Nov 03, 2020 telecommunications

Enabling the hyper-connected world with the intelligent edge

By Nermin Mohamed, Head of Telecommunications Solutions at Wind River


In our era of connected devices, edge computing is transforming the way data is processed, delivered, and stored. Whether it’s a smartphone, autonomous car, or medical device, edge-computing systems bring fast, real-time data computing power close to where it’s generated—“the edge.”

The practical applications are endless. This cloud edge approach of processing data as close as possible to where it’s generated is gaining wide adoption in many industries, such as telecommunications, healthcare, and automotive. Indeed, a recent Wind River study revealed that, in the wake of COVID-19, more than 70% of Telco industry 5G projects have been put on a development fast-track.

There are many market trends driving the fast adoption rate. According to IDC, there will be a projected 75 billion connected devices by 2025, and the data generated from these will reach 79.4 zettabytes. With these seismic numbers of customers to serve with 5G, it also brings the increasing need for intelligence and compute power at the edge of the network to meet the real time processing requirements for all the applications running on those billions of devices.

The intelligent edge provides new, valuable capabilities that enable enterprises across all industries to innovate and introduce new products and services. In fact, according to GSMA, communications service providers (CSPs) expected to invest over $880 billion in 5G network infrastructure in the next five years, from 2020 to 2025.

The intelligent edge: what it is and why it’s useful

To understand the momentum behind its wide adoption, it’s important to first understand what edge computing is and the vast opportunities it brings with it. When it comes to edge computing, the old adage holds true: it’s all about location, location, location. Rather than a new home with a view, in this case, we’re talking about bringing virtualization to the edge to deliver on the key 5G requirements: ultra-low latency, high availability, scalability, and security—delivered in a cost-effective way across a dynamic landscape with multiple remote nodes. This is a key to enabling new 5G services.

Together, edge computing connectivity combined with advanced analytics enables enterprises to take advantage of fast data processing close to where it is requested. It applies the power of cloud scale to run applications and provide real-time insights, which is especially helpful for enterprise use cases that require high-speed or real-time response. This high-efficiency processing enables valuable, new use cases across industries, such as connected manufacturing and smart buildings, hospital systems, farms, and cities.

Edge computing is focused on optimizing data-driven capabilities by bringing data collection, processing, and reporting as close to the end user as possible. To deliver this data, the edge needs a strong pipe, and 5G will provide that. 5G, the fifth generation of wireless technology, is a technology paradigm shift that is the underlying backbone of fully connected devices across the globe. According to the World Economic Forum, intelligent connectivity, enabled by 5G, will be a catalyst for social and economic growth with an estimated $13.2 trillion of global economic value reached by 2035. Artificial intelligence (AI) and machine learning (ML) play a key role as they transform 5G into a scalable real-time network that is data-driven.

Enterprises that step into the intelligent edge ‘arena” will gain quantifiable benefits for their businesses and customers:

Reduced latency

In a smart hospital or digital factory (or any other edge-required application for that matter) milliseconds matter. Confining data analysis to the edge where it is created eliminates latency, which translates into faster response times.

Greater security and privacy

Edge computing leverages the portability of containerized facilities with modular servers. Just as security best practices have been developed for each evolution in networking and compute processing, rigorous security measures and protocols are a foundational piece of cloud edge deployments. In addition, edge computing greatly reduces the volume of traffic, which, in turn, shrinks the cyberattack surface.

Efficient use of bandwidth and cost savings

Edge computing allows organizations to categorize the value of their data and to decide which data is critical or nonessential for the service and applications to operate. By prioritizing storing critical data at edge locations, enterprises reduce the need for costly bandwidth to connect all of their data locations. This reduced bandwidth consumption delivers significant cost savings.

Greater reliability and scalability

Scalability is one of the most valuable and predominant features of network edge computing. Distributed clouds extend beyond the network edge and onto the customer edge, enabling customers to run low-latency applications close to the data source to reduce traffic volumes and costs.

An inside look at what makes up “the edge”

Edge computing combines deploying the cloud to mobile devices—with fast connectivity and powerful analytics processing. With these elements in mind, we know there are some key components for the infrastructure of edge computing:

Distributed cloud

A distributed cloud is a type of cloud that has geographically dispersed infrastructure that primarily runs services at the network edge. A distributed cloud environment is strategically placed where application components are at appropriate geographic locations to best meet the demands placed on the application.

Information processing

Successfully taking advantage of edge data processing requires powerful analytics that can readily handle the data volume and processing requirements. According to 451 Research, the IoT analytics workloads being performed at the edge are becoming increasingly sophisticated, with a shift away from rules-based actions towards artificial intelligence and machine learning. Whatever AI form it takes, advanced analytics systems at the edge compute application data and reveal, in milliseconds, insights about the connected things, devices, the surrounding environment, and more.

Advanced connectivity (5G)

By deploying edge computing into the 5G network, it minimizes the physical distance between the cloud and the mobile device, providing faster speeds with only millisecond latency to millions of device connections per square kilometer. As put by Forrester Research, “5G networks promise exponential bandwidth and ultra-low latency to edge computing solutions that empower near-real-time insights gained from business systems built on internet of things (IoT) technologies.”

Wind River: Making the intelligent edge a reality

5G creates the increasing need to bring greater intelligence and compute to the edge of the network. Virtualizing this intelligent edge is key to making 5G a reality. To meet this challenge, Wind River introduced best-in-class solutions to help enterprises virtualize the edge network by moving to a cloud-native, container-based virtualized architecture with standardized interfaces that lead to greater flexibility and scalability, faster services delivery, and improved cost efficiency.

Wind River enables the high-scale deployment of 5G network infrastructure for major service providers worldwide, providing a production-grade Kubernetes for managing edge cloud infrastructure. With an approach that is focused on simplifying deployment and management of complex distributed networks, our platform provides zero-touch, automated management, deployment, and upgrades of thousands of notes across a geo-distributed cloud.

Our solutions provide the cloud network analytics that make it possible to effectively manage a distributed cloud system by consuming and processing the data to gain meaningful insights to make proactive and informed decisions that will keep your cloud up and optimized.

Want to learn more about Wind River’s edge computing solutions? Visit us here.

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