Edge Computing – A Complete Guide
Edge computing is a new iCloud technology that has had a positive impact on the IT environment in recent years. It has the potential to dramatically change the way information is generated and processed.
In this article, we intend to fully explore margin calculations and learn about the importance of using this technology and its applications in various fields.
What is Edge Computing?
Edge computing is a mesh network that processes or stores important records locally. In a high-level system, processing takes place only within the source. In fact, the purpose of designing edge computing is to process around the source.
Edge Computing can be said to be a centralized network that minimizes the hassle of delay and bandwidth usage as much as possible. In simple terms, Edge Computing works less functionality in the cloud and moves those functions to premises on the user’s computer, such as the IoT system or the Edge server.
How Does Edge Computer Technology Work?
In this technology, each device has artificial intelligence and much wider processing power. In other words, each device becomes its own data center. So far, all smart devices that use cloud space have stored and processed all their information sent to clouds after processing sent to clouds end devices.
Because of the large size of smart devices and the high level of communication between these devices, they make use of edge computing. Currently, in Edge Computing, each device has its own information processing function, and only the most important and vital components are shipped to the CLOUDs.
EDGE is poised to launch the next-generation IoT technology. These centers are not Internet hotspots, but places that can serve certain users. These centers increase data transfer speeds and reduce transmission costs.
Another advantage of on-premises data centers using EDGE technology is that it enhances network security in accordance with data management regulations.
Features Of Edge Computing
Edge computing has many of the same characteristics as cloud computing but expands the cloud with its own structure:
Geographical Distribution: Sensor network-based IoT applications greatly benefit from processing data locally through EDGE computing sites. Large data analyzes can be done quickly and with great accuracy. Edge systems implement real-time analysis and AI processing at the scale. Examples include sensor networks for monitoring the environment, for example, pipeline monitoring systems or conflict avoidance systems.
Movement Support: As the number of mobile devices is growing rapidly, Edge Computing supports the movement for direct communication with mobile devices.
Location Authentication: it allows the use of technologies such as GPS to locate devices. Therefore, location awareness can be utilized by edge computing applications such as fog-based vehicle safety applications and margin-based disaster management.
Proximity: The availability of computing resources and services in the local vicinity allows users to use network context information to make offline and service usage decisions.
Low Latency: The low latency for Edge computing enables users to implement their resource-intensive and delay-sensitive applications on the margin device. These applications include connected vehicles, remote health monitoring, warehouse logistics, and industrial control systems.
Diversity: it refers to the presence of different sites, computer configurations, and communication technologies that use Edge computing elements (peripherals, Edge servers, and networks).
Bandwidth Intensive Use Cases: Increasing the amount of data generated by today’s IoT deployments requires extensive bandwidth, especially from surveillance cameras (video analysis) data from video. Placing
computational sources as close as possible to high bandwidth data sources means sending very little data to remote cloud data centers. For example, video clips and sensor data can be processed locally from hazardous locations to provide real-time information to responders in public security applications.
Types Of Edge Computing
These are just some of the goal-setting shareware that you can use.
- Device Edge
- Sensor Edge
- On-Premise Edge
- Near Edge
- Network Edge
- Mobile Edge
- Far Edge
- Multi-Access Edge Computing
- Data Center Edge
- Cloud Edge
- Internet of Things Edge
- Wireless Access Edge
- Router Edge
- Service Provider Edge
- Branch Edge
- Enterprise Edge
Some of these overlap in their definition. It is named from several perspectives i.e end-user view, service provider view, enterprise user view, etc. Very briefly, without going into too much detail let us see what they mean.
The second type is edge computing machine edge. It has one or more mini servers or nano DC. It will have only one or some allocations and will have limited processing power. Databases in this section may not be installed on a single rack. Also, we need to run without cooling.
These are also commonly found in places unrelated to data centers. Such as warehouses, wind generators and durable ones that can withstand severe weather. They are placed next to IoT sensors, so there are limited latency, bandwidth, or connection issues. The disadvantage is that these small devices can only have low power and applications.
It consists of a set of sensors installed to detect a specific action or environmental event or situation. The pre-defined level triggers it, and the corresponding computing process is located at or near the sensor. For example, a motion detection camera that can detect faces and set an alarm.
