Adopting a Cloud Platform Solution refers to implementing a comprehensive infrastructure and service framework that leverages cloud technologies. It enables organizations to harness the benefits of scalability, flexibility, cost optimization, and streamlined operations, empowering them to innovate and thrive in the digital landscape.

In recent years, developers have increasingly opted for deploying microservices-based applications in the cloud instead of traditional monolithic applications. Microservices architecture provides better scalability, flexibility, and fault tolerance.

Microservices architecture in the cloud allows developers to break complex applications into small, independently scalable services, providing more agility and faster response times.

In this blog, we'll explore the best practices for deploying microservices in the cloud, covering aspects like service discovery, load balancing, scaling, and more.

We will also delve into cloud platforms suited for the Middle East to address the region's unique needs. This blog will help you deploy robust and scalable microservices. Read till the end for valuable insights.

Best Practices for Deploying Microservices in the Cloud#

Service Discovery#

Imagine a big city with all similar-looking buildings housing thousands of businesses without any brand boards. Without a map or reliable directory, it would be impossible for you to find the service you are looking for. In the same way, service discovery is crucial for microservices in the cloud. Service discovery connects different microservices to work together seamlessly.

Service Discovery Best Practices#

There are different methods of navigating a business in a big city. Likewise, service discovery has different methods to navigate and connect microservices.

DNS-based Service Directory#

In this method, service names are mapped to their IP addresses. Services can query and find other services, similar to an online phone directory.

Client-side Service Directory#

In this method, each available service registers itself with the service discovery server. Clients can easily find and communicate with the required service.

Comparison of Cloud Platforms#

Here is a comparison of cloud application development services. Google Cloud Platform has its own service discovery service called Cloud DNS. Cloud DNS creates DNS records and simplifies deploying microservices in Google Cloud. On the other hand, Amazon offers Route 53, which creates DNS records and routes microservices, making it easier to deploy Java microservices in AWS.

Nife is another cloud platform providing a seamless service discovery solution that integrates with both Google Cloud and AWS. Nife's service discovery module automatically registers and updates microservices information in the service registry, facilitating communication between microservices.

Load Balancing#

Load balancing is another critical aspect of microservices architecture. With multiple microservices applications working independently with varying loads, managing these microservices efficiently is essential for a streamlined workflow. Load balancing acts as a traffic controller, distributing incoming requests to all available service instances.

Load Balancing Best Practices#

Just as there are different methods for controlling traffic, there are various practices for load balancing in a microservices architecture.

Round Robin#

In this load-balancing method, requests are distributed among services in a rotating fashion. Services are queued, and each new request is transferred to service instances following their position in the queue.

Weighted Round Robin#

In this method, each service is assigned a weight, and requests are served proportionally among all services based on their weight.

Least Connections#

In this load-balancing method, requests are directed according to the load on service instances. Requests are sent to services handling the least amount of load.

Comparison of Cloud Platforms#

Here is a comparison of two renowned cloud application development services. Google Cloud Platform offers load balancing services including HTTP(S) Load Balancing, TCP/UDP Load Balancing, and Internal Load Balancing, simplifying the deployment of microservices in Google Cloud. In contrast, Amazon provides Elastic Load Balancing (ELB), offering various load balancing options to handle load efficiently and making it easier to deploy Java microservices in AWS.

Nife is another cloud platform offering comprehensive load-balancing options. It integrates with both Google Cloud and AWS, leveraging effective load-balancing techniques for microservices architecture to ensure an efficient and streamlined workflow.

Scaling#

Scaling is another crucial aspect of microservices deployment, especially for cloud platforms in the Middle East region. Microservices break down complex applications into smaller, manageable services. The workload on each of these services can increase dramatically with higher demand. To manage these loads, a scalable infrastructure is essential. Here are some primary scaling approaches:

Horizontal Scaling#

In this practice, additional microservices are added to handle increasing load.

