Introduction to Edge computing

Edge computing is a new model of computing that involves networks and devices located near the end-user. It focuses on processing data closer to its source, allowing for faster and larger data processing, resulting in more timely and effective outcomes.

Compared to traditional centralized models that rely on on-premise data centers, edge computing offers some distinctive benefits. By bringing computing power closer to the source of data generation, it enables companies to optimize their use of physical assets and create new interactive, human experiences. Edge computing has numerous applications, such as production QA, inventory management, self-driving cars, autonomous robots, smart equipment data, and automated retail.

Additionally, With Edge Computing, latency can be reduced therefore real time inferencing can be achieved with AI. Since data doesn't travel outside the enterprise, data sovereignty is maintained.

Broad Classification

Edge computing is composed of various components, including edge devices, network edge, and on-premises infrastructure.

Edge devices
Edge devices, such as smart speakers, watches, and phones, are commonly used and locally collect and process data while interacting with the physical world. IoT devices, point of sale systems, sensors, robots, and vehicles can also function as edge devices if they compute locally and connect with the cloud.

Network edge
The network edge is another component that may not require a separate network but can leverage 5G to provide powerful wireless connectivity with low latency and high speed, enabling opportunities like autonomous drones, remote telesurgery, and smart city projects.

on-premises infrastructure
Finally, on-premises infrastructure includes servers, routers, containers, hubs, or bridges that manage local systems and connect to the network.

Edge devices, Network edge, and On-premises infrastructure

Why is Edge computing required?

Edge computing has already found its way into critical systems that need to function reliably and safely, such as hospitals, factories, and retail locations. These places require low-latency solutions that don't rely on network connections.

Edge computing has the potential to transform businesses across every industry and function, enabling real-time, proactive, and adaptive business operations that lead to optimized experiences for people. Edge computing can benefit various business functions, such as customer engagement, marketing, production, and back-office operations.

Edge computing enables businesses to merge digital and physical worlds by incorporating online data and algorithms into physical environments. This leads to improved retail experiences, training opportunities for workers, and the creation of smart environments that prioritize safety and comfort.

Edge computing allows companies to run critical applications on-site with high reliability, real-time performance, and data requirements. This ultimately results in faster innovation, quicker deployment of new products and services, and the potential for new revenue streams.

Edge computing has the potential to transform businesses in various industries and functions, including customer engagement, marketing, production, and back-office operations.

Edge computing allows organizations to access and process data that was previously unavailable or difficult to obtain. This can lead to new insights and opportunities for innovation, driving business growth and creating a competitive advantage for the future.

Edge computing combined with other technologies

Edge computing complements centralized cloud architectures by integrating distributed components. While the cloud enables the collection and processing of data from various sources in one centralized location, edge computing uses locally generated data to enable real-time responsiveness and create new experiences.

By reducing latency and controlling sensitive data, edge computing makes it possible to respond quickly and efficiently to changing circumstances. This approach can lead to cost savings on data transmission to the cloud and also allows for a greater level of control over data processing and privacy.

5G
5G enhances the efficiency, reliability, and management of edge implementations by ensuring the transmission of critical control messages that enable devices to make autonomous decisions. This technology connects the edge to the internet backhaul and guarantees that edge devices have the appropriate software-defined network configurations to perform their tasks.

Service and data mesh
Service and data mesh enable the deployment and querying of data and services distributed across containers and data stores throughout the edge. These meshes present a single interface that abstracts away the routing and management of service and data interfaces, enabling bulk queries for entire populations within the edge, rather than on each device.

IoT and connected devices
IoT and connected devices are unique data sources that require protection and registration in the cloud. Edge computing is typically located near or on these data sources.

Software-defined networking
Software-defined networking permits users to configure the overlay networks and customize routing and bandwidth to determine how to connect edge devices to each other and to the cloud.

Containers
Containers provide a standardized deployment environment for developers to build and package applications, regardless of device capabilities, settings, and configurations.

Digital twin
Digital twin is another critical enabler that organizes physical-to-digital and cloud-to-edge. This twin allows data and applications to be configured using domain terms around assets and production lines, enabling domain experts to configure applications to sense, think, and act on the edge.


AI and blockchain are two technologies that can further enhance the capabilities of edge computing.

By implementing AI algorithms on edge devices, the need for centralized computing power can be reduced, resulting in faster and more efficient processing. Blockchain can also benefit from edge computing as it allows for more reliable and trustworthy data, reducing the chance of human error.

Edge computing can enable the capture and relay of real-time data directly by machines, while the increasing use of sensors and cameras can provide richer data to be analyzed and acted upon.

Edge computing is also transforming the field of automation, enabling complex processes to be performed in open, uncontrolled environments like agriculture, instead of just systematic processes in closed, controlled environments like factories.

