What we mean by “hybrid cloud” has always needed to be clarified for the cloud industry. Once defined as a private cloud paired with a public cloud, it’s now a catch-all for any system that’s not a public cloud working together with a public cloud.
Hybrid clouds have become the battle cry for every enterprise hardware and software company looking to stay relevant. They can’t afford to build a public cloud with billions of buy-in. However, they can sell systems that work with public clouds, a cheap way to modernize your 20-year-old technology.
GenAI changes everything
The interest in generative AI is pushing more enterprises toward hybrid clouds. In most instances, companies are looking to leverage their data for training data where it exists, which is typically in the enterprise’s data center, colo, or managed services provider. Of course, it’s way more convenient to use genAI from public cloud providers, so we end up sharing training data with a public cloud provider, thus creating a hybrid cloud.
Of course, you will rarely find a single public cloud provider in a hybrid cloud mix. Most hybrid clouds are multicloud, using more than one public cloud provider. That adds complexity. You may have training data residing on edge computing systems, IoT devices, or even other cloud providers or data providers. You’re right that this looks like a vast, complex mess.
The most significant drawback to these types of deployments is lackluster performance. I can often trace this to engineering issues, not the fact that it’s a hybrid cloud. Engineering and architecture issues are easy to diagnose but difficult and costly to fix, especially after the system is in production.
High performance, high complexity
The complexity of hybrid environments demands meticulous performance engineering to ensure operational efficiency. Let’s delve into the labyrinth of performance engineering within hybrid cloud architectures and get to the essence of the problems.
Why is there a performance problem in the first place? The fundamental allure of hybrid clouds lies in their ability to provide businesses with a tailored fit for varying computational and storage needs. However, the intricacies involved in managing disparate systems operating across different environments necessitate a performance engineering approach that is proactive and systemic.
How do you engineer your hybrid cloud right the first time? Here are some key issues to consider:
Performance engineering begins with clear, measurable objectives aligned with business outcomes. Key performance indicators (KPIs) such as response times, throughput, and system availability should be defined, and these goals should interlock neatly with user expectations and service-level agreements (SLAs).
Without metrics, how do you know you have a performance problem? I often hear, “I know it when I see it.” That is not good enough. It’s best to have clear and measurable objectives written down and understood by the engineers, the architect, and the users.
Architecture is pivotal in ascertaining performance excellence. Selecting the right mix of services and designing for redundancy, load distribution, and fault tolerance is integral. This is complemented by using performance-focused design patterns such as microservices. Or it can be implementing robust caching mechanisms to facilitate faster data retrieval.
Most performance issues can be traced back to poor architecture, even deploying a technology stack that costs more than it should and worsens performance. I’m looking at you, any architect who keeps deploying the same technology configuration no matter what problem you want to solve. It does not work that way.
A robust hybrid cloud deployment undergoes varied testing protocols before deployment. From unit and load testing to stress and soak testing, each layer of the cloud stack is verified to uphold the current load and potential scalability challenges. Tools and frameworks automate tests, simulate user behavior, and ensure the cloud infrastructure can endure and perform under diverse conditions.
Once deployed, the hybrid cloud system enters a phase of perpetual observability. Performance monitoring tools gather real-time data throughout the deployment, facilitating immediate action on emerging issues. AIops and similar services provide insights into resource usage patterns, enabling engineers to make informed decisions about system optimization. You would not believe the number of unmonitored systems I see.
My more considerable fear is that we’ll deploy hybrid cloud solutions that perform poorly, and the blame will be unfairly placed on the deployment model—hybrid cloud. People fall into the trap of making generalizations. It is possible to deploy hybrid cloud systems quickly that are speedy and easy to manage. It just takes a bit of planning and following the concepts presented above.
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