Vertical Scaling#

In this practice, the resources of microservices are increased to handle growing demand.

Nife: Simplifying Microservices Deployment in the Cloud | Cloud Platform Solution#

Developers are always seeking efficient and streamlined solutions for deploying microservices. That's where Nife comes in, a leading platform for cloud application development services. It simplifies the deployment of microservices and provides a wide range of features tailored to developers' needs. With Nife, you can enjoy a unified experience, whether deploying microservices in Google Cloud or Java microservices in AWS.

By leveraging Nife's Cloud Platform for the Middle East, developers can address the unique needs of that region. Nife's strength lies in its seamless integration of service discovery, load balancing, and scaling capabilities. Nife provides a service discovery mechanism to enable communication between microservices, automatic load balancing to distribute traffic across services, and automatic scaling to ensure optimal resource utilization based on demand.

To experience the power of Nife and simplify your microservices deployment, visit nife.io.

Discover Nife's Cloud Platform for Efficient Deployment of Microservices

Conclusion#

Are you looking to deploy microservices in the cloud? Discover the best practices for developers in this comprehensive article. Explore how to deploy microservices in Google Cloud and AWS, utilizing their cloud application development services.

Learn about service discovery, load balancing, and scaling techniques to ensure seamless communication and optimal resource utilization.

Discover how the Cloud Platform for the Middle East caters to developers' unique needs in the region. Experience the power of Nife's cloud platform solution, simplifying microservices deployments and empowering developers to build exceptional applications. Revolutionize your cloud journey today with Nife's comprehensive suite of tools and services.

Computer vision solutions are becoming a very important part of our daily life. It has many valuable applications in various fields from facial recognition to self-driving vehicles and medical imaging. It has applications everywhere. It allows machines to analyze images and identify people and objects with great accuracy and precision.

No doubt the technology is very powerful, but its capabilities are being limited by traditional cloud infrastructure. This is where cloud edge computing steps in. Cloud edge computing has provided the necessary speed and infrastructure to utilize computer vision applications at their best.

The importance of Cloud edge computing in providing efficient deployment of computer vision applications can not be overstated. Cloud Edge infrastructure processes the data of users at the edge of the network, where it is being generated. It provides low latency and real-time processing power, making it ideal for various computer vision applications.

In this article, we will explore the challenges as well as strategies for efficiently deploying computer vision solutions with edge computing. Read the full article for complete insights.

Computer Vision and Edge Computing#

Before jumping into the topic let's explore cloud vision technology and edge computing in detail.

What is Computer Vision?#

Computer Vision is a field of AI (Artificial Intelligence) that enables machines to interpret and analyze visual data (images and videos) intelligently. It uses different algorithms, machine learning, and deep neural networks for that.

In the last few years, it has improved very much in capabilities. It has various applications in different fields. Some computer vision applications are facial recognition, object detection, and self-driving vehicles.

What is Edge Computing?#

Edge computing is a type of cloud computing that uses IoT devices to process data closer to the source of its generation. It provides many benefits including low latency, high bandwidth, high speed, reliability, and security. It reduces the dependence on a centralized cloud solution.

Relationship#

Computer vision applications need to process large amounts of data. Edge computing enables the processing of a large amount of visual data in real-time. Which allows machines to make informed decisions at a higher speed.

Their relationship can significantly improve different fields including manufacturing, retail, healthcare, and more.

Challenges in Deploying Computer Vision Solutions with Edge Computing#

The advantages of deploying computer vision solutions with edge computing can not be denied. But there are also some challenges and concerns that need to be addressed. These challenges include security and privacy concerns, power constraints, latency and bandwidth issues, and security.

Latency and Bandwidth Issues#

One of the important challenges in the deployment of computer vision solutions with edge computing is latency and bandwidth issues. Data is processed at the edge of the network, close to the source in edge computing. The processing capabilities of edge devices are limited and computer vision applications usually require a large amount of processing power.

