Emerging Technologies in Cloud

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Emerging Technologies in the cloud

Innovative technologies like Edge computing, Containers, Artificial Intelligence (AI), Machine Learning (ML), and Serverless computing are transforming the cloud big time. It has greatly enhanced the way businesses function especially with enterprises learning to use cloud capabilities tactfully and strategically. With the eruption of digital revolution, the cloud is an imperative enabler in optimizing costs, providing flexibility, reliability, and automation at scale.

So, in today’s post, let us have a look at the emerging trends in cloud technology, and their significance for those wanting to use them on the cloud.


A container is a technology used to bundle software along with all its library and config files that are required to run applications in a more flexible, reliable and accelerated manner on the cloud. They can be easily moved and run on any operating system in any context in portable cloud environments.

Containerization is a virtualization technique that plays a vital role in modernizing application development. It allows the developers to streamline and simplify the software development process through container orchestration. This enables automated provisioning, deployment, management, scaling, load balancing and networking of operations needed to run the packaged workloads and services as container units.

The adoption of containerization is on the rise as many tech-oriented organizations see them as an alternative to traditional virtual machines (VMs). Kubernetes is the most popular and widely used orchestration platform which is also provided by most of the cloud service providers. Most companies have developed cloud-native applications leveraging containerization to –

  • Build once but portably run it anywhere
  • Bring in significant savings on resources and operations
  • Enable quicker software development
  • Accomplish robust yet seamless horizontal scaling

It’s limitations

  • Containers provide lightweight isolation from the host OS s well as containers within the same system, leading to weaker security boundaries when compared to virtual machines.
  • It can run well if using only one operating system, but it’s a disadvantage if you need to use it across other operating systems. You can run previous versions of the same operating system using lightweight virtual machines.


Serverless is a function-as-service model that handles critical hardware and software maintenance, provisioning and scaling to accelerate efficient solutions on the cloud. It allows developers to build and run services without managing the underlying infrastructure.

Serverless computing is reviving the trend towards pay-as-you-go and pay-as-you-use computing models, addressing much of the software burden. 

Serverless still use physical servers, but they are taken care of by external vendors. The responsibility of scaling, scheduling, patching, provisioning, and other routine infrastructure management tasks are offloaded to cloud service providers and tools using the “serverless”, also known as the cloud computing application execution model. Thus, it enables developers to focus more on specific business logic for their apps or processes. Few benefits of serverless include –

  • Easy scalability of code
  • Pay-as-you-go model enabling cost-effectiveness
  • Reduced time-to-market
  • Innovation and flexibility

It’s limitations

  • Each time a serverless instance starts, it creates a new version of itself. This means it’s difficult to replicate the created environment to verify how the code would work. Also, developers do not have the visibility of backend processes making debugging a big challenge, as the applications are compartmentalized into distinct, smaller functions.
  • Building serverless functions on one platform can make it difficult to migrate to another. For example, moving from AWS to Azure or Google Cloud requires code rewrites, APIs that exist on one platform may not exist on another. It usually requires additional manpower and costs as well.


Microservices is a cloud-native architecture where large-sized or monolithic applications are compartmentalized into smaller components to simplify and fast-track software delivery. Here, a single application comprises of independent smaller services or modules that can be deployed easily. This modular approach facilitates multiple small teams within an enterprise to deploy loosely coupled broken-down modules, regardless of the actual size of the application. This enables continuous delivery of the latest updated software, ultimately resulting in faster app delivery cycles. With Microservices –

  • Code can be updated with ease 
  • New functionalities can be incorporated without disturbing the entire application
  • Developers can use discrete stacks & programming languages for different components
  • Broken-down modules can be scaled independently

The microservice architecture enables enhanced productivity, better resiliency and greater business agility as a whole.

It’s limitations

  • Microservices are typically written in multiple programming languages, use different technology stacks and have limited ability to reuse code. This can lead to increased development time and costs. Also, sharing code between microservices can become a challenge as well.
  • Microservices are designed to be self-contained, and they rely extensively on the network to communicate with each other. This can result in increased network traffic and slower response times (network latency). Additionally, it is a challenge to track down errors when several microservices are interacting with each other.

