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Amazon Web Services (AWS) is having problems. And unfortunately for anyone who owned a vacuum or doorbell that relied on AWS, a lot of the so-called smart objects are no longer functional.

AWS is the worlds biggest cloud hosting software. In just over a decade, Amazons product has essentially become the backbone of the internet.

And early on Thursday morning Australia time, one of its major server centres began to have an issue not for the first time either.

Soon after people noticed, Amazon acknowledged the problem on its internal dashboard for users.

We continue to work towards recovery of the issue affecting the Kinesis Data Streams API in the US-EAST-1 Region. We also continue to see an improvement in error rates for Kinesis and several affected services, but expect full recovery to still take up to a few hours, it read.

Many of the services that you know and love Adobe cloud software, 1Password, Flickr have all been having issues because of the outage.

But there were some unexpected issues as well: namely, a lot of people began to notice things that they didnt know needed cloud computing were actually very reliant on them.

First, peoples beloved vacuum robot Roombas stopped working.

One user tweeted about Amazon in frustration Some part of AWS is down and apparently its screwing up the Roomba,

The company that produces Roombas, iRobot, confirmed they (along with their robot mops) were no longer working.

And next came the doorbells:

My fucking doorbell doesnt work because AWS us-east-1 is having issues, another user tweeted.

Some people even lost control of their Christmas lights!

Anyone else unable to turn on their Christmas lights because of the AWS outage? one Twitter user asked.

I guess what we can take from this is: probably not a good idea to have half of the infrastructure of the internet depend on the same company, hey?

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People Cant Vacuum Or Use Their Doorbell Because Amazons Cloud Servers Are Down - Gizmodo Australia

Industry experts expect increased investments in edge capacity to reduce latency and support personalized content delivery and custom security policies.

Gartner predicts that by 2024 at least 40% of enterprises will have plans to adopt secure access service edge services, up from less than 1% at the end of 2018.

Image: Gartner

IDC predicts that the edge computing market worldwide will grow to $250.6 billion by 2024. Dave McCarthy, the firm's research director of edge strategies, thinks edge products and services will power the next wave of digital transformation.

Companies will need to think about how to build out edge capacity with new infrastructure and services. Industry experts predict that companies will start to incorporate edge computing capabilities into the software deployment pipeline and use this infrastructure to support personalized content and streaming services.

Ali Fenn, president of data center consulting firm ITRenew, said that edge computing is fundamentally about how to do compute close to data sources and data users.

"2021 will see major enterprises and tech companies driving towards homogenous, cost-effective infrastructure across these tiers, from public cloud, to private in colos, to modular commercial, and to consumer-proximal," he said. "Winners will look to advanced and modern paradigms for IT and networking, and untether from conventional stacks, racks, and vendors, to deliver plug-and-play, operationally efficient IT, at the lowest possible cost."

SEE: Top cloud providers in 2020: AWS, Microsoft Azure, and Google Cloud, hybrid, SaaS players (TechRepublic)

Keith Higgins, vice president of digital transformation for Rockwell Automation, describes the edge as the new cloud. He predicts that real-time availability of mission-critical workloads will be vital for companies scaling smart factory initiatives in 2021.

"Edge computing will complement existing cloud infrastructure by enabling real-time data processing where the work takes place: motors, pumps, generators, or other sensors," he said.

The industry will continue to move toward more decentralized compute environments, and the edge will add significant value to digital transformation initiatives.

"By integrating edge functionalities with existing cloud infrastructure, organizations will worry less about logistical IT considerations and, instead, focus on rethinking what's possible in a smart machine," he said.

Here's a preview of what 2021 trends in edge computing will look like.

Aruba launched its Edge Services Platform (ESP) in June 2020 as an AI-powered, cloud-native platform to automate, unify, and protect the edge.

Aruba CTO Partha Narasimhan said the company defines "the edge" as where the users and the actions are, which could be offices, sports stadiums, or homes. He said that edge computing provides the infrastructure to understand how people use physical spaces as well as to deliver experiences.

