Contextual Ads at Edge are buzzing around the OTT platforms. To achieve the perfect mix of customer experience and media monetization, advertisers will need a technology framework that harnesses various aspects of 5G, such as small cells and network slicing, to deliver relevant content in real time with zero latency and lag-free advertising.

Why Contextual Ads at Edge?#

"In advertising, this surge of data will enable deeper insights into customer behaviors and motivations, allowing companies to develop targeted, hyper-personalized ads at scale — but just migrating to 5G is not enough to enable these enhancements. To achieve the perfect mix of customer experience and media monetization, advertisers will need a technology framework that harnesses various aspects of 5G, such as small cells and network slicing, to deliver relevant content in real-time with zero latency and lag-free advertising."

Contextual Video Ads Set to Gain#

A recent study shows that 86% of businesses used videos as their core marketing strategy in 2021 compared to 61% in 2016. A report by Ericsson estimates videos will account for 77% of mobile data traffic by 2025 versus 66% currently.

Read more about Contextual Ads at Edge in the article covered by Wipro.

Wipro Tech Blogs - Contextual Ads Winning in a 5G World

Are you familiar with the differences between edge computing and cloud computing? Is edge computing a type of branding for a cloud computing resource, or is it something new altogether? Let us find out!

The speed with which data is being added to the cloud is immense. This is because the growing number of devices in the cloud are centralized, so it must transact the information from where the cloud servers are, hence data needs to travel from one location to another so the speed of data travel is slow. If this transaction starts locally, then the data travels at a shorter distance, making it faster. Therefore, cloud suppliers have combined Internet of Things strategies and technology stacks with edge computing for the best usage and efficiency.

In the following article, we will understand the differences between cloud and edge computing. Let us see what this is and how this technology works.

EDGE COMPUTING#

Edge Computing is a varied approach to the cloud. It is the processing of real-time data close to the data source at the edge of any network. This means applications close to the data generated instead of processing all data in a centralized cloud or a data center. It increases efficiency and decreases cost. It brings the storage and power closer to the device where it is most needed. This distribution eliminates lag and saves a scope for various other operations.

It is a networking system, within which data servers and data processing are closer to the computing process so that the latency and bandwidth problems can be reduced.

Now that we know what the basics of edge computing are, let's dive in a little deeper for a better understanding of terms commonly associated with edge computing:

Latency#

Latency is the delay in contacting in real-time from a remotely located data center or cloud. If you are loading an image over the internet, the time to show up completely is called the latency time.

Bandwidth#

The frequency of the maximum amount of data sent over an Internet connection at a time is called Bandwidth. We refer to the speed of sent and received data over a network that is calculated in megabits per second or MBPS as bandwidth.

Leaving latency and bandwidth aside, we choose edge computing over cloud computing in hard-to-reach locations, where there is limited or no connectivity to a central unit or location. These remote locations need local computing, and edge computing provides the perfect solution for it.

Edge computing also benefits from specialized and altered device functions. While these devices are like personal computers, they are not regular computing devices and perform multiple functions benefiting the edge platform. These specialized computing devices are intelligent and respond to machines specifically.

Benefits of Edge Computing#

• Gathering data, analyzing, and processing is done locally on host devices on the edge of the network, which has the caliber to be completed within a fraction of a second.

• It brings analytical capabilities comparatively closer to the user devices and enhances the overall performance.

• Edge computing is a cheaper alternative to the cloud as data transfer is a lengthy and expensive process. It also decreases the risk involved in transferring sensitive user information.

• Increased use of edge computing methods has transformed the use of artificial intelligence in autonomous driving. Artificial Intelligence-powered and self-driving cars and other vehicles require massive data presets from their surroundings to function perfectly in time. If we use cloud computing in such a case, it would be a dangerous application because of the lag.

• The majority of OTT platforms and streaming service providers like Netflix, Amazon Prime, Hulu, and Disney+ to name a few, create a heavy load on cloud network infrastructure. When popular content is cached closer to the end-users in storage facilities for easier and quicker access. These companies make use of the nearby storage units close to the end-user to deliver and stream content with no lag if one has a stable network connection.

The process of edge computing varies from cloud computing as the latter takes considerably more time. Sometimes it takes up to a couple of seconds to channel the information to the data centers, ultimately resulting in delays in crucial decision-making. The signal latency can translate to huge losses for any organization. So, organizations prefer edge computing to cloud computing which eliminates the latency issue and results in the tasks being completed in fractions of a second.

