What Are The Key Benefits Of Edge Computing?

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Written By abhraham Denis

As IoT devices increase, the information they generate increases. While this massive IoT-generated data stored in data centers is a fundamental part of cloud computing, managing and processing it is a costly and slow affair. There are also significant bandwidth and latency issues. And this is where edge computing comes into play.

Edge computing is a new computing model in which the data on the network is processed in the same source that generates data. This technology solves many bandwidth and latency issues, providing faster, reliable data in real time.

How Edge Computing is Different from Cloud Computing:

The main distinguishing factor between the two is that edge computing is time-sensitive, while cloud computing processes data that is not time-sensitive. Clouds are often located in central locations where all data processing occurs. On the other hand, edge computing is used in remote geographical areas where there is less or no connection to a central location.

Additionally, edge computing reduces energy consumption, bandwidth, and latency (related to responsiveness), which poses a problem when accessing data from centralized cloud locations. There are other such advantages of edge computing that scores over traditional cloud computing.

An edge server can bring server-like computation to the edge. They might be installed in NEMA enclosures, custom cabinetry in the desert, a closet, a warehouse, on a desk, or even right in the middle of a welding studio.

Edge servers process data physically, close to end-users and the on-site apps. These devices process requests quickly compared to centralized servers.

These devices process raw data and return content to client machines instead of sending unprocessed data on a trip to and from a data center.

Diversification advantage:

As the number of IoT devices increases, edge computing will likely be of paramount importance to ensure identification and avoid system overload.

Cybersecurity at its core:

Edge computing provides more security than the cloud because there is less data in a cloud location. So even if a data environment is attacked by malware, you know the damage is minimal.

High speed machining:

Since the data is processed at the edge (source), latency is reduced, ensuring real-time response. Such a feature is particularly beneficial for automated vehicles and life-saving devices.

In a sense, we can say that edge computing expands the capabilities of cloud computing while leveraging its analysis and improvement capabilities.

Edge computing and IoT:

Edge computing can be viewed as an extension of cloud computing, where the IoT data is processed at the source where the data is generated or nearby. Edge computing is done regionally and therefore does not follow the strict guidelines that cloud computing adheres to.

Edge computing and its importance for other technologies?

With IoT, edge computing shares a close relationship with many other digitally transmitted technologies. Some of the notable ones are:

5G:

When considering 5G, its relevance to edge computing cannot be underestimated. An example of this association is automated or driverless vehicles. Here, real-time processing is achieved through edge computing, while low latency is achieved through 5G. So as you can see, one technology can drive others to reach their maximum potential.

Big data:

All IoT information in big data center is not valuable. In order to derive meaningful information from an enormous amount, something more is needed. This is where the role of edge computing comes into play. Edge computing filters the IoT information based on predefined methods and parameters. Once this is done, the filtered information is sent to the cloud, saving a lot of energy.

Machine learning:

Machine learning and the edge computing connection dramatically improve the analysis performance and communication of automated devices. These technologies together help the connected devices respond in real-time and also help them make better decisions without the need for human intervention. This is what the future of machine learning is supposed to be.

Mobile edge computing makes remote access possible:

Just like edge computing, MEC reduces the distance of the edge (source) where the data is produced, collected and analyzed. Processing takes place virtually in data centers. Mobile Edge Cloud stores and processes information on wireless devices within the cloud network. By being close to devices and users, mobile edge computing ensures higher bandwidth, lower latency, and faster decision making and response time.

Mobile edge computing was originally developed to ensure the connectivity of mobile networks and focused more on mobile networks. Later it evolved to cover various fields such as technology, manufacturing and more support. Some examples are:

Autonomous vehicles:

Edge computing can enable automated cars to be aware of their surroundings, such as traffic signs, other vehicles, nearby pedestrians and more. High speed processing ensures vehicles respond in real time, making this new mode of transportation successful.

Video games and other remote services:

The lower the latency, the better the responsiveness and the best performance for gamers. With edge computing, AR and VR are also getting a boost, improving remote services like healthcare, etc.

Automated factories:

Through edge computing, industries can benefit from automated production, helping them ensure efficiency, quality and better interaction between robots and humans, thereby preventing accidents and their recurrence.

Conclusion:

Edge computing addresses real-world challenges that require robust solutions in terms of faster processing, insights and better performance. According to an analysis, edge computing is the fastest growing computing segment. And it is expected to continue to grow (around 50% of IT companies are expected to move to the edge). But no matter what business area you belong to, edge computing is the solution for you.

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