This may increase the latency of the speed and affect the real-time decision-making capabilities. However, this problem can be resolved by selectively sending data to the cloud for low latency.

Security and Privacy Concerns#

Edge computing infrastructure involves the deployment of multiple connected devices. These devices are deployed in an unsafe environment and are always vulnerable to cyber attacks. Important data collected by these devices can be compromised. These security and privacy concerns can be addressed by using encryptions and access controls.

Power Constraints#

Edge devices usually have limited battery capacities. These batteries can dry up pretty quickly during the processing of vast amounts of data. In that case, it can create operational challenges. It is important to take necessary actions to avoid these types of problems.

Scalability#

Another big challenge in the deployment of computer vision applications is scalability. As processing requirements of computer vision applications are huge. To fulfill these processing requirements, a large number of edge devices are required. It can be difficult to manage these large numbers of devices which can eventually create scalability challenges.

Strategies for Efficient Deployment of Computer Vision Solutions with Edge Computing#

Efficient deployment of computer vision solutions with edge computing can be done by implementing some useful strategies. Here are some of the strategies that can be used to improve efficiency.

Edge Device Selection#

Choosing edge devices is a very important strategy in deploying computer vision solutions. Edge devices need to be selected based on capabilities such as processing power, battery, memory, connectivity, and reliability. Computer vision deployment requires the processing of vast amounts and latency for real-time decision-making. That is why it is crucial to select devices carefully.

Machine Learning Models and Algorithms#

Machine learning models and algorithms play a crucial role in the efficient deployment of computer vision solutions. Edge devices are not capable of processing these language models and algorithms. Therefore lightweight language models and algorithms can be used for speed and accuracy. These lightweight models deliver without compromising quality.

Cloud Edge Hybrid Solutions#

Another important strategy for the deployment of Computer Vision solutions with edge computing is the use of hybrid solutions. Computer vision applications require large storage and processing power. By implementing hybrid solutions these needs can be addressed efficiently. Organizations can use cloud resources for important data while day-to-day processing edge devices can be used. Hybrid infrastructure provides security, reliability, and speed.

Use Cases:#

Here are some of the applications of efficient deployment of computer vision solutions with edge computing.

Smart Cities and Traffic Management#

Computer vision combined with edge computing can be used in smart cities for surveillance and traffic management. Edge camera devices with censors utilizing computer vision algorithms can be used to control traffic flow. These devices can analyze real-time data and adjust traffic effectively by making informed decisions. In this way, accidents can be avoided and a proper traffic flow can be maintained.

Healthcare#

Another important application of computer vision and edge computing is healthcare. Edge devices enable remote diagnosis of patients. Edge devices with sensors allow patients to detect diabetes, heart diseases, and respiratory illnesses from their homes. These are some diseases that need regular checkups. Edge devices allow patients to transfer their medical history to their hospitals. Moreover, edge devices also allow patients to consult doctors from their homes using cameras and get their diagnosis.

Manufacturing#

Efficient deployment of computer vision solutions with edge computing can be used to improve the efficiency of manufacturing plants. Edge devices with computer vision technology can be used to monitor product lines, inventory, and manufacturing processes. Edge devices can be used to make real-time adjustments in the manufacturing process.

Agriculture#

Another important application of computer vision with edge computers is agriculture. Edge devices with computer vision technology can provide many benefits to farmers. These devices can automatically detect water levels in crops and give water whenever required. These devices are also capable of detecting pesticides and diseases in crops.

There are many more applications of edge computing and computer vision in agriculture fields. With proper deployment, these applications can provide many benefits to farmers.

Conclusion:#

Efficient deployment of computer vision solutions with edge computing can provide many benefits in different industries, from healthcare and automotive to manufacturing and agriculture.

Edge computing combined with computer vision allows room for efficiency, accuracy, scalability, and cost-effective solutions.

