8 Principles of Cloud-Native Development
Building cloud-native applications involves holistic adherence to eight major principles. These principles serve as guiding pillars for architects and developers, helping them build applications that are not merely hosted in the cloud but can fully harness its potential.
Let’s take a closer look at each of them.
Service-oriented design
This principle emphasizes breaking down applications into smaller, modular services that can operate independently and perform specific business functions. While microservices architecture is a prominent manifestation of service-oriented design, the flexibility inherent in this approach extends far beyond it.
For instance, service-oriented design can be seamlessly integrated into serverless computing models. There, it enables the creation of individual functions or serverless components that encapsulate specific business logic. Furthermore, its ability to encapsulate functionality into independently deployable services facilitates the integration of on-premises legacy systems with modern cloud services. This allows for a gradual migration towards a more distributed architecture.
Elasticity
This principle ensures that cloud-native apps can efficiently handle varying workloads, optimize resource utilization, and maintain performance under changing conditions. Auto-scaling mechanisms make it possible by adjusting the resources given to an application based on set criteria like CPU usage, network traffic, or other performance metrics.
Elasticity advocates designing the system to support horizontal scaling, allowing the dynamic addition or removal of instances in response to changes in demand. This can involve designing stateless services, using load-balancing mechanisms, and implementing distributed data storage.
All of it makes the elasticity principle a pivotal point in cloud cost optimization, as it allows you to use resources only for the current workload and pay for precisely what you require. This, in turn, helps to avoid unnecessary resource idling.
Observability
This principle involves collecting, analyzing, and visualizing data from software to gain insights into its performance, health, and behavior. Building a comprehensive observability system relies on three main components:
- Metrics — give you insights into key performance indicators, throughput, and resource utilization
- Tracing — allows you to track the flow of requests across your services, identify bottlenecks, and understand dependencies
- Logging — captures detailed events and errors, providing a timeline of activities for effective debugging and post-incident analysis
In practice, however, cloud providers offer robust logging tools, enabling efficient handling of hundreds of gigabytes of logs. Adding a correlation ID to the logs erases the fundamental distinction between logs and tracing, facilitating the identification and troubleshooting of issues across the entire application stack.
Beyond the traditional pillars of observability, alerting is a widely utilized aspect in fostering cloud observability. It covers services that notify stakeholders through various channels about exceeding specific parameters in metrics or the presence of logs with specific attributes (e.g., log levels fatal or even error).
Resilience
Resilience is the core principle of cloud-native app development. It focuses on the system’s ability to withstand and recover from failures, disruptions, or unexpected changes.
Resilient architecture is built upon the following strategies:
- Fault tolerance. It involves designing and implementing mechanisms to detect and mitigate failures, preventing them from causing widespread outages.
- Auto-healing. The system can identify issues, initiate corrective actions, and restore regular operation without manual intervention.
- Redundancy. It involves having backup systems or components to ensure recovery in case of failure.
- Graceful degradation. It occurs when the system adjusts its functionality or performance during adverse conditions. Instead of a complete outage, the system continues to operate with reduced capabilities.
Great news: cloud providers can handle many failures automatically. So when an instance fails, the cloud promptly creates a new one, which will manage the failed request (if you’ve implemented a retry policy correctly).
Recovery mechanisms can be more involved in more intricate scenarios, such as complex distributed transactions and lasting outages. However, cloud infrastructure and a well-designed architecture minimize the likelihood of such problems, making their handling relatively straightforward. As a result, in practice, cloud developers don’t need to build their own resilience mechanisms and worry about them daily.
Automation
Since cloud-native applications are distributed across dozens or even hundreds of components, automation is a vital principle allowing effective system management. Embracing automation also brings additional benefits, like allowing cloud-native applications to adapt to dynamic demands and efficiently reduce manual intervention. Your dedicated development team can automate a long list of processes and activities in cloud computing, including server auto-provisioning, data backups, or discovery and elimination of idle processes.
Automation also ensures consistent, rapid, and error-free deployment and management of apps through:
- Infrastructure as Code (IaC)
- Continuous Integration and Continuous Deployment (CI/CD) pipelines
- Auto-scaling and configuration management
It extends to container orchestration, where tools like Kubernetes automate the coordination of containerized applications, simplifying their deployment and scaling. Additionally, automation is pivotal in monitoring, security, and backup processes, enabling proactive responses to issues.
However, cloud developers must ensure the automation process is controllable and its impact on the system is predictable. Thus, if any exceptions do occur, the scope of the error’s influence can be managed and deemed acceptable.
Zero Trust
Traditional security methods involve creating a protective barrier around your infrastructure and monitoring and blocking external threats. However, this perimeter dissolves when you transition to cloud-native technologies like serverless. That’s when the Zero Trust security model comes in.
Zero Trust security assumes that no entity, whether internal or external, should be trusted by default. It supports the following principles:
- Always verify
- Contextualize request
- Secure admin environment
- Grant least privilege
The last ensures that entities have the minimum level of access required to perform their tasks. This minimizes the attack surface and limits the potential damage a compromised entity can cause.
Micro-segmentation is a key strategy in zero trust. It involves dividing the network into small, isolated segments to limit lateral movement in case of a security breach. Each segment is treated as an independent zone with specific access controls. This helps to reduce the potential impact of a compromised entity.
Zero Trust advocates for the use of encryption, multi-factor authentication, and authorization at every layer. This approach helps protect sensitive data and resources, especially in a dynamic and distributed cloud environment.
Stateless processing
In a stateless system, each request from a client to a server is treated as an independent and complete transaction. So, the server retains no information about the session state or user data between consecutive requests. This independence simplifies the design and scalability of applications, as servers can efficiently distribute requests across multiple instances without needing to synchronize state.
Stateless processing enhances scalability, as servers can be added or removed from the environment without affecting the overall system. It also simplifies application design and maintenance. Since each request is self-contained, developers can focus on building independent and modular components. The absence of a shared state also makes the system’s behavior more predictable, leading to easier debugging and troubleshooting.
Continuous evolution of architecture
This principle emphasizes the iterative and adaptive nature of designing and refining the architecture of cloud-native applications. It involves continuously assessing, enhancing, and incorporating new technologies, methodologies, and practices.
For instance, existing products offered by cloud providers continuously evolve, expanding their range of features. So, at some point, it may become worth replacing a custom part of the infrastructure with a new feature of a cloud-native service. This will allow you to improve productivity, keep pace with evolving business needs, and reduce costs.
These eight principles make cloud-native architecture beneficial, especially in the area of cost optimization. So, let’s dive deep to see how it does it.
Leobit
