Think of a farm somewhere out there. This farm has sensors that have been installed at various points on the farm. The sensors are meant to capture data. They capture data on irrigation, soils, seeds, and weather. The farm also has drones that are used to hover over the farm and capture images for further imaging analysis. All this data is then integrated into various appliances that can model yields, make predictions and send updates to other connected equipment. All this data is collected together using a private network which connects all the sensors together and forms an intelligent edge. The immediate analysis and modelling can be carried out on equipment which is also a part of the edge. All working together to make better predictions that will ensure higher yields.
Consider a hospital which is a ‘smart’ hospital. Doctors in this smart digital hospital have access to the data they need right at their patients’ bedsides. The patients can receive better care when doctors can use the insights that have been created by analyzing data that has been collected from various labs and instruments located in and around the hospital and which can be processed quickly and efficiently and made available without any delays. These insights can help with quicker diagnosis and decisions on the further course of action. Here the network that ties all the various instruments and devices and the computing equipment together forms the intelligent edge which provides the analysis and the insights to the doctors.
A hotel offers mobile check-in and other services. Guests who need to check-in at this hotel need not wait in lines to be checked in at the reception. They can go directly to their rooms and enter with a digital room key provided on their apps. The room would be set as per their preferences of lighting, cooling and TV channels.
The use cases shown above all have one thing in common. They all integrate connectivity, compute, analytics and security, to connect people and things within a local environment to enable control and action locally. All of them work on the intelligent edge. The intelligent edge refers to the local processing of data close to the point of generation and collection of the data. Instead of sending data out to the cloud or a data center every time, analysis is performed at the location where the data is generated.
What is edge computing?
Over the past few years, many applications and workloads have been migrated by many enterprises to centralized clouds that offer flexible infrastructure and ease of use. We live now in a world where everything is hyper-connected which is resulting in a growing amount of data from an increasing number of connected devices and equipment. This is giving rise to many new use cases closer to where the data is generated, which is at the edge. Then there is the question of latency and cost tradeoffs that is challenging the rationale and value proposition of using cloud computing to execute these use cases. Emerging technologies at the edge such as Augmented Reality (AR), Virtual Reality (VR) and Internet of Things (IoT) all have requirements which are not served by the current cloud computing model.
Edge computing was developed due to these reasons and the exponential growth of Internet of Things (IoT) devices. These devices are being used in many contexts and many of the IoT devices generate enormous amounts of data during the course of their operations. All these devices connect to the internet for either receiving information from the cloud or delivering data back to the cloud. The problem begins when latency starts impacting the response times of the IoT devices to the equipment they control. The time taken for the data to traverse from the point of generation/collection to the application in the cloud, computation and analysis in the cloud, and the required insight/action returned to the device sometimes is not quick enough for critical operations. At such times local computation and analysis is required. Edge computing brings computation and data storage closer to the devices where it’s being gathered. This removes the dependency on a central location that can be very far away. This way computation involving real-time data does not suffer latency issues that can affect an application’s performance.
Gartner defines edge computing as “a part of a distributed computing topology in which information processing is located close to the edge – where things and people produce or consume that information.”
There are a lot of IoT devices out there. Today it is around 7 billion, a little less than as many people as there are on this planet. In another 5 years it will be a little over 30 billion, a lot more than the number of people on this planet at that time. And these devices generate huge amounts of data and will continue to into the future. Edge computing is transforming the way data is being handled, processed, and delivered from millions of devices around the world. It is this explosive growth of internet-connected devices – the IoT – along with new applications that require real-time computing power, that continues to drive edge-computing systems.
Think of devices that monitor manufacturing equipment on a factory floor. Think of multiple internet-connected video cameras that send live footage from remote locations. Think of any number of other devices like IoT sensors, employee’s notebook computers, their latest smartphones, the security cameras or even the internet-connected microwave ovens or other gadgets. Edge gateways themselves are considered edge devices within an edge-computing infrastructure. The amount of data being generated that requires quick round trips to the cloud is huge and is a cost in terms of both bandwidth and time. There are Edge-computing hardware and services that help solve this problem by being a local source of processing and storage for many of these systems. They can process data from an edge device, and then send only the relevant data back through the cloud, reducing bandwidth needs. Or it can send data back to the edge device in the case of real-time application needs.
Another factor that plays a big part in accelerating and creating support for real-time applications is faster networking technologies, such as 5G wireless and WiFi-6. These are allowing for edge computing systems to accelerate the creation or support of real-time applications, such as video processing and analytics, self-driving cars, artificial intelligence and robotics, to give some examples. While earlier the goals of edge computing were to address bandwidth cost for data traveling long distances because of the growth of IoT-generated data, now the rise of real-time applications that need processing at the edge is what will drive the technology ahead.
It is important to remember that none of this removes the cloud from the equation. The data will still need to be sent to the cloud for further analysis and storage. The intelligent edge is not a replacement for enterprise and hyper scale cloud data centers but only a way to distribute tasks across the network based on timeliness, connectivity, and security.
Why an Intelligent Edge?
So what is in it for the businesses? Why intelligent edge? Using intelligent edge technology can help maximize a business’s efficiency. It makes possible real-time analysis of data for critical next best actions. Which simply mean this analysis can be performed more quickly, and also decreases the likelihood that the data will be intercepted or otherwise breached. By doing this, the intelligent edge reduces latency, costs, and security risks, thus making the business more efficient. It also means that businesses become much more self-contained and do not rely on potentially faulty network connections to do their work. The edge enables the delivery of superior digital experiences such as personalized recommendations in a shopping mall or a seamless check-in process in a hotel. In addition to digital experiences, these edge use cases also enable business outcomes that cannot be achieved via traditional computing methods including that of the cloud.
In terms of services on the intelligent edge we have to consider that for several years enterprises have extended their problem solving and data-processing activities away from the head offices and into the field. This has always been possible because of the cloud which has allowed reliable and secure connectivity between the field operations that use various devices and the central data centers within the enterprise. This is becoming challenging now with the proliferation of IoT-enabled computing devices being used for data collection in the field and huge amounts of data that needs to be transferred to the enterprise data computing resources. This is where the intelligent edge will become increasingly important to devices that operate in the field, as they become more sophisticated and autonomous. With the intelligent edge, businesses can gain much greater visibility into their physical operations while deputizing AI technology to handle more high-demand tasks autonomously. At the scale of data centers, factories, and supply chains, reducing downtime, predicting demand, and optimizing for efficiency can deliver tremendous economic gains.
Every enterprise today has a vision which is to transform how they work, live and play at the edge. They are forever looking for ways in which new business outcomes can be achieved and new experiences can be crafted to delight their users. They imagine new ways to interact with their customers, for products to be designed and made, and how their employees will interact with their peers and surroundings. They look to providing their customers with better ways to shop, explore, and entertain. This, today, is becoming a common vision that is being shared across industries like retail, healthcare, manufacturing, hospitality and education. It is a vision that will be made possible by the all the innovations that will be made in the data era at the Intelligent Edge.