Public cloud computing systems enable businesses to complement their data centers with worldwide servers that can scale processing capabilities up and down as required. In terms of value and security, hybrid public-private clouds are unparalleled.
However, real-time AI applications demand substantial local processing capacity, frequently in areas distant from centralized cloud servers. speedpak tracking is among the services including AI for the safety of your goods and parcels.
Moreover, some workloads demand low latency or data residency and must stay on-premises or specified locations.
That is why many businesses use edge computing to implement AI applications.
Instead of storing data in a centralized cloud, edge computing saves data locally in an edge device. Moreover, the gadget may function as a stand-alone network node without an internet connection.
Cloud and edge computing offer many advantages and application cases.
Cloud computing is a computing approach in which scalable and elastic IT-enabled capabilities are supplied as a service through the Internet.
Cloud computing's popularity is growing as a result of its many advantages. Cloud computing, for example, has the following benefits:
Edge computing is the process of physically bringing computational capacity closer to the source of data, which is generally an Internet of Things device or sensor. Edge computing, so named because of how computing power is delivered to the network's or device's edge, enables quicker data processing, higher bandwidth, and data sovereignty.
Edge computing lowers the need for huge volumes of data to travel between servers, the cloud, and devices or edge locations to be processed by processing data at the network's edge. It is especially relevant for current applications like data science and artificial intelligence.
Edge and cloud computing have unique advantages, and most businesses will utilize both. Here are some things to think about when deciding where to deploy certain workloads.
In contrast, cloud computing is ideal for non-time-sensitive data processing, but edge computing is ideal for real-time data processing.
Also, the former requires a dependable online connection, while the latter should encompass rural regions with little or no internet access.
Furthermore, cloud computing stores data in the cloud, but edge computing includes very sensitive data and tight data rules.
Medical robotics is one example of when edge computing is superior to cloud computing because surgeons want real-time data access. These systems include a significant amount of software running on the cloud.
Still, the sophisticated analytics and robotic controls increasingly used in operating rooms cannot tolerate latency, network stability difficulties, or bandwidth limits. In this case, edge computing provides the patient with life-saving advantages.
Convergence of cloud and edge is required for many enterprises. Organizations centralize when possible and disseminate when necessary.
Firms may benefit from the security and management of on-premises systems with hybrid cloud architecture. It also makes use of a service provider's public cloud resources.
For each firm, a hybrid cloud solution implies something different. It might imply training in the cloud and deploying at the edge, training in the data center and deploying at the edge using cloud management tools, or training at the edge and deploying in the cloud to centralize models for federated learning. There are several options to connect the cloud and the edge.
Though both the computing systems are equally important, each carries distinctive perks. As the world is moving toward the hybrid approach, understanding the right computing choice will ease your process. Our guide will assist in this regard.
Hi Friends! Hope you’re well today. In this post, I’ll walk you through What is Edge Computing?
Edge computing is the extension of cloud computing. Cloud computing is used for data storage, data management, and data processing. While Edge Computing does serve the same purpose with one difference: edge processing is carried out near the edge of the network which means data is processed near the location where it’s produced instead of relying on the remote location of the cloud server.
Confused?
Don’t be.
We’ll touch on this further in this article.
Curious to know more about what is edge computing, the difference between edge computing and cloud computing, benefits, and applications?
Keep reading.
Edge computing is the process where data is processed near or at the point where it’s produced. The word computing here is used for the data being processed. Simply put, Edge computing allows the data to be processed closer to the source of data (like computers, cell phones) rather than relying on the cloud with data centers. This process is used to reduce bandwidth and latency issues.
For instance, Surveillance cameras. When these cameras are required simultaneously to record a video, if you use cloud computing and run the feed through the cloud, it will increase its latency (latency is the time delay between actual data and processed data) and reduce the quality of the video.
This is where edge computing comes in handy. In this particular case, we can install a motion detector sensor that will sense the movement of the physical beings around the camera. This motion-sensing device will act as an edge device that is installed near the data source (camera). When live feed data is processed near the edge devices instead of the cloud or data centers, it would increase the video quality and practically reduce the latency to zero.
Cloud storage takes more time to process and store data, while edge computing can locally process data in less time. The market of edge computing is expected to grow from $3.5 billion to $43.4 billion by 2027, according to experts in Grand View Research. Many mobile network carriers are willing to apply edge computing into their 5G deployment to improve their data processing speed instead of picking the cloud server.
Normally in cloud computing, two components are used: the device and the cloud server. In edge computing an intermediate node is introduced between the device and the cloud server, this node is called an edge device.
How data was stored in data centers before edge computing stepped in? Yes, this is the main question to discuss before we explain how edge computing works.
Before edge computing, data was gathered from distributed locations. This data was then sent to the data center which could be an in-house facility or the public cloud. These data centers were used to process the stored data.
In edge computing that data processing is carried out near or at the point from where data originates. This is very useful for making real-time decisions that are time-sensitive. Like in the case of automatic cars interacting with each other.
