Edge Computing and 5G Economy

Distributed Edge Computing Infrastructure is Key to 5G

- Overview

5G Edge Computing Infrastructure is a combination of 5G cellular networks and edge computing that improves network performance and speeds up data processing:

• 5G cellular networks: 5G networks offer faster data speeds, higher bandwidth, and lower latency than previous generations of mobile technology.
• Edge computing: Edge computing involves placing small data centers and processing units closer to the end users and devices that generate data.
• 5G Edge: 5G Edge combines these two technologies to reduce latency and enable near real-time data processing. This allows for faster and more efficient services.

5G Edge Computing Infrastructure is important for many applications, including: 

• Smart cities: 5G Edge can improve network performance in smart cities.
• Autonomous vehicles: 5G Edge can support autonomous vehicles.
• Healthcare: 5G Edge can improve healthcare services.
• Industrial automation: 5G Edge can support industrial automation.
• IoT applications: 5G Edge is important for deploying large-scale IoT applications.
• AI, AR, and VR: 5G Edge can support emerging use cases that use AI, augmented reality (AR), and virtual reality (VR).

5G cellular networks are becoming vital to edge computing and are therefore shaping the future of enterprise IT. 5G connects wireless devices to the internet more quickly than fourth-generation LTE, offering much higher bandwidth, higher download speeds, and lower latency than what has traditionally been possible.

- Creating The Next-Generation Edge-Cloud Ecosystem

5G and edge computing are closely related. Edge computing has great potential to help communication service providers improve content delivery, enable extreme low-latency use cases and meet stringent legal requirements on data security and privacy. With the 5G network infrastructure creating a completely new layer of “fog,” 5G will allow companies to feel more secure within their own private networks. 5G will drive the transformation in edge intelligence.

Edge computing is a distributed computing model in which computing takes place near the physical location where data is being collected and analyzed, rather than on a centralized server or in the cloud. This new infrastructure involves sensors to collect data and edge servers to securely process data in real-time on site, while also connecting other devices, like laptops and smartphones, to the network.

Edge computing is important because it creates new and improved ways for industrial and enterprise-level businesses to maximize operational efficiency, improve performance and safety, automate all core business processes, and ensure “always on” availability. It is a leading method to achieve the digital transformation of how you do business. Across industries and global organizations, edge computing is driving seismic business change

Edge computing works hand in hand with the cloud to provide a flexible solution based on the data collection and analysis needs of each organization. For real-time collection and analysis, the edge is ideal for certain workloads. At the same time, the cloud can provide a centralized location for large scale analytics. Together they provide real-time and longer term insights into performance and power initiatives like machine learning and asset performance management. 

- Edge AI

The edge is where new AI use cases will take off, especially those for personal use. Powering these programs will not only become more affordable, but also more reactive and customizable, a win-win for consumers and researchers alike. 

AI training and inference can be done on a range of GPU types, including consumer GPUs in mobile devices. The hardware that powers our mobile devices has been steadily improving since smartphones hit the market, and it shows no signs of slowing down. Industry-leading mobile GPUs like Apple’s A17 Pro and Qualcomm’s Adreno 750 (used in high-end Android devices like the Samsung Galaxy and Google Pixel) are redefining the AI ​​tasks that can be done on mobile devices. 

Now, new chips called neural processing units (NPUs) are being produced specifically for consumer AI computing, supporting on-device AI use cases while managing the thermal and battery constraints of mobile devices. Add smart system design and architecture that can route jobs to the hardware best suited for the job, and the network effect created can be very powerful. 

Despite the huge potential of edge AI, it still faces a series of challenges. Optimizing artificial intelligence algorithms for various mobile hardware, ensuring consistent performance under different network conditions, solving latency issues, and maintaining security are all key obstacles. However, ongoing research in artificial intelligence and mobile technologies is steadily addressing these challenges, paving the way for this vision to become a reality. 