Companies such as large corporations, industrial production sites, and large retail operations take advantage of the scope of deployments to gain the ability to process data near the point of origin while owning the necessary hardware and property rights.
From a service provider’s perspective, NearEdge is generally closer to the core network. It may fall under the Network Edge or Cloud Edge category.
It can be broadly described as a set of infrastructure that serves portable goods such as cell phones, drones, and autonomous vehicles. To serve these mobile devices, a distributed network of service access points has been created.
In the eyes of a telecommunications service provider, the telecommunications service provider has a set of infrastructures that are far from cloud data centers but very close to the end-users. Mobile Edge, Device Edge, and Sensor Edge generally fall into this category.
Multiple Access Computing (MEC)
MEC is defined by the ETSI ISG, which is a network architecture that provides computational and storage resources within RAN. The main characteristics of MEC are very low latency, high bandwidth, density and closeness to the end-user.
Internet of Things Edge
Most of the collection of connected devices, objects, and equipment fall into this category. A delay of less than 1 millisecond is usually the controlling factor.
Wireless Access Edge
The infrastructure surrounding the Wireless Access Network (RAN) creates this type of margin. The default RAN and Open RAN components changed this feature with the advent of 5G
You usually have routers (virtual or standard functions)
Service Provider Edge
Service Edge is a router that usually connects one part of the network provider to the others. This type of peripheral router includes devices with a wide range of routing protocols such as Border Gateway Protocol (PGP), Open Shortest Path First (OSBF), and Multi-Protocol Label Switching (MBLS). This can also be mentioned. as the edge of the router.
Referring to a location outside the head office, this edge has enough computing power to host multiple business-critical applications with minimal lag. For example, medical imaging systems, POS systems, etc.
Computing resources are used throughout the central shared platform connected to the enterprise’s network rather than at every location of the enterprise, usually with high computing capabilities. Can be combined with edge-type sorting in the workplace
Typically, data from multiple edge locations and small spaces are combined into a single edge center, providing specific geographic boundaries before reaching the central data center. This is usually referred to as Network Edge and Near Edge
Data Center Edge
Small differences emerged in data centers to address the torrent of data processing needs. These are commonly used as small data centers, micro data centers, or micro data centers.
Cloud service providers generally use this type of edge scope to develop certain types of functionality such as a Content Distribution Network (CDN).
Designed for resource-intensive low-latency computing, these are small mobile data centers that enhance mobility near high-end devices and objects
The industry is constantly evolving, and new and innovative edge service models are being developed in various edge models.
Edge Computing Vs Cloud Computing
At one time, computing was the dominant paradigm in personal computers. Applications are run and data is stored locally or sometimes in the virtual data center on the user’s device.
A new paradigm of cloud computing and data processing and storage offers many advantages over the on-premises computer. Since cloud services are concentrated in the “cloud”. we can access this data from any device on the Internet.
The main difference between EDGE and cloud computing is the processing site:
- In cloud computing, all data operations are done in a central location.
- In edge computing, most data-related operations occur locally (at the edge of the environment).
Edge computing is ideal for application cases that rely on time-sensitive data processing for decision-making. Another application in which edge computing is better than cloud solutions is working in remote sites without an internet connection.
However, edge computing is not a substitute for the cloud. These techniques are not interchangeable; Edge complements the computing cloud, and both technologies ensure optimal performance for specific application events.
However, cloud computing can cause delays due to the distance between users and the data centers where cloud services are hosted. But edge computing solves this problem while at the same time maintaining the centralized nature of cloud computing, bringing operations closer to end-users, and reducing the distance that data has to travel.
Edge Computing Architecture
The key components that make up a edge computing framework are:
Edge Hardware: A piece of special-purpose equipment with limited computing power.
The Edge Note: Any device, server, or gateway that does edge computing.
Edge Server: A computer located in a facility adjacent to the Edge machine. These devices take on the workload of shared applications and services, so they require more computer power than high-end devices.
Edge Gateway: An Edge server that performs network functions such as tunneling, firewall management, protocol translation, and wireless communications. The portal can also host utility workloads.
Cloud: A public or private cloud that acts as a repository for containerized workloads such as applications and machine learning models. Cloud hosting also manages applications that manage marginal nodes.
