Introduction to Edge Computing
INTRODUCTION TO EDGE COMPUTING
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What is Edge Computing?
Edge computing brings computation and data storage closer to the devices where it’s being gathered, rather than relying on a central location that can be thousands of miles away. This is done so that data, especially real-time data, does not suffer latency issues that can affect an application’s performance.
In addition, companies can save money by having the processing done locally, reducing the amount of data that needs to be processed in a centralized location. Edge computing was developed due to the exponential growth of IoT devices, which connect to the internet for either receiving information from the cloud or delivering data back to the cloud.
Why does Edge computing matter?
Before edge computing, a smartphone scanning a person’s face for facial recognition would need to run the facial recognition algorithm through a cloud-based service, which would take a lot of time to process. With an edge computing model, the algorithm could run locally on an edge server or gateway, or even on the smartphone itself, given the increasing power of smartphones.
With enhanced interconnectivity enabling improved edge access to more core applications, and with new IoT and industry-specific business use cases, edge infrastructure is poised to be one of the main growth engines in the server and storage market for the next decade and beyond. Increasingly, though, the biggest benefit of edge computing is the ability to process and store data faster, enabling more efficient real-time applications that are critical to companies.
How does Edge computing Works?
In traditional enterprise computing, data is produced at a client endpoint, such as a user's computer. That data is moved across a WAN such as the internet, through the corporate LAN, where the data is stored and worked upon by an enterprise application. Results of that work are then conveyed back to the client endpoint. The number of devices connected to the internet, and the volume of data being produced by those devices and used by businesses, is growing far too quickly for traditional data center infrastructures to accommodate. The prospect of moving so much data in situations that can often be time- or disruption-sensitive which puts incredible strain on the global internet, which itself results in congestion and disruption.
IT architects have shifted focus from the central data center to the logical edge of the infrastructure -- taking storage and computing resources from the data center and moving those resources to the point where the data is generated. Edge computing puts storage and servers where the data is, often requiring little more than a partial rack of gear to operate on the remote LAN to collect and process the data locally. In many cases, the computing gear is deployed in shielded or hardened enclosures to protect the gear from extremes of temperature, moisture and other environmental conditions. Processing often involves normalizing and analyzing the data stream to look for business intelligence, and only the results of the analysis are sent back to the principal data center.
Major Benefits of Edge Computing
Speed - edge computing allows processing data on the spot or at a local data center, thus reducing latency. As a result, data processing is faster than it would be when the data is ping-ponged to the cloud and back.
Scalability - a combination of local data centers and dedicated devices can expand computational resources and enable more consistent performance.
Security- By implementing computing at the edge, any data traversing the network back to the cloud or data center can be secured through encryption.
Versatility - Edge computing handles raw data and allows the device service. In addition to this, the central network can receive data already prepared for further machine learning or data analysis.
Reliability - with the operation proceedings occurring close to the user, the system is less dependent on the state of the central network.
Drawbacks of Edge Computing
The greatest challenge of edge computing is making these distributed networks secure. Edge computing’s reliance upon smaller data centers and IoT edge devices presents a different range of security concerns than traditional cybersecurity approaches.
Data at the edge can be troublesome, especially when it’s being handled by different devices that might not be as secure as a centralized or cloud-based system
Since edge computing framework is more widely distributed than a traditional server-based network, there is a greater number of possible attack vectors for hackers to exploit.
Edge computing requires more local hardware. For example, while an IoT camera needs a built-in computer to send its raw video data to a web server, it would require a much more sophisticated computer with more processing power in order for it to run its own motion-detection algorithms.
How do 5G and Edge Computing work together?
Around the world, carriers are deploying 5G wireless technologies, which promise the benefits of high bandwidth and low latency for applications, enabling companies to grow further ahead with their data bandwidth. Instead of just offering the faster speeds and telling companies to continue processing data in the cloud, many carriers are working edge-computing strategies into their 5G deployments in order to offer faster real-time processing, especially for mobile devices, connected cars and self-driving cars.
A report says - Applications using 5G technology will change traffic demand patterns, providing the biggest driver for edge computing in mobile cellular networks. It cites low-latency applications that include IoT analytics, machine learning, virtual reality, autonomous vehicles as those that “have new bandwidth and latency characteristics that will require support from edge-computer infrastructure. It’s clear that while the initial goal for edge computing was to reduce bandwidth costs for IoT devices over long distances, the growth of real-time applications that require local processing and storage capabilities will drive the technology forward over the coming years.
Other Applications of Edge Computing
Self Driving Cars: Autonomous vehicles need to react in real time, without waiting for instructions from a server.
Medical monitoring devices: It is crucial for medical devices to respond in real time without waiting to hear from a cloud server.
Video Conferencing: Interactive live video takes quite a bit of bandwidth, so moving backend processes closer to the source of the video can decrease lag and latency.
IoT Devices: Smart devices that connect to the Internet can benefit from running code on the device itself, rather than in the cloud, for more efficient user interactions.
Other examples involve predictive analytics that can guide equipment maintenance and repair before actual defects or failures occur.
Future of Edge Computing
Shifting data processing to the edge of the network can help companies take advantage of the growing number of IoT edge devices, improve network speeds, and enhance customer experiences. The scalable nature of edge computing also makes it an ideal solution for fast growing, agile companies, especially if they’re already making use of cloud infrastructure. Taking advantage of edge computing, companies can optimize their networks to provide flexible and reliable service that will bolster their brand and keep their customers happy.
Edge computing offers several advantages over traditional forms of network architecture and will surely continue to play an important role for companies going forward. With more and more internet-connected devices hitting the market, innovative organizations have likely only scratched the surface of what’s possible with edge computing.
References:
[1] https://www.stl.tech/blog/edge-computing/
[2]https://www.cloudflare.com/en-in/learning/serverless/glossary/what-is-edge-computing/
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