While the proliferation of IoT computer devices increases the overall area of network attacks, and offers some important security benefits. The traditional technology of cloud computing is centralized, making it vulnerable to attacks on service denials (DDoS) and power outages. Edge computing distributes processing, storage and applications across a wide range of devices and data centers, making it difficult for any single disruption to slow down the entire network.
Another major concern with IoT devices on the computer edge is that they can be used as a hack for cyberattacks, allowing malware or other issues to infect the network from a single weak spot. While this is a real threat, the widespread state of edge-building software makes it easy to use security agreements that can shut down damaged parts without shutting down the entire network.
As more data is processed on local devices rather than returning it to a central data center, edge computing also reduces the amount of data at risk by one second. There is little data that can be captured during transit, and even if the device is compromised, it will only contain data collected locally rather than data that can be displayed by a centralized server.
Although edge-to-computer computing includes specialized edge data centers, this often provides additional security measures to prevent DDoS attacks and other cyber threats.
The quality data center should provide a variety of tools that clients can use to protect and monitor their networks in real time.
Speed is very important in any company's core business. Take the reliance of the financial sector on high frequency trading algorithms, for example. A small millisecond drop in its trading algorithms can lead to costly results. In the health care industry, where the poles are very high, losing a fraction of a second can be a matter of life or death.
For businesses that provide data-driven services to customers, the remaining speed can frustrate customers and cause long-term damage to the product. This may not sound as bad as life and death, but poor network performance and slow speed could mean the end of your company altogether. Speed is no longer just a competitive advantage - a very good practice.
The most important advantage of Edge computing is its ability to increase network performance by reducing delays. As IoT edge computing devices process data in a location or nearby data centers, the data they collect does not have to go that far under traditional cloud technology.
In today's world, it is easy to forget that data is not always fast; bound by the same laws of physics as anything else in the known universe. Current fiber-optic technology allows data to move at 2/3 of the speed of light, from New York to San Francisco in about 21 milliseconds.
However, as more data continues to be conveyed, the digital traffic congestion in the future is likely to be a certainty. By 2020, the earth produced about 44 zettabytes (one zettabyte equivalent to a trillion gigabyte) of data. By 2025, 463 exabytes (one exabyte equivalent to billions of gigabytes) of data will be processed daily.
There is also the problem of the "mile storage" bottle, where the data must be transmitted via a local network connection before reaching its final destination. Depending on the quality of this connection, the "last mile" can add anywhere between 10 and 65 milliseconds.
By processing the data near the source and reducing the apparent distance to go, edge computing can significantly reduce delays. This means higher speeds for end users, with latency measured in microseconds rather than milliseconds. If you think that even one minute of relaxation or rest can cost companies thousands of dollars, the speed benefits of using edge computing are very important for your network.
As companies grow, they cannot always anticipate their IT infrastructure needs. Building a dedicated data center is an expensive proposal, making it very difficult to plan for the future.
In addition to the high cost of high-quality construction and ongoing care, there is the question of future needs. Traditional independent services limit performance growth, preventing companies from predicting their future computer needs. If business growth exceeds expectations, they may not be able to take advantage of opportunities due to insufficient computer resources.
Fortunately, the development of cloud-based technology and edge computing has made it easier for businesses to increase their performance. Computer skills, storage, and analytics are increasingly being integrated with devices with small steps that can be close to end users.
Expanding data collection and analysis no longer requires companies to establish central, independent, cost-effective data centers to build, maintain, and replace when it is time to grow again. By integrating regional data collection services with regional computer data centers, organizations can expand their network edge and access faster and cost-effectively. As they grow, the flexibility of edge computing power enables them to quickly adapt to changing markets and maximize their data and computer needs efficiently.
In short, end-to-end computing provides a more cost-effective approach to scalability, allowing companies to maximize their computing power through integration of IoT devices and edge data centers. The use of computer devices on the active edge also reduces the cost of growth because each new additional device does not impose large bandwidth requirements in the context of the network.
The decline of the computer on the edge also plays into its various functions. By working with data centers on the edge, local companies can easily target desirable markets without investing in expanding expensive infrastructure.
Edge data centers allow them to use end users efficiently with minimal body distance or latency. This is especially important for content providers who want to deliver uninterrupted streaming services. Nor do they force companies with more violence, allowing them to switch to other markets if economic conditions change.
Edge computing enables IoT devices to collect unprecedented amounts of possible data. Instead of waiting for people to sign in with devices and interact with central servers, edge computing devices are always open, always connected, and always generate data for future analysis.
Randomized data collected by end-to-end networks can be processed locally to deliver faster services or be returned to a network environment, where powerful analytics and machine learning programs will be distributed to identify trends and noticeable data points. Armed with this knowledge, companies can make better decisions and meet real market needs effectively.