CLOUD COMPUTING#

A cloud is an information technology environment that abstracts, pools, and shares its resources across a network of devices. Cloud computing revolves around centralized servers stored in data centers in large numbers to fulfill the ever-increasing demand for cloud storage. Once user data is created on an end device, its data travels to the centralized server for further processing. It becomes tiresome for processes that require intensive computations repeatedly, as higher latency hinders the experience.

Benefits of Cloud Computing#

• Cloud computing gives companies the option to start with small clouds and increase in size rapidly and efficiently as needed.

• The more cloud-based resources a company has, the more reliable its data backup becomes, as the cloud infrastructure can be replicated in case of any mishap.

• There is little to no service cost involved with cloud computing as the service providers conduct system maintenance on their own from time to time.

• Cloud enables companies to help cut expenses in operational activities and enables mobile accessibility and user engagement framework to a higher degree.

• Many mainstream technology companies have benefited from cloud computing as a resourceful platform. Slack, an American cloud-based software as a service, has hugely benefited from adopting cloud servers for its application of business-to-business and business-to-consumer commerce solutions.

• Another largely known technology giant, Microsoft has its subscription-based product line ‘Microsoft 365' which is centrally based on cloud servers that provide easy access to its office suite.

• Dropbox, infrastructure as a service provider, provides a service- cloud-based storage and sharing system that runs solely on cloud-based servers, combined with an online-only application.

KEY DIFFERENCES#

• The main difference between edge computing and cloud computing is in data processing within the case of cloud computing, data travel is long, which causes data processing to be slower but in contrast edge computing reduces the time difference in the data processing. It's essential to have a thorough understanding of the working of cloud and edge computing.

• Edge computing is based on processing sensitive information and data, while cloud computing processes data that is not time constrained and uses a lesser storage value. To carry out this type of hybrid solution that involves both edge and cloud computing, identifying one's needs and comparing them against monetary values must be the first step in assessing what works best for you. These computing methods vary completely and comprise technological advances unique to each type and cannot replace each other.

• The centralized locations for edge computing need local storage, like a mini data center. Whereas, in the case of cloud computing, the data can be stored in one location. Even when used as part of manufacturing, processing, or shipping operations, it is hard to co-exist without IoT. This is because everyday physical objects that collect and transfer data or dictate actions like controlling switches, locks, motors, or robots are the sources and destinations that edge devices process and activate without depending upon a centralized cloud.

With the Internet of Things gaining popularity and pace, more processing power and data resources are being generated on computer networks. Such data generated by IoT platforms is transferred to the network server, which is set up in a centralized location.

The big data applications that benefit from aggregating data from everywhere and running it through analytics and machine learning to prove to be economically efficient, and hyper-scale data centers will stay in the cloud. We chose edge computing over cloud computing in hard-to-reach locations, where there is limited connectivity to a cloud-based centralized location setup.

CONCLUSION#

The edge computing and cloud computing issue does not conclude that deducing one is better than the other. Edge computing fills the gaps and provides solutions that cloud computing does not have the technological advancements to conduct. When there is a need to retrieve chunks of data and resource-consuming applications need a real-time and effective solution, edge computing offers greater flexibility and brings the data closer to the end user. This enables the creation of a faster, more reliable, and much more efficient computing solution.

Therefore, both edge computing and cloud computing complement each other in providing an effective response system that is foolproof and has no disruptions. Both computing methods work efficiently and in certain applications, edge computing fills and fixes the shortcomings of cloud computing with high latency, fast performance, data privacy, and geographical flexibility of operations.

Functions that are best managed by computing between the end-user devices and local networks are managed by the edge, while the data applications benefit from outsourcing data from everywhere and processing it through AI and ML algorithms. The system architects who have learned to use all these options together have the best advantage of the overall system of edge computing and cloud computing.

Learn more about different use cases on edge computing-

Condition-based monitoring - An Asset to equipment manufacturers (nife.io)

Large-scale manufacturing units, especially industrial setups, have complicated equipment. Condition-based monitoring at the edge is unprecedented. Can this cost be reduced?

Learn More!