"If you create a connectivity layer that is always on, seamless, and secure, that drives participation," he said.

One trend in edge computing that he sees is a push to bring services closer to the connectivity layer to reduce latency.

"Authentication could be centralized but fulfillment will be local," he said. "Companies will have to rethink how to extend zero trust to the edge."

Narasimhan said that Aruba has built its services around a zero-trust model since 2002.

"We don't trust our own access points, they have to authenticate," he said. "Built-in authentication determines what policy gets applied to each device."

Automating some of this process is the only way edge computing will work well, Narasimhan added.

"Policies should be used on demand when and where you need them," he said. "That means not just automation for provisioning from the cloud, but provisioning locally where security rights and policies have to be plumbed in where the user connects."

SEE: 5 Internet of Things (IoT) innovations (free PDF) (TechRepublic)

Narasimhan said that companies should use two types of tactics to accomplish this: Automation that relies on scripting and automation that learns and changes over time. Developers should monitor how services are working, measure user satisfaction, and monitor new devices and use this information to tweak automated processes as needed.

"This learning type of automation is even more important at the edge," he said.

Narasimhan also sees potential in low-power sensors than run on ambient light or ambient radio-frequency energy to expand compute power and data collection at the edge.

"If you can run these lick and stick sensors without batteries, you'll deploy more of them and you're not just installing sensors but also building analysis power," he said.

Companies should think beyond traditional workloads and network design to leverage all forms of available infrastructure to expand edge compute options, Narasimhan said.

Aruba predicts these business outcomes will be powered by edge computing:

Limelight Networks is expanding its expertise with content delivery networks to the world of edge computing. Steve Miller-Jones, vice president of edge strategy and solution architecture at Limelight, said that the company is helping industrial, retail, business, and telco customers distribute apps, content, and services in disparate locations served by Limelight's private network.

"Edge functions allows you to customize the experience and gives you instant scale into more locations than most clients can easily manage," he said.

SEE: Future of 5G: Projections, rollouts, use cases, and more (free PDF) (TechRepublic)

Miller-Jones said he has seen a big shift over the last year in Limelight customers using the edge as code.

"Customers are integrating edge capability into their deployment pipeline to deploy their own processes and workflows," he said. "They don't have to do any distribution, the code just runs where their requests show up."

Customers are using edge capabilities to personalize content, aggregate data, and direct traffic.

"Companies are taking those decisions and abstracting them into code," he said.

Using a serverless approach is key to reducing latency and getting the ROI out of edge solutions, Miller-Jones said.

"If you can use a serverless environment and have processes run on demand, that delivers the value of edge sensors and you get more agility and operational ability," he said.

This is particularly importanting for the gaming industry to be able to offer customers faster access to servers and more intelligent routing without having to send everything to the cloud.

Miller-Jones said that he expects to see an expansion of capacity to support streaming services as well as content personalization.

As more workloads move to the cloud, chief information security officers are looking to take a cloud-first approach to security, Gartner Senior Analyst Nat Smith said, and that's where secure access service edge (SASE, pronounced sassy) services come in.

"SASE is largely about connecting people or devices to service, which can be private or public," he said. "The big advantage of this approach is that IT teams don't have to set up and maintain access."

This approach reduces operational mistakes which in turn improves security, Smith said.

Gartner predicts that by 2024 at least 40% of enterprises will have plans to adopt SASE up from less than 1% at the end of 2018.

In a research paper about SASE "The Future of Network Security is In the Cloud," Gartner analysts determined that "digital business and edge computing have inverted access requirements with more users, devices, applications, services and data located outside of an enterprise than inside."

Also, complexity, latency, and the need to decrypt and inspect encrypted traffic one time will increase demand for consolidation of networking and security capabilities into a SASE platform delivered via the cloud. Companies also need a "worldwide fabric of points of presence" to ensure low-latency to users and devices.

To accomplish this, Gartner recommends IT security leaders take these steps:

Companies should look for security vendors with significant experience setting up other customers in the cloud and protecting those environments.