By incorporating new IoT devices into their network design, companies can provide new and better services to their customers without completely overhauling their IT infrastructure. Targeted devices offer many exciting opportunities for organizations that value design as a way to drive growth. It is a great benefit to industries that want to increase network access in regions with limited connectivity (such as health, agriculture and manufacturing sectors).
Given the security benefits offered by edge computing, it should come as no surprise that it offers better reliability as well. With IoT edge computing devices and edge data centers located close to end users, there is less chance of a remote network problem affecting local customers. Even in the event of a nearby data center outage, IoT devices on the computer edge will continue to work successfully on their own because they handle important traditional processing tasks.
By processing data close to the source and prioritizing traffic, edge computing reduces the amount of data flowing to or from the main network, resulting in lower latency and greater overall speed. Physical distance is also important for performance.
By getting edge systems in geographical data centers by approaching end users and distributing processing accordingly, companies can significantly reduce the distance data that has to go before services can be delivered. These reduced networks ensure a faster, seamless experience for their customers, who expect access to their content and requests instantly anywhere, anytime.
With many edge computing devices and data centers on the edge connected to the network, it becomes very difficult for any single failure to shut down the service completely. Information can be retrieved on multiple routes to ensure users keep access to the products and information they need. Successfully installing IoT computer devices and data centers on the edge in complete edge design can therefore provide unparalleled reliability.
It sometimes makes sense to treat edge computing not as a generic category but as two distinct types of architectures: cloud edge and device edge.
Most people talk about edge computing as a singular type of architecture. But in some respects, it makes sense to think of edge computing as two fundamentally distinct types of architectures: Device edge and cloud edge.
Although a device edge and a cloud edge operate in similar ways from an architectural perspective, they cater to different types of use cases, and they pose different challenges.
Here’s a breakdown of how device edge and cloud edge compare.
First, let’s briefly define edge computing itself.
Edge computing is any type of architecture in which workloads are hosted closer to the “edge” of the network — which typically means closer to end-users — than they would be in conventional architectures that centralize processing and data storage inside large data centers.
#cloud #edge computing #cloud computing #device edge #cloud edge
Most of the companies in today’s era are moving towards cloud for their computation and storage needs. Cloud provides a one shot solution for all the needs for services across various aspects, be it large scale processing, ML model training and deployments or big data storage and analysis. This again requires moving data, video or audio to the cloud for processing and storage which also has certain shortcomings compared to do it at the client like
If you look at other side, cloud have their own advantages and I will not talk about them right now. With all these in mind, how about a hybrid approach where few requirements can be moved to the client and some remain on the cloud. This is where EDGE computing comes into picture. According to Wiki here is the definition of the same
Edge has a lot of use cases like
Look at Gartner hype cycle for emerging technologies. Edge is gaining momentum.
There are many platforms in the market specialised in edge deployments right from cloud solutions like azure iot hub, aws greengrass …, open source like _kubeedge, edgeX-Foundary _and third party like Intellisite etc.
I will focus this article on using one of the platforms for building an “Attendance platform” on the edge using facial recognition. I will add as many links as possible for your references.
Let us start with taking the first step and defining the requirements
Choosing the right platform from so many options was a bit tricky. For the POC, we looked at few pieces in the platform
There were other metrics as well but these were on top of our mind. Azure IoT looked pretty good in terms of above evaluation. We also looked at Kubeedge which provided deployments on Kubernetes on the edge. It is open source and looked promising. Looking at many components (cloud and edge) involved with maintenance overhead, we decided not to move ahead with open source. We were already using Azure cloud for other cloud infra, this also made our work a little more easier in choosing this platform. This also helped
Leading platform players
Azure IoT hub provided 2 main components. One is the cloud component responsible for managing the deployments on edge and collection of data from them. The other is the edge component consisting of
I will not go into the details, you can find more details here about the Azure IoT edge. To give a brief, Azure edge requires modules as containers which can to be pushed to the edge. The edge device first needs to be registered with the IoT Hub. Once the Edge agent connects with the hub, you can push your modules using a deployment.json file. The container runtime that Azure Edge uses is moby.
We used Azure IoT free tier which was sufficient for our POC. Check the pricing here
As per the requirements of the POC, this is what we came up with
The solution consists of various containers which are deployment on the edge as well as few cloud deployments. I will talk about each components in details as we move ahead.
As part of the POC, we assumed 2 sites where attendance needs to be taken at multiple gates. To simulate, we created 4 ubuntu machine. This is the ubuntu desktop image we used. For attendance, we created a video containing still photos of few filmstars and sportsperson. These videos will be used for attendance in order to simulate the cameras, one for each gate.