Background#

The world is leaning toward the Industrial 4.0 transformation, and so are the manufacturers. The manufacturers are moving towards providing services rather than selling one-off products. Edge computing in manufacturing is used to collect data, manage the data, and run the analytics. It becomes essential to monitor assets, check for any faults, and predict any issues with the devices. Real-time data analysis of assets detects faults so we can carry out maintenance before the failure of the system occurs. We can recognize all the faulty problems with the equipment. Hence, we need condition-based monitoring.

Why Edge Computing for Condition-Based Monitoring?#

Edge computing is used to collect data and then label it, further manage the data, and run the system's analytics. Then, we can send alerts to the end enterprise customer and the OEM to notify them when maintenance service is required. Using network edge helps eliminate the pain of collecting data from many disparate systems or machines.

The device located close to the plants or at the edge of the network provides condition-based monitoring, preempts early detection, and correction of designs, ensuring greater productivity for the plant.

Key Challenges and Drivers of Condition-Based Monitoring at Edge#

• Device Compatibility
• Flexibility in Service
• Light Device Support
• Extractive Industries

Solution#

To detect machinery failures, the equipment has a layer of sensors. These sensors pick up the information from the devices and pass it to a central processing unit.

Here, edge computing plays a crucial part in collecting and monitoring via sensors. The data from the sensors help the OEM and the system administrators monitor the exact device conditions, reducing the load on the end device itself. This way, administrators can monitor multiple sensors together. With the generation of the events, failure on one device can be collated with another device.

Edge also allows processing regardless of where the end device is located or if the asset moves. The same application can be extended to other locations. Alternatively, using edge helps remove the pain of collecting data from many disparate systems/machines in terms of battery.

The edge computing system based on conditions is used to collect statistics, manage the data, and run the analytics without any software hindrance. A system administrator can relax as real-time data analysis detects faults to carry out maintenance before any failure occurs.

Condition-based monitoring can be used in engineering and construction to monitor the equipment. Administrators can use edge computing industrial manufacturing for alerts and analytics.

On-Prem vs. Network Edge#

Given that the on-prem edge is lightweight, it's easy to place anywhere on the location. On the other hand, installing a device is overridden if the manufacturing unit decides to go with the network edge; hence, flexibility is automatically achieved.

How Does Nife Help with Condition-Based Monitoring at Edge?#

Use Nife as a network edge device to compute and deploy applications close to the industries.

Nife works on collecting sensor information, collating it, and providing immediate response time.

Benefits and Results#

• No difference in application performance (70% improvement from Cloud)
• Reduce the overall price of the Robots (40% Cost Reduction)
• Manage and monitor all applications in a single pane of glass
• Seamlessly deploy and manage navigation functionality (5 min to deploy, 3 min to scale)

Edge computing is an asset to different industries, especially device manufacturers, helping them reduce costs, improve productivity, and ensure that administrators can predict device failures.

You might like to read through this interesting topic of Edge Gaming!

Computer Vision at Edge is a growing subject with significant advancement in the new age of surveillance. Surveillance cameras can be primary or intelligent, but Intelligent cameras are expensive. Every country has some laws associated with Video Surveillance.

How do Video Analytics companies rightfully serve their customers, with high demand?

Nife helps with this.

Introduction#

The need for higher bandwidth and low latency processing has continued with the on-prem servers. While on-prem servers provide low latency, they do not allow flexibility.

Computer Vision can be used for various purposes such as Drone navigation, Wildlife monitoring, Brand value analytics, Productivity monitoring, or even Package delivery monitoring can be done with the help of these high-tech devices. The major challenge in computing on the cloud is data privacy, especially when images are analyzed and stored.

Another major challenge is spinning up the same algorithm or application in multiple locations, which means hardware needs to be deployed there. Hence scalability and flexibility are the key issues. Accordingly, Computing and Computed Analytics are hosted and stored in the cloud.

On the other hand, managing and maintaining the on-prem servers is always a challenge. The cost of the servers is high. Additionally, any device failure adds to the cost of the system integrator.

Thereby, scaling the application to host computer vision on the network edge significantly reduces the cost of the cloud while providing flexibility of the cloud.

Key Challenges and Drivers of Computer Vision at Edge#

• On-premise services
• Networking
• Flexibility
• High Bandwidth
• Low-Latency

Solution Overview#

Computer Vision requires high bandwidth and high processing, including GPUs. The Edge Cloud is critical in offering flexibility and a low price entry point of cloud hosting and, along with that, offering low latency necessary for compute-intensive applications.

