starting Latent AI, Kandasamy sold his previous startup OtoSense to Analog Devices (in addition to managing HPE Mid-Market Security business before that). OtoSense used data from sound and vibration sensors for predictive maintenance use cases. Before its sale, the company worked with the likes of Delta Airlines and Airbus.

Image Credits: Latent AI

In some ways, Latent AI picks up some of this work and marries it with IP from SRI International .

“With OtoSense, I had already done some edge work,” Kandasamy said. “We had moved the audio recognition part out of the cloud. We did the learning in the cloud, but the recognition was done in the edge device and we had to convert quickly and get it down. Our bill in the first few months made us move that way. You couldn’t be streaming data over LTE or 3G for too long.”

At SRI, Chai worked on a project that looked at how to best manage power for flying objects where, if you have a single source of power, the system could intelligently allocate resources for either powering the flight or running the onboard compute workloads, mostly for surveillance, and then switch between them as needed. Most of the time, in a surveillance use case, nothing happens. And while that’s the case, you don’t need to compute every frame you see.

“We took that and we made it into a tool and a platform so that you can apply it to all sorts of use cases, from voice to vision to segmentation to time series stuff,” Kandasamy explained.

What’s important to note here is that the company offers the various components of what it calls the Latent AI Efficient Inference Platform (LEIP) as standalone modules or as a fully integrated system. The compressor and compiler are the first two of these and what the company is launching today is LEIP Adapt, the part of the system that manages the dynamic AI workloads Kandasamy described above.

Image Credits: Latent AI

In practical terms, the use case for LEIP Adapt is that your battery-powered smart doorbell, for example, can run in a low-powered mode for a long time, waiting for something to happen. Then, when somebody arrives at your door, the camera wakes up to run a larger model — maybe even on the doorbell’s base station that is plugged into power — to do image recognition. And if a whole group of people arrives at ones (which isn’t likely right now, but maybe next year, after the pandemic is under control), the system can offload the workload to the cloud as needed.

Kandasamy tells me that the interest in the technology has been “tremendous.” Given his previous experience and the network of SRI International, it’s maybe no surprise that Latent AI is getting a lot of interest from the automotive industry, but Kandasamy also noted that the company is working with consumer companies, including a camera and a hearing aid maker.

The company is also working with a major telco company that is looking at Latent AI as part of its AI orchestration platform and a large CDN provider to help them run AI workloads on a JavaScript backend.

Krishna Rangasayee, founder and CEO, at SiMa.ai, has 30 years of experience in the semiconductor industry. He decided to put that experience to work in a startup and launched SiMa.ai last year with the goal of building an ultra low-power software and chip solution for machine learning at the edge.

Today he announced a $30 million Series A led by Dell Technologies Capital with help from Amplify Partners, Wing Venture Capital and +ND Capital. Today’s investment brings the total raised to $40 million, according to the company.

Rangasayee says in his years as a chip executive he saw a gap in the machine learning market for embedded devices running at the edge and he decided to start the company to solve that issue.

“While the majority of the market was serviced by traditional computing, machine learning was beginning to make an impact and it was really amazing. I wanted to build a company that would bring machine learning at significant scale to help the problems with embedded markets,” he told TechCrunch.

The company is trying to focus on efficiency, which it says will make the solution more environmentally friendly by using less power. “Our solution can scale high performance at the lowest power efficiency, and that translates to the highest frames per second per watt. We have built out an architecture and a software solution that is at a minimum 30x better than anybody else on the frames per second,” he explained.

He added that achieving that efficiency required them to build a chip from scratch because there isn’t a solution available off the shelf today that could achieve that.

So far the company has attracted 20 early design partners, who are testing what they’ve built. He hopes to have the chip designed and the software solution in Beta in the Q4 timeframe this year, and is shooting for chip production by Q2 in 2021.

