Author: Dev Mookerjee, Technology Leader, Red Hat Synergy, Asia Pacific, IBM Global Markets
I don’t quite remember when I first heard the phrase Edge Computing, it was probably three or so years ago – but I do remember how I felt when I heard it. I went through the four stages of teaming (with apologies to Bruce Tuckman) – all at the same time.
“Hmm, that makes sense.”
“No… that’s not new!”
“Ah, I get the bigger picture now and the future possibilities!”
“Let’s blog about it.”
Well, no – blogging wasn’t my first thought, but I do remember being very excited about what Edge Computing along with IoT devices, AI, and 5G and so much more, meant for the future of consumers and enterprises.
The idea of Edge Computing is not new of course – its origins can be traced back to the Content Delivery Networks of the early nineties. Leaving jargon aside, Edge Computing is about running your computing process close to where the data is created, i.e. computing in a distributed manner in contrast to a centralised manner.
Simple? I mean, isn’t that what people did before public clouds came and rocked our world? Not quite.
Over the last decade, new waves of technology development have seen both the advent of low cost, high powered, easily accessible centralised computing power with public clouds like IBM Cloud, and also at the same time, the growth in distributed localised computing power. If you are reading this post on a mobile phone, take a moment to look at the device and recognise the technological marvel you hold in your palm. From amateur hobbyists using Arduino toolkits and Raspberry Pis to autonomous cars, from the multitude of COTS surveillance cameras to intermittently disconnected deep-sea exploration devices – all have various degrees of computing power available on their device. These devices are all part of the growth of Edge Computing – computing on the edge of the network.
You get the picture. Billions of devices providing computing capacity at the point where the applications are running, and where their data is created. Until recently PCs, local servers, tablets, phones and gaming consoles were the key components of the Edge, today it includes a vast range of technologies including autonomous vehicles, medical devices, virtual reality headsets, robots, building sensors, smart whiteboards, drones and so much more.
While various technologies have had an impact on Edge Computing, there are three that particularly stand out. The growth of Edge servers and Edge devices have been and will continue to be deeply impacted by IoT, AI and the advent of 5G.
IoT or Internet of Things has emerged in the same time frame I am talking about. In an industry with a proud history of often cryptic acronyms, IoT seems to stand uniquely ordinary – it’s basically like saying “connected stuff.” However, the name is anything but ordinary, and the credit for it goes to Kevin Ashton, who is the co-founder of the Auto-ID Center at the Massachusetts Institute of Technology (MIT), which created a global standard system for RFID and other sensors. IoT devices form a significant growing subset of Edge Computing devices, and each IoT device has unique identifiers (UID) to communicate with each other. We already see Edge devices all around us. Medical sensors, smartwatches, home automation components, fitness trackers, surveillance cameras are all examples of IoT devices. Look around you – and it’s highly likely that you have multiple IoT devices surrounding you.
According to US start-up Lucid Motors, an autonomous car could generate between three and 40 gigabytes of data per second. That is way too much data to be analysing through brute force computing algorithms or having it streamed back to a central server for processing – enter embedded AI on devices.
Many Edge devices run Linux as an operating system, and this allows us to deploy sophisticated AI models limited only by the ever-increasing hardware components on board. Automated cars, surveillance cameras, live translation devices, medical devices that can run diagnostics in real-time, computers that can self-heal when major disruptions take place – these are real examples of AI in Edge devices. The growth in AI techniques and Edge has given us the capability to distribute not only computing to the Edge but also the devices, which can understand, reason and learn in a localised manner.
Advent of 5G
The distributed nature of Edge Computing makes connectivity to each other and back to a central point a key component of the architectural design. 5G is being rolled out globally as the Next Generation network with high bandwidth, low latency and much higher capacity to handle more devices at the same time. This promises to remove many obstacles that Edge Computing faces today.
While it’s not rolled out everywhere, 5G networks, along with its wireless connectivity to low-cost public clouds, change the variables of the decision equation of what workloads can be run on Edge devices.
IBM Edge Computing™ is an advanced platform supporting real-time AI, 5G and IoT applications. You can deploy and remotely manage these applications more securely — on Edge devices, servers and gateways across hybrid, multicloud environments. Unlocking joint value spanning telecommunication providers and enterprises.
IBM Edge Computing provides a full range of deployment flexibility to manage, move and secure workloads. It can be deployed as either a full or light-stack Kubernetes cluster on Edge servers and gateways or lightweight dockerized containers on Edge devices.
According to Garter, around 10% of enterprise-generated data is created and processed outside a traditional centralised data centre or cloud. By 2025, they predict this figure will reach 75%. Forrester adds that 2020 will be the breakout year for Edge Computing.
The immediate future holds a massive opportunity for Edge and brings with it enterprise application considerations. IBM has already launched discussions and best-practice methodologies around:
Security standards for such heterogeneous products
Seamless management of the deployment of applications and devices
Resiliency considerations and much more
Instrumented and connected lives for both consumers and as an extension, enterprises providing such capabilities will be the norm. The Pew Research Center estimates that more than 5 billion people in the world have smartphones. Today there are 15 billion Edge devices, by 2025 it’s estimated that there will be 150 billion. That’s a lot of humans on this Earth who are already on track to be part of that Edge.
We don’t have to look too far into the future as many Edge Computing stories today sound like they are straight out of science fiction. My favourite Edge Computing use case is truly out of this world – literally!
In 2018, IBM, together with Airbus and the German Aerospace Center DLR launched the technology-demonstration Project CIMON – the first AI-powered robot in space. CIMON was a floating Edge device like none other as it processed data onboard and connected to IBM Watson on the IBM Cloud in Germany. The team shrank a typical 8-10 seconds communications to 1-2 seconds by using satellite-based data connection, optimisation of the CIMON software architecture and the end-to-end connectivity of the cloud. Inspired? Find out more here.
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