Application of Edge Computing
Edge accounts can be integrated into a wide variety of applications, products, and services. Here are some excerpts:
Security System Monitoring: As described above.
Self-Driving Cars: Vehicles do not have to wait for server instructions to execute the required real-time feedback. So they must use advanced computer applications and pass instructions.
Highly Efficient Storage: By running the code on CDN’s external network, an application can adjust how content is stored to deliver more efficient content to users.
Medical Tracking Devices: It is important that medical devices such as automated vehicles respond in real-time without waiting for a response from the cloud server.
Video Conferencing: Edge computing is very useful for high-quality, real-time video conferencing, which takes up less bandwidth and minimizes lag.
Another important application of margin calculations is in the field of economics. One of the most important functions of edge computing is to detect and prevent the storage of failed transactions.
Transactions that are not compliant with the banking system will be detected and stopped quickly. As such change will have a very positive impact on the analysis and processing.
Reasons For Edge Computing Development
Nowadays, the Internet of Things (IoT) may be presented as one of the most widely used technologies. The technology is expected to reach 50 billion products by 2020.
Of course, connecting these devices together requires a lot of bandwidth, and cloud technology is not designed for such a large amount of data. Another problem with cloud computing is data overload.
The network that grows your online business offers many online games and many people use their online tutorials. Therefore, edge computing technology was introduced instead of sending information to large clouds to increase the speed of data usage and solve specific problems.
Benefits of Edge Computing
Cost Savings: Edge computing help reduce the use of bandwidth and server resources. You know, bandwidth and cloud resources are cheap and expensive. Is expected by the process
By 2025, more than 75 billion devices worldwide will have access to the Internet in every home and office with smart cameras, printers, and thermostats.
Performance: Another important advantage of moving functions to Edge accounts is the delay reduction. This causes a delay every time a device connects to the remote server.
For example, two co-workers in the same company are chatting in different rooms. Both of these face significant delays because every message has to leave the building and communicate with one.
The server is in an unspecified location. Wait for the reply from that server. Implementing these steps will result in a significant delay for each request. If the company’s internal router is responsible. Transferring chats within the office will reduce delays.
Similarly, users of various web applications experience delays when they encounter processes that require them to connect to an external server.
The duration of these delays will vary depending on the available bandwidth and server location. By bringing additional processes to the edge of the network, these delays can generally be avoided.
Disadvantages Edge Computing
Cyber-Attacks: A major problem in EDGE computing is the large number of cyber-attacks. As “smart” devices, such as IoT devices with Edge servers and powerful internal computers
For the network, new opportunities are created for malicious attackers to compromise these devices.
Powerful Hardware Resources: High-end computers require more local hardware resources. For example, an IoT camera may send its original video data from a web server to an internal computer
Motion detection algorithms can be implemented. So this requires an advanced system with more processing power. But reducing hardware costs makes for better hardware
Example Of Edge Computing
Imagine a building with dozens of IoT standard CCTV cameras. These cameras silently generate a series of RAW video signals and send these signals continuously to the cloud server. In the cloud, only videos detected by motion detection software that have clips that are active will be stored in the database.
Because the size of the images to be converted is high. There must be a large bandwidth to get the right internet infrastructure. In addition, the cloud server has a very high load, which means it can process video from all cameras at once.
Now imagine that the motion sensor calculations are moved to the edge of the network. What if each camera worked and used motion detection on its internal system to send useful images to the cloud server?
In this case, bandwidth usage is greatly reduced because many videos and camera photos never need to be transferred to the cloud server.
In addition, the cloud server is only responsible for storing important videos and photos. So they can interact with more cameras and have no extra load. This example actually shows what margin calculations do.
Mobile network processing is necessary to cloud computing in providing services to mobile users. Processing covers the downsides of cloud computing, including high latency, high cost, and security and privacy issues.
However, for the widespread implementation of this concept, it is necessary to lay the foundations of its theory properly to achieve the best results. For this purpose, it is necessary to examine the model and various aspects of the real problem more simply.
One of these aspects is the effect of network status on how functions are outsourced to the edge of the network. It is important for academic researchers around the world to take a deep look at it and prepare the ground for the flourishing of this new method of processing. Hopefully, the article on cross-border computing or edge computing is useful.