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With the rise of cloud computing and the internet of behaviors (IoT), users can access the data, files, content, and information they need over the internet, whenever and wherever they may choose. No longer are users tethered to in-house or local computing where data is stored physically on a single device. However, the next advancement in computing has arrived in the form of edge computing, as the emerging technology tackles some of the issues that hinder cloud computing.
Edge computing is an extension of cloud computing, so it is essential first to comprehend the cloud. Like we stated earlier, with cloud computing, IoT devices such as a laptop or smartphone utilize the internet to provide users with access to the information they need. Additionally, the data accessed in cloud computing is stored in a central location, referred to as a data center. Data centers allow for greater control, security, and reliability since all data is collected and stored in one spot.
Two prime examples of cloud computing service providers include Amazon Web Services (AWS) and Microsoft Azure. Furthermore, companies of all sizes use cloud computing services, including tech giants such as Netflix, Apple, and Instagram. However, small and medium-sized businesses frequently utilize cloud computing as well.
Edge computing continues to gain traction as the supply of data increases exponentially every year. This is because sending, retrieving, and saving large sums of data has led to inefficiencies with cloud computing, including strains on bandwidth and latency. Latency is the delay in time it takes for the information to reach the recipient.
Accordingly, edge computing aims to remove the physical distance that plagues cloud computing, which means the distance between an IoT device and a data center. This distance can be thousands of miles. Hence, removing this physical distance equates to fewer latency issues and better bandwidth for users.
For example, if a user in Chicago sends an email to a coworker in Paris, a delivery delay is expected. This is because the user’s email application in Chicago has to communicate with a server that is located in New York City. Then, the server in New York City has to communicate with your coworker’s application in Paris to deliver the email. All this takes time, and greater physical distance further exacerbates latency. Therefore, latency is reduced in edge computing because the edge data centers are closer physically to the email sender and recipient than a standard cloud data center. Edge computing can deliver faster processing of the data and real-time responses within applications.
Edge computing works in a few different ways. The first is to implement edge data centers closer to where the data is sourced. This often means building edge data centers in less populated cities and towns instead of sticking solely to major cities like cloud computing. Therefore, when an IoT device collects data, it can be processed at a nearby edge center. After the data is processed at the edge data center, the necessary data can be sent to the cloud data center for safekeeping. Furthermore, the data collected at the edge computing center deemed irrelevant will not be sent to the cloud center, thus reducing strain on the cloud.
Another method is integrating IoT devices with computing power so they can process the collected data themselves. This results in less data being sent to the edge and cloud data centers because the IoT device can identify which information needs to be sent to the centers and which information is unnecessary.
An example of this is a security camera that is tasked with collecting footage where movement is detected. If the video camera is an IoT device with internal processing capabilities, it can identify the footage with motion and solely send that information to the data centers. Furthermore, all footage that doesn’t have movement is not sent to the data centers and subsequently does not add additional labor to the cloud. Lastly, since the cloud centers will only be accountable for safeguarding the necessary footage, the cloud can connect to more IoT devices without hitting its capacity.
Research shows that the demand for edge computing will expand from $3.5 billion in 2021 to $43.4 billion by 2027. This indicates that the need for quickly processing and storing data will continue to increase for years to come. In addition, as companies make the switch to edge computing, we can expect advances in emerging IoT technologies that require quicker processing times.
Do you have questions about edge or cloud computing, or are you looking to integrate the technology into your business? We would be thrilled to help. Contact us at info@quantilus.com for a consultation and learn more about what Quantilus has to offer here.
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