“Not all the data processing in our system is close to us”
Didn’t understand what I mean? Let us just look at cloud computing and how data is accessed and used and processed in it. Data is stored at a remote location and can be accessed from anywhere around the globe.
But this feature of cloud computing has led to some security threats. This makes the working vulnerable as all the data is stored and processed online. Data stored in the cloud is constantly at a threat of getting misused.
It is prone to cyber-attacks and data leakages. Some way was needed to protect the data stored on the cloud from these cyber-attacks and threats.
That is when “edge computing” came to light.
Edge computing is a networking paradigm that focuses on placing processing as close as feasible to the source of data to reduce latency and bandwidth usage.
In layman's terms, edge computing is transferring fewer processes from the cloud to local locations, such as a user's PC, an IoT device, or an edge server.
In simple words, Edge computing is a distributed IT architecture that brings computing resources from clouds and data centers as near to the source as possible. Its major purpose is to reduce latency while processing data and lowering network expenses.
The most important thing about this network edge is that it should be geographically close to the device.
(Related blog: Cloud computing guide)
Now you must be thinking, how does this data come close to the device? How does this “Edge Computing” work? Let us understand how it works.
In traditional computing methods, the data is first displayed on the screen of a user and is then sent to the server using the internet, intranet, LAN e.t.c. Here, at the server, the data is stored and processed.
But, as this data kept on increasing with the growth of the internet and so did the devices connected to the internet, these data storage infrastructures started seeming incapable of storing that much data.
According to a Gartner study, by 2025, 75% of company data would be generated outside of centralized data centers. This volume of data places tremendous pressure on the internet, causing congestion and interruption.
To tackle this, the concept of “Edge Computing” was introduced.
The idea behind edge computing is straightforward: rather than bringing data closer to the data center, the data center is moved closer to the data. The data center's storage and processing resources are installed as close as possible to where the data is generated (preferably in the same place).
For example, an edge gateway can process data from an edge device i.e. the device used to control the flow of data between two networks, and then send just the necessary data back to the cloud, minimizing bandwidth requirements. In the case of real-time application requirements, it can also send data back to the edge device.
An IoT sensor, an employee's notebook computer, their latest smartphone, the security camera, or even the internet-connected microwave oven in the office break room are examples of edge devices. Within an edge-computing infrastructure, edge gateways are considered edge devices.
(Must catch: What is Neuromorphic Computing? Working and Features)
Let us try to compare the working of all the types of computing used to play with data.
Only one isolated computer was used to store data.
Data is stored either in the user's device or data centers.
Data is stored in data centers and is processed via the cloud.
Data is brought close to the user, either to the user’s device or to any nearby device.
Now that we know about the working of Edge Computing, let us try to know where it is used.
Edge computing can be used in a wide range of products, services, and applications. Among the possibilities are:
Security system monitoring: With the help of edge computing, security system monitoring is performed.
IoT devices: For more effective user interactions, smart devices that connect to the internet can benefit from running code on the device itself rather than in the cloud.
Self-driving cars: Self-driving cars must react in real-time, rather than waiting for commands from a server.
Caching that is more efficient: An application can alter how content is cached to more efficiently serve content to users by running code on a CDN edge network.
Medical monitoring equipment: It must be able to reply in real-time without having to wait for a response from a cloud service.
Video conferencing: Because interactive live video consumes a significant amount of bandwidth, putting backend functions closer to the video source can reduce lag and latency.
Gaming on the cloud: Latency is critical in cloud gaming, a new type of gaming that sends a live feed of the game straight to devices.
To reduce latency and provide a completely responsive and immersive gaming experience, cloud gaming businesses are looking to install edge servers as close to gamers as feasible.
(Related blog: What is virtualization in cloud computing?)
All these uses suggest that there must be so many benefits of edge computing over other ways of computing. Let us now try to look at the advantages or benefits of Edge Computing.
Here are some of the most important advantages of Edge Computing:
The time it takes to send data between two places on a network is referred to as latency. Delays can be caused by large physical distances between these two points, as well as network congestion. Latency difficulties are essentially non-existent thanks to edge computing, which brings the points closer together.
The rate at which data is transported over a network is referred to as bandwidth. Because all networks have a finite amount of bandwidth, the amount of data that can be transferred and the number of devices that can process it are also constrained.
Edge computing allows multiple devices to function over a much smaller and more efficient network by installing data servers at the places where data is created.
Despite the fact that the Internet has changed through time, the sheer volume of data created every day by billions of devices can cause significant congestion. Local storage is available in edge computing, and local servers can execute critical edge analytics in the event of a network failure.
(Also read: What is SaaS?)
There are some drawbacks too. Here are some of the major drawbacks of Edge computing:
Implementing an edge infrastructure in a company may be both complicated and costly. Before deployment, it requires a clear scope and goal, as well as extra equipment and resources.
Edge computing can only process subsets of data, which should be determined in advance of implementation. Companies may lose crucial data and information as a result of this.
Because edge computing is a distributed system, it might be difficult to ensure proper security. Processing data outside of the network's edge has several hazards.
The adoption of edge computing has ushered in a new era of data analytics. This technology is being used by an increasing number of businesses for data-driven operations that require lightning-fast results.
Although it's still developing, the future holds a lot for Edge Computing. If the drawbacks are put aside, Edge seems like a perfect replacement for data centers, which indeed is a very important thing. So, let us wait and watch and observe what lies in the lap of the future for Edge Computing.
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