Scaling the application to host on the network edge significantly reduces the camera's cost and minimizes the device capex. It can also help scale the business and comply with data privacy laws, e.g. HIPAA, GDPR, and PCI, requiring local access to the cloud.

How does Nife Help with Computer Vision at Edge?#

Use Nife to seamlessly deploy, monitor, and scale applications to as many global locations as possible in 3 simple steps. Nife works well with Computer Vision.

• Seamlessly deploy and manage navigation functionality (5 min to deploy, 3 min to scale)
  • No difference in application performance (70% improvement from Cloud)
  • Manage and Monitor all applications in a single pane of glass.
  • Update applications and know when an application is down using an interactive dashboard.
  • Reduce CapEx by using the existing infrastructure.

A Real-Life Example of the Edge Deployment of Computer Vision and the Results#

In the current practice, deploying the same application, which needs a low latency use case, is a challenge.

• It needs man-hours to deploy the application.
• It needs either on-prem server deployment or high-end servers on the cloud.

Nife servers are present across regions and can be used to deploy the same applications and new applications closer to the IoT cameras in Industrial Areas, Smart Cities, Schools, Offices, and in various locations. With this, you can monitor foot-fall, productivity, and other key performance metrics at lower costs and build productivity.

Conclusion#

Technology has revolutionized the world, and devices are used for almost all activities to monitor living forms. The network edge lowers latency, has reduced backhaul, and supports flexibility according to the user's choice and needs. We can attribute IoT cameras to scalability and flexibility, which are critical for the device. Hence, ensuring that mission-critical monitoring would be smarter, more accurate, and more reliable.

Want to know how you can save up on your cloud budgets? Read this blog.

For scaling the robots, the biggest challenge is management and deployment. Robots have brought a massive change in the present era, and so we expect them to change the next generation. While it may not be true that the next generation of robotics will do all human work, robotic solutions help with automation and productivity improvements. Learn more!

Introduction#

In the past few years, we have seen a steady increase and adoption of robots for various use-cases. When industries use robots, multiple robots perform similar tasks in the same vicinity. Typically, robots consist of embedded AI processors to ensure real-time inference, preventing lags.

Robots have become integral to production technology, manufacturing, and Industrial 4.0. These robots need to be used daily. Though embedded AI accelerates inference, high-end processors significantly increase the cost per unit. Since processing is localized, battery life per robot also reduces.

Since the robots perform similar tasks in the same vicinity, we can intelligently use a minimal architecture for each robot and connect to a central server to maximize usage. This approach aids in deploying robotics, especially for Robotics as a Service use-cases.

The new architecture significantly reduces the cost of each robot, making the technology commercially scalable.

Key Challenges and Drivers for Scaling Deployment of Robotics#

• Reduced Backhaul
• Mobility
• Lightweight Devices

How and Why Can We Use Edge Computing?#

Device latency is critical for robotics applications. Any variance can hinder robot performance. Edge computing can help by reducing latency and offloading processing from the robot to edge devices.

Nife's intelligent robotics solution enables edge computing, reducing hardware costs while maintaining application performance. Edge computing also extends battery life by removing high-end local inference without compromising services.

Energy consumption is high for robotics applications that use computer vision for navigation and object recognition. Traditionally, this data cannot be processed in the cloud; hence, embedded AI processors accelerate transactions.

Virtualization and deploying the same image on multiple robots can also be optimized.

We enhance the solution's attractiveness to end-users and industries by reducing costs, offloading device computation, and improving battery life.

Solution#

Robotics solutions are valuable for IoT, agriculture, engineering and construction services, healthcare, and manufacturing sectors.

Logistics and transportation are significant areas for robotics, particularly in shipping and airport operations.

Robots have significantly impacted the current era, and edge computing further reduces hardware costs while retaining application performance.

How Does Nife Help with Deployment of Robotics?#

Use Nife to offload device computation and deploy applications close to the robots. Nife works with Computer Vision.

• Offload local computation
• Maintain application performance (70% improvement over cloud)
• Reduce robot costs (40% cost reduction)
• Manage and Monitor all applications in a single interface
• Seamlessly deploy and manage navigation functionality (5 minutes to deploy, 3 minutes to scale)

A Real-Life Example of Edge Deployment and the Results#

In this customer scenario, robots were used to pick up packages and move them to another location.

If you would like to learn more about the solution, please reach out to us!