Internet of Things (IoT)

According to the market research platform – IoT Analytics, the number of IoT-connected devices is expected to grow by 22% to 27 billion devices by 2025.

The Internet of Things (IoT) describes the network of physical objects— “things”—that are embedded with internet connectivity, sensors, software, and other hardware to connect and exchange data with other devices and controls via the web. IoT can be present and utilized in ordinary household devices as well as in sophisticated industrial tools. Devices and objects embedded with sensors connect to an Internet of Things platform that integrates data from a variety of devices, applies analytics and shares the most important information with applications designed for specific needs. 

These powerful IoT platforms can identify useful information and the ones that are safe to ignore. We can use this information to identify patterns, make recommendations, and identify potential problems before they occur.

Benefits of the Internet of Things –

  • Effective use of resources
  • Reduction of human efforts
  • Decreases cost and improves productivity
  • Enhances customer experience

It’s limitations

  • IoT systems are interconnected and communicate through networks. Therefore, despite all security measures, the system remains largely uncontrollable and is highly prone to different types of network attack.
  • There is high dependency on the internet as it cannot function effectively without the same.

Edge Computing

With the anticipated surge in data volumes, there is a common concern that enterprises will struggle to reduce latency and inefficiency in data processing. This is where edge computing comes into play. Edge computing allows enterprises to optimize their systems by offloading data processing to the source where the data was created, rather than relying on the data center to process and analyze data.

Edge computing architecture moves critical processing tasks from a central location to servers and devices at the “edge” of a network. In this framework, much of the data collected from edge endpoints are never returned to the network core for processing and analysis. Instead, this data is processed almost instantly by on-premise computing resources, allowing edge devices and applications to respond rapidly to the changing condition and needs.

 To put it simply, edge computing is computing that runs at or near data sources to reduce latency and enhance bandwidth.

Advantages of Edge Computing –

  • Bandwidth relaxation
  • Improved data management
  • Improved security
  • Enhanced reliability & resilience

It’s limitations

  • Ensuring adequate security is often a challenge in edge distributed environments. Because data processing takes place on the edge of the network, the risk of identity theft and cyber security breaches is high. Also, with each new IoT device added, the chances of an attacker breaking into the device increases.
  • Implementing edge infrastructure can be costly and complex. It requires additional equipment and resources that are expensive and need high maintenance. Edge also requires additional local hardware for IoT devices to function aptly which pose as an add-on investment.

Artificial Intelligence 

Artificial intelligence is the next-gen of technology solutions that portray an all-together new perspective towards the world of digitization. With solutions featuring machine intelligence independent of human support, AI gains significant market dominance among existing tools today.

However, building AI applications are complex for many enterprises. With abundant computing and storage options, the cloud plays a key role in offering deep learning tools. Cloud-based AI solutions are powerful and popular technologies that help with the automation of tasks, increase business productivity and enable improved decision-making. AI on the cloud support the building of models and apps as well as operating, monitoring and sharing them through machine learning algorithms.

Benefits of Artificial Intelligence –

  • Improved monitoring and insights
  • Intelligent automation
  • Higher productivity and cost-efficiency 
  • Enhanced data analytics and management 

It’s limitations

  • With absence of human intelligence, machines can only perform the tasks they were designed or programmed to do. However, upon requesting an unscripted command, machines would fail to provide appropriate results leading to dissatisfying experiences.
  • Being complex, AI comes with a huge price. Apart from the cost of installation, their repair and maintenance also add up significantly to the costs. In addition, software programs are updated frequently to meet the needs of a changing environment, leading to a continuous rise in expenses.


With the continuous advancement of technology, enterprises are investing heavily in intelligent solutions, robotics and AI-powered services to secure and strengthen their business while sketching out a blueprint for the future of cloud computing.

The above trending technologies assure us how promising the cloud is to the IT industry today. For an innovative and future-fit tomorrow, it is of utmost importance that businesses adopt emerging cloud strategies, models and technologies to build cost-effective, unique and robust solutions in the long run.

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