edge computing
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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.
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At CES, the Chinese tech giant Baidu today announced OpenEdge, its open-source edge computing platform. At its core, OpenEdge is the local package component of Baidu’s existing Intelligent Edge (BIE) commercial offering and obviously plays well with that service’s components for managing edge nodes and apps.
Because this is obviously a developer announcement, I’m not sure why Baidu decided to use CES as the venue for this release, but there can be no doubt that China’s major tech firms have become quite comfortable with open source. Companies like Baidu, Alibaba, Tencent and others are often members of the Linux Foundation and its growing stable of projects, for example, and virtually ever major open-source organization now looks to China as its growth market. It’s no surprise, then, that we’re also now seeing a wider range of Chinese companies that open source their own projects.
“Edge computing is a critical component of Baidu’s ABC (AI, Big Data and Cloud Computing) strategy,” says Baidu VP and GM of Baidu Cloud Watson Yin. “By moving the compute closer to the source of the data, it greatly reduces the latency, lowers the bandwidth usage and ultimately brings real-time and immersive experiences to end users. And by providing an open source platform, we have also greatly simplified the process for developers to create their own edge computing applications.”
A company spokesperson tells us that the open-source platform will include features like data collection, message distribution and AI inference, as well as tools for syncing with the cloud.
Baidu also today announced that it has partnered with Intel to launch the BIE-AI-Box and with NXP Semiconductors to launch the BIE-AI-Board. The box is designed for in-vehicle video analysis while the board is small enough for cameras, drones, robots and similar applications.

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Many AWS customers have to run in multiple zones for many reasons, including performance requirements, regulatory issues or fail-over management. Whatever the reason, AWS announced a new tool tonight called Global Accelerators designed to help customers route traffic more easily across multiple regions.
Peter DeSantis, VP of global infrastructure and customer support at AWS speaking at an event Monday night at AWS Re:Invent, explained that much of AWS customer traffic already flows over their massive network, and customers are using AWS Direct Connect to help applications get consistent performance and low network variability as customers move between AWS regions. He said what has been missing is a way to use the AWS global network to optimize their applications.
“Tonight I’m excited to announce AWS Global Accelerator. AWS Global Accelerator makes it easy for you to improve the performance and availability of your applications by taking advantage of the AWS global network,” he told the AWS re:Invent audience.
Graphic: AWS
“Your customer traffic is routed from your end users to the closest AWS edge location and from there traverses congestion-free redundant, highly available AWS global network. In addition to improving performance AWS Global Accelerator has built-in fault isolation, which instantly reacts to changes in the network health or your applications configuration,” DeSantis explained.
In fact, network administrators can route traffic based on defined policies such as health or geographic requirements and the traffic will move to the designated zone automatically based on those policies.
AWS plans to charge customers based on the number of accelerators they create. “An accelerator is the resource you create to direct traffic to optimal endpoints over the AWS global network. Customers will typically set up one accelerator for each application, but more complex applications may require more than one accelerator,” AWS’s Shaun Ray wrote in a blog post announcing the new feature.
AWS Global Accelerator is available today in several regions in the U.S., Europe and Asia.
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Once upon a time, it looked like cloud-based serviced would become the central hub for analyzing all IoT data. But it didn’t quite turn out that way because most IoT solutions simply generate too much data to do this effectively and the round-trip to the data center doesn’t work for applications that have to react in real time. Hence the advent of edge computing, which is spawning its own ecosystem of startups.
Among those is Swim.ai, which today announced that it has raised a $10 million Series B funding round led by Cambridge Innovation Capital, with participation from Silver Creek Ventures and Harris Barton Asset Management. The round also included a strategic investment from Arm, the chip design firm you may still remember as ARM (but don’t write it like that or their PR department will promptly email you). This brings the company’s total funding to about $18 million.
Swim.ai has an interesting take on edge computing. The company’s SWIM EDX product combines both local data processing and analytics with local machine learning. In a traditional approach, the edge devices collect the data, maybe perform some basic operations against the data to bring down the bandwidth cost and then ship it to the cloud where the hard work is done and where, if you are doing machine learning, the models are trained. Swim.ai argues that this doesn’t work for applications that need to respond in real time. Swim.ai, however, performs the model training on the edge device itself by pulling in data from all connected devices. It then builds a digital twin for each one of these devices and uses that to self-train its models based on this data.

“Demand for the EDX software is rapidly increasing, driven by our software’s unique ability to analyze and reduce data, share new insights instantly peer-to-peer – locally at the ‘edge’ on existing equipment. Efficiently processing edge data and enabling insights to be easily created and delivered with the lowest latency are critical needs for any organization,” said Rusty Cumpston, co-founder and CEO of Swim.ai. “We are thrilled to partner with our new and existing investors who share our vision and look forward to shaping the future of real-time analytics at the edge.”