AR Mirrors or Smart mirrors, the future of mirrors, is known as the world's most advanced Digital Mirrors. Augmented Reality mirrors are a reality today, and they hold certain advantages amidst COVID-19 as well.

Learn More about how to deploy and scale Smart Mirrors.

Introduction#

AR Mirrors are the future and are used in many places for ease of use for the end-users. AR mirrors are also used in Media & Entertainment sectors because the customers get easy usage of these mirrors, the real mirrors. The AI improves the edge's performance, and the battery concern is eradicated with edge computing.

Background#

Augmented Reality, Artificial intelligence, Virtual reality and Edge computing will help to make retail stores more interactive and the online experience more real-life, elevating the customer experience and driving sales.

Recently, in retail markets, the use of AR mirrors has emerged, offering many advantages. The benefits of using these mirrors are endless, and so is the ability of the edge.

For shoppers to go back to the stores, the touch and feel are the last to focus on. Smart Mirrors bring altogether a new experience of visualizing different garments, how the clothes actually fit on the person, exploring multiple choices and sizes to create a very realistic augmented reflection, yet avoiding physical wear and touch.

About#

We use real mirrors in trial rooms to try clothes and accessories. Smart mirrors have become necessary with the spread of the pandemic.

The mirrors make the virtual objects tangible and handy, which provides maximum utility to the users building on customer experience. Generally, as human nature, the normal mirrors in the real world more often to get a look and feel.

Hence, these mirrors take you to the virtual world, help you with looking at jewellery, accessories and even clothes making the shopping experience more holistic.

Smart Mirrors use an embedded processor with AI. The local processor ensures no lag when the user is using the Mirrors and hence provides an inference closest to the user. While this helps with the inference, the cost of the processor increases.

In order to drive large scale deployment, the cost of mirrors needs to be brought down. Today, AR mirrors have a high price, hence deploying them in retail stores or malls has become a challenge.

The other challenge includes updates to the AR application itself. Today, the System Integrator needs to go to every single location and update the application.

Nife.io delivers by using minimum unit architecture, each connected to the central edge server that can lower the overall cost and help to scale the application on Smart Mirror

Key challenges and drivers of AR Mirrors#

• Localized Data Processing
• Reliability
• Application performance is not compromised
• Reduced Backhaul

Result#

AR Mirrors deliver a seamless user experience with AI. It is a light device that also provides data localization for ease of access to the end-user.

AR Mirrors come with flexible features and can easily be used according to the user's preference.

Here, edge computing helps in reducing hardware costs and ensures that the customers and their end-users do not have to compromise with application performance.

1. The local AI processing moves to the central server.
2. The processor now gets connected to a camera to get the visual information and pass it on to the server.

Since the processing is moved away from the server itself, this helps AR mirrors also can help reduce battery life.

The critical piece here is lag in operations. The end-user should not face any lag, the central server then must have enough processing power and enough isolations to run the operations.

Since the central server with network connectivity is in the control of the application owner and the system integrator, the time spent to deploy in multiple servers is completely reduced.

How does Nife Help with AR Mirrors?#

Use Nife to offload device compute and deploy applications close to the Smart Mirrors.

• Offload local computation
• No difference in application performance (70% improvement from Cloud)
• Reduce the overall price of the Smart Mirrors (40% Cost Reduction)
• Manage and Monitor all applications in a single pane of glass.
• Seamlessly deploy and manage applications ( 5 min to deploy, 3 min to scale)

What's happening with 5G and the 5G networks innovation and rollout? How are these shaping innovation and the world we know? Are you curious? Read More!

We will never forget the year 2020 as the year of the COVID-19 pandemic. We all remember how we witnessed a lengthy lockdown during 2020, and it put all our work on a halt for some time. But we all know that the internet remains one of the best remedies to spend time while at home. We have a 4G network, but there was news that the 5G network would soon become a new normal. Interestingly, even during COVID-19, there were several developments in the 5G network. This article will tell you how the 5G network testing and development stayed intact even during the pandemic.

Innovative Tools That Helped in 5G Testing Even During the Pandemic (Intelligent Site Engineering)#

To continue the 5G testing and deployment even during a pandemic, Telcos used specific innovative tools, the prominent being ISE (Intelligent Site Engineering).

What is Intelligent Site Engineering?#

Intelligent Site Engineering refers to the technique of using laser scanners and drones to design network sites. It is one of the latest ways of network site designing. In this process, they collected every minute detail to create a digital twin of a network site. If the company has a digital twin of a network site, they can operate it anywhere, virtually.

