Edge computing will grow in importance as manufacturers see the development of the Industrial Internet of Things (IIoT). The interconnected factory, designed for manufacturing automation, will become an essential corporate goal.
The digital transformation of the industrial network edge continues:
With the continuous pursuit of improvement and innovation for business processes, edge computing, supported by the Industrial Internet of Things, has become widely recognized and applied. The industrial network edge is the point where information technology (IT) and cloud-based applications intersect with the operational technology (OT) environment. Edge computing is expected to become a "foundational technology" that is expected to be adopted by up to 90% of industrial enterprises by 2022.
Connectivity requirements continue to escalate as data-hungry analytics and other enterprise applications require reliable, pervasive information from OT assets, processes, people, and other components. Application requirements in critical areas, such as latency, security, and local execution, are constantly being tested for improvements in edge capabilities. The roles of various edge components have also evolved, including expanding computing and storage capabilities in "thick edge" devices, and the continued convergence of IT, OT, and security on a network-centric "thin edge". A growing number of mobile and autonomous devices such as robots and Automated Guided Vehicles (AGVs), are integrating edge computing with cloud computing, the Industrial Internet of Things (IIoT), artificial intelligence, big data analysis, and 5G. The IIoT Edge is taking its place alongside these key technologies.
Edge-to-cloud integration, edge computing, and convergence of IT, OT, and security within the industrial network infrastructure layer:
From cloud computing, Industrial Internet of Things (IIoT), artificial intelligence, big data analysis to 5G networks, edge computing can be said to be one of the key technologies.
- The IIoT edge is considered a key enabler of digital transformation strategies.
- The Industrial IoT edge continues to evolve in response to the escalating demands of data-driven digital transformation strategies.
- Computation-centric, thick-edge devices are going in a different direction than network-centric thin-edge components.
- Edge-to-cloud integration and edge computing remain largely the domain of thick-edge devices, which are defined by their greater computing and storage capabilities.
- IT/OT convergence, especially the convergence of automated control, centralized management, and security, is most prominent at the thin network-centric edge.
- The thin edge remains a dynamic environment in terms of emerging network alternatives, seeing a constant introduction of new technologies and standards.
Different requirements for the digitally transformed industrial network edge:
Customers pursuing a digital business improvement strategy need a deeper understanding of the entire enterprise, as well as broad connectivity across all devices and services. Cloud-based solutions for analytics, machine learning, service-oriented revenue streams, automation control devices, and other applications located at the Edge will generate meaningful data to create business value.
Edge capabilities can accelerate the implementation of a full range of connected products, processes, and services. Edge is capable of reducing response times when sending data to the cloud. Edge devices play a key role in supporting OT environments by providing integration and isolation from higher-level architectures. This is reflected in better connectivity and OT-friendly visualization and security.
The industrial network infrastructure segment at the IIoT edge is the flashpoint for delivery and increasingly transforms data and information between physical assets and processes into digital transformation applications. Continued improvements in network performance, driven by standardization and innovation, and an increasing emphasis on integrated operational security, drive the dynamic nature of the architecture.
Edge capabilities can accelerate the implementation of a full range of connected products, processes, and services. Edge is capable of reducing response times when sending data to the cloud. Edge devices play a key role in supporting OT environments by providing integration and isolation from higher-level architectures. This is reflected in better connectivity and OT-friendly visualization and security.
Cloud computing and edge computing technologies complement each other:
Cloud computing provides enterprises with computing, storage, and network services. In hybrid cloud architecture, edge computing is the intermediary between devices, clouds or data centers. It is mainly used to access device data and provide instant analysis as a transmission pipeline between the data source and the cloud to reduce round trips. Since the edge can process and filter the data that needs to be sent to the cloud, it can also reduce bandwidth costs. The local processing characteristics of edge computing help enterprises gain local data and dominance. In the future technical environment, edge computing can be regarded as an extension of cloud architecture. In the hybrid structure, the functions of traditional cloud computing and edge computing can be combined, and both parties can make up for their respective weaknesses.