He recognizes that it’s hard to raise this kind of money in the current environment and he’s grateful to the investors, and the design partners who believe in his vision. The timing could actually work in the company’s favor because it can hunker down and build product while navigating through the current economic malaise.

Perhaps by 2021 when the product is in production, the market and the economy will be in better shape and the company will be ready to deliver.

AWS held its annual re:Invent customer conference last week in Las Vegas. Being Vegas, there was pageantry aplenty, of course, but this year’s model felt a bit different than in years past, lacking the onslaught of major announcements we are used to getting at this event.

Perhaps the pace of innovation could finally be slowing, but the company still had a few messages for attendees. For starters, AWS CEO Andy Jassy made it clear he’s tired of the slow pace of change inside the enterprise. In Jassy’s view, the time for incremental change is over, and it’s time to start moving to the cloud faster.

AWS also placed a couple of big bets this year in Vegas to help make that happen. The first involves AI and machine learning. The second, moving computing to the edge, closer to the business than the traditional cloud allows.

The question is what is driving these strategies? AWS had a clear head start in the cloud, and owns a third of the market, more than double its closest rival, Microsoft. The good news is that the market is still growing and will continue to do so for the foreseeable future. The bad news for AWS is that it can probably see Google and Microsoft beginning to resonate with more customers, and it’s looking for new ways to get a piece of the untapped part of the market to choose AWS.

Pensando, an edge computing startup founded by former Cisco engineers, came out of stealth mode today with an announcement that it has raised a $145 million Series C. The company’s software and hardware technology, created to give data centers more of the flexibility of cloud computing servers, is being positioned as a competitor to Amazon Web Services Nitro.

The round was led by Hewlett Packard Enterprise and Lightspeed Venture Partners and brings Pensando’s total raised so far to $278 million. HPE chief technology officer Mark Potter and Lightspeed Venture partner Barry Eggers will join Pensando’s board of directors. The company’s chairman is former Cisco CEO John Chambers, who is also one of Pensando’s investors through JC2 Ventures.

Pensando was founded in 2017 by Mario Mazzola, Prem Jain, Luca Cafiero and Soni Jiandani, a team of engineers who spearheaded the development of several of Cisco’s key technologies, and founded four startups that were acquired by Cisco, including Insieme Networks. (In an interview with Reuters, Pensando chief financial officer Randy Pond, a former Cisco executive vice president, said it isn’t clear if Cisco is interested in acquiring the startup, adding “our aspirations at this point would be to IPO. But, you know, there’s always other possibilities for monetization events.”)

The startup claims its edge computing platform performs five to nine times better than AWS Nitro, in terms of productivity and scale. Pensando prepares data center infrastructure for edge computing, better equipping them to handle data from 5G, artificial intelligence and Internet of Things applications. While in stealth mode, Pensando acquired customers including HPE, Goldman Sachs, NetApp and Equinix.

In a press statement, Potter said “Today’s rapidly transforming, hyper-connected world requires enterprises to operate with even greater flexibility and choices than ever before. HPE’s expanding relationship with Pensando Systems stems from our shared understanding of enterprises and the cloud. We are proud to announce our investment and solution partnership with Pensando and will continue to drive solutions that anticipate our customers’ needs together.”

Speaking today at the Microsoft Government Leaders Summit in Washington, DC, Microsoft CEO Satya Nadella made the case for edge computing, even while pushing the Azure cloud as what he called “the world’s computer.”

While Amazon, Google and other competitors may have something to say about that, marketing hype aside, many companies are still in the midst of transitioning to the cloud. Nadella says the future of computing could actually be at the edge, where computing is done locally before data is then transferred to the cloud for AI and machine learning purposes. What goes around, comes around.

But as Nadella sees it, this is not going to be about either edge or cloud. It’s going to be the two technologies working in tandem. “Now, all this is being driven by this new tech paradigm that we describe as the intelligent cloud and the intelligent edge,” he said today.

