Edge Computing Helps 5G Networks: A Practical Guide to Optimising Campus Network Architecture

With the gradual deployment of the fifth generation (5G) network, the global communication network is undergoing an unprecedented upgrade. 5G not only promises faster speeds, lower latency, and higher reliability, but will also promote the popularisation of emerging technologies such as the Internet of Things (IoT), augmented reality (AR), and autonomous driving. However, with the explosive growth in the number of access devices, traditional cloud computing architecture has been unable to effectively cope with these high-bandwidth, low-latency application requirements.In this context, edge computing came into being. As an important part of the 5G network, it greatly improves the network's response speed and processing power by migrating computing resources from centralised data centres to the edge of the network.

In this article, we will take a deep look at the key role of edge computing in 5G networks and the opportunities and challenges it brings.

What is edge computing?

Edge computing is a decentralised computing model that migrates computing resources, data storage, and application services from traditional centralised data centres to locations closer to data generation sources. This means that data can be processed closer to where it is generated, reducing the distance and time of data transmission. The main goal of edge computing is to perform computing tasks on edge nodes of the network (such as base stations, routers, or IoT devices) to reduce latency, save bandwidth, and improve real-time data processing capabilities.

How Edge Computing Improves 5G Network Performance?

• Improve Broadband Utilisation

Edge computing significantly reduces the amount of data that needs to be transferred to core data centres by processing data close to its source. This localised data processing greatly reduces the demand for core network bandwidth. Only filtered and compressed important data will be transmitted to the central system, thereby optimising the network's bandwidth utilisation and enabling 5G networks to more efficiently support a large number of devices and data-intensive applications.

• Reduce Network Latency

Another significant advantage of edge computing is its ability to effectively reduce network latency. By processing data at edge nodes close to end users, edge computing significantly reduces data round-trip time. By reducing the transmission path of data, edge computing helps 5G networks achieve near-instant response speeds.

• Enhance Network Reliability

Edge computing also improves the reliability of 5G networks through its distributed architecture. In the edge computing model, tasks can be distributed and processed across multiple edge nodes, which means that even if a node fails, other nodes can continue to perform tasks, thus avoiding the single point of failure problem in traditional centralised computing architecture. This enhanced reliability not only ensures service continuity, but also improves the stability of user experience.

Application Scenarios of Edge Computing in 5G Networks

• Autonomous Driving

Autonomous driving vehicles need to make decisions within milliseconds, which requires data processing and response to be very fast. By deploying edge computing nodes in vehicles or infrastructure close to the road, data from on-board sensors can be processed in real time, greatly improving the safety and reliability of autonomous driving. In addition, edge computing can also share real-time road information between multiple vehicles, further improving traffic safety and efficiency.

• Smart Cities

Smart cities need to process a large amount of traffic, environment, and pedestrian flow data in real time for intelligent management. Edge computing can transmit this data to edge devices closer to the data source through IoT devices, sensors, etc., thereby improving data processing speed and accuracy. For example, edge computing can analyse traffic flow data in real time, dynamically adjust the timing of traffic lights, and optimise traffic flow, thereby reducing congestion and carbon emissions.

• Augmented Reality (AR) and Virtual Reality (VR)

Augmented reality and virtual reality applications have extremely high requirements for network bandwidth and latency. By processing AR/VR content at edge nodes close to users, edge computing can significantly reduce latency and provide a smoother immersive experience. For example, in AR applications, edge computing can quickly process changes in user perspectives and instantly update the position and interactive effects of virtual objects, making the user experience more natural and realistic.

• Industrial Internet of Things (IIoT)

In the context of Industry 4.0, the Industrial Internet of Things (IIoT) relies on edge computing to achieve real-time monitoring and automated control of production equipment. In the manufacturing workshop, by deploying edge computing nodes, data from various sensors can be processed in real time, and the operating status of the production line can be adjusted immediately based on the analysis results. In addition, edge computing can predict and prevent equipment failures before they occur, thereby further improving the operational efficiency and safety of the factory.

Upgrade Networks to Meet Challenges of 5G Network

Application of Edge Computing in Campus Network

With the popularisation of 5G technology, campus network is gradually evolving into an intelligent and highly integrated network environment. The application of edge computing in campus network is crucial, as it can extend data processing capabilities to the edge of the network, thereby supporting more complex applications and higher data traffic.

