February 20, 2023

By: Padmaja Doddi, Senior Manager - Cloud Services, LTIMindtree

The Google Cloud Platform (GCP) revolutionized how we create applications and caused a paradigm shift in the application migration and modernization field. The GCP ecosystem drives some of the strongest, largest, and most innovative applications pertaining to cloud-native application development and application modernization with Google Cloud Platform(GCP). Some of these principles go against conventional application development best – practices, despite being vital to the success of cloud-native applications.

The Cloud Native Ecosystem

The cloud-native ecosystem encompassing application development and cloud application modernization constitutes three building blocks:
1. Cloud Platform
2. Cloud Architecture,
3. Cloud Native Application

It is important to note that, the cloud platform is the enabler to cloud application possibilities.

For instance, the limitless computing and storage capabilities of a cloud platform provide cloud-native applications with the following characteristics:

• Scalability is a distinguishing feature.
• The pay-per-use model reduces the cost of the applications.
• Managed services that make cloud-native applications both versatile and developer-friendly.

There are numerous reasons why the industry gravitates toward cloud-native architecture as the foundation for its applications and, in turn cloud application modernization. The architecture dictates how software is engineered, and developers have far more control over cloud-native architecture. It enables developers to use DevOps, containers, automation, microservices, and other technologies. Microservices are one of the essential components of a cloud-native architecture, and they are responsible for the rest of the defining characteristics of cloud-native applications: agility and resilience.

When an application can take advantage of the cloud platform, it is considered cloud-native, and to do so entirely, it is usually built on a cloud-native architecture. As a result, a cloud-native application should be the following:

• Scalability: quickly increase or decrease resources to meet demand.
• Managed Infrastructure: use the cloud platform as an infrastructure
• Loosely associated: parts of the application should be isolated enough to be changed or removed without causing downtime.
• Resilience: a single bug or crash should not bring the application down.

The Five Principles of Cloud Native Architecture

There are five principles to a cloud-native architecture. These principles help you to leverage the overall cloud-native ecosystem.

Principle 1 – Self-contained and Independently Deployable Microservices

Cloud-native architected applications are based on independently deployable microservices and are self-contained. A consistent effort should be made to keep the microservices – light and focused. If microservices are large and complex, they lose the initial purpose of being – Modular and Agile.

Other Key factors to keep in mind are:

Architecture-based API Communication – The containers interact with each other using APIs.

Multiple Technology Stack – Encourages multi-programming language diverse environment.

Independent Deployment – Continuous and concurrent deployment of microservices as and when required. This helps in mass automation testing.

Principle 2 – Leverage Automation

Google Cloud Platform and its subsequent architecture are extremely automation friendly. This includes the repetitive task of monitoring, deploying, repairing, and scaling.

Other Key factors to keep in mind are:

Setting Up Infrastructure and Continuous Automation – With Google Cloud Platform (GCP) Deployment Manager or Terraform tool, infrastructure, update, creation, and maintenance can be automated.

Automating Development – Google Cloud Platform (GCP) is enabled with development automation tools. This boosts productivity and takes care of repetitive tasks. For instance, setting up CI/CD pipeline using Google Cloud Build.

Image: Google Cloud Platform (GCP) – Development Automation Tools

Monitoring App Performance – Developers can integrate logging and monitoring systems since the app’s inception. Machine Learning (ML) can be leveraged to analyze data streams in real-time for effective decision-making.

Principle 3 – Encouraging DevOps
Adopting DevOps encourages ability and agility in problem-solving. Leveraging consistent workflows and tools for collaboration reduces burden and balances workloads amongst cross-functional teams.

Principle 4 – Managed Services
Modern managed solutions from Google Cloud Platform (GCP) are incredibly advanced. It helps save resources and time and helps reduce operational overhead.

Principle 5 – Innovation and You
Google Cloud Platform (GCP) empowers you with tools to innovate with. You can easily integrate the Internet of Things (IoT) and Machine Learning (ML) to drive innovation.

Our Success Story with Clients

Various ad marketing agencies utilize the Google Ad Platforms that would let them help business advertisers run their Campaigns by providing various marketing strategies to achieve and increase their business goals and revenue overall. Although Google Ad Product teams do provide reporting tools for each Ad platform that lets them visualize their business performance, currently, they need to make individual analyses, and it makes it hard for them to make a customized reporting system for the various Advertisers/clients they’re working with at a single place at scale. Having this in place would help these agencies make better data-driven decisions by suggesting and improving their strategic decisions, which let them make reinvestments and eventually uplift Google’s revenue by generating more traffic and better leads. Since these Agencies act as enablers between Google Platforms and end advertisers, Google needs to give customized reporting tools or visualization by utilizing the various Google APIs that are available, basis their own needs and requirements. We can say this is the value addition Google is providing in return to these Marketing agencies by building customized tools and reporting dashboards to these marketing agencies by partnering with various partners like LTI Mindtree.