He said that to truly understand the impact the edge is going to have on computing, you have to look at research, which predicts there will be 50 billion connected devices in the world by 2030, a number even he finds astonishing. “I mean this is pretty stunning. We think about a billion Windows machines or a couple of billion smartphones. This is 50 billion [devices], and that’s the scope,” he said.

The key here is that these 50 billion devices, whether you call them edge devices or the Internet of Things, will be generating tons of data. That means you will have to develop entirely new ways of thinking about how all this flows together. “The capacity at the edge, that ubiquity is going to be transformative in how we think about computation in any business process of ours,” he said. As we generate ever-increasing amounts of data, whether we are talking about public sector kinds of use case, or any business need, it’s going to be the fuel for artificial intelligence, and he sees the sheer amount of that data driving new AI use cases.

“Of course when you have that rich computational fabric, one of the things that you can do is create this new asset, which is data and AI. There is not going to be a single application, a single experience that you are going to build, that is not going to be driven by AI, and that means you have to really have the ability to reason over large amounts of data to create that AI,” he said.

Nadella would be more than happy to have his audience take care of all that using Microsoft products, whether Azure compute, database, AI tools or edge computers like the Data Box Edge it introduced in 2018. While Nadella is probably right about the future of computing, all of this could apply to any cloud, not just Microsoft.

As computing shifts to the edge, it’s going to have a profound impact on the way we think about technology in general, but it’s probably not going to involve being tied to a single vendor, regardless of how comprehensive their offerings may be.

Cloudian, a company that enables businesses to store and manage massive amounts of data, announced today the launch of Edgematrix, a new unit focused on edge analytics for large data sets. Edgematrix, a majority-owned subsidiary of Cloudian, will first be available in Japan, where both companies are based. It has raised a $9 million Series A from strategic investors NTT Docomo, Shimizu Corporation and Japan Post Capital, as well as Cloudian co-founder and CEO Michael Tso and board director Jonathan Epstein. The funding will be used on product development, deployment and sales and marketing.

Cloudian itself has raised a total of $174 million, including a $94 million Series E round announced last year. Its products include the Hyperstore platform, which allows businesses to store hundreds of petrabytes of data on premise, and software for data analytics and machine learning. Edgematrix uses Hyperstore for storing large-scale data sets and its own AI software and hardware for data processing at the “edge” of networks, closer to where data is collected from IoT devices like sensors.

The company’s solutions were created for situations where real-time analytics is necessary. For example, it can be used to detect the make, model and year of cars on highways so targeted billboard ads can be displayed to their drivers.

Tso told TechCrunch in an email that Edgematrix was launched after Cloudian co-founder and president Hiroshi Ohta and a team spent two years working on technology to help Cloudian customers process and analyze their data more efficiently.

“With more and more data being created at the edge, including IoT data, there’s a growing need for being able to apply real-time data analysis and decision-making at or near the edge, minimizing the transmission costs and latencies involved in moving the data elsewhere,” said Tso. “Based on the initial success of a small Cloudian team developing AI software solutions and attracting a number of top-tier customers, we decided that the best way to build on this success was establishing a subsidiary with strategic investors.”

Edgematrix is launching in Japan first because spending on AI systems there is expected to grow faster than in any other market, at a compound annual growth rate of 45.3% from 2018 to 2023, according to IDC.

“Japan has been ahead of the curve as an early adopter of AI technology, with both the governmetn and private sector viewing it as essential to boosting productivity,” said Tso. “Edgematrix will focus on the Japanese market for at least the next year, and assuming that all goes well, it would then expand to North America and Europe.”

Microsoft today announced an interesting update to its database lineup with the preview of Azure SQL Database Edge, a new tool that brings the same database engine that powers Azure SQL Database in the cloud to edge computing devices, including, for the first time, Arm-based machines.

Azure SQL Edge, Azure corporate vice president Julia White writes in today’s announcement, “brings to the edge the same performant, secure and easy to manage SQL engine that our customers love in Azure SQL Database and SQL Server.”