In campus network, edge computing can achieve real-time data analysis and processing, support intelligent security systems, IoT device management, and high-bandwidth applications such as high-definition video surveillance and AR/VR interaction. By deploying computing resources at the edge nodes of the campus network, enterprises can better manage and optimise their internal data flows, reduce dependence on external cloud computing resources, and improve data security and privacy.

Small and Medium-sized Enterprise Office Network Solutions

Increasing the speed of small and medium-sized campus networks is one of the key steps to meet the challenges of 5G. Traditional Gigabit Ethernet can no longer meet the bandwidth and speed requirements of 5G applications. Therefore, upgrading to a higher-speed network solution is crucial. For small and medium-sized campuses, FS utilises PicOS® switches and the AmpCon™ unified management platform to build a basic two-layer network architecture. The SMB office network solution helps to create an efficient, stable, secure, and easy-to-maintain office network, significantly enhancing network security and user experience.

In this small campus network architecture, the access layer switch (S3910-24TS) supports 10G uplink capability to ensure that it meets the needs of future business expansion and upgrades. The aggregation layer switch (S5860-20SQ) supports 10G and 25G uplinks, and can handle a large number of concurrent data streams, ensuring that network traffic from each access layer can be efficiently and quickly transmitted to the egress layer. The egress layer switch (NSG-3230) is responsible for forwarding data traffic from the campus network to external networks, such as the Internet or WAN. It can also perform firewall, routing, NAT and other functions to ensure data security of the campus network and efficient access to the external network.

With the popularisation of 5G technology and the increase in the number of devices in the campus network, 10G modules provide guarantees for network expansion and performance improvement. In this small campus network architecture, 10G and 25G optical modules play a key role, ensuring efficient connection and data transmission between different network layers.

• 10G Module (Access Layer and Egress Layer)

At the access layer, 10G optical modules (SFP-10GSR-85) are used to connect switches and terminal devices (such as PCs, printers, cameras, etc.). The switches at this level are connected to the aggregation layer switches through 10G modules to ensure that the terminal devices access the network at a higher speed.

The 10G optical module (SFP-10GSR-85) at the egress layer is connected to the aggregation layer switch to achieve high-speed data transmission. At the same time, it also supports data transmission through optical fibre, which enhances the security of data transmission.

The 10G module can provide sufficient bandwidth to meet the network rate requirements of applications such as daily office and monitoring systems, and ensure fast data transmission to avoid network bottlenecks.

• 25G Module (Aggregation Layer)

At the aggregation layer, 25G optical modules (SFP28-25GSR-85) are used to connect aggregation switches to aggregation or egress layer devices. 25G modules aggregate data from multiple access layers switches through high-speed links and forward them to the egress layer. Compared with 10G, 25G optical modules provide higher bandwidth and data processing capabilities, which can effectively support larger-scale data transmission needs within the campus, especially in application scenarios that require high concurrency and low latency, such as high-definition video conferencing and large-scale data backup.

By using 10G and 25G optical modules, this small campus network architecture achieves efficient connections between different network levels, ensuring the overall performance and stability of the network. This design not only improves the bandwidth utilisation of the network, but also provides flexibility for future network expansion and upgrades.

Advantages of Upgrading Network: Improving Campus Network Data Processing and Transmission Efficiency

Upgrading the network can not only meet the high bandwidth requirements of the 5G network, but also significantly improve the data processing and transmission efficiency of the campus network. By introducing edge computing and high-speed switches and optical modules, campus networks can process large amounts of data more quickly, thereby reducing latency and improving user experience. Efficient data transfer reduces network congestion and ensures that critical applications always receive the bandwidth they need. In addition, the upgraded network is more scalable and can easily handle future increases in device and data traffic.

Final Words

Taking the above points into account, edge computing significantly improves the overall performance of 5G networks by optimising bandwidth utilisation, reducing latency, and enhancing reliability. These improvements directly translate into a better user experience, allowing users to enjoy faster connection speeds, lower latency and more stable services, meeting the demand for high-quality network services. In multiple scenarios such as the Internet of Things, smart homes, and remote offices, edge computing applications are leading the next generation of digital experience.

As 5G technology continues to evolve, we can foresee that future networks will be more distributed and intelligent, and the deployment of edge computing nodes will be further expanded to cover more application scenarios and devices. In addition, with the development of artificial intelligence and machine learning technology, edge computing will be able to process and analyse data more intelligently, further improving the adaptability and responsiveness of the network.