Cloud computing relies on being connected to a centralized data center, so requires more time to receive data from the computing end. Conversely, edge computing is that at the "edge" of the network, so data is processed nearby, saving the time of connecting to the data center. Shortened response times are necessary for such applications as in Automated Guided Vehicles where immediate information processing would be necessary to reduce the risk of car crashes. For retail, medical, and other industries, where IoT is relatively mature, edge computing can help reduce latency.
What is a thick edge?
Edge-to-cloud integration, edge computing, and cloud-native architectures
Digitally transformed businesses have more opportunities to fully use IT, cloud-native technologies, and IP-based networking by moving closer to the edge. Enterprise cloud architectures are also transitioning to using the edge as their primary data source, and for overcoming cloud limitations.
Data processing at the edge reduces cloud service charges for data-intensive installations and can address concerns about the deployment, scale, and management complexity of cloud-based solutions. The edge layer can also be used to generate, access, and process data that is too difficult, expensive, or slow to access, or to bypass the control system architecture and send data directly to the cloud.
Edge computing can deliver concrete business outcomes in areas such as reducing machine downtime and maximizing asset utilization. This pursuit has expanded to related capabilities related to AI, AR/VR, machine vision, and video analytics. Extensive data preprocessing and reliability requirements, along with the need for local output, are pushing the execution of these applications to the edge. Analytics, video, machine learning, and similar applications require powerful data collection and computing power, making direct integration with enterprise clouds prohibitive from a cost and performance standpoint. The resulting upgrade in edge computing and storage capabilities is driving the use of the thick edge, including in IPCs, edge servers, and industrial IoT gateways and routers.
Standardization of IP-based wired and wireless networks is eliminating the need for hardware-based protocol translation, especially in new installations, and driving container-based application-layer protocol translation. These devices contain a lot of memory and provide software support for containers. Gateway and router vendors continue to add computing capabilities to their devices. As a result, the value focus of the industrial IoT gateway and router market has evolved from one based on automated protocol conversion to one that relies more on differentiation through software-enabled functions and application execution, including edge-to-cloud integration and edge computing.
5G + edge computing promotes lag-free audio and video entertainment experience:
5G networks are transforming the manufacturing, healthcare, retail, and automotive industries. The infrastructure running on telecom provider facilities connected via 5G networks has low latency. Telecom providers are moving towards a multi-tenant, managed infrastructure layer that bridges the gap between cloud and end-users.
In recent years, cloud computing has been integrated into almost everyone's life. We place an order from an app on a smartphone and receive the item a few hours later at the specified time and address. We use Netflix to watch series and movies on our phones and Spotify to listen to music almost anywhere in the world. Next, 5G and multi-access edge computing will affect the technological innovation progress of enterprises and consumers in the next few years, especially 5G applications such as games and AR/VR/UHD streaming media for the vast consumer market. What is expected is an audio-visual entertainment experience with richer sound and light effects and no delay.
Edge computing market outlook:
As the network connects industrial environments more closely, edge computing will become important for the management of enterprise automation equipment and remote capital equipment monitoring. Emerging technologies will accelerate the development of data processing equipment, automated robots, self-driving cars, smart factories, traffic management etc. Large enterprises and telecom service providers will continue to build the IIoT to deploy enterprise-specific wireless networks for Industry 4.0, automated mining, precision agriculture, smart healthcare, and smart retail development.
Edge computing is at the core of the Industrial Internet of Things (IIoT), and is an important key for enterprises wanting to accelerate their journey into Industry 4.0. The edge must provide secure access to docked devices, monitor operational conditions, detect and remotely troubleshoot, manage software, patch updates, and provide hardware maintenance. From device deployment to decommissioning, each device will undergo lifecycle management through edge computing device management services.
In the past, information technology (IT) and operational technology (OT) were two different disciplines with their own management goals. As cloud-based IoT grew, IIoT devices and connected machines become network virus intrusion points. In response to these malicious attacks, integrated management of IT and OT has been gradually developed and edge computing has become a common language between the two. Industries will rely more and more on edge computing platforms to realize the next generation of automated smart production.
