Migrating to the cloud can be daunting, especially when dealing with complex applications, which can have a life of their own. These applications can act in seemingly random ways when exposed to unexpected stimuli, such as moving from a stable data center environment to a more chaotic cloud environment. This inherent complexity makes migrating to the cloud risky, but there are ways to mitigate the risk.
Piecemeal Migration
Proper pre-migration preparation is critical to a successful cloud migration.You can often make simple—or more complex—changes to your application to prepare it for the migration. Common changes include reducing the dependency on specific networking topology, changing how you establish database connections, changing data and caching strategies, removing reliance on server-local data, refactoring configuration mechanisms and strategies, and changing how firewalls and other security components interact with the application.
Often, a large monolithic application is split into smaller applications or services before it is considered safe to be moved to the cloud. Smaller pieces are easier to move to the cloud, especially when each is moved independently. By performing a migration one component at a time, you limit the risk of a given migration step and simplify the inherent complexity in each migrated module by limiting the scope of the migration.
This piecemeal strategy, often called a service-by-service migration, is common for applications composed of multiple services or microservices. However, it can also be used for monolithic applications by performing pre-migration application architecture changes.
Using this technique can assist in migrating large monoliths to a more service-based architecture during the migration process, yet the complexity of such a migration can still be extensive.
Post-Migration Complexity
The journey doesn't end once the migration to the cloud is complete. Post-migration optimization is crucial to ensure that the application not only survives but thrives in its new environment.
For example, after the migration is complete, continuous monitoring is essential. Cloud environments are dynamic, with resources being scaled up or down based on demand. This can lead to new performance bottlenecks that weren’t present in the static data center environment. Implementing robust monitoring tools that can provide real-time analytics will help you understand how your application performs under various loads and identify areas for improvement.
Cost Optimization
Complex applications can also impact expected cloud cost savings. Without proper management, an unwieldy complex application can spiral cloud costs out of control. Preventing this requires additional analysis and work. Cloud cost management tools can help with this, but a lack of understanding of how the application's complexities impact cloud costs will make cost control problematic long term.
Security Complexity
Finally, your migrated application will have new and evolving security challenges in its new environment. It’s critical the migrated application undergo a security review. The more complex your application is, the more likely an unknown security vulnerability will impact your application’s ability to perform as needed.
Remember, even though the cloud provider is responsible for the security of the cloud itself, you are responsible for the security of your application in the cloud.
Cloud-Native Capabilities
Cloud-native features such as auto-scaling, serverless computing, and managed services are among the values of the cloud that attract many companies to consider cloud migration. Yet many of these features are unavailable for existing applications. Refactoring a highly complex monolith application into something that can leverage, for example, serverless computing, is a big and risky undertaking that is often too costly to justify.
Pre-Migration Preparation to Reduce Complexity Growth
Often, the best strategy for a successful migration is to reduce the uncertainty involved in the migration itself. Uncertainty during the migration can lead to random or ununified decision-making, which can lead to anincrease in the complexity of an application during the migration.
These sorts of migration-related issues are often unavoidable, but pre-migration preparation can help immeasurably reduce this complexity growth. Pre-migration preparation is essential to maintaining control during the migration.
Yet pre-migration preparation is difficult with many large applications simply due to their initial complexity.
Managing Complexity Via Chaos
Chaos engineering is a practice often considered for managing application complexity and is particularly useful in a cloud environment. Chaos engineering involves deliberately injecting failures into a system to test its resilience and ability to withstand turbulent conditions in production. By doing so, you can identify weaknesses and improve the overall reliability of your application. The goal is to make your system more resilient to failures so that it can continue to function even when things go wrong. This is especially important in the cloud, where failures are more common due to the dynamic and distributed nature of the infrastructure.
Intentionally injecting failures allows you to build robust mechanisms to counteract those failures. This includes improving the application code itself and improving the tooling used to monitor and resolve issues in production. These tools can include improved performance monitoring tools, complexity analysis tools, and problem diagnostics tools. Chaos engineering involves improving your team’s processes and procedures for responding to issues that arise. By building more resilient tools and processes to respond to these failures, you increase the overall reliability of your application in the long term, and your overall application resiliency naturally increases.
Yet, chaos engineering can be challenging to implement, and the cultural changes required to accept a chaos-oriented culture can be daunting. This makes it impractical for many companies to consider.
Managing Complexity Using Software Intelligence
Given all of this, understanding the complexities of how your application works internally is critical to a successful cloud migration. However, for large, unwieldy, monolithic applications, this is not an easy or straightforward task. You can’t simply study your way to understanding your application’s intricacies.
Recent advances in software analysis are making understanding complex applications more practical. With the advent of AI and software intelligence technology, understanding complex software systems is becoming more viable.
These software intelligence technologies analyze your application and can be used to answer critical questions necessary to make your migration a success. They can help determine what underlying assumptions are built into a complex code base and which require adjustments when moving to the cloud. Software intelligence technology can also assist in answering migration-related questions, such as how will timing changes inherent in the planned cloud infrastructure migration impact my application.
Previously, many of these questions and concerns were answered haphazardly, but AI-driven software intelligence technologies can make these determinations faster and more comprehensive. This reduces the risk of migration-related failures.
Static vs. Dynamic Analysis
Dynamic analysis, such as that provided by performance monitoring tools like Dynatrace, gives intelligence about software behavior during runtime. In other words, it sheds light on how the application behaves post-migration. While helpful, it doesn’t give clear and concise guidance on an application’s issues before the migration starts. Hence, the migration is often an unstable, iterative process.
Static software intelligence, such as CAST, provides actionable insights into the internal software structures before the migration process begins. This results in less random decision-making, making the migration itself less risky and more predictable.
Conclusion
Cloud migration is a complex process, particularly for intricate applications accustomed to stable data center environments. The unpredictability of cloud infrastructures increases the risk, but strategic preparation can mitigate it.
Large, monolithic applications often benefit from being divided into smaller, independent services, easing the transition and reducing risks. This service-by-service migration approach simplifies complexity and is also applicable to monolithic systems through architectural modifications. Despite the challenges, this strategy can gradually transform monoliths into service-oriented architectures during the migration.
After a migration is complete, additional complexity added to an application during the migration becomes a challenge. Continuous monitoring is vital to identify new performance bottlenecks and optimize resource scaling. Cloud costs become hard to manage.
AI and software intelligence technology can help immeasurably ease the application complexity that can burden a cloud migration by reducing the risk of unknowns prevalent in a complex application. This technology can turn a risky, complex, error-prone migration into a well-orchestrated, well-planned, manageable process.
This article originally appears on Lee Atchinson's Software Architecture Insights blog here.
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