By reducing latency and controlling sensitive data, edge computing makes it possible to respond quickly and efficiently to changing circumstances

Edge Computing - Advantages

Faster response times: Edge computing can provide real-time or near-real-time results because data is processed locally, without the need for transmission to the cloud and back.

Connectivity in remote areas: Edge computing can be used in areas with limited connectivity, such as offshore oil drilling platforms, or in mobile or transportation scenarios.

Efficient processing of high data volumes: In cases where there is a high volume of data, it may be more efficient to process it at the edge rather than sending it to the cloud due to physical network limitations and transmission costs.

Autonomous operations: In situations where connectivity to the cloud is not possible or unreliable, end-to-end processing within the local environment can help keep operations running smoothly.

Privacy and security: Sensitive data can be kept locally at the edge without sending it to the cloud, which can help with privacy and security concerns.

Cost optimization: The processing of data in different parts of the cloud continuum can have different cost profiles, and edge computing can help optimize costs to minimize overall system costs.

Edge computing offers various benefits such as rapid response, high data volume processing, privacy control, remote accessibility, cost optimization, and autonomous operations. These advantages improve the user experience by increasing relevance, unlocking valuable data, and generating more reliable and safer data processing. The combination of edge and cloud knowledge results in better predictions and more relevant information, leading to continuous improvement.

Edge computing - examples

We will now explore a few examples of how edge computing is already being used today and how these use cases are expected to benefit from the advancement of 5G and other emerging technologies.

Retail
The retail industry is transforming into a more flexible and customer-centered experience with the advent of the Store of Tomorrow concept. Edge technology plays a critical role in enabling human-centered experiences in this model. One example of edge application is the frictionless store checkout, which solves the issue of long lines that often result in missed sales. By using on-site cameras and AI trained to recognize inventory items, data can be processed in an edge network in the store. This allows customers to walk out of the store and charges their accounts accurately through a kiosk, without waiting in line. Retailers can benefit from increased customer satisfaction, improved inventory and supply chain management, and reduced theft.

Healthcare
In the field of robot-assisted surgery, edge computing has the potential to make a significant impact. By using edge computing, surgeons are able to perform less invasive and shorter procedures with the help of robotic assistants. This is made possible by several small changes that add up to a big impact, such as smaller incisions, better views of the site, and more intuitive controls. Ultimately, the use of edge computing in this context results in a better user experience for both surgeons and patients.

Surgeons are able to perform less invasive and shorter procedures with the help of robotic assistants

Edge computing - challenges and opportunities

Organizations that aim to leverage edge computing may encounter various obstacles that hinder the adoption of this technology. Some common challenges that organizations may face when implementing edge computing include:

Lack of standard and integrated architectures: The absence of a standardized architecture for edge computing and the need for infrastructure alignment often hinder enterprises from adopting edge technology.

Fast-moving ecosystem with multiple tech options: With a vast selection of potential partners and technologies, it can be challenging to make critical decisions for edge computing, especially with continuous innovation in network capabilities.

Lack of cloud talent to understand what belongs at the edge, why and when: While leveraging cloud capabilities is essential for edge computing, companies may face a shortage of cloud talent who understand how to extend those capabilities to the edge.

Unique security challenges at the edge: There are distinct security challenges in the IoT and edge domain, including the need for rapid patching and physical security measures due to devices being located remotely or in untrusted environments.

Innovation fatigue and pilot purgatory: Industrializing and scaling edge solutions can be daunting, and organizations may get stuck in the proof-of-concept stage without moving towards scaling.

Unrealized business value at the edge: Organizations may struggle to understand the full potential of edge computing solutions and may only focus on easy-win use cases that drive quick returns.

Edge computing - use cases

Intelligent machines and real-time productivity
Edge use cases often involve processing data with velocity, which can enhance the productivity of intelligent machines in real-time. This has the potential to transform various industries, from smart signage to quality assurance in assembly lines.

Optimized close to consumption
Edge computing can optimize digital production and consumption for the best user experience and cost efficiency. Use cases may include content delivery and offshore oil well optimization, among others.

Experience with extended reality
Extended reality use cases, which can involve digital twins, can create optimized experiences in healthcare, the workforce, and entertainment. Examples may include smart health applications and mixed-reality gaming.

Privacy and security by default
Edge computing can enhance the reliability and privacy of sensitive data by processing it locally. Examples include wearable health devices and regulated data processing.

Always-on and untethered
Edge computing can enable mission-critical and remote applications, such as POS or autonomous operations, to make decisions and process data independently of connectivity.

Processing data with velocity, which can enhance the productivity of intelligent machines in real-time