"Moving things to the cloud is a fairly important trend from a security perspective because in most cases we have just opened up a lot more of our real estate and we're not protecting it as well as we would like to," he said.

This is your go-to resource for XaaS, AWS, Microsoft Azure, Google Cloud Platform, cloud engineering jobs, and cloud security news and tips. Delivered Mondays

The rest is here:
Will edge computing become the new cloud in 2021? - TechRepublic

Surveillance systems demands huge amounts of storage media. With most surveillance based around image capture, that will always likely be the case.

That also means that input/output (I/O) will generally be sequential think one movie frame after another being written or read to a drive. But, that changes somewhat with the use of artificial intelligence (AI) to add intelligence to surveillance.

But what are the storage needs of surveillance systems, what type of storage media can be used for surveillance, and what kinds of storage products are aimed at surveillance?

Monitoring and retention of video camera footage still forms a central core to the world of surveillance, and so a key requirement of storage in surveillance is the ability to handle sequential I/O well.

Meanwhile, surveillance is increasingly being supplemented with AI or so-called vision intelligence to bring actionable insight to camera-captured imagery.

So, where once security cameras would simply have been monitored by humans and retained as a record, movement of potential interest can be identified and alerts raised, for example. Thats of obvious utility to physical security where systems can be primed to recognise an intruder by size, shape, movement, and so on.

But AI has also been implemented in a wide range of other surveillance applications such as phasing traffic lights to improve vehicle flow, monitoring retail centre footfall to adjust product placement, and being able to recognise concerns over wellbeing such as a fallen patient in healthcare settings.

But no matter what the application and level of AI applied to it, sequential I/O will likely be the core storage performance characteristic required for surveillance systems.

However, not all apparently sequential I/O is as smooth as you think when it comes to video. Some video codecs, for example, comprise different types of frames that represent full images and differential changes that contain only changes portions. A hardly changing image would see little in the way of change, until a person walked across the cameras view, which means I/O flow would be quite wave-like.

While the requirements of image capture are largely sequential, the application of analytical and machine learning functionality is likely to involve a whole load more randomness.

Thats because while ingest of an image is pretty sequential, reading and comparing patterns within images will make reference to potentially large amounts of existing patterns already saved. The AI side of things is potentially quite random in its I/O needs.

Spinning disk HDD

With sequential I/O forming the vast bulk of surveillance traffic, spinning disk HDDs are predominant in the field. Spinning disk is well-suited to sequential I/O as the mechanical nature of the drives means time savings are made when read heads dont need to cycle in and out of different locations on disk platters.

Drives can be SATA, or the more performant and more expensive serial-attached SCSI (SAS), in terms of back-end connectivity. Both will work well with the main job of video ingest.

Solid state and NVMe

However, for the kind of heavy lifting that analytics requires, solid state storage where random access of storage cells is done electronically rather than mechanically doesnt incur time penalties.

This kind of media means flash, or NVMe flash, perhaps with enhanced processing power from a GPU card will possibly be required.

That was the case with Hong Kong-based cloud service provider Vivavo, where it leveraged NVMe-based storage for facial recognition on street cameras.

Solid state is a lot more pricey than spinning disk, so it will often make sense to have a tier of flash where data being processed is held, while HDD is fine for bulk storage of video.

Tape

Tape is also a potentially useable bulk storage medium, and when tapes are not in use they consume no power, unlike HDDs. But access to data held on tape is a lot slower than other media. Automating that via use of LTFS a way of layering a NAS-like file system on top of tape would be one option. Using tape as another tier for bulk storage is another way to incorporate it.

Cloud storage

Finally, there is cloud storage, which in some senses may be an ideal medium for video surveillance footage and analytics, which can be leveraged in the form of as-a-service offerings in the cloud.

But there are drawbacks too, such as the bandwidth required to upload video that could multiply as the number of cameras increases. There are also charges for the retention of data in the cloud to be considered. Compliance may also be an issue if images are stored outside particular jurisdictions.

The easiest way to get IP surveillance is by buying a network video recorder (NVR) that comes with everything you need cameras, servers, connecting hardware. These may be well suited to many smaller businesses, but may also be limited in terms of expansion, customisation and integration possible.