They developed Intelligent Site Engineering to meet the increasing data traffic needs and solve the network deployment problems of the Communication Service Providers (CSPs). This incredible technology enabled the site survey and site design even during the pandemic. We all know that site design and site surveys are vital for the proper installation of a network. But it was not possible to survey the site physically. Therefore, these companies used high technologies to launch and deploy 5G networks even in these lockdown times.

Intelligent Site Engineering uses AI (Artificial Intelligence) and ML (Machine Learning) to quickly and efficiently deliver a network. This helps CSPs to deploy frequency bands, multiple technologies, and combined topologies in one place. This advanced technology marks the transition from the traditional technique of using paper, pen, and measuring tape for a site survey to the latest styles like drones carrying high-resolution cameras and laser scanning devices.

How Does Intelligent Site Engineering Save Time?#

The Intelligent Site Engineering technique saves a lot of time for CSPs. For example, in this digitized version, CSPs take only 90 minutes for a site survey. Previously, they had to waste almost half a day in site surveys using primitive tools in the traditional method. Instead, the engineers can use the time these service providers save for doing other critical work.

Also, this process requires fewer people because of digitalization. This means that it saves the headcount of the workforce and the commuting challenges. Also, it reduces the negative impact on the environment.

What is the Use of Digital Twins Prepared in This Process?#

Using Intelligent Site Engineering, the CSPs replicate the actual site. They copied digital twins through 3D scans and photos clicked from every angle. The engineers then use the copies to get an accurate analysis of the site data. With the highly accurate data, they prepared the new equipment. The best example of digital twins is that CSPs can make a wise decision regarding altering the plan for future networks. Therefore, a digital twin comes in handy, from helping in creating material bills to detailed information about the networking site and related documents.

The technique is helpful for customers as well. For example, through the digital twin, customers can view online documents and sign them. In this way, this advanced technology and innovation enable remote acceptance of network sites even with 5G.

How Did the COVID Pandemic Promote the Digitalization of the 5G Network?#

We all know that the COVID pandemic and the subsequent lockdown put several restrictions on travel. Since no one could commute to the network site, it prepared us to switch to digital methods for satisfying our needs. The result was that we switched to Intelligent Site Engineering for 5G network deployment, bringing in 5G networks innovation.

When physical meetings were restricted, we switched to virtual conversations. Video meetings and conference calls became a new normal during the pandemic. Therefore, communication service providers also used the screen share features to show the clients the network sites captured using drones and laser technology. The image resolution was excellent, and the transition from offline to online mode was successful. Training of the personnel also became digitized.

The best part of this digitalization was that there was no need to have everyone on one site. Using these digital twins and technological tools, anyone can view those designs from anywhere. The companies could share the screen, and the clients could review the site without physical presence.

How Much Efficiency Were the CSPs Able to Achieve?#

When communication service providers were asked about the experience of these new technological tools for network sites, they felt it was better than on-site conversations. They reveal that these online calls help everyone look at the same thing and avoid confusion, which was the biggest problem in on-site meetings. Therefore, this reduces the queries, and teams could complete the deal in less time than offline sales.

The most significant benefit is for the technical product managers. They can now work on online techniques for vertical inspection of assets and sites. In addition, 3D modeling is enhanced, and the ground-level captured images ensure efficiency.

Rounding Up About 5G Networks Innovation:#

The year 2020 was indeed a gloomy year for many of us. But the only silver lining was the announcements of technological advancements like the 5G launch, even during these unprecedented times. The technology advancement enabled us to use this pandemic wisely, and we deployed the 5G network at several places. So, we can say that the innovations remained intact even during the pandemic because of intelligent and relevant technologies. Therefore, it would not be wrong to conclude that technological advancements have won over these challenging times and proved the future.

AI (Artificial Intelligence) and ML (Machine Learning) are all set to become the future of technology. According to reports, AI and ML will become crucial for intelligent edge management.

Summary#

We can't imagine Intelligent Edge computing without AI and ML. If you are unaware of the enormous impact of AI and ML on Intelligent edge management, this article will help you uncover all the aspects. It will tell you how AI and ML will become the new normal for Intelligent Edge Management.

What is Intelligent Edge Computing?#

Edge Cloud computing refers to a process through which the gap between computing and network vanishes. We can provide computing at different network locations through storage and compute resources. Examples of edge computing include “on-premises at an enterprise or customer network site” or local operators like Telco.

