“Edge computing can be defined as a technique of processing/computing data obtained from IoT devices at local servers instead of the cloud, improving response time, and saving bandwidth. This technique is particularly important in RTOS (Real-time operating systems)”
Edge computing is remodeling the means information is being handled, processed, and delivered from various devices around the world. The explosive growth of internet-connected devices – the IoT – besides new applications that need real-time computing power, continues to drive edge-computing systems.
Statista Facts and Figures:
“Around 62 percent of respondents from the financial services industry stated that their organization currently made use of edge computing.”
While early goals of edge computing were to handle the cost of bandwidth for data traveling long distances as a result of the expansion of IoT-generated data, the increase of real-time processing applications that require process at the sting /edge can drive the technology ahead.
The motive of edge computing:
At its basic level, edge computing brings computation and data storage nearer to the devices wherever it’s being gathered, instead of wishing on a central location that may be thousands of miles away. this can be done so knowledge, particularly real-time processing data, doesn’t suffer latency issues that may affect associate application’s performance. additionally, companies can save cash by having the processing done locally, reducing the quantity of data that has to be processed in a very centralized or cloud-based location.
Edge computing was introduced due to the exponential growth of the internet of things(IoT) devices, that connect with the web for either receiving info from the cloud or delivering data back to the cloud. and lots of IoT devices generate enormous amounts of data during their operations.
You can visualize the need and importance of edge computing by a real-world example. For example, you are running IoT connected networks of buildings security in a city. Suppose IoT system senses some fire in one of the buildings. This requires immediate action. So, if we send the data to the cloud, we will fill face following outcomes
- The cloud server may be much far from the city, so, data will take much time to reach the cloud
- Cloud receives a lot of data at a time. So, there will be a lot of time required for the processing of data in the cloud.
If the above situation occurs, then, the security system will become dangerous. As these are real-time applications that require immediate action. If we don’t respond immediately, it will cause much loss. So, in order to reduce the loss, a local server is developed at the edge of the city/system for processing data in the meantime and increasing response time.
Similarly, in the accident control IoT based system, where we have to process data in milliseconds, we cannot rely on a cloud server for computation and processing. We just have to process data in milliseconds and it is where edge computing finds its application. On the other hand, if you are to process data obtained from fields/agriculture where real-time processing is not required, you can rely on the main cloud server for processing.
Benefits of edge computing:
Now you are familiar with uses and need for edge computing. Let’s have a look at the benefits of edge computing
- Speed and latency: The much time required for data processing, less relevant it becomes relevant to the application. In the above application of accident control and building security systems, if a delay of milliseconds takes place, these applications will become irrelevant for us. So, in this case, we have t rely on edge computing. Edge computing not only increases the speed of data processing, but it also reduces the latency of data at the edge server.
- SECURITY: When all of your knowledge should eventually feed to its cloud analyzer through one pipe, the vital business and operational processes that have confidence unjust data are extremely vulnerable. As a result, one DDoS attack will disturb entire operations for an international company. Once you distribute your knowledge analysis tools across the enterprises, you distribute the danger additionally. whereas it may be argued that edge computing expands the potential attack surface for would-be hackers, it can jointly diminish the impact on the organization as an entire. Another inherent truth is that once you transfer less knowledge, there’s less knowledge that may be intercepted.
- RELIABILITY: edge computing provides greater reliability when it is compared to cloud computing. The chances for network outage are minimum in edge computing, as all the data centers are nearer to the server. If somehow edge server down due to bandwidth issue, IoT devices can handle most of the data on their own. So, all you have to do is to increase the bandwidth for better response.
- Scalability: However, it is considered much difficult to expand IT infrastructure, but we can easily scale up our infrastructure of edge computing. You can buy devices at the very low cost having enough power for computation at the edge server. You can also combine your edge server with colocation services for expanding the edge server.
- COST: Edge computing permits you to categorize your information from a management perspective. By retentive the maximum amount of information at intervals your edge locations, you scale back the requirement for expensive information measure to attach all of your locations, and information measure interprets directly into bucks. Edge computing isn’t regarding eliminating the requirement for the cloud, it’s regarding optimizing the flow of your information so as to maximize your operational prices. Edge computing additionally helps to scale back some level of knowledge redundancy.
DISADVANTAGES OF EDGE COMPUTING:
As every coin has two sides, Similarly, edge computing has some disadvantages as well.
- Incomplete Data: IoT based systems are also called smart systems that learn from data obtained from devices and perform computation accordingly. The more data is obtained, the more efficient it is. But in the case of edge computing, the edge server is less intelligent as it obtains fewer data and doesn’t learn a lot.
Other disadvantages are
- Limited Redundancy
- The potential loss of data corruption
- Higher Risk from local hackers
Impact of edge computing on the global market:
Due to its widespread usage and importance, the market of edge computing is increasing every year. It seems to be increasing by 35.27% every year. In 2016, its market was 138 Million USD which by 2020 has increased up to 633 Million USD. It is suspected by market analytics that will increase up to 1.5 Billion USD by the end of 2024.
“Asia Pacific is the region that will have the largest edge computing footprint worldwide, accounting for 36.7 percent of the overall IT power footprint for edge computing infrastructure by 2028 – the equivalent to 25,409 megawatts (MW). According to the source, the edge footprint is measured by the aggregate IT power rating of the edge infrastructure equipment and the edge data center facilities that are deployed.”