There are some challenges associated with the technology which can be addressed through proper planning. Overall the potential of edge computing and computer vision is limitless. With more innovations in the field, the applications are expected to grow.

Infrastructure as Code can help your organization manage IT infrastructure needs while also improving consistency and reducing errors and manual configuration.

When you use the cloud, you most likely conduct all your activities using a web interface (e.g., ClickOps). After some time has passed, and you feel that you have gained sufficient familiarity, you will probably begin writing your first scripts using the Command Line Interface (CLI) or PowerShell. And when you want to have full power, you switch to programming languages such as Python, Java, or Ruby and administer your cloud environment by making SDK (software development kit) calls. Moreover, Even while all of these tools are quite strong and may help you automate your work, they are not the best choice for doing activities such as deploying servers or establishing virtual networks.

What is Infrastructure as Code (IaC)?#

The technique of automatically maintaining your information technology infrastructure via scripts rather than doing it by hand is called "Infrastructure as Code" or "IaC." One of the most important aspects of the DevOps software development methodology is that it enables the complete automation of deployment and setup, paving the way for continuous delivery.

The term "infrastructure" refers to the collection of elements that must be present to facilitate your application's functioning. It comprises several kinds of hardware like servers, data centers, and desktop computers, as well as different kinds of software like operating systems, web servers, etc. In the past, a company would physically construct and oversee its Infrastructure on-site. This practice is still common today. Cloud hosting, offered by companies like Microsoft Azure, and Google Cloud, is now the most common technique for housing infrastructure in the modern world.

Companies in every sector want to begin using Amazon Web Services (AWS) to write their Infrastructure as code for various reasons, including a scarcity of qualified workers, a recent migration to the cloud, and an effort to reduce the risk of making mistakes due to human error.

Amazon Web Services and Microsoft Azure are just two examples of cloud service providers that make it feasible and increasingly simple to set up a virtual server in minutes. Spinning up a server linked with the appropriate managed services and settings to function in stride with your existing Infrastructure becomes the hardest part.

How does Infrastructure as Code work?#

If the team did not have IaC, each deployment would need the team to individually set up the Infrastructure (servers, databases, load balancers, containers, etc.). It takes time for environments that were supposed to be identical to develop inconsistencies (these environments are sometimes referred to as "snowflakes"), which makes it more difficult to configure them and thus slows down deployments.

IaC thus uses software tools to automate performing administrative chores by specifying Infrastructure in code.

It's implemented like this:

• The group uses the necessary programming language to draught the infrastructure settings.
• The files, including the source code, are uploaded to a code repository.
• The code is executed by an IaC tool, which also carries out the necessary operations.

Managing Infrastructure as code#

"Managing infrastructure as code," or IAC refers to creating, supplying, and managing infrastructure resources using code rather than human procedures. The process of establishing and maintaining infrastructure resources may be automated with the aid of tools such as Terraform and CloudFormation. This makes it much simpler to manage and maintain Infrastructure on a large scale.

The following is a list of the general stages involved in managing Infrastructure as code with the help of various tools:

1. Define infrastructure resources:#

Code should be used to define the necessary infrastructure resources for your application. Virtual machines, load balancers, databases, and other resources may fall under this category.

2. Create infrastructure resources:#

Use the code to create the necessary resources using your chosen tool, such as CloudFormation or Terraform. The resources will be created in the cloud provider of your choosing, such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud, via the tool.

3. Manage infrastructure resources:#

Utilize the code to handle the infrastructure resources after they have been generated. This involves keeping the resources updated as required, keeping track of their current state, and adjusting them as necessary.

5. Test infrastructure changes:#

Test the code before making any modifications to the Infrastructure to ensure it will still function as intended after the changes. This helps prevent problems and lowers the possibility of making mistakes while applying modifications.

6. Deploy infrastructure changes:#

After the code has been validated and checked over, the modifications should be deployed to the Infrastructure. You can do this automatically using tools like Jenkins or Travis CI, or you can do it manually by running the code through the automated tool.