The new service, which will also run on x64-based devices and edge gateways, promises to bring low-latency analytics to edge devices as it allows users to work with streaming data and time-series data, combined with the built-in machine learning capabilities of Azure SQL Database. Like its larger brethren, Azure SQL Database Edge will also support graph data and comes with the same security and encryption features that can, for example, protect the data at rest and in motion, something that’s especially important for an edge device.

As White rightly notes, this also ensures that developers only have to write an application once and then deploy it to platforms that feature Azure SQL Database, good old SQL Server on premises and this new edge version.

SQL Database Edge can run in both connected and fully disconnected fashion, something that’s also important for many use cases where connectivity isn’t always a given, yet where users need the kind of data analytics capabilities to keep their businesses (or drilling platforms, or cruise ships) running.

Docker and Arm today announced a major new partnership that will see the two companies collaborate in bringing improved support for the Arm platform to Docker’s tools.

The main idea here is to make it easy for Docker developers to build their applications for the Arm platform right from their x86 desktops and then deploy them to the cloud (including the Arm-based AWS EC2 A1 instances), edge and IoT devices. Developers will be able to build their containers for Arm just like they do today, without the need for any cross-compilation.

This new capability, which will work for applications written in JavaScript/Node.js, Python, Java, C++, Ruby, .NET core, Go, Rust and PHP, will become available as a tech preview next week, when Docker hosts its annual North American developer conference in San Francisco.

Typically, developers would have to build the containers they want to run on the Arm platform on an Arm-based server. With this system, which is the first result of this new partnership, Docker essentially emulates an Arm chip on the PC for building these images.

“Overnight, the 2 million Docker developers that are out there can use the Docker commands they already know and become Arm developers,” Docker EVP of Strategic Alliances David Messina told me. “Docker, just like we’ve done many times over, has simplified and streamlined processes and made them simpler and accessible to developers. And in this case, we’re making x86 developers on their laptops Arm developers overnight.”

Given that cloud-based Arm servers like Amazon’s A1 instances are often significantly cheaper than x86 machines, users can achieve some immediate cost benefits by using this new system and running their containers on Arm.

For Docker, this partnership opens up new opportunities, especially in areas where Arm chips are already strong, including edge and IoT scenarios. Arm, similarly, is interested in strengthening its developer ecosystem by making it easier to develop for its platform. The easier it is to build apps for the platform, the more likely developers are to then run them on servers that feature chips from Arm’s partners.

“Arm’s perspective on the infrastructure really spans all the way from the endpoint, all the way through the edge to the cloud data center, because we are one of the few companies that have a presence all the way through that entire path,” Mohamed Awad, Arm’s VP of Marketing, Infrastructure Line of Business, said. “It’s that perspective that drove us to make sure that we engage Docker in a meaningful way and have a meaningful relationship with them. We are seeing compute and the infrastructure sort of transforming itself right now from the old model of centralized compute, general purpose architecture, to a more distributed and more heterogeneous compute system.”

Developers, however, Awad rightly noted, don’t want to have to deal with this complexity, yet they also increasingly need to ensure that their applications run on a wide variety of platforms and that they can move them around as needed. “For us, this is about enabling developers and freeing them from lock-in on any particular area and allowing them to choose the right compute for the right job that is the most efficient for them,” Awad said.

Messina noted that the promise of Docker has long been to remove the dependence of applications from the infrastructure on which they run. Adding Arm support simply extends this promise to an additional platform. He also stressed that the work on this was driven by the company’s enterprise customers. These are the users who have already set up their systems for cloud-native development with Docker’s tools — at least for their x86 development. Those customers are now looking at developing for their edge devices, too, and that often means developing for Arm-based devices.

Awad and Messina both stressed that developers really don’t have to learn anything new to make this work. All of the usual Docker commands will just work.