Predictions of Edge computing:

We expect the future of edge computing to grow at a spectacular rate. Since edge computing is the foundation of the network computer fabric, experts predict a steady growth of the popularity of edge computing shortly. Adding to these predictions are the new applications like IoT, 5G, smart devices, extended reality and Industry 4.0 that will enable rapid growth of edge computing. According to a prediction by Ericsson, by 2023, almost 25% of 5G users will start using intelligent edge computing. These predictions reflect the expected growth of edge computing shortly.

Challenges with Edge computing

Every coin has two sides. Similarly, if edge computing is expected to grow substantially, it will not come without common problems and challenges. The first problem is the gap between existing cloud management solutions and computing at the edge. The cloud management solutions that exist today work on large pools of homogeneous hardware, making it difficult to manage. Besides that, it requires 24/7 system administration. But if you look at the suitable environment for edge computing, you would see significant differences.

• It has limited and constrained resources:

Unlike the existing cloud management solutions, edge computing is limited by constrained resources. This is because the location and servers are made with a small factor of rack space in mind. This might seem like an advantage because you will require less space, money, etc. But the challenge with this is that one needs to have optimum utilisation of resources to get efficient computing and storing facilities.

• Heterogeneous hardware and dynamic factors:

The other significant difference is that, unlike the existing resources that require homogeneous hardware, edge computing requires diverse hardware. Therefore, the requirement can vary at different times. Requirements for hardware can vary according to varying factors like space, timing, the purpose of use etc. Let's look at some of the diverse factors that influence the heterogeneity and dynamics of edge computing:

• Location: If edge computing is for a commercial area, it will get overburdened during rush hours. But in contrast, if you are using it in residential areas, the load will be after working hours because people will use it after coming home. So in this way, the location can matter a lot for edge computing.

• Timing: There are several hours in the day when edge computing is widely used, while at some hour's its application is negligible.

• Purpose of application: The goal of computing is to determine what kind of hardware we require for edge computing. If, for IoT, the application will need the best services. But if it is for a simple purpose like gaming, even low latency computing would work.

• In this way, we see that edge computing has to overcome heterogeneity and diversity for optimum performance.
• Requirement of reliability and high performance from edge computing:

The third challenge for edge computing is to remain reliable and offer high performance. There is a dire need to reduce the chances of failure that are most common in software infrastructure. Therefore, to mitigate these failures, we need timely detection and analysis and remedy for the problem. If it is not correct, it can even transfer from one system to another.

• The problem of human intervention with remote computing:

If edge servers are in a remote area, there will be a problem with human intervention. Administrators can't visit these remote areas regularly and check on the issues. Therefore, there is a need for the part of computing to become self-managing.

How AI and ML are expected to become of utmost importance for edge computing?

Artificial intelligence and machine learning are expected to become crucial for computing because the distribution of computer capability and the network has several challenges in operation. Hence AI and ML can overcome these challenges. AI and ML will simplify cloud edge operations and ensure a smooth transition of edge computing.

• AI and ML can extract knowledge from large chunks of data.
• Decisions, predictions, and inferences reached through AI and ML are more accurate and faster at the edge.
• By detecting data patterns through AI and ML, Edge computing can have automated operations.
• Classification and clustering of data can help in the detection of faults and efficient working of algorithms.

How to use AI and ML for edge computing?#

Enterprises can use AI and ML in different mechanisms at edge computing locations.

Let's look at the different tools and processes involved.

• Transfer learning (new model training from previously trained models)
• Distributed learning
• Federated learning
• Reinforcement learning
• Data monitoring and management
• Intelligent operations.

Conclusion#

We can expect extended artificial intelligence and machine learning on edge to become a new normal. It will affect almost all technological tools, including edge computing. In this article, we looked at how artificial intelligence and machine learning would help edge computing in the future to overcome its challenges. But it will always remain essential to have a robust framework for technological tools not to be misused.

Why are videos the best to use with Edge? What makes edge special for Videos? This article will cover aspects of Video at Edge why it is a Unilateral choice! Read on!

The Simplest, Smartest, Fastest way for Enterprise to deploy any application

With advancements in computing, we are making newer technologies to improve end-user performance. The tool to help us in getting the best user experiences is edge computing. As cloud computing is gaining momentum, we have created better applications that were impossible earlier. Given the vast arena of edge computing, we can get several benefits from it. Therefore, we should not restrict to only content and look beyond what is available presently.