Benefits of Infrastructure as Code#

Reduced costs.#

Cloud computing is more cost-effective than traditional methods since spending money on expensive gear or staff is unnecessary to maintain it. When you automate using IoC, you reduce the work required to run your Infrastructure, which frees up your staff to concentrate on the more critical duties that create value for your company. As a result, you save money on infrastructure expenditures. In addition, you will only be charged for the resources you use.

Consistency.#

Manual deployment results in many discrepancies and variants, as previously discussed. IaC prevents configuration or environment drift by guaranteeing that deployments are repeatable and setting up the same configuration each time. This is done using a declarative manner, which will be discussed in more detail later.

Version control.#

In IaC, the settings of the underlying Infrastructure are written into a text file that can be easily modified and shared. It may be checked into source control, versioned, and evaluated along with your application's source code using the procedures already in place, just like any other code. It is also possible for the infrastructure code to be directly connected with CI/CD systems to automate deployments.

Conclusion#

If you follow these instructions, you can manage your Infrastructure as code with the help of technologies like Terraform and CloudFormation. This strategy makes it possible for you to generate, manage, and keep your infrastructure resources up to date consistently and repeatedly. As a result, the possibility of making a mistake is decreased, and you are given the ability to scale your infrastructure resources effectively.

As a developer, you're likely familiar with new technologies that promise to enhance software production speed and app robustness once deployed. Cloud computing technology is a prime example, offering immense promise. This article delves into multi-access edge computing and deployment in cloud computing, providing practical advice to help you with real-world application deployments on cloud infrastructure.

Why is Cloud Simplification Critical?#

Complex cloud infrastructure often results in higher costs. Working closely with cloud computing consulting firms to simplify your architecture can help reduce these expenses [(Asmus, Fattah, and Pavlovski, 2016)]. The complexity of cloud deployment increases with the number of platforms and service providers available.

The Role of Multi-access Edge Computing in Application Deployment#

[Multi-access Edge Computing] offers cloud computing capabilities and IT services at the network's edge, benefiting application developers and content providers with ultra-low latency, high bandwidth, and real-time access to radio network information. This creates a new ecosystem, allowing operators to expose their Radio Access Network (RAN) edge to third parties, thus offering new apps and services to mobile users, corporations, and various sectors in a flexible manner [(Cruz, Achir, and Viana, 2022)].

Choose Between IaaS, PaaS, or SaaS#

In cloud computing, the common deployment options are Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). PaaS is often the best choice for developers as it manages infrastructure, allowing you to focus on application code.

Scale Your Application#

PaaS typically supports scalability for most languages and runtimes. Developers should understand different scaling methods: vertical, horizontal, manual, and automatic [(Eivy and Weinman, 2017)]. Opt for a platform that supports both manual and automated horizontal scaling.

Consider the Application's State#

Cloud providers offering PaaS often prefer greenfield development, which involves new projects without constraints from previous work. Porting existing or legacy deployments can be challenging due to ephemeral file systems. For greenfield applications, create stateless apps. For legacy applications, choose a PaaS provider that supports both stateful and stateless applications.

Select a Database for Cloud-Based Apps#

If your application doesn't need to connect to an existing corporate database, your options are extensive. Place your database in the same geographic location as your application code but on separate containers or servers to facilitate independent scaling of the database [(Noghabi, Kolb, Bodik, and Cuervo, 2018)].

Consider Various Geographies#

Choose a cloud provider that enables you to build and scale your application infrastructure across multiple global locations, ensuring a responsive experience for your users.

Use REST-Based Web Services#

Deploying your application code in the cloud offers the flexibility to scale web and database tiers independently. This separation allows for exploring technologies you may not have considered before.

Implement Continuous Delivery and Integration#

Select a cloud provider that offers integrated continuous integration and continuous delivery (CI/CD) capabilities. The provider should support building systems or interacting with existing non-cloud systems [(Garg and Garg, 2019)].