Plus, less computing power is required in edge computing since we don’t need to push back all data to the data center. Like in the case of a motion-detecting sensor installed near the camera. In case we require a video of a particular instance, we need to pull out the entire information recorded inside the camera to reach that particular instant clip. However, when the motion sensor is installed near the camera that acts as an edge device, we only require that information where that sensor has detected the movement of any physical beings, and we can easily discard the rest of the information and we don’t need to store that information into the cloud server.
Know that edge data centers are not the only way to store and process data. Rather, edge computing involves the network of different technologies. Some IoT devices can become a part of this edge computing and can process data onboard and send that data to the smartphone or edge server to do the difficult calculations and efficiently handle the data processing.
An edge computing environment is developed using a network of data centers spread across the globe. The data centers in edge computing are different than the data centers at cloud computing. In former data centers store and process information locally and comes with the ability to replicate and transfer that information to other locations. While in the latter, data centers are located hundreds of thousands of miles away. The network latency issues and unpredictable pricing model of the cloud storage allow the organizations to prefer private data centers and edge locations over public cloud.
Google Cloud, Amazon Web Services, and Microsoft Azure are the best examples of cloud computing. They use cloud computing infrastructure which is developed to transfer the data from data source to one centralized location called data centers.
While facial recognition lock feature of the iPhone uses an edge computing model. If the data in this feature runs through cloud computing, it would take too much time to process data, while the edge computing device, which is the iPhone itself, in this case, does this processing within a few seconds and unlocks the mobile screen.
For massive data storage or for software or apps that don’t require real-time processing needs, cloud computing is the better solution and is commonly called the centralized approach. And if you require less storage with more real-time processing power that is carried out locally, edge computing is the answer and is called a decentralized approach where not a single person is making a decision, rather decision power is distributed across multiple individuals or teams.
Know that companies typically harness the power of both cloud computing and edge computing to develop advanced IoT frameworks. These two infrastructures are not opposite but are complementary for designing a modern framework.
Edge computing is a form of distributed computing infrastructure that is location-sensitive while IoT is a technology that can use edge computing to its advantage. Edge computing is a process that brings the processing data as near to an IoT device as possible.
Don’t confuse an edge device with an IoT device. The device is the physical device where data is stored and processed while the IoT device, on the other hand, is the device connected to the internet. It is nothing but the source of the data.
Edge computing is changing the way how data is stored and processed. This gives a more consistent and reliable experience at a significantly lower cost.
With new technology comes new security issues and edge computing is no different. From a security point of view, data at the edge computing can become vulnerable because of the involvement of local devices instead of the centralized cloud-based server. A few ricks of edge computing include:
Hackers always seek to steal, modify, corrupt, or delete data when it comes to edge computing. They strive to manipulate edge networks by injecting illegal hardware or software components inside the edge computing infrastructure. The common practice followed by these hackers is node replication where they inject malicious node into the edge network that comes with an identical ID number as assigned to the existing node. This way they can not only make other nodes illegitimate but also can rob sensitive data across the network.
Tampering of connected physical devices in edge networks is another malpractice carried out by potential hackers. Once they approach the physical devices they can extract sensitive cryptographic information, change node software and manipulate node circuits.
Routing attach is another security risk in edge computing. This approach can affect the way how data is transferred within the edge network. The routing information attacks can be categorized into four different types:
In wormholes attach, hackers can record packets at one location and tunnel them to another. In grey holes attach, they slowly and selectively delete the data packets within the network. In a hello food attack, they can introduce a malicious node that sends hello packets to other nodes, creating routing confusion within the network. While in black holes attach the outgoing and incoming packets are deleted which increases the latency.
Know that these practices can be avoided by establishing reliable routing protocols and incorporating effective cyber security practices within the network. It’s wise to put your trust in manufacturers who have proper policies in practice to guarantee the effectiveness of their edge computing solutions.
Edge computing comes in handy where quick data processing is required. With computing power near the data source, you can make better and quick real-time decisions.
A few edge computing examples include:
Predictive maintenance is another example where edge computing can play a key role. It helps to identify if the instrument needs maintenance before its major failure or total collapse. This saves both time and money which would otherwise require for entire instrument replacement.
Edge computing becomes common practice among many organizations since it provides more control over processed data.
This trend will continue to grow with time and it is expected by 2028 edge services will become widely available across the globe.
Wireless technologies such as WiFi 6 or 5G will work in favor of edge computing, giving chance to virtualization and other automation capabilities, at the same time making the wireless network more economical and flexible. Many carriers are now working to incorporate edge computing infrastructure into their 5G developments to provide fast real-time processing capabilities, particularly for connected cars, mobile devices, and automatic vehicles.
It is not about which one is better cloud computing or edge computing. It’s about the requirement. If you want data to be processed quickly near the source, you’ll adopt edge computing and if you want more data storage and data management, you will pick cloud computing.
The prime goal of edge computing is to reduce bandwidth and practically reduce the latency to zero. With the extension of real-time applications that require local computing and storage power, edge computing will continue to grow over time.
That’s all for today. Hope you find this article helpful. If you have any questions, you can reach out in the comment section below. I’d love to help you the best way I can. Thank you for reading this article.