Understanding Edge Computing: A Comprehensive Overview

What Is Edge Computing?

Edge computing is a distributed computing concept that integrates intelligence into edge devices (also known as edge nodes), allowing data to be processed and analyzed in real time close to the source of data collection. Because edge computing processes data locally at the edge of the network rather than in the cloud or a centralized data center, it minimizes latency and data transmission costs, enabling real-time feedback and decision-making.

How Does Edge Computing Work?

Edge computing brings computing power closer to the data source, where sensors and other data capturing instruments are located. The entire edge computing process takes place inside intelligent devices that speed up the processing of the various data collected before the devices connect to the IoT.

The goal of edge computing is to boost efficiency. Instead of sending all the data collected by sensors to the enterprise applications for processing, edge devices do the computing and only send important data for further analysis or storage. This is possible thanks to edge AI, i.e., artificial intelligence at the edge.

After the edge devices do the computation of the data with the help of edge AI, these devices group the data collected or results obtained into different categories. The three basic categories are:

• Data that doesn’t need further action and shouldn’t be stored or transmitted to enterprise applications.

• Data that should be retained for further analysis or record keeping.

• Data that requires an immediate response.

The work of edge computing is to discriminate between these data sets and identify the level of response and the action required, then act on it accordingly.

Depending on the compute power of the edge device and the complexity of the data collected, the device may work on the outlier data and provide a real-time response. Or send it to the enterprise application for further analysis in real-time with immediate retrieval of the results. Since only the important and urgent data sets are sent over the network, there’s reduced bandwidth requirement. This results in substantial cost savings, especially with wireless cellular networks.

Why Edge Computing?

Edge computing gains popularity in enterprise computing due to the imperative nature of data-dependent digital transformation initiatives, encompassing robotics, advanced automation, AI, and data analytics. Most industries utilizing these technologies are also time-sensitive, and any system congestion and network outage will result in huge economic losses. Choosing edge computing has the following benefits:

Low Latency - Edge processing minimizes latency by handling data from sensors and IoT devices locally, eliminating the need to transmit it to a centralized cloud for processing. This gives a more reliable and consistent network.

Reduced Bandwidth - Every network has a limited bandwidth, especially wireless communications. Edge computing solves bandwidth limitations by processing immense volumes of data near the network’s edge then only sending the most relevant information through the network. This minimizes the volume of data that requires a cellular connection.

Ensuring Security - When processing data at its source, edge computing empowers organizations to retain both data and computation in a proper location. This minimizes susceptibility to cybersecurity threats and ensures compliance with stringent and dynamic data location regulations.

Data Compliance and Governance – Organizations that handle sensitive data, are subject to data regulations of various countries. By processing this set of data near the source, these companies can keep the sensitive customer/employee data within their borders, hence ensuring compliance.

Types of Edge Computing

There are three main categories of edge computing:

Provider Edge: The provider edge constitutes a network of computing resources accessible via the internet, primarily leveraged for delivering services by telecommunication firms, service providers, media entities, and other content delivery network (CDN) operators.

Enterprise Edge: The enterprise edge is an extension of the enterprise data center, including data centers at remote office sites, micro data centers, etc. While IT typically owns and manages this environment akin to a centralized data center, there may exist constraints related to space or power that necessitate adjustments in design for these setups.

Industrial Edge: The industrial edge, often referred to as the far edge, encompasses smaller computing instances like one or two compact, ruggedized edge servers or embedded systems deployed beyond traditional data center environments. Operating outside the confines of typical data centers presents a range of distinctive challenges, including those related to space, cooling, security, and management.

Edge Computing Use Cases

Over the years, edge data centers have found several use cases across industries, thanks to rapid tech adoption and the benefits of processing data at the network edge. Below are the different ways several industries use edge computing in their day-to-day operations:

Transportation - Autonomous vehicles produce around 5 to 20 terabytes of data daily from information about speed, location, traffic conditions, road conditions, etc. This data must be organized, processed, and analyzed in real-time, and insights fed into the system while the vehicle is on the road. This time-sensitive application requires accurate, reliable, and consistent onboard computing.

Manufacturing - Several manufacturers now deploy edge computing to monitor manufacturing processes and enable real-time analytics. By coupling this with machine learning and AI, edge computing can help streamline manufacturing processes with real-time insights, predictive analytics, and more.

Healthcare - The combination of edge computing and artificial intelligence can improve the work efficiency of medical staff and ensure the personal safety of patients. For Example, Human pose estimation, a widely adopted task in vision AI, involves predicting key points on an individual's body, including eyes, arms, and legs. This technology can be applied to alert healthcare staff in case of patient movement or falls from a hospital bed.

The other areas where edge computing has been adopted include healthcare facilities to help patients avoid health issues in real-time and retail to optimize vendor ordering and predict sales.

Edge Computing Challenges

Edge computing isn’t without its challenges, and some of the common ones revolve around security and data lifecycles. Applications that rely on IoT devices are vulnerable to data breaches, which could comprise security at the edge. As far as data lifecycles are concerned, the challenge comes in with the large amount of data stored at the network’s edge. A ton of useless data may take up critical space; hence businesses should keenly choose the data to keep and discard.

Edge computing relies on network connections, making network limitations another issue worthy of concern. It’s, therefore, necessary to plan for connectivity problems and design an edge computing deployment that can accommodate common networking issues.

Implementing Edge Computing

Developments in artificial intelligence, IoT, and 5G will continue to drive the adoption of edge computing. The number of use cases and workload types deployed at the edge will increase. The possibilities at the edge are truly endless. Regardless of the industry you are in, edge computing comes with several benefits, but only if it’s designed well and deployed to solve the challenges common with centralized data centers.