What is Edge Computing and How its Work? Edge Computing vs Cloud Computing

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Here in this post, we discuss related Edge computing technology means and How Edge Computing Works. What are the different Types of Edge Computing with Advantages and Disadvantages of Edge Computing Technology?

Edge Computing is related to technology but it will be one of the emergings in the Future. There are different Edge computing companies like 5g Edge Computing, IBM Edge Computing, Google Edge Computing, AWS edge computing, Intel Edge Computing, and Nvidia Edge Computing is working on that.

The Vision of Edge Computing:

One vision for edge computing is to create a distributed network of computing resources that can be deployed at the edge of the network, near the devices that are generating or collecting data. These resources could include servers, storage devices, and other types of computing hardware, as well as Edge Computing Software and applications that are designed to run on these devices.

What is Edge Computing?

Let’s see that what Edge computing technology definition says and clear all the Edge Computing Basics. BE aware that 5G and Edge Computing are not the same things.

Edge computing is a type of computing that involves bringing processing power and data storage closer to the edge of the network, or closer to the devices that are generating or collecting data. The goal of edge computing is to reduce the amount of data that needs to be transmitted over the network and to improve the responsiveness of applications by reducing the latency of data processing.

Edge computing can be contrasted with traditional centralized computing, in which data is collected and processed in a central location, such as a data center or cloud computing platform. In edge computing, data is processed and stored closer to the devices that are generating it, which can reduce the amount of data that needs to be transmitted over the network and improve the speed and efficiency of data processing.

Edge computing has become increasingly important in recent years due to the growth of the Internet of Things (IoT) and the emergence of new applications and services that require real-time data processing and analysis. Edge computing can be used in a variety of settings, including industrial and manufacturing environments, transportation and logistics systems, and smart cities.

One thing you read often is Edge Computing 5G and Multi-Access Edge Computing. So what is this MEC in 5g or in Edge Computing?

5G Technology

How Edge Computing Works:

Edge computing involves bringing processing power and data storage closer to the edge of the network, or closer to the devices that are generating or collecting data. In an edge computing system, data is collected and processed at the network’s edge, rather than being transmitted back to a central location for processing.

There are several key components that are involved in edge computing:

  1. Edge devices: These are the devices that are located at the edge of the network and are responsible for collecting and processing data. Edge devices can include sensors, cameras, and other types of IoT devices that are capable of generating and collecting data.
  2. Edge gateways: These are devices that act as intermediaries between the edge devices and the rest of the network. Edge gateways can be used to filter and process data before it is transmitted to the central network, and they can also provide connectivity and power to edge devices.
  3. Edge servers: These are servers that are located at the edge of the network and are responsible for processing and storing data. Edge servers can be used to run applications and perform analytics tasks, and they can be deployed in a variety of settings, including data centers, offices, and other locations.
  4. Centralized servers and cloud platforms: These are servers and platforms that are located at the center of the network and are responsible for storing and processing data from a large number of devices. Centralized servers and cloud platforms can be used to run applications and perform analytics tasks, and they can provide a central repository for data that is collected from edge devices.

Types of Edge Computing:

There are several different types of edge computing, depending on the location and purpose of the computing resources:

  1. Fog computing: This type of edge computing involves bringing computing resources closer to the edge of the network, but not necessarily all the way to the devices generating or collecting data. Fog computing can be used to support the needs of IoT and other types of distributed systems that require low-latency data processing and analysis.
  2. Cloudlet: This is a type of edge computing that involves bringing cloud computing resources closer to the edge of the network, typically in the form of a small-scale data center. Cloudlets can be used to support the needs of mobile devices and other types of edge devices that require fast and reliable access to cloud computing resources.
  3. Mobile edge computing (MEC): MEC Edge Computing is a type of edge computing that involves bringing processing power and data storage closer to the edge of the network, specifically in the areas served by cellular networks. MEC is designed to enable real-time data processing and analysis at the edge of the network, which can help to reduce latency and improve the performance of applications and services that rely on low-latency data transmission.

We know that MEC 5th G is coming and handling the MEC server is a very complex task. But as we know Edge computing and 5G are not the same. That’s why MEC Computing is now in the growing phase.