The future of AI innovation lies not in building bigger data centers, but in harnessing the power we already have in our pockets and homes. By shifting the focus to edge computing, a more inclusive, efficient and innovative AI ecosystem can be formed. This decentralized approach not only democratizes AI but also aligns with global sustainability goals, ensuring that the benefits of AI are available to everyone, not just the privileged few.  

[Edge Computing - Wikipedia]

- Edge Computing for Digital Transformation

Edge computing will be critical if the digital transformation is going to be able to deliver business value over time in line with our high expectations. This is obviously driven by the strong IoT and mobility trend. The surge in data volume that will come from the massive number of devices enabled by 5G has made edge computing more important than ever before. Beyond its abilities to reduce network traffic and improve user experience, edge computing will also play a critical role in enabling use cases for ultra-reliable low-latency communication in industrial manufacturing and a variety of other sectors.

With the emergence of new technologies such as augmented and virtual reality, autonomous cars, drones and IoT with smart cities, data is increasingly being produced at the user end of the network. These use cases demand real-time processing and communication between distributed endpoints, creating the need for efficient processing at the network edge. 

Many use-cases for IoT and 5G span the device, access-, distributed-, national- and global sites. For example, an augmented reality solution comprises a client on a device, a component supporting video processing, a CDN/caching function at a distributed site and a backend at a national- or global site. This requires a solution that can handle any workload, anywhere in the network, with end to end orchestration. Distributed cloud is doing this - managing different types of sites where the location of the edge depends on the use case.

Edge computing devices - especially IoT devices – depend on network access to the cloud to receive machine learning and complex event processing models. Likewise, these devices need network access to send sensor and status data back to the cloud. In an enterprise environment, many of these devices are already on SCADA (Supervisory Control and Data Acquisition) networks and will continue to operate there.  

- 5G Multi-access Edge Computing

Multi-access edge computing (MEC) is the way that high-bandwidth, low-latency applications will be delivered in the future. With innovative and cutting-edge applications, MEC enables Mobile Network Operators (MNOs) host content close to the edge of the network. MEC offers application developers and content providers cloud-computing capabilities and an IT service environment at the edge of the network. This environment is characterized by ultra-low latency and high bandwidth as well as real-time access to radio network information that can be leveraged by applications. 

MEC allows running applications and processing data traffic closer to the cellular customer, reducing latency and network congestion. Computing data closer to the edge of the cellular network enables real-time analysis for providing time sensitive response - essential across many industry sectors, including healthcare, telecoms, finance, and so on. Implementing distributed architectures and moving user plane traffic closer tonthe edge by supporting MEC use cases is an integral part of the 5G evolution.

MEC provides a new ecosystem and value chain. MNOs can open their Radio Access Network (RAN) edge to authorized third-parties, allowing them to flexibly and rapidly deploy innovative applications and services towards mobile subscribers, enterprises and vertical segments.  

5G multi-access edge computing is a form of edge computing that brings the capabilities of cloud computing into telcos’ points of presence, as opposed to the traditional cloud which is housed in remote data centers. As 5G evolves and we move toward a connected ecosystem, MNOs are challenged to maintain the status quo of operating 4G along with 5G enhancements such as edge computing, NFV and SDN. The success of edge computing can not be predicted (the technology is still in its infancy), but the benefits might provide MNOs with critical competitive advantage in the future.

- The Future of Network Design Is White Box

When it comes to building telecom infrastructure, the white box trend isn’t going away anytime soon. This approach to universal customer premises equipment (uCPE) sees service providers increasingly disaggregating network hardware and software. Using hardware and software from different vendors provides a “more open, flexible, and cost-effective alternative to traditional proprietary, integrated networking equipment.

Similar to 5G, edge computing is not a single technology, but a set of technologies being deployed in unison to achieve a business outcome. And to achieve the scale needed with edge deployments, white box hardware will undoubtedly play a part. The disaggregated nature of 5G adds further complexity. White box hardware alongside open source software stacks provide significantly more innovation and development opportunities. But as simple as it may sound, it is complicated.