Prevent Vendor Lock-In#

Avoid cloud providers that offer proprietary APIs that can lead to vendor lock-in, as they might limit your flexibility and increase dependency on a single provider.

References

Asmus, S., Fattah, A., & Pavlovski, C. ([2016]). Enterprise Cloud Deployment: Integration Patterns and Assessment Model. IEEE Cloud Computing, 3(1), pp.32-41. doi:10.1109/mcc.2016.11.

Cruz, P., Achir, N., & Viana, A.C. ([2022]). On the Edge of the Deployment: A Survey on Multi-Access Edge Computing. _ACM Computing Surveys (CSUR).

Eivy, A., & Weinman, J. ([2017]). Be Wary of the Economics of ‘Serverless' Cloud Computing. IEEE Cloud Computing, 4(2), pp.6-12. doi:10.1109/mcc.2017.32.

Garg, S., & Garg, S. ([2019]). Automated Cloud Infrastructure, Continuous Integration, and Continuous Delivery Using Docker with Robust Container Security. In 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (pp. 467-470). IEEE.

Noghabi, S.A., Kolb, J., Bodik, P., & Cuervo, E. ([2018]). Steel: Simplified Development and Deployment of Edge-Cloud Applications. In 10th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 18).

Organizations continue to build new apps on the cloud or move current applications to the cloud. A company that adopts cloud technologies and/or selects cloud service providers (CSPs) and services or applications without first thoroughly understanding the hazards associated exposes itself to a slew of commercial, economic, technological, regulatory, and compliance hazards. In this blog, we will learn about the hazards of application deployment, Cloud Deployment, Deployment in Cloud Computing, and Cloud deployment models in cloud computing.

What is Cloud Deployment?#

Cloud computing is a network access model that enables ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or interaction from service providers [(Moravcik, Segec and Kontsek, 2018)].

Essential Characteristics:#

1. On-demand self-service
2. Broad network access
3. Resource pooling
4. Rapid elasticity
5. Measured service

Service Models:#

1. Software as a service (SaaS)
2. Platform as a service (PaaS)
3. Infrastructure as a service (IaaS)

Deployment Models:#

1. Private Cloud
2. Community cloud
3. Public cloud
4. Hybrid cloud

Hazards of Application Deployment on Clouds#

At a high level, cloud environments face the same hazards as traditional data centre settings; the threat landscape is the same. That is, deployment in cloud computing runs software, and software contains weaknesses that attackers aim to exploit.

1. Consumers now have less visibility and control.

When businesses move assets/operations to the cloud, they lose visibility and control over those assets/operations. When leveraging external cloud services, the CSP assumes responsibility for some rules and infrastructure in Cloud Deployment.

2. On-Demand Self-Service Makes Unauthorized Use Easier.

CSPs make it very simple to add Cloud deployment models in cloud computing. The cloud's on-demand self-service provisioning features enable an organization's people to deploy extra services from the agency's CSP without requiring IT approval. Shadow IT is the practice of employing software in an organisation that is not supported by the organization's IT department.

3. Management APIs that are accessible through the internet may be compromised.

Customers employ application programming interfaces (APIs) exposed by CSPs to control and interact with cloud services (also known as the management plane). These APIs are used by businesses to provide, manage, choreograph, and monitor their assets and people. CSP APIs, unlike management APIs for on-premises computing, are available through the Internet, making them more vulnerable to manipulation.

4. The separation of several tenants fails.

Exploiting system and software vulnerabilities in a CSP's infrastructure, platforms, or applications that allow multi-tenancy might fail to keep tenants separate. An attacker can use this failure to obtain access from one organization's resource to another user's or organization's assets or data.

5. Incomplete data deletion

Data deletion threats emerge because consumers have little insight into where their data is physically housed in the cloud and a limited capacity to verify the secure erasure of their data. This risk is significant since the data is dispersed across several storage devices inside the CSP's infrastructure in a multi-tenancy scenario.