  1. Internet of Things (IoT) edge computing: This is a type of edge computing that is specifically designed to support the needs of IoT devices and systems. IoT edge computing involves bringing computing resources closer to the edge of the network, near the devices generating or collecting data. This can help to reduce the amount of data that needs to be transmitted over the network and can improve the responsiveness of IoT applications and services.
5G Technology

Edge Computing vs Cloud Computing:

Here you will find the difference between Edge Computing and Cloud Computing. There are various Edge Cloud Platforms are you can find if you want to learn Edge Computing Cloud.     


Edge ComputingCloud Computing
Location of computing resourcesCloser to the edge of the networkCentralized data centers or other remote locations
Scale of computing resourcesSmaller-scale resourcesLarge-scale resources
LatencyLower latency due to closer proximity to devices generating or collecting dataHigher latency due to the need to transmit data over the network to centralized resources
FlexibilityLimited flexibility due to the specific location and scale of the computing resourcesGreater flexibility due to the large-scale and centralized nature of the computing resources
CostMay be more expensive due to the need to deploy and maintain computing resources at the edge of the networkGenerally less expensive due to the shared nature of the resources and the ability to pay for only the resources that are used

Advantages and Disadvantages of Edge Computing Technology:

Here are some of the main advantages of edge computing:

  1. Lower latency: Because edge computing involves bringing computing resources closer to the devices generating or collecting data, it can result in lower latency and faster response times for applications and services that rely on real-time data processing.
  2. Improved efficiency: By reducing the amount of data that needs to be transmitted over the network, edge computing can improve the efficiency of data transmission and processing. This can be especially important in environments where there are large amounts of data being generated and transmitted, such as in IoT and industrial applications.
  3. Increased reliability: Because edge computing involves distributed computing resources, it can be more resilient to failures and outages. If one edge device or server goes offline, the others can continue to operate and provide data processing and storage capabilities.
  4. Greater security: By processing and storing data closer to the devices that are generating it, edge computing can offer greater security and privacy protections. This is especially important in environments where data is sensitive or confidential.

However, there are also some limitations and challenges associated with edge computing:

  1. Cost: Deploying and maintaining edge computing resources can be more expensive than relying on centralized computing resources, especially if the edge devices or servers are located in remote or difficult-to-reach locations.
  2. Complexity: Managing a distributed network of edge computing resources can be more complex than managing a centralized computing infrastructure. This can require specialized skills and expertise to ensure that the resources are properly configured and maintained.
  3. Limited flexibility: Because edge computing resources are typically located closer to the devices that are generating or collecting data, they may be less flexible in terms of being able to support a wide range of applications and services.

Up to that you know Edge computing definition, Edge computing examples, Cloud and Edge Computing differences, MEC edge means different types of Edge Computing. Let’s see where these Edge Computing Solutions are useful in our real life. There are lots of Edge computing devices that are currently working.

Applications of Edge Computing:

Edge computing has a wide range of Edge Computing Applications across many different industries and sectors. Some examples of the types of applications that can benefit from edge computing include:

  1. Internet of Things (IoT): Edge computing can be used to support the needs of IoT devices and systems, which often generate large amounts of data and require real-time data processing and analysis. By bringing computing resources closer to the edge of the network, edge computing can help to reduce the amount of data that needs to be transmitted over the network and can improve the responsiveness of IoT applications and services.
  2. Industrial and manufacturing: Edge computing can be used to support the needs of industrial and manufacturing environments, which often require real-time data processing and analysis for tasks such as process control, predictive maintenance, and quality control.
  3. Transportation and logistics: Edge computing can be used to support the needs of transportation and logistics systems, which often require real-time data processing and analysis for tasks such as fleet management, route optimization, and predictive maintenance.
  4. Smart cities: Edge computing can be used to support the needs of smart cities, which often require real-time data processing and analysis for tasks such as traffic management, public safety, and environmental monitoring.
  5. Virtual and augmented reality: Edge computing can be used to support the needs of virtual and augmented reality applications, which often require low-latency data transmission and processing to provide a seamless and immersive user experience.

Conclusion:

In conclusion, edge computing is a type of computing that involves bringing processing power and data storage closer to the edge of the network, or closer to the devices that are generating or collecting data. The goal of edge computing is to reduce the amount of data that needs to be transmitted over the network and to improve the responsiveness of applications by reducing the latency of data processing.

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