We all know that our computer applications depend on the cloud for efficient operations. Still, certain drawbacks of this dependence include buffering, loading time, reduced efficiency, irritation, etc. This article will look at how we can get the best video experience at the Edge.

How can the Edge help us in getting the best video experience?#

We all love to watch different genre videos on our smartphones, laptops, PC and other devices. But these are only limited to a restricted view; we don't feel them in reality. Therefore, several tech projects look into the possibility of creating a 360-degree video experience. Several tools like head-mounted Display (HMD), also called virtual reality, can come in handy for this 360-degree video viewing. It creates more interest and unique ways than a traditional video viewing experience. However, there are several challenges that this technology has to overcome to provide a better user experience.

Challenges for better user experience for videos at edge#

• High Bandwidth is required to run these immersive videos
• Latency sensitivity is another problem.
• Requirement of Heterogeneous HMD devices for getting a 360-degree experience

However, edge computing can help us overcome these challenges and enhance the user experience.

What is edge computing?#

The Edge often termed the next-gen solution, can help us get the best video experience because it allows us to view unlimited content on different devices. The quality is improved because the content is stored near the end-user. Interestingly, Edge can help get an enjoyable experience with no regard to the location.

Take videos loading, for example, it is faster in edge computing rather than cloud computing. To check the video experience in edge computing, users played an edge gaming app(a smartphone multiplayer video game). They did the entire process on edge rather than on a mobile phone. This experiment showed spectacular results with remarkable speed.

In a video operating system that gets help from the Edge, the viewers get a 360-degree viewpoint on edge servers. The algorithms involved in Edge can help implement and solve the problems of video streaming systems.

Benefits for using Edge for video streaming

• Edge helps in reducing bandwidth usage. Therefore, there is a reduction in loading and buffering issues.
• The computation workload on HMD (Head Mounted Display) is reduced because lightweight models are used.
• The users could realise lower network latency.
• Let's compare it with traditional video streaming platforms. We will get 62% better performance because it reduces bandwidth consumption by almost sixty-two per cent and renders the highest video quality to the viewer.
• The battery life is also enhanced because the Edge consumes far less battery than traditional video streaming platforms.

Imagine the possibility of hosting a whole application on edge computing

We have seen how edge computing can offer wonderful video experiences. Let's see how edge computing can help us in hosting a whole application and getting maximum satisfaction. According to the study, if we upload the entire application at the Edge, we would need o

nly a front-facing client to operate with no other requirement.

An excellent example to understand this concept is Google glass. If we watch an application on Google glass, we can see that it is not hosting the application, but it is only a medium to view it. Similarly, smartphones would not host the application but become a medium to view the application. It could therefore show spectacular performance.

Enhanced Experience is not the only benefit by hosting on Edge

• We will see the first change in the landscape of application.

Edge will make the application more interactive, intelligent and exciting, thus giving a better user experience.

• The application hosted on Edge will not need to depend on a smartphone but only on the network.
• The requirements for allied technology like power, battery, memory for smartphones will reduce since we host the applications on Edge and not on the smartphone.

In addition, it will help smartphone manufacturers to give necessary attention to hardware components like display, screen, etc.

Hosting applications at the Edge will bring a revolution and how we perceive smartphones. We will, then, use smartphones only for viewing the application and not for storing the application.

As the load on smartphones reduces, companies can remove unnecessary technology from smartphones. Users can get slim, thin, foldable (as the latest technology is trying to give) and even unimagined smartphones in the future.

How does an application get to be a part of edge computing?#

We saw how edge computing could help us in getting an excellent video and application experience. But we don't want to give this to theory, only instead bring it to practical use. For making it a reality, there are specific requirements. The first requirement for hosting applications or videos on Edge rather than a smartphone is an edge computing platform. Only an edge computing platform will enable us to get the benefit of network and application. Therefore, several companies like Nife.io are working on creating an ‘OS for the edge'.

Rounding up:

In this article, we saw how edge computing could help render better quality videos, solve the existing problems of video streaming platforms and give the best user experience. But for all of this to be a reality, we require platforms to adopt edge computing.

Therefore, welcome the future of video streaming and reap the benefits of edge devices by reaching out to us. We are soon to realise the benefits of the videos listed above due to edge computing.

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