What Is an Edge Network?
Across industries, near-real-time access to data is transforming business and everyday life, enabling life-saving use cases, increasing efficiency and productivity, and delivering innovative experiences.
Achieving these outcomes requires faster data processing and analysis—accomplished with edge computing that brings compute capabilities closer to where data is generated—and faster, more efficient data delivery, accomplished with edge networking.
Simply put, an edge network brings resources—including network, compute, and storage—closer to the point of data creation and consumption. As a result, an edge network can process and deliver data to consumers and systems faster and more efficiently than alternate cloud computing and data storage models.
This allows the network to power many use cases that demand a fast, low-latency connection, including AI-enabled factories, robotics, connected retail, and media use cases, such as virtual reality (VR) and video on demand (VOD).
How Does Edge Networking Work?
Existing networks that primarily rely on fixed-function physical infrastructure are challenged to cost-effectively deliver the near-real-time responsiveness required by many enterprise use cases.
Networks need to evolve to support the growing demand for edge AI, which is expected to rise by 300 percent by 2027.1 They also need to support increased demand for visually rich media experiences like spatial computing, cloud gaming, and VOD, as the streaming media market alone is forecasted to grow twelvefold by 2031.2
Network infrastructure must become agile, scalable, and intelligent to respond to these mounting expectations while controlling capital investment and operational costs. A modernized edge network helps answer these challenges through physical and software-defined infrastructure that can dynamically adjust workloads to meet variable system demands and enable capitalization of every data point in the network.
Benefits of Edge Networks
Moving data infrastructure to the network edge makes it possible to intelligently manage workload placement, providing some key operational benefits:
- Low latency: Moving data analysis to the edge helps speed up system response times, enabling faster transactions and better experiences that are often vital in near-real-time applications, including AI-enabled commercial use cases.
- Reduced bandwidth usage: Minimizing the distance that data is sent over the network can reduce the bandwidth needed to transmit large volumes of data, reducing costs.
- Enhanced data protection: Processing data on the local edge network helps protect data by limiting the amount transmitted over the internet. This can be particularly important in some industries, such as healthcare, where local or regional requirements limit the storage or transmission of personal data.
- More agility and scalability: With the ability to distribute computing strategically across an edge cloud, service providers can ensure applications settle in the place of highest value and make the best use of their resources. This opens up a range of opportunities, such as establishing new services, targeting service delivery between localized markets, expanding capacity without the need to open a new data center, and improving quality of service (QoS).
Edge Network Challenges
The network edge sits within your network just outside the network core. It includes converging locations such as regional data centers, next-generation central offices (NGCOs), fixed wireline access points, wireless access base stations, and radio access networks (RANs). The network edge can also include on-premises locations, such as universal customer premises equipment (uCPE)—edge devices that can host multiple functions and services such as SD-WAN and enterprise applications.
Transformation of the network edge is about system optimization and enabling increased control over traffic management, including how and where to process and transmit data at the data-packet level.
Service providers have already adopted software-defined networking (SDN) and network functions virtualization (NFV) to bring flexibility into their networks, but this approach can be extended further. Adopting cloud-like management practices and implementing a software-defined edge can bring increased operational agility and resource efficiency.
A software-defined edge facilitates resource management across the heterogeneous hardware elements that are necessary to enact an edge environment. It also helps to orchestrate services and workloads such as media and AI, allowing the network to optimize workload placement on shared physical resources and drive higher returns. Ultimately, it is what makes edge deployment viable for service providers and developers.
Achieving successful business outcomes at the network edge—whether that’s combining regional fixed and mobile network functions into a new central office, deploying private 5G networks, or ensuring secure enterprise uCPE solutions—will take a combination of agility and performance.
Edge Network Security
Securing an edge network involves multiple considerations. Protecting the expanded attack surface of distributed computing infrastructure requires a zero-trust model that authenticates the identity of every device on the network. Additionally, all levels of the solution stack need to be protected. Furthermore, securing AI models and associated data against threats such as data breaches and model inversion attacks is a priority. Not to mention, tools are needed to help control the movement of sensitive data and manage the deployment of security updates across distributed infrastructure.
These challenges call for a secure foundation that protects the network across the entire data infrastructure, from edge to network core to cloud to RAN. Having a common security framework streamlines security management and provides a consistent way to respond to threats across the network, helping to protect data at rest, in flight, and in use.
Edge Network Use Cases
By placing compute resources at the edge of the network, service providers can meet the demand for many edge use cases. They include:
- AI/ML: Enterprises are integrating more IoT edge devices and data into their operations. Therefore, they need access to low-latency edge computing that can run AI and machine learning (ML) analytics over a wireless connection. Industrial robotics, smart manufacturing, advanced image processing workloads, and next-gen customer experiences in retail are a few of the many use cases that rely on near-real-time analytics.
- Streaming media: Distributed computing allows media workloads—such as live transcoding, video on demand, content delivery networks (CDN), and cloud gaming—to be orchestrated at nodes at the edge of the network, creating new monetization opportunities. For example, service providers could offer high-resolution cloud gaming with superior performance over a low-latency 5G edge network.
- Spatial computing: Edge computing is instrumental for many latency-sensitive emerging use cases, including augmented reality/virtual reality (AR/VR) and collaborative conferencing. These technologies are rapidly finding a foothold in sectors such as military and defense, manufacturing, and entertainment.
The Future of Edge Networks
As wireless technology continues to advance, edge networks can help provide the connectivity and resources needed for transformative experiences. Future generations of wireless networks are expected to merge connectivity with advanced sensing capabilities. This could enhance network performance through improved channel awareness, enabling new services and applications. The applications are expected to include intelligent transportation systems, environmental monitoring, enhanced security, real-time intruder detection, and digital twins for smart cities and factories. Together with an intelligent, automated RAN and AI/ML distribution between the edge and the cloud, these advancements will help define the edge networks of tomorrow.