The company doesn’t disclose any current customers, but it is focusing its efforts on manufacturers, service providers and smart city solutions. Update: Swim.ai did tell us about two customers after we published this story: The City of Palo Alto and Itron.
Swim.ai plans to use its new funding to launch a new R&D center in Cambridge, UK, expand its product development team and tackle new verticals and geographies with an expanded sales and marketing team.
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The infrastructure that underpins our lives is not something we ever want to think about. Nothing good has come from suddenly needing to wonder “where does my water come from?” or “how does electricity connect into my home?” That pondering gets even more intense when we talk about cellular infrastructure, where a single dropped call or a choppy YouTube video can cause an expletive-laden tirade.
Recently, I visited Verizon’s cellular switch for the New York City metro area (disclosure: TechCrunch is owned by Oath, and Oath is part of Verizon). It’s a completely nondescript building in a nondescript suburb north of the city, so nondescript that it took Verizon’s representative about 15 minutes of circling around just to find it (frankly, the best security through obscurity I have seen in some time).
This switch, along with its sister, powers all cellular service in New York City, including three million voice or voice over LTE (VoLTE) calls and 708 million data connections a day. High-reliability and redundancy is a must for the facility, where dropping even one in 100,000 connections would create more than 7,000 angry customers a day. As Christine Williams, the senior operations manager who oversees the facility, explained, “It doesn’t matter what percentage of dropped calls you have if you are that person.”
As we walked through the server rows that processed those hundreds of millions of connections, I was surprised by just how little digital equipment was actually in the switch itself. “Software-defined networking” has taken full hold here, according to Michele White, who is Verizon’s Executive Director for Network Assurance in the U.S. northeast. As the team has replaced older equipment, the actual physical footprint has continued to downsize, even today. All of New York City’s traffic is run from a handful of feet of server racks.
The key to network assurance is two-fold. First is multiple levels of redundancy at every level of the infrastructure. Inside the switch, independent server racks can take over from other servers that fail, providing redundancy at the machine level. If the air conditioning — which is critical for machine performance — were to fail, mobile AC units can be deployed to pick up the burden.
All equipment in the building is serviced by DC power, and in the event of an external power loss, two diesel generators connected to a large fuel storage tank will take over. The facility is also equipped with battery backups that can sustain the facility for eight hours if the generators themselves don’t function appropriately.
Diesel generators can sustain power to the switch in the event of an external power outage
At a higher level, the switch and its sister share all New York City cellular traffic, but either one could handle the full load if necessary. In short, the goal of the switch’s design is to ensure that that no matter how small or large a problem it might experience, there is an instant backup ready to go to keep those cellular connections alive.
The other half of network assurance is centralization, something that I was surprised to hear in this supposed era of decentralization. Cellular sites in an urban area like New York are often placed on buildings, as anyone looking at roof lines can see from the street. Given those locations, it can be hard to provide backup generators and other failover infrastructure, and servicing them can also be challenging. With centralization, increasingly only the antenna is located at the site, with almost all other operations handled in central control offices and switches where Verizon has greater control of the environment.
Even with intense focus on redundancy, natural disasters can overwhelm even the best laid plans. The telecom company has an additional layer of redundancy with its mobile units, which are placed in a “barnyard” owing to the names of the equipment stored there. There are GOATs (generator on a truck), and COWs (cell on wheels), and BATs (bi-directional amplifier on a truck). These units get deployed to areas of the network that either are experiencing unusually strong demand (think the U.S. Open or a presidential inauguration) or where a natural disaster has stuck (like Hurricane Harvey).
A barnyard filled with animal-named mobile cell infrastructure, including COWs, COLTs, HORSEs, and others
That said, both White and Williams noted that mobile cell deployment is much rarer than people would guess. One reason is that cell sites are increasingly being installed with Remote Electrical Tilt, which allows nearby cell sites to adjust their antennas so as to provide some signal to an area formerly covered by an out-of-commission cell. That process I was told is increasingly automated, allowing the network to essentially self-heal itself in emergencies.
The other reason their deployment is rare is that network assurance already has to handle a remarkable amount of surging traffic throughout the normal ebb and flow of a dense urban city. “Rush hour in Times Square is pretty heavy,” noted Williams. Even something as heavy as a parade through Midtown Manhattan won’t typically exceed the network’s surge capacity.
