The Concept of Edge Computing - By Azgari Lipshy
To reduce the amount of data transmission and storage required, many organizations are turning to a technique called “edge computing,” in which computations are performed at or near the point where data is being generated.
“The time when people owned computers is long gone. Power and connectivity are the most important things in computing right now.”
Edge computing is a shared information technology architecture that brings data storage and computation closer to data sources like local edge servers and IoT devices. But that isn’t all there is to know about edge computing. In this article, I’ll comprehensively explain the meaning of edge computing, how it works, its importance, and its uses!
Meaning of Edge Computing
Edge computing is capturing, keeping, processing, and examining data close to the client, where the data originated from. It transmits some parts of data and storage resources in the central data center closer to the data source. Instead of transmitting raw data to the primary data center for analysis and processing, that work is done at the origin of the data, whether it’s a factory store, retail store, or across cities.
The result of the computing work at the edge, like maintenance predictions, or real-time business insights, is sent back to the central data center for analysis and other human interactions. It is safe to say edge computing is modifying business and IT computing.
How Edge Computing Works
Edge computing has to do with location. In existing enterprise computing, data is generated at a client’s endpoint, like a user’s computer. The generated data is then moved across a Wide area network (WAN), like the internet, through the corporate Local area network(LAN). The data is kept and worked on by an enterprise application. The work results are then transferred back to the client’s endpoint. This is a proven approach to client-server computing for many standard business applications.
But the amount of internet-connected devices, and the size of data generated by those devices and utilized by businesses, is growing too rapidly for existing data center infrastructures to take in. The idea of edge computing isn’t new. It is deeply rooted in years-old ideas of remote computing, like branch offices and remote offices. It was more efficient to put computing resources at the location of choice instead of relying on one central location.
Edge computing places servers and storage where the data is, often needing more than an incomplete gear rack to work on the remote Local area network to gather and locally process the data. In most cases, the computing gear is used in hardened or shielded enclosures to safeguard the gear from moisture extremes and every other environmental condition. Processing involves analyzing and normalizing the data stream to search for business intelligence, and only the analysis results are sent back to the central data center.
Why is Edge Computing Important?
Computing tasks need compatible architectures, and the architecture compatible with one type of computing task might not fit every computing task. Edge computing has become a vital and viable architecture that supports shared computing to use storage and compute resources closer to the exact physical location of the data source. Generally, shared computing models are barely new, and the concept of data center colocation, cloud computing, remote offices, and branch offices have a trusted and proven record.
Edge computing has significantly grown because it provides solutions to new network problems related to moving large volumes of data that present-day organizations develop and consume. It’s not only a matter of amount but also time. Applications rely on time-sensitive responses and processing.
Edge computing is capturing, keeping, processing, and examining data close to the client, where the data originated from. It transmits some parts of data and storage resources in the central data center closer to the data source. Instead of transmitting raw data to the primary data center for analysis and processing, that work is done at the origin of the data, whether it’s a factory store, retail store, or across cities.
The result of the computing work at the edge, like maintenance predictions, or real-time business insights, is sent back to the central data center for analysis and other human interactions. It is safe to say edge computing is modifying business and IT computing.
How Edge Computing Works
Edge computing has to do with location. In existing enterprise computing, data is generated at a client’s endpoint, like a user’s computer. The generated data is then moved across a Wide area network (WAN), like the internet, through the corporate Local area network(LAN). The data is kept and worked on by an enterprise application. The work results are then transferred back to the client’s endpoint. This is a proven approach to client-server computing for many standard business applications.
But the amount of internet-connected devices, and the size of data generated by those devices and utilized by businesses, is growing too rapidly for existing data center infrastructures to take in. The idea of edge computing isn’t new. It is deeply rooted in years-old ideas of remote computing, like branch offices and remote offices. It was more efficient to put computing resources at the location of choice instead of relying on one central location.
Edge computing places servers and storage where the data is, often needing more than an incomplete gear rack to work on the remote Local area network to gather and locally process the data. In most cases, the computing gear is used in hardened or shielded enclosures to safeguard the gear from moisture extremes and every other environmental condition. Processing involves analyzing and normalizing the data stream to search for business intelligence, and only the analysis results are sent back to the central data center.
Why is Edge Computing Important?
