What is edge computing?
According to Gartner, “a component of a distributed computing architecture in which information processing is positioned at the edge—where devices and people create or consume that information.”
At its most basic, edge computing brings processing and data storage closer to the devices that gather it, rather than relying on a central location that may be thousands of miles distant. This is done to guarantee that data, particularly real-time data, does not suffer from latency concerns that might decrease application performance. Furthermore, doing the processing locally saves money by reducing the amount of data that must be processed at a centralized or cloud-based site.
Edge computing is transforming the way data is being handled, processed, and delivered from millions of devices around the world. The explosive growth of internet-connected devices—the IoT along with new applications that require real-time computing power, continues to drive edge-computing systems.
Faster networking technologies, such as 5G wireless, are allowing for edge computing systems to accelerate the creation or support of real-time applications, such as video processing and analytics, self-driving cars, artificial intelligence, and robotics, to name a few.
Edge computing arose from the exponential growth of the Internet of Things (IoT) devices that connect to the internet to receive information from the cloud or give data back to the cloud. Furthermore, many IoT devices generate huge amounts of data throughout their operations.
Because they provide a local source of processing and storage, many of these systems benefit from edge computing hardware and services. An edge gateway, for example, can analyze data from an edge device and then transmit just the essential data back to the cloud, reducing bandwidth needs. It can also provide data back to the edge device in the case of real-time application needs. (See also: Edge gateways are adaptable and long-lasting IoT enablers.)
Edge devices include an IoT sensor, an employee’s notebook computer, their latest smartphone, the security camera, and even the internet-connected microwave oven in the office break room. Edge gateways are edge devices in an edge computing ecosystem.
Edge computing use cases
Although there are as many possible edge use cases as there are users – each arrangement will be unique – several industries have been at the forefront of edge computing. Manufacturers and heavy industry employ edge technology to deliver delay-intolerant applications, keeping computing power close to where it’s needed for things like automated coordination of heavy equipment on a production floor. Companies may also utilize the edge to integrate IoT applications like predictive maintenance close to the equipment. In the meanwhile, agricultural clients may use edge computing as a data collection layer for data from a range of connected devices, such as soil and temperature sensors, combines and tractors, and so on.
Different sorts of deployment will demand various types of equipment. Industrial customers, for example, may prioritize durability and low latency, needing ruggedized edge nodes that can function in the harsh environment of a manufacturing floor, as well as specialized communication lines, to fulfill their goals (private 5G, dedicated Wi-Fi networks, or even wired connections). Environmental sensors, on the other hand, are likely to have both a larger range and lower data requirements, therefore an LP-WAN connection, Sigfox, or similar technology may be the ideal option.
Other usage situations raise entirely other concerns. Retailers may utilize edge nodes as an in-store clearinghouse for a range of activities, such as combining point-of-sale data with targeted promotions, tracking foot traffic, and more, for a unified shop management application. The connectivity component might be as simple as in-house Wi-Fi for all devices or as complex as Bluetooth or other low-power connectivity handling traffic tracking and promotional services while Wi-Fi is kept for point-of-sale and self-checkout.
Edge technologies
The physical architecture of the edge is complicated, but the basic idea is that client devices connect to a nearby edge module for quicker processing and smoother operations. The modules are referred to as “edge servers” and “edge gateways,” among other things.
Edge computing, cloud computing, and fog computing are all different types of computing.
“Edge computing” and “cloud computing” are words that are frequently used interchangeably. While these concepts have some overlap, they are not the same and should not be used interchangeably. It is useful to compare and contrast the notions in order to understand their differences.
One of the simplest ways to understand the differences between edge, cloud, and fog computing is to concentrate on their common theme: distributed computing. All three concepts are concerned with the physical deployment of processing and storage resources in relation to the data that is being generated. It’s simply a matter of where those resources are positioned that matters.
Edge- Edge computing refers to the deployment of computer and storage facilities at the point where data is produced. This optimizes computing and storage near the data source at the network edge. A compact box containing several servers and storage, for example, maybe installed atop a wind turbine to collect and evaluate data collected by sensors within the turbine. A railway station, for example, may use a minimal amount of computer and storage to collect and analyze data from track and train traffic sensors. Any such processing discoveries can then be sent to another data center for human review, archiving, and combination with other data results for more thorough analytics.
Cloud- Cloud computing is the large, highly scalable distribution of computing and storage resources over several global sites (regions). Cloud providers also provide a number of pre-packaged IoT services, making the cloud a popular alternative for IoT setups. Despite the fact that cloud computing offers far more resources and services than traditional data centers, the nearest regional cloud facility can be hundreds of miles away from where data is collected, and connections rely on the same erratic internet connectivity that supports traditional data centers. In practice, cloud computing is utilized as a substitute for existing data centers or, in certain situations, as a supplement. The cloud enables considerably closer centralization of computation to a data source, but not at the network level.
Fog- The cloud and the edge aren’t the only places where compute and storage may be deployed. Although a cloud data center may be too far away, the edge deployment may be too resource-constrained, geographically fractured, or distributed to allow for rigorous edge computing. In this case, the notion of fog computing may be beneficial. In contrast, fog computing takes a step back and places compute and storage resources “within” rather than “at” the data.
Fog computing configurations can generate unfathomable volumes of sensor or IoT data over broad geographical regions that are just too large to detect an edge. Examples include smart buildings, smart cities, and even smart energy networks. Consider a smart city that uses data to analyze, assess, and optimize public transit, municipal utilities, city services, and long-term urban planning. Because a single edge deployment is insufficient to handle such a load, fog computing may gather, process, and analyze data by deploying a series of fog nodes inside the scope of the environment.