6. Credentials have been stolen.

If an attacker acquires access to a user's cloud credentials, the attacker can utilise the CSP's services such as deployment in cloud computing to provide new resources (if the credentials allow provisioning) and target the organization's assets. An attacker who obtains a CSP administrator's cloud credentials may be able to use them to gain access to the agency's systems and data.

7. Moving to another CSP is complicated by vendor lock-in.

When a company contemplates shifting its deployment in cloud computing from one CSP to another, vendor lock-in becomes a concern. Because of variables such as non-standard data formats, non-standard APIs, and dependency on one CSP's proprietary tools and unique APIs, the company realises that the cost/effort/schedule time required for the transition is substantially more than previously estimated.

8. Increased complexity puts a strain on IT staff.

The transition to the cloud can complicate IT operations. To manage, integrate, and operate in Cloud deployment models in cloud computing, the agency's existing IT employees may need to learn a new paradigm. In addition to their present duties for on-premises IT, IT employees must have the ability and skill level to manage, integrate, and sustain the transfer of assets and data to the cloud.

Conclusion

It is critical to note that CSPs employ a shared responsibility security approach. Some features of security are accepted by the CSP. Other security concerns are shared by the CSP and the consumer. Finally, certain aspects of security remain solely the consumer's responsibility. Effective Cloud deployment models in cloud computing and cloud security are dependent on understanding and fulfilling all customs duties. The inability of consumers to understand or satisfy their duties is a major source of security issues in Cloud Deployment.

Introduction#

Consumers want quicker, better, more convenient, and revolutionary data speeds in this internet age. Many people are eager to watch movies on their smartphones while also downloading music and controlling many IoT devices. They anticipate a 5G connection, which will provide 100 times quicker speeds, 10 times more capacity, and 10 times reduced latency. The transition to 5G necessitates significant expenditures from service providers. To support new income streams and enable better, more productive, and cost-effective processes and exchanges, BSS must advance in tandem with 5G network installations (Pablo Collufio, 2019). Let's get ready to face the challenges of 5G monetization.

Why 5G monetization?#

The appropriate 5G monetization solutions may be a superpower, allowing CSPs to execute on 5G's potential from the start. The commercialization of 5G is a hot topic. "Harnessing the 5G consumer potential" and "5G and the Enterprise Opportunity" are two studies that go through the various market prospects. They illustrate that, in the long term, there is a tremendous new income opportunity for providers at various implementation rates, accessible marketplaces, and industry specializations. “Getting creative with 5G business models” highlights how AR/VR gameplay, FWA (Fixed Wireless Access), and 3D video encounters could be offered through B2C, B2B, and B2B2X engagement models in a variety of use scenarios. To meet the 5G commitments of increased network speeds and spectrum, lower latency, assured service quality, connectivity, and adaptable offers, service suppliers must discuss their BSS evolution alongside their 5G installations, or risk being unable to monetize those new use cases when they become a real thing (Munoz et al., 2020). One of the abilities that will enable providers to execute on their 5G promises from day one is 5G monetization. CSPs must update their business support systems (BSS) in tandem with their 5G deployment to meet 5G use scenarios and provide the full promise of 5G, or risk slipping behind in the 5G race for lucrative 5G services (Rao and Prasad, 2018).

Development of the BSS architecture#

To fully realize the benefits of 5G monetization, service providers must consider the growth of their telecom BSS from a variety of angles:

• Integrations with the network - The new 5G Basic standards specify a 5G Convergent Charging System (CCS) with a 5G Charging Function (CHF) that enables merged charging and consumption limit restrictions in the new service-based architecture that 5G Core introduces.
• Service orchestration - The emergence of distributed systems and more business services need more complicated and stricter service coordination and fulfillment to ensure that goods, packages, ordeals, including own and third-party products, are negotiated, purchased, and activated as soon as clients require them.
• Expose - Other BSS apps, surrounding levels such as OSS and Central networks, or 3rd parties and collaborators who extend 5G services with their abilities might all be consumers of BSS APIs (Mor Israel, 2021).
• Cloud architecture - The speed, reliability, flexibility, and robustness required by 5G networks and services necessitate a new software architecture that takes into consideration BSS deployments in the cloud, whether private, public, or mixed.

