Edge computing reduces latency and bandwidth by placing computing close to data sources. It involves running fewer processes in the cloud and moving them to local devices, such as computers, IoT devices, or edge servers. Computation at the network’s edge minimizes long-distance communication between clients and servers.
Parts of an Edge Computing
One way to view edge computing is a circle from the code data center. Moving closer to the edge, each tier represents a different level.
- Provider/enterprise core: Traditionally, these are the “non-edge” tiers operated and owned by cloud providers, telcos, and large enterprises.
- Service provider edge: Located between core data centers and last-mile access, these tiers are usually owned and operated by telcos or internet service providers, and these providers serve multiple customers from them.
- End-user premises edge: Last-mile access edges include enterprise edges (e.g., retail stores, factories, trains) and consumer edges (e.g., homes, cars).
- Device edge: Sensors and actuators are directly connected via non-internet protocols in stand-alone (non-clustered) systems. This is where the network ends.
How Does Edge Computing Work?
Compared to traditional methods, edge computing offers businesses and organizations a faster, more efficient way to process data. There was a time when edge points generated vast amounts of data that would often go unused. Thanks to mobile computing and the Internet of Things (IoT), IT architecture can now be decentralized, allowing for near real-time insights with less latency and lower cloud server bandwidth demands.
The Evolution of Edge Computing
Creating the first content delivery network (CDN) in the 1990s led to the creation of edge computing. However, this technology was only suited to images and videos, not massive amounts of data. As mobile and early smart devices became more prevalent in the 2000s, existing IT infrastructure was under increased strain. Overlays networks like peer-to-peer have relieved some of the pressures caused by pervasive computing.
Despite this, true IT decentralization was only achieved once the mainstream cloud computing application provided end users with enterprise-level processing power, increased flexibility, on-demand scalability, and collaboration capabilities. The need to process more data outside the data center at the source and manage it from a central location arose as more end users demanded cloud-based applications, and businesses worked from multiple locations.
It was then that mobile edge computing became a reality. Companies allocate IT differently as the “Era of IoT” unfolds, making previously complicated data collection more accessible.
Why is Edge Computing Important?
The most sensitive data and critical systems that must function safely and reliably are processed at the edge in places like hospitals, factories, and retail locations. A low-latency solution without a network connection is needed for these places. The potential of edge technology to transform business across all industries and functions makes edge so exciting. All edges enable proactive, adaptive business functions, often in real-time, resulting in new, improved user experiences.
Bringing the digital world to the physical world is what edge allows businesses to do. Bringing online data and algorithms into brick-and-mortar stores to improve the retail experience. By creating training systems and situations enabling workers to learn from machines, workers can train themselves. The design of intelligent environments that ensure our safety and comfort. These examples use edge computing, enabling companies to run applications that require reliability, real-time, and data requirements on-site. It allows companies to innovate faster, launch new products and services more quickly, and generate new revenue streams more efficiently.
Benefits of Edge Computing
Modern networks face numerous challenges in moving vast volumes of data, and edge computing has emerged as one of the most effective solutions. Among the essential advantages of edge computing are the following:
An indicator of latency is the time it takes for data to be transferred between two points on a network. The distance between these two points and network congestion can cause delays. Using edge computing, latency issues are virtually eliminated because the points are closer.
When data is transferred over a network, bandwidth refers to how fast it can be shared. All networks have a limited bandwidth, which limits both the volume of data that can be transferred and the number of devices that can process it. Using edge computing, many devices can operate over a smaller and more efficient bandwidth by deploying data servers near the data sources.
The Internet has evolved, but the amount of data generated daily across billions of devices can cause high congestion levels. Using local storage, edge computing servers can perform essential edge analytics during a network outage. Click Here
The example above shows that edge computing minimizes bandwidth consumption and server resources. There are finite resources and costs associated with bandwidth and cloud computing. By 2025, Statista predicts that more than 75 billion IoT devices will be installed in households and offices, including intelligent cameras, printers, thermostats, and even toasters. Moving significant amounts of computation to the edge to support all those devices will be necessary.
