For those not yet open cloud migration journey of businesses, one thing is clear: hands-off era is over.
To determine which applications to migrate to the cloud, which applications you want to keep locally is not easy. How to use the native cloud technology reconstruction of these applications, or create a hybrid cloud setup, you can continue to use the data and applications, which is another potential problem in many DevOps teams face. This is a complex process.
However, the disadvantage of not rebuilding cloud legacy application investments are: competitive disadvantage technical debt, agility and flexibility and customer due to poor user experience and frustrated. We have no choice but to move on, to accept cloud technologies and processes.
Each organization's native cloud journey is different, but any business needs to take some steps.
First, companies want to cloud the original biochemical need to take the following three steps:
1. You must define a strategy system for the cloud vision. What customer needs are? How to plan the delivery of products and services? Clouds set in DevOps and delivery pipeline What is the role? How to ensure reliable system performance and overall robust end-user experience? Choose cloud platform is public or private? single cloud, or hybrid cloud cloudy? these questions seem simple, but the answers to these questions form the primary building blocks of the cloud.
2. a comprehensive understanding of the existing legacy system. Application profiling to understand how they work, and their performance benchmarks in order to compare them later in the cloud with their performance - and make sure they run better. They know how and where does not meet the baseline is also important. In this analysis, monitoring plays a key role in the stage: from the creation of the entire technology stack topology mapping, mapping the interdependencies between systems, automatic performance baselining, and then to complete the stress test. These are essential factors to ensure a comprehensive understanding of the existing system architecture, services, processes and performance.
3. Define migration policy itself. Program which applications to keep or retire, you want to keep what applications, which applications to migrate to the cloud, or re-platform reconstruction. Each method has its advantages and disadvantages. Lifting and transfer applications is the fastest, there is no need to modify the code. The downside to this is basically retained the internal architecture, which means that applications can not take advantage of the new cloud environment. On the other hand, the reconstruction is the most resource-consuming because of the requirement from scratch to build the application architecture. Typically, this involves a single application that contains millions of lines of code into multiple micro service more dynamic, easy to maintain and service these micro-expansion. However, since this process creates an application built specifically for cloud computing, it also received the greatest return on investment, compared with the lifting and transfer, it has a more long-term operational and cost advantages.
Second, the answer lies in intelligent automation and software
After developing a vision for cloud migration, analysis of legacy applications and defines the migration strategy, followed by the actual migration of specific work itself. This is a process full of technical challenges and significant organizational changes, including:
The highly specialized tissue from the chimney and waterfall method to rebuild flexible process automation and DevOps team Dalian gynecological where good mobile.0411fuke.com
Establish continuous integration and continuous delivery system
Integration with legacy components and cloud native components
Miss the deadline or risk migration targets, including system performance under realistic conditions (think Black Friday or global events)
This is where automation and artificial intelligence as applied.
Companies need to automate everything. Successful cloud migration depends on the continuous build automation, integration and delivery (across all stages of the test); on automation, performance monitoring and monitoring equipment; starting from the root cause analysis, an improved approach. And automated baseline performance and configuration.
This "all automation" approach is the use of artificial intelligence. Modern network scale cloud applications are too complex to be operated by human beings alone. Intelligent software built on a strong foundation of artificial intelligence, monitor the health of the entire system from end to end. Intelligent anomaly detection, real-time root-cause analysis and business impact assessment is a key pillar of support to bring artificial intelligence.
This cloud migration and cloud local conversion means what? First of all, the software creates intelligent and automated visibility and actionable insight. This enables software engineers to have full ownership of the entire value chain: from initial coding to deployment of the final product. It promotes a powerful, flexible creation of DevOps culture. In this culture, engineers can truly promise "you build it, you run it."
Artificial intelligence can also be used to further improve the CI / CD pipeline to meet the migration deadline and ensure excellent software quality. Intelligent software helps narrow the gap between the existing automation, such as the decision to build a door or manual approval step verification at. It also provides new performance test identification constructed in accordance with the production scene.
Finally, intelligent software for operators is key to providing excellent customer experience. AIOps ensures real-time detection of performance problems and their causes, and can automatically correct the problem.
Third, are you ready?
Clouds began to implement the policy requiring major changes on the organization. Artificial intelligence and automation to provide a navigable as far as possible to make the journey and seamless tool. By automating performance monitoring, repair, CI / CD pipes, root cause analysis, stress testing, system configuration and more steps, AI is that it saves a lot of tedious manual work and errands - and the attendant costs and headaches thing. More importantly, artificial intelligence and automation helped to lay the foundation for the adoption of DevOps culture and AIOps of. Eventually, a fully formed, flexible DevOps culture - driven by automation and artificial intelligence - is the key to a successful cloud journey of conversion.