Challenges to 5G Monetization#

Even though monetizing 5G networks appears to be a profitable prospect for telecommunications, it is not without flaws. The following are the major challenges:

• Massive upfront investments in IT infrastructure, network load, and a radio access system, among other things.
• To get optimal ROI, telecommunications companies must establish viable monetization alternatives (Bega et al., 2019).
• The commercialization of 5G necessitates a change in telecom operations.

Case of Augmented Reality Games and Intelligent Operations#

With the 5G Core, BSS, and OSS in place, it's time to bring on a new partner: a cloud gaming firm that wants to deliver augmented reality monetization strategies to the operator's users (Feng et al., 2020). For gaming traffic, they want a specific network slice with assured service quality. Through a digital platform, a member in a smart, completely automated network may request their network slice and specify their SLAs. BSS decomposes this order into multiple sub-orders, such as the construction and provisioning of the particular portion through the OSS, once it receives it. The operator additionally uses their catalog-driven design to describe the item offered that its customers will acquire to get onboard new on the partner's network slice all in one location. This deal is immediately disseminated to all relevant systems, including online charging, CRM, and digital platforms, and may be generally consumed.

Conclusion#

5G can impact practically every industry and society. Even though there is a lot of ambiguity around 5G and a lot of technical concerns that need to be resolved, one thing is certain: 5G is the next big thing. Finally, whenever a user buys a new plan, he or she is automatically onboarded in the particular portion, often without affecting the system. The partnership will be able to monitor the network health and quality of various types of services for each customer in real time and will be able to take immediate decisions or conduct promotions based on this data (Bangerter et al., 2014). New platforms may adapt to changes based on factual resource use thanks to the BSS cloud architecture. All information regarding purchases, items, network usage, and profitability, among other things, is given back into circulation and utilized as feeds for infrastructure and catalog design in a closed-loop method.

References#

• Bangerter, B., Talwar, S., Arefi, R., and Stewart, K. (2014). Networks and devices for the 5G era. IEEE Communications Magazine, 52(2), pp.90–96.
• Bega, D., Gramaglia, M., Banchs, A., Sciancalepore, V. and Costa-Perez, X. (2019). A Machine Learning approach to 5G Infrastructure Market optimization. IEEE Transactions on Mobile Computing, pp.1–1.
• Feng, S., Niyato, D., Lu, X., Wang, P. and Kim, D.I. (2020). Dynamic Game and Pricing for Data Sponsored 5G Systems With Memory Effect. IEEE Journal on Selected Areas in Communications, 38(4), pp.750–765.
• Mor Israel (2021). How BSS can enable and empower 5G monetization. online Available at: https://www.ericsson.com/en/blog/2021/4/how-bss-can-enable-and-empower-5g-monetization.
• Munoz, P., Adamuz-Hinojosa, O., Navarro-Ortiz, J., Sallent, O. and Perez-Romero, J. (2020). Radio Access Network Slicing Strategies at Spectrum Planning Level in 5G and Beyond. IEEE Access, 8, pp.79604–79618.
• Pablo Collufio, D. (2019). 5G: Where is the Money? e-archivo.uc3m.es. online.
• Rao, S.K. and Prasad, R. (2018). Telecom Operators’ Business Model Innovation in a 5G World. Journal of Multi Business Model Innovation and Technology, 4(3), pp.149–178.

Learn more about Edge Computing and its usage in different fields. Keep reading our blogs.

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.