It captures IP camera feed and pushed the frames for consumption
The module was configured to use a lot of environment variables, one being sampling rate of the video frames. Processing all frames require high memory and CPU, so it is always advisable to drop frames to reduce cpu load. This can be done in either camera module or inferencing module.
#deep-learning #edge-computing #azure #edge
Over the years of computing, the processing and storage of data systems that are used in the interconnected computers have been based on the technology of cloud computing. Cloud computing has been based on the centralized data storage systems where all the devices performing some internet operations depend on the efficiency of the cloud service provider.
Since the data has often been centralized, various concerns including the security and the speed in operation have been raised regarding this setup of infrastructure. Since the data is centralized, a single breach can sabotage a large number of users. Moreover, people’s right to privacy may be violated since the service providers have an opportunity to access and monitor people’s details and demographic characteristics.
Latency to the information required may be experienced when the data is being transmitted from the cloud to the end-user due to factors such as the traffic and the distance.
The introduction of edge computing has proved to be effective in the problems associated with cloud computing. Let’s have a look at what edge computing is and the advantages.
The introduction of edge computing has led to the successful proximity of internet data to the end-user. This is done by installing the edge devices close to the end-user by different service providers. A system of interconnected nodes enables the transfer of data from one edge device to the other, hence resulting in the ease of accessing information.
The response time which has been a critical concern especially to the heavy commercial consumers has been solved by this great technology of edge computing. Since the edge devices are close to the end-user, the time of travel of the information from one end-user to the other or from an end-user to a system of AI in the edge devices is minimized. Besides, the traffic that exists in cloud computing is eliminated since the decentralized edge devices serve few users, consequently, the efficiency in the response time and rate.
The system of a computer program that functions to avail data to users at their location and delivers it, can be referred to as an edge device.
Most service providers such as the CCTV cameras, traffic systems in roundabouts and other critical points that heavily depend on the real-time processing of data find the edge computing useful in these functions. The CCTV cameras collect a huge amount of data that can be as high as 10 GB per second especially in a moving car for about a mile. For the data to be transferred to the cloud for the AI (artificial intelligence) to assist in its processing, there can be latency experienced in the process resulting in poor decision making especially in the self-driving cars or the AI dependent systems.
Edge devices enable the real-time processing of the data in huge volumes and at the shortest distance hence the elimination of the latency experienced when cloud computing is adopted. Cloud computing might be efficient in the operation of huge data for its capacity and the extent of specialized and sophisticated hardware installed in it, the edge devices are unchallenged in the operation of real-time data.
#cloud computing #data #edge computing #edge #interner of things #cloud
This article will hopefully introduce you to edge computing. We will compare it to cloud computing, discuss its main advantages & disadvantages and some use cases. The cherry on the top: a prediction on edge computing at the end of the article, and whether cloud computing will be made obsolete by edge computing.
loud computing is now well anchored in our daily lives. To the point that whether you are aware of it or not, you are probably using it right now. It ranges from the obvious online cloud storage (Dropbox and OneDrive come to mind), communication services (email & messaging), digital assistants (Siri, Alexa, Google Assistant), to entertainment content providers (Spotify, Netflix, …).
Those services are centralized. Whenever you send a request it is sent to the cloud provider, processed, and returned to you. To put it simplistically:
The level of dematerialization has increased over the last decades. This is a change from the former paradigm, where physical storage was used, think of accessing a CD/DVD:
Author creation — Right-hand image by PNG Creation (Source)
Another paradigm has now emerged between those two extremes: edge computing.
Edge computing could be defined as ‘a local, distributed extension to centralized cloud computing’.
To understand the place of Edge computing, let’s compare it to cloud Computing.
Cloud computing is centralized:
Image by Robert White on Focus-works.com
Edge computing is distributed:
Additionally, it interacts with the physical world via IoT devices e.g. sensors and cameras (more on this later).
Image by Robert White on Focus-works.com
How does cloud and edge computing interact?
Creation by NoMore201 — CC BY-SA 4.0, on Wikipedia
Let’s now review a few use cases of edge computing to understand which industries it could disrupt in the future.
#predictions #iot #edge-computing #cloud-computing #technology #cloud
In many situations, using cloud computing and edge computing at the same time can lead to the best overall outcome from a performance perspective.
Discussions about the relationship between cloud computing and edge computing have a tendency to present the cloud and edge as opposite types of architectures and to treat them as an either/or proposition.
This isn’t the best way to think about cloud and edge. Although there are key differences between cloud and edge computing, the cloud and the edge don’t compete with each other as much as they complement each other.
#cloud #edge computing #cloud-native #cloud computing