Edge Computing

Edge computing is transforming the way data is being handled, processed, and delivered from millions of devices around the world. The explosive growth of internet-connected devices – the IoT – along with new applications that require real-time computing power, continues to drive edge-computing systems.

Faster networking technologies, such as 5G wireless, 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 name a few.

Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth.

The origins of edge computing lie in content delivery networks that were created in the late 1990s to serve web and video content from edge servers that were deployed close to users. In the early 2000s, these networks evolved to host applications and application components at the edge servers, resulting in the first commercial edge computing services that hosted applications such as dealer locators, shopping carts, real-time data aggregators, and ad insertion engines.

Modern edge computing significantly extends this approach through virtualization technology that makes it easier to deploy and run a wider range of applications on the edge servers.

Urban areas are quickly becoming massive information gathering centers, with sensors collecting data on traffic patterns, utility usage, and key infrastructure every day. While that data allows city officials to respond to problems faster than ever before, all of that information must be collected, stored, and analyzed before it can be put to use. Traditional cloud solutions aren’t able to provide immediate response times for the multitude of devices operating on the outskirts of the network.

Edge computing architecture makes it possible for devices regulating utilities and other public services to respond to changing conditions in near real-time. Coupled with the rising number of autonomous vehicles and the ever-expanding internet of things, smart cities have the potential to transform how people live and utilize services in an urban environment. Since all edge computing use cases rely upon devices collecting data to carry out basic processing tasks, the city of the future will have the ability to react dynamically to changing conditions as they occur.

Banking institutions are adopting edge computing in conjunction with smartphone apps to better target services to customers. They’re also incorporating the same principles to provide ATMs and kiosks with the ability to gather and process data, making them more responsive and allowing them to offer a broader suite of features.

For high-volume finance firms dealing in hedge funds and other markets, even a millisecond of lag in a trading algorithm computation can mean a substantial loss of money. Edge computing architecture allows them to place servers in data centers near stock exchanges around the world to run resource-intensive algorithms as close to the source of data as possible. This provides them with the most accurate and up to date information to keep their business moving.

The healthcare industry has long struggled to integrate the latest IT solutions, but edge computing offers exciting new possibilities for delivering patient care. With IoT devices capable of delivering vast amounts of patient-generated health data (PGHD), healthcare providers could potentially have access to critical information about their patients in real time rather than interfacing with slow and incomplete databases. Medical devices themselves could also be made to gather and process data throughout the course of diagnosis or treatment.

Edge computing could make a significant impact on the delivery of healthcare services to hard-to-reach rural areas. Patients in these regions are often many miles from the nearest health provider and even if a healthcare professional evaluates them on-site, they may not be able to access crucial medical records. With edge computing, devices could gather, store, and deliver that information in real time, and even use their processing capabilities to recommend treatments.

While regulatory requirements for the sharing and disclosure of medical information would make any edge solution challenging to implement, other emerging security measures such as blockchain technology could provide new ways to address such concerns.

Between smartphones and AI-powered virtual assistants like Amazon’s Alexa and Google’s Assistant, the modern household is becoming a fully integrated network unto itself. As more and more of these devices enter homes, there will be a greater strain on service provider networks as more requests flood into their servers and different forms of streaming content are delivered to users.

By incorporating edge computing architecture into their networks, companies can improve performance significantly and reduce latency. Rather than every AI virtual assistant sending processing and data requests to a centralized server, they can instead distribute the burden among edge data centers while performing some computing functions locally.

As these edge computing use cases become more widespread, many more industries are sure to benefit from the versatility and advantages it can provide. The proliferation of localized data centers for both cloud and edge computing make it easier than ever for organizations to expand their network reach and put themselves in a position to make the most of their data resources.