In this context, LTIMindtree is working with an agency that’s making enhancements to the tools currently hosted in a Google Cloud App engine that lets them visualize the various performance reports retrieved through various Google APIs that store the data on Big Query. And this data is scaled automatically, managed through cloud tasks & cloud schedulers periodically connected through the full form (ETL) pipelines in cloud run, which eventually helps them analyze and compare their various investments across the ad platforms and channels and strategize their data-driven business decisions.

Benefits of Cloud Native Architecture

Cloud-native applications offer numerous benefits that place them ahead of traditional applications in many aspects. These benefits are the sheer rationale for why developers build cloud-native applications. Albeit, not all the benefits are guaranteed automatically, as these are not inherent in characteristics.

For Instance, simply re-hosting to a cloud platform does not guarantee faster time-to-market, or for that matter, application resilience. The developer can ensure that characteristic benefits and architecture reach the end user. To date, the developer’s expertise makes cloud-native applications so popular and powerful for business.

Increased Delivery Speed

First-move advantage is highly valued in the market, for products and services before competitors. Also, consistent updates and periodic bug fixes improve credibility.

Microservices Architecture

Cloud-native applications are quick to build and even quicker to push important updates. The approach and architecture play a pivotal role here. For instance, there is a paradigm shift from monolithic architecture to loosely coupled architecture. The cloud-native architecture encourages applications to build on blocks and then connected. These blocks are called ‘Microservices’ and work in isolation within containers. The connected communication is via APIs.

With a microservices architecture, bug fixes are also easy as each microservice is monitored independently.

Increase in Scalability

As a result of the vast hardware capabilities of the Google Cloud Platform, it is highly scalable. However, with cloud-native applications scaling is horizontal rather than vertical.

An example of horizontal scaling is to increase the number of units. Instead of one 8 gigabytes, one can have four sticks of 2 gigabytes. This opens more possibilities for resource allocation. From an investment perspective, pay-per-use and auto-scale make it a very viable option.

Tech Stack and Workforce – Mixed

Cloud-native applications support multiple programming languages, within the same applications and databases. For instance, a mix of SQL and NoSQL variants. The Google Cloud Platform reads and executes containers, irrespective of dependencies – language and libraries. This provides an amazing functional value for a vivid workforce.

Increase in Resilience

Cloud-native applications respond to failures, for instance – automated failsafe. Other practices such as Continuous Integration and Continuous Delivery (CI/CD), automated testing, and canary development help respond to failures.

Continuous Integration / Continuous Delivery (CI/CD)

The development model for CI/CD is based on DevOps, making the process continuous and concurrent. Using Continuous Integration (CI), one can constantly validate and monitor the code, after driving change management to avoid failures. This is usually done by setting up CI servers on the Google cloud platform.

These days developers are practicing automated delivery. Post-change management, and all test, though, it is automatically deployed to the production environment/code base. This, in turn , helps in shorter and quicker release cycles, increasing reliability.

Increase in Automation

Google Cloud Platform offers managed services, to configure infrastructure and to provision resources. Automated environments – creation and maintenance help the cloud to do the lift and shift. Moreover, environment automation also promotes autoscaling.

Managed Data Analytics Solutions

Now you can monitor complex metrics and analysis using Real-time Machine Learning (ML). Analyze traffic patterns and resource utilization to get valuable data insights on application performance.

For Instance, Automated – market analysis and demand generation, for Supply Chain Management within a warehouse. Automated interactions with external Business Intelligence (BI) tools.

Google Cloud Platform’s dedicated Research and Innovation (R&I) for day-to-day analytics leverages the smart power of Artificial Intelligence (AI) and Machine Learning (ML).

Summarizing it all

Cloud-native applications seek to transform software delivery by leveraging cloud platform tools, techniques, and technologies while also supporting cloud platform operations and DevOps capabilities.
What distinguishes cloud native applications from traditional web apps is that in order to be successful in application development today, we must accelerate the development of new applications, optimise optimize existing ones, and find ways to rapidly deploy them into production. In business, minimising minimizing the time it takes to get a product to market and profit is critical.

When these strategies are combined to create cloud native applications, the end result is software that can deliver what users need at the speed that the company requires, adding real value to the organization