The Evolution of Deployment Strategies in the Era of DevOps

Aashima Kapur
Evolution of Deployment

What Is a Deployment Strategy?

A deployment strategy is more than just a mere step-by-step plan, it is an all-encompassing and extensive approach that extends to cross-functional team collaborations and planning to the implementation, testing and analysis of a software deployment.

The purpose of an effective software deployment strategy is to ensure a successful software application launch and experience for the users – faster release and change roll-outs, minimized downtime for issue resolution, streamlining operations, risk mitigation, and evaluation and analysis.

Types of Deployment Strategies

Types of Deployment Strategies

It is crucial to choose a deployment strategy that suits the specific needs of the software. While there are many strategies to choose from, let’s have a look at the most widely practised strategies.

1. Canary Deployment

Canary deployment is a popular strategy in DevOps. Taking its inspiration from miners’ use of canary birds to detect toxic gases in the mines, this approach involves deploying a new version of software to a small group of users, the ‘canaries’. The DevOps team closely monitors the performance, stability and user experience to identify and resolve any potential bugs and issues before gradually deploying the software to a larger user base.

This deployment strategy offers a controlled approach and allows for instant rollbacks in case of user dissatisfaction or potential technical issues.

2. Blue-Green Deployment

Also known as red-black deployment, this strategy in DevOps includes using two identical environments – ‘Blue’, the environment currently in production and ‘Green’, the environment used for the launch of a new version.

This strategy is ideal for a time-sensitive seamless shift that allows redirecting traffic from the blue environment to the green environment while keeping the blue environment at stand-by in case of rollbacks.

3. Rolling Deployment

This Software deployment approach, also known as ‘phased rollout’ involves gradually deploying new software versions across the system. This allows slow replacement of the previous versions of an application with the latest version by replacing the infrastructure.

While this deployment strategy is quicker than the blue-green deployment strategy, it complicates the rollback process.

4. Shadow Deployment

Shadow deployment is yet another widely used deployment strategy that involves running the old and new versions of an application simultaneously. In short, a replica of the live environment is created to allow the DevOps team to analyse the performance of the latest application in a production-like environment without affecting the users.

How DevOps Changed the Software Deployment Landscape

Gaining momentum in 2007 and 2008, the DevOps movement emerged directly in response to the dissatisfaction expressed by IT professionals regarding the dysfunction between IT and software development teams. DevOps was created to improve the collaboration between developers and operations teams and change traditional software development encompassing manual handoffs and extensive development cycles. DevOps has since come a long way, keeping up with the demands of digitalization and digital transformation.

1. Code Integration

Integration of DevOps practices such as CD/CI has automated the process of code integration in the source code repository. The goal is to accelerate the software deployment by conducting tests to ensure the functionality of the code and resolve any issues or bugs in the later stages to ensure that the software is deployable.

2. User Experience

Using DevOps in the deployment strategies also ensures a positive user experience. The users receive software applications that undergo rigorous automated testing, monitoring, early detection of potential bugs and issues and repeated feedback loops.

3. Security

At the same time as improving the workflow, and optimising the resources, agility and user experience, DevOps is also crucial for maintaining security at every step of software deployment – a concept called DevSecOps.

4. Resource Management

DevOps processes like Infrastructure as Code (IaC) have replaced the manual infrastructure processes – setting up, maintaining and updating operating systems, database connections, and storage, with a code-integrated automated process. This allows for a time-efficient and reliable development, testing and deployment of software.

With the growth of DevOps in the digital industry, the software deployment and development have improved significantly. By using key concepts such as CD/CI (Continuous Delivery/Continuous Integration), IaC (Infrastructure as Code), Version Control System (VCS) and automation, software deployment processes have and will continue to evolve for better, secure and faster delivery.

AI in DevOps & Software Deployment

DevOps and AI-3

Because both DevOps and AI put automation at the forefront, the synergy of the two is powerful and transformative. Owing to the advancement in AI and machine learning, DevOps practices have significantly evolved. The integration of AI in the software development lifecycle revolutionized the methods used by the teams to build, test, deploy and monitor software applications.