Note – It’s worth noting that fog computing and edge computing have almost identical definitions and designs, and the terms are routinely used interchangeably even among technology experts.
What is the importance of edge computing?
Good designs are required for computing tasks, and an architecture that is appropriate for one form of computing activity may not be appropriate for all types of computing chores. Edge computing has emerged as a viable and necessary architecture for distributed computing, enabling processing and storage resources to be deployed closer to the data source, ideally in the same geographic region. In general, distributed computing models are not novel, and concepts like remote offices, branch offices, data center colocation, and cloud computing are widely known.
Decentralization, on the other hand, might be problematic since it demands a high level of monitoring and control, which is sometimes overlooked when departing from a traditional centralized computing strategy. Edge computing has gained popularity as a feasible solution to the expanding network challenges associated with conveying the huge volumes of data generated and consumed by today’s enterprises. It isn’t only a matter of quantity. It’s also a matter of time; applications increasingly rely on time-sensitive processing and responses.
Consider the development of self-driving autos. Intelligent traffic signaling will be necessary. Automobiles and traffic control systems will be required to generate, analyze, and share data in real-time. When you multiply this requirement by a huge number of self-driving cars, you can understand the scope of the potential problems. This needs a network that is both fast and responsive. Edge — and fog — computing addresses three fundamental network limits: bandwidth, latency, and congestion or reliability.
Bandwidth- The amount of data that a network can move in a given amount of time, measured in bits per second, is referred to as bandwidth. Every network has a bandwidth limit, and wireless communication has even more. This suggests that there is a limit to the amount of data — or the number of devices — that may be transferred through the network. Although expanding network bandwidth to accommodate more devices and data is feasible, the cost can be significant, there are still (greater) limited limits, and it does not address other difficulties.
Latency- Latency is the amount of time it takes to send data between two points on a network. Although data should move at the speed of light, enormous physical distances, as well as network congestion or outages, may cause data to be delayed. All analytics and decision-making processes are slowed as a result, restricting a system’s ability to respond in real-time. It has even cost lives in the case of driverless vehicles.
Congestion- In essence, the internet is a global “network of networks.” Despite the fact that it has evolved to provide good general-purpose data exchanges for the majority of everyday computing tasks, such as file transfers or basic streaming, the sheer volume of data generated by tens of billions of devices can overwhelm the internet, causing high levels of congestion and forcing time-consuming data retransmissions. In other cases, network outages can exacerbate congestion and even shut off access to certain internet users, rendering the internet of things inoperable.
Edge computing connects numerous devices across a much smaller and more efficient LAN, where sufficient bandwidth is used completely by local data-generating devices, substantially reducing delay and congestion. Local storage gathers and secures raw data, while local computers may perform essential edge analytics — or at the very least pre-process and reduce data — in real-time to make decisions before uploading discoveries, or just significant data, to the cloud or central data center
What are the advantages of edge computing?
Edge computing addresses essential infrastructure challenges such as bandwidth restrictions, excessive latency, and network congestion, but it also offers a variety of potential additional benefits that make it appealing in other situations.
Autonomy- Edge computing is useful when connections are inconsistent or bandwidth is constrained due to environmental considerations. Examples include oil rigs, ships at sea, rural farms, and other isolated locations such as a rainforest or desert. Edge computing computes on-site, sometimes on the edge device itself, such as water quality sensors on water purifiers in remote villages, and can save data for transmission to a central location only when a connection is available. The amount of data that must be sent may be dramatically reduced by processing it locally, which requires significantly less bandwidth and connectivity time than would otherwise be necessary.
Data sovereignty- When it comes to transporting enormous amounts of data, it’s not only a technological challenge. The transfer of data across national and regional borders can intensify worries about data security, privacy, and other legal issues. Edge computing may be used to keep data close to its source while remaining compatible with current data sovereignty requirements, such as the GDPR, which governs how data is stored, processed, and exposed in the European Union. This enables raw data to be processed locally, masking or securing any sensitive data before moving it to the cloud or central data center, which may be situated in various jurisdictions.
Edge Security- Finally, edge computing offers a novel approach to developing and maintaining data security. Despite the fact that cloud providers offer IoT services and specialize in complex analysis, businesses are concerned about data security after it leaves the edge and returns to the cloud or data center. By placing computers at the edge, all data traveling across the network back to the cloud or data center may be encrypted, and the edge deployment itself can be safeguarded against hackers and other harmful acts – even if IoT device security remains a problem.
Security and privacy
Data at the edge can be problematic in terms of security, especially when handled by a range of devices that aren’t as secure as centralized or cloud-based systems. As the number of IoT devices grows, it is vital that IT recognizes the security concerns and ensures that those systems are secure. Encrypting data, utilizing access-control methods, and maybe VPN tunneling are all components of this.
Furthermore, an edge device’s reliability may be influenced by diverse device requirements for processing power, electricity, and network connectivity. Redundancy and failover management is necessary for devices that handle data at the edge to ensure that data is delivered and processed effectively even if a single node fails.
Conclusion-
Edge computing has made things even more efficient. As a result, the quality of the company’s procedures has improved. Depending on the current state of affairs, edge computing might be a viable solution for data-driven tasks that require lightning-fast results and a high level of flexibility.