When it comes to HDDs for surveillance storage, any enterprise drive will work. But with retention of large amounts of imagery for long periods with potentially small amounts of access, higher capacities with less rigorous performance requirements fit the bill. That most probably means 7,200rpm SATA most of the time.

Some hard drive vendors, such as Seagate with its Skyhawk AI products, sell HDDs which they claim to be optimised for surveillance workloads. These are SATA drives, with an emphasis placed on MTBF (mean time between failures); 2 million hours in the case of the Seagate product.

Increasingly, surveillance is moving to the cloud, in so-called video surveillance as a service, in which the customer just has cameras on-site plus the ability to connect to the internet.

With so-called vSaaS one big trade-off is ongoing data storage costs against the one-time outlay for an NVR product. Having said that, cloud costs are likely to be more predictable than spend on on-site storage capacity or dealing with failed components.

Issues could also arise if internet connectivity is broken and images are not captured. Hybrid systems have been developed that use some on-site storage to counter that eventuality.

A big plus for cloud-based surveillance is that the added intelligence of AI can be added as a service to existing provision.

Read more:
IP surveillance: The storage it needs, on-premise and in the cloud - ComputerWeekly.com

Nov. 25, 2020 SK Telecom (SKT) today unveiled its self-developed artificial intelligence (AI) chip named SAPEON X220 and shared its AI semiconductor business vision.

SAPEON X220 is optimally designed to process artificial intelligence tasks faster, using less power by efficiently processing large amounts of data in parallel. Its deep learning computation speed is 6.7 kilo-frames per second, which is 1.5 times faster than that of Graphics Processing Units (GPUs) for inference that are being widely used by AI-service companies. At the same time, it uses 20% less power than GPU by consuming 60 watts of energy and is about half the price of a GPU.

SKT explained that SAPEON X220 will enable the provision of high-quality AI services by enhancing the performance of AI data centers through speedy computation of massive amounts of data.

From next year, the company will apply SAPEON X220 to its AI service NUGU to improve the voice recognition capability. SAPEON X220 will also be utilized by SKTs affiliate companies. For instance, ADT Caps will apply the chip to enhance the performance of its AI-based video monitoring service named T View.

In addition, SAPEON X220 will be applied to the cloud server of the next-generation media platform of Cast.era, a joint venture of SKT and Sinclair Broadcast Group.

SKT will also provide the AI chip to enable AI-based projects promoted under the Korean New Deal Initiative put forth by the Korean Government.

The company aims to generate synergies by combining AI semiconductor chips and 5G edge cloud. The application of an AI chip to a cloud located close to the end users will enable the provision of high-quality AI services with ultra-low latency regardless of customer device.

Moreover, the company said that it will go beyond just providing the AI chip to data centers by actively promoting the AI as a Service (AIaaS) business. It will offer a complete solution package as a service by combining its AI chip and AI software, including diverse AI algorithms for features like content recommendation, voice recognition, video recognition and media upscaling, along with Application Programming Interfaces (APIs).

For instance, an over-the-top (OTT) service provider wanting to adopt an AI-based content curation service will be able to easily implement the service by simply using SKTs solution that comes with all that is needed, from high-performance data center applied with AI chip to AI-based software like content recommendation algorithm and APIs.

Meanwhile, SKT is also playing a leading role in Koreas efforts to secure differentiated competitiveness in AI semiconductor technology. The industry-academia-research consortium led by SKT is currently carrying out a large-scale project assigned by the Ministry of Science and ICT to develop next-generation AI chips and interfaces that can be utilized for high-performance servers like cloud data centers. The consortium members include SK Hynix, Seoul National University and Korea Electronics Technology Institute. SKT brings to the consortium its capability in AI processor core and is working closely with SK Hynix, the worlds second-largest memory chipmaker, in the area of memory technology to promote advancement of AI semiconductor technology.

Originally posted here:
SKT Unveils its AI Chip and New Plans for AI Semiconductor Business - HPCwire