Computing tasks need compatible architectures, and the architecture compatible with one type of computing task might not fit every computing task. Edge computing has become a vital and viable architecture that supports shared computing to use storage and compute resources closer to the exact physical location of the data source. Generally, shared computing models are barely new, and the concept of data center colocation, cloud computing, remote offices, and branch offices have a trusted and proven record.
Edge computing has significantly grown because it provides solutions to new network problems related to moving large volumes of data that present-day organizations develop and consume. It’s not only a matter of amount but also time. Applications rely on time-sensitive responses and processing.
Principle Network Limitations Addressed by Edge Computing
Consider the call to fame of self-driving cars. They rely on intelligent traffic control signals. Traffic and Car controls are required to produce, examine and swap data in real-time. Multiply the large numbers of autonomous vehicles by the requirement, and the scope of the possible problems becomes clearer. To solve the problem, a responsive and fast network is needed. An effective edge computing model should be able to address three significant network limitations: Latency, bandwidth, and reliability or congestion.
1. Bandwidth: Bandwidth is the data size a network can carry over some time, in bits per second. Every network has finite bandwidth, and the restraints are more severe for wireless communication. There is a limit to the amount of data or devices that can convey data across the network. While it is possible to boost network bandwidth to allow more data and devices, it can come at a high cost, there are higher limits, and it doesn’t resolve other problems.
Consider the call to fame of self-driving cars. They rely on intelligent traffic control signals. Traffic and Car controls are required to produce, examine and swap data in real-time. Multiply the large numbers of autonomous vehicles by the requirement, and the scope of the possible problems becomes clearer. To solve the problem, a responsive and fast network is needed. An effective edge computing model should be able to address three significant network limitations: Latency, bandwidth, and reliability or congestion.
1. Bandwidth: Bandwidth is the data size a network can carry over some time, in bits per second. Every network has finite bandwidth, and the restraints are more severe for wireless communication. There is a limit to the amount of data or devices that can convey data across the network. While it is possible to boost network bandwidth to allow more data and devices, it can come at a high cost, there are higher limits, and it doesn’t resolve other problems.
2. Latency: Latency is the time required to transmit data from one point to another on a network. While communication occurs at the speed of light, long physical distances, together with network outages or congestion, can delay the movement of data over the web. This can delay the decision-making and analytics processes and, at the same time, reduce system responses in real time.
3. Congestion: The internet is a universal “network of networks.” Although it has developed to provide excellent general-purpose data swaps for major everyday computing tasks like basic streaming or file exchanges. The size of data associated with billions of devices can overwhelm the internet, which causes congestion and forces time-consuming data retransmissions. In other cases, network congestion or outages can worsen congestion and even cut off some users’ communication entirely, rendering the IoT useless during outages.
By utilizing servers and data storage sources, edge computing can work on many devices over a more efficient and smaller local area network where sufficient bandwidth is exclusively used by local data-generating devices, thereby addressing the issue of congestion and latency.
Local storage gathers and secures the raw data. In contrast, local servers can execute crucial edge analytics or reduce the data to make real-time decisions before transmitting results to the central data center or the cloud.
Conclusion
Edge computing assists in unlocking the potential of the numerous untapped data generated by connected devices. By employing edge computing models, you can increase operational efficiency, discover new business opportunities, and provide more reliable and faster experiences for your customers.
About Author
Azgari Lipshy is passionate and writes about technology, yoga, and her solo travels worldwide.
SOURCE CREDIT:
https://azgarilipshytumbler.tumblr.com/post/695530003678576640/the-concept-of-edge-computing-by-azgari-lipshy
By utilizing servers and data storage sources, edge computing can work on many devices over a more efficient and smaller local area network where sufficient bandwidth is exclusively used by local data-generating devices, thereby addressing the issue of congestion and latency.
Local storage gathers and secures the raw data. In contrast, local servers can execute crucial edge analytics or reduce the data to make real-time decisions before transmitting results to the central data center or the cloud.
Conclusion
Edge computing assists in unlocking the potential of the numerous untapped data generated by connected devices. By employing edge computing models, you can increase operational efficiency, discover new business opportunities, and provide more reliable and faster experiences for your customers.
About Author
Azgari Lipshy is passionate and writes about technology, yoga, and her solo travels worldwide.
SOURCE CREDIT:
https://azgarilipshytumbler.tumblr.com/post/695530003678576640/the-concept-of-edge-computing-by-azgari-lipshy