Latencies are also reduced when processes are moved to the edge. Devices that need to communicate with distant servers cause delays every time. Suppose two coworkers, for example, chat over an IM platform in the same office. In that case, there might be a significant delay because every message must travel outside the building, communicate with a server across the globe, and then return before it appears on the recipient’s screen. The noticeable delay would not exist if that process were brought to the edge and the company’s internal router handled intra-office chats.
In the same way, users of any web application that has to communicate with an external server will experience delays. In addition to bringing more processes to the network edge, these delays will also vary based on the bandwidth and the server’s location.
Edge computing can also provide new functionality that wasn’t previously available. For example, it is possible to analyze and process data in real-time using edge computing.
To recap, the key benefits of edge computing are:
- Decreased latency
- Decrease in bandwidth use and associated cost.
- Reduction of server resources and associated cost
- Added functionality
Drawbacks of Edge Computing
Even though edge computing offers several benefits, it is still relatively new and far from foolproof. The following are some of the most significant drawbacks of edge computing:
It can be costly and complex to implement edge infrastructure in an organization. For it to function, additional equipment and resources and a clear scope and purpose are required before deployment.
Edge computing should be clearly defined during implementation because it can only handle partial data sets. As a result, companies may lose valuable data and information.
Since edge computing is a distributed system, ensuring adequate security can be challenging. When data is processed outside the network edge, there are risks involved. Adding new IoT devices increases the possibility of an attacker infiltrating the device.
Edge Computing use Cases and Examples.
With edge computing techniques, data is collected, filtered, processed, and analyzed “in place” at or near the network edge. It allows you to use data that can’t be moved to a centralized location. For example, the sheer volume makes that impractical, costly, or might otherwise violate compliance obligations, like data sovereignty. Many real-world examples and use cases have emerged from this definition:
Manufacturing errors were discovered, and product quality improved using real-time analytics and machine learning at the edge, enabled by edge computing in an industrial manufacturer. A manufacturing facility with edge computing added environmental sensors, providing insight into how product components were assembled and stored and how long they remained in stock. It is now possible for the manufacturer to make faster and more accurate business decisions regarding the factory facility and manufacturing operations.
Think about a business that grows crops indoors without sunlight, soil, or pesticides. By using this process, grow times are reduced by over 60%. Using sensors, companies can track water consumption, nutrient density, and harvesting efficiency. Data is collected and analyzed to find the effects of environmental factors and continuously improve crop-growing algorithms.
3. Network optimization
With edge computing, users can measure network performance across the Internet and then use analytics to determine their traffic’s most reliable, low-latency network path. Traffic is “steered” across the network using edge computing for optimal performance in time-sensitive traffic.
4. Workplace safety
Using edge computing, businesses can monitor workplace conditions or ensure employees follow established safety protocols by combining and analyzing data from on-site cameras, employee safety devices, and various other sensors — especially when the workplace is remote or unusually dangerous, such as a construction site or oil rig.
5. Improved healthcare
As medical devices, sensors, and other equipment have become increasingly sophisticated, the amount of patient data collected has increased dramatically. As a result of this enormous data volume, edge computing must apply automation and machine learning to access the data, ignore “normal” data, and detect problem data in real time to help clinicians prevent health incidents.
To operate autonomously, they gather information about the location, speed, condition of the vehicle, road conditions, traffic conditions, and other vehicles, requiring and producing anywhere from 5 TB to 20 TB per day. Real-time aggregation and analysis of the data must be performed while the car is moving. Autonomous vehicle data can also help authorities and businesses manage vehicle fleets based on real-time conditions. Independent vehicle data can also help firms and authorities manage vehicle fleets based on real-time conditions.
A retail business can produce enormous volumes of data from surveillance, stock tracking, sales data, and other real-time business information. It is possible to analyze this diverse data and identify business opportunities, such as an effective endcap or campaign, to predict sales and optimize vendor orders using edge computing. The local environment of retail businesses can differ dramatically, so that that edge computing can be an effective solution for local processing.