Wind River CTO: Building Intelligent Systems in the 5G and Edge AI Era

Author: Paul Miller, CTO of Wind River

More computing and processing power will be deployed at the edge of the network to provide a positive, powerful and secure user experience. This computing and processing requirement will increase the complexity of the network architecture and require a higher and more detailed level of management and maintenance to work effectively. As the number of connected devices grows exponentially, enterprises will need to design intelligent systems to ensure that these devices are effectively coordinated.

Communications service providers and global enterprises have begun to build and shape their business models around the intelligent edge and 5G. The vast majority of enterprise use cases supported by 5G are located at the edge of the network, thanks to ultra-low latency communications and increased network speeds. Intelligent systems are essential to support and coordinate the ecosystem of 5G.

The introduction of edge systems implies the deployment of highly physically distributed architectures, which introduces significant operational complexity. Not surprisingly, AI will play a key role in the creation of intelligent edge devices, where data is often abundant but processing resources are limited.

New opportunities in a tightly connected ecosystem

Edge AI and 5G offer tremendous opportunities for organizations looking to leverage a host of innovative new use cases, applications, and services using real-time data processing and analytics.

Both technologies have stimulated a huge change in autonomous embedded systems such as planes, trains, cars and robots. Across industries, there is a huge effort to automate processes and reduce human intervention to bring greater product innovation, production efficiency and safety improvements.

Manufacturers like Bosch have already begun deploying 5G in some of their factories in hopes of fulfilling their ambitions for Industry 4.0. What manufacturers hope to achieve with 5G is a factory floor that operates in real time, with machines able to communicate more intelligently with computing resources located at the edge of the network.

Similar to self-driving cars, 5G provides the necessary real-time connectivity required for the vehicle’s built-in sensors to communicate with the edge cloud, where data can be processed close to the physical vehicle location to maximize passenger safety and enhance the passenger experience.

Artificial intelligence will bring further business opportunities

According to Gartner, by 2025, 75% of data will be computed at the edge of the network. In addition, the amount of data collected is expected to reach 175 ZB by 2025, with the vast majority of data generated by connected devices. With the promise of 5G and smart technology so great, a key challenge will be how to make sense of the explosion of data they will create.

Artificial intelligence, which can enhance human cognitive abilities, is very important for information processing in 5G. Whatever form AI takes, advanced analytical systems can be computed at the edge and reveal insights about connected things, devices, surroundings, etc., often within milliseconds. Integrating AI into embedded systems to coordinate the connected device-driven ecosystem will be key to transforming 5G into a data-driven, real-time network.

AI will help increase the value of big data in the future. As edge systems grow and geographically distributed environments become more and more common, AI can enable new operational capabilities. For example, consider using machine learning algorithms to perform predictive analytics on network infrastructure to avoid network outages.

True AI will eventually enable self-healing within the network, identifying and resolving issues in real time without human intervention and process delays. AI capabilities and AI for IT operations (AIOps) support intelligent network automation, workload placement, enhanced infrastructure planning, and big data aggregation and event identification, which are important applications to help operators deliver 5G services. Gartner predicts that by 2023, large enterprises will use AIOps and digital experience monitoring tools to monitor applications and infrastructure.

Supporting the right infrastructure

In this new AI, edge, and 5G-enabled ecosystem, the right infrastructure to support AI in the seamless management and decoding of data will be critical. 5G is driving cloud-native application development, enabling services to run on distributed edge cloud infrastructure. As 5G introduces more devices than ever before, new imperatives will include distributed computing, single-interface management, coordination of synchronized network tasks, and continuous monitoring and management of the analytical tools needed to deliver rapid, continuous uptime, etc.

With so many device endpoints, a multi-node, geographically distributed edge cloud scenario, where applications, devices, and use cases run closer to the edge of the network, will be key to supporting the compute resources AI needs to successfully analyze, orchestrate, and enhance processes.

While there is a focus on using AI to streamline processes and provide strategic information to businesses, embedded developers must begin to adopt the technology themselves. As the transformative promise of 5G and the intelligent edge is increasingly realized by leaders across vertical industries, the demand for smart, connected devices and embedded systems will accelerate.

Adding AI to the mix will enable new use cases to reach peak efficiency, speed up processes, and take business to the next level. The growth of endpoints will require more complex coordination and management, which only AI can enable. Crucially, it will rely on infrastructure that provides computing resources to the edge of the network so that the entire connected ecosystem begins to operate seamlessly and successfully in real time.