One other redundancy that Verizon has been exploring is using drones to provide more adaptive coverage. The company has been testing “femto-cell” drone aircraft designed by American Aerospace Technologies that can provide one square mile of coverage for about sixteen hours. A drone capability could be particularly useful in cases like hurricanes, where roads are often littered with debris, making it hard for network engineers to deploy ground-based mobile cells.
I asked about 5G, which I have been covering more heavily this year as telecom deployments pick up. Given the current design of 5G, White and Williams didn’t expect too much change to happen at the switch level, where most of the core technology was likely to remain unchanged.
The trend that is changing things though is edge computing, which is in vogue due to the need for computing to be located closer to users to power applications like virtual reality and autonomous cars. That’s critical, because 50 milliseconds of extra latency could be the difference between an autonomous car hitting another vehicle or a new support pylon and swerving out of the way just in time.
Edge computing in many ways is decentralizing, and therefore there is a tension with the increasingly centralized nature of mobile communications infrastructure. Switches like this one are getting outfitted with edge technology, and more installations are expected in the coming years. 5G and edge are also deeply connected at the antenna level, and that will likely affect cell deployments far more than the switch infrastructure itself.
Edge, internet of things, 5G — all will increase the quantity and scale of the connections flowing through these networks. In the future, a cellular outage may not just inconvenience that YouTube user, but could also prevent an automobile from successfully navigating to a hospital during a natural disaster. It takes backups, backups, and backups to prevent us from ever having to ask, “where does that signal come from?”
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At its Build developer conference this week, Microsoft is putting a lot of emphasis on artificial intelligence and edge computing. To a large degree, that means bringing many of the existing Azure services to machines that sit at the edge, no matter whether that’s a large industrial machine in a warehouse or a remote oil-drilling platform. The service that brings all of this together is Azure IoT Edge, which is getting quite a few updates today. IoT Edge is a collection of tools that brings AI, Azure services and custom apps to IoT devices.
As Microsoft announced today, Azure IoT Edge, which sits on top of Microsoft’s IoT Hub service, is now getting support for Microsoft’s Cognitive Services APIs, for example, as well as support for Event Grid and Kubernetes containers. In addition, Microsoft is also open sourcing the Azure IoT Edge runtime, which will allow developers to customize their edge deployments as needed.
The highlight here is support for Cognitive Services for edge deployments. Right now, this is a bit of a limited service as it actually only supports the Custom Vision service, but over time, the company plans to bring other Cognitive Services to the edge as well. The appeal of this service is pretty obvious, too, as it will allow industrial equipment or even drones to use these machine learning models without internet connectivity so they can take action even when they are offline.
As far as AI goes, Microsoft also today announced that it will bring its new Brainwave deep neural network acceleration platform for real-time AI to the edge.
The company has also teamed up with Qualcomm to launch an AI developer kit for on-device inferencing on the edge. The focus of the first version of this kit will be on camera-based solutions, which doesn’t come as a major surprise given that Qualcomm recently launched its own vision intelligence platform.
IoT Edge is also getting a number of other updates that don’t directly involve machine learning. Kubernetes support is an obvious one and a smart addition, given that it will allow developers to build Kubernetes clusters that can span both the edge and a more centralized cloud.
The appeal of running Event Grid, Microsoft’s event routing service, at the edge is also pretty obvious, given that it’ll allow developers to connect services with far lower latency than if all the data had to run through a remote data center.
Other IoT Edge updates include the planned launch of a marketplace that will allow Microsoft partners and developers to share and monetize their edge modules, as well as a new certification program for hardware manufacturers to ensure that their devices are compatible with Microsoft’s platform. IoT Edge, as well as Windows 10 IoT and Azure Machine Learning, will also soon support hardware-accelerated model evaluation with DirextX 12 GPU, which is available in virtually every modern Windows PC.
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OpenStack, the open-source infrastructure project that aims to give enterprises the equivalent of AWS for the private clouds, today announced the launch of its 17th release, dubbed “Queens.” After all of those releases, you’d think that there isn’t all that much new that the OpenStack community could add to the project, but just as the large public clouds keep adding… Read More
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As we develop increasingly sophisticated technologies like self-driving cars and industrial internet of things sensors, it’s going to require that we move computing to the edge. Essentially this means that instead of sending data to the cloud for processing, it needs to be done right on the device itself because even a little bit of latency is too much. Intel announced a new chip… Read More
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Big data analytics — where vast troves of information are structured and used to help businesses gain more insights into their operations and customers, to develop new products, and to run more efficiently — are a cornerstone of how many tech-centric enterprises run their businesses today. Now the focus is on building solutions that the rest of the enterprise world can use, even if… Read More
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