Let’s take a look at some of the most significant impacts of AI on DevOps and subsequently in software development and software deployment.

1. Data-Driven Decisions

AI-driven analytics across vast datasets provides the DevOps teams with actionable insights on user feedback, business KPIs, and performance metrics. These comprehensive data-driven insights by the AI algorithm allow the DevOps teams to build collaborative and strategic plans for continuous improvement and innovation in the software development and software deployment process.

2. Problem Resolution

AI-driven Incident management systems aid in building resilient IT environments. AI systems can accelerate issue resolution by detecting an issue, responding to the issue and identifying the root cause for the issue in an automated manner. AI systems can also forecast possible issues by learning the pattern from old data and detecting live anomalies. This ensures a reliable and secure system and improves the overall system service.

3. DevSecOps

AI enhances security by automating threat detection, identifying anomalies and mitigating cyber threats. Machine learning algorithms continuously monitor and analyze network traffic to detect malicious activities and mitigate the risks of breaches. By providing real-time alerts, AI can improve threat detection and safeguard sensitive data by fortifying cyber defence

4. Resource and Infrastructure Management

AI facilitates the DevOps teams with efficient resource utilization. By optimizing the use of cloud infrastructure and automating resource allocation based on demands, AI improves the software development and software deployment processes. AI and machine learning algorithms also improve the management and scalability of complex infrastructures by analysing workload patterns and minimizing downtime. By optimising resource and infrastructure management, AI enables an organization to improve its scalability, resilience, workloads and performance.

Conclusion

In conclusion, while the integration of DevOps has significantly improved and revolutionized the software development lifecycle, its role and contribution to emerging technologies are also noteworthy.
In an era of continuous digital transformation, DevOps is at the forefront of the seamless integration of technology like AR, Blockchain (DApps), IoT devices, edge computing and cloud computing.

Navigating the trends and new technologies, Revinfotech provides comprehensive backend DevOps solutions and services. You can read more about our DevOps services on Revinfotech company page.

Seeking service experts for your business solutions? Our team offers expert guidance for business growth.

Frequently Asked Questions

Depending on your business’s specific requirements you can choose amongst the many AI tools available to enhance DevOps. The most widely used tools are Kubiya, Amazon CodeGuru, PagerDuty, Atlassian Intelligence, Snyk, Datadog APM, and Harness.

Simply put, Artificial Intelligence is a technology that enables computer systems or machines to perform complex tasks such as decision-making and problem-solving, in a way that is associated with human intelligence. AI comprises several other technologies such as Machine Learning, Natural Language Processing (NLP) and Deep Learning.

Using a software development strategy is essential for secure and timely rollouts of a software. This in turn ensures a high-quality user experience and overall enterprise growth. Having an efficient software deployment strategy is also crucial for efficient team collaborations, improved business operations, reduced risk of human errors, and efficient cost and time management.

DevOps is the integration of software Development (Dev) and Operations (Ops). In short, DevOps is a combination of philosophies, tools and practices used to enhance an organisation’s ability to develop and deploy high-quality software and services swiftly and securely.

There are many options available depending on your business’s specific needs. The most widely used tools are GitLab CI/CD, Bamboo, Jenkins, CircleCI AWS code Deploy, Microsoft Azure DevOps server and Semaphore.

Depending on your business model and specific requirements, the choice of deployment strategy can vary. For example, the Canary deployment and Blue-Green deployment strategy are used for the gradual and monitored release of a software, while the Big Bang deployment strategy is used to deploy all the changes across the entire user base at once.

Aashima Kapur
Article written by

Aashima Kapur

Aashima Kapur is a marketing strategist with a passion for creating impactful digital Campaigns. Her passion lies in digital marketing, social media engagement, product marketing, and content writing. With an educational and professional background i... read more

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