Once your cloud architecture works, it’s time to optimize it for efficiency and cost. An audit will reveal how much value it adds to the business. Credit: Getty Images We’ve discussed the notion of cloud architecture optimization here in the past. Now it’s time to understand how it’s measured. You really have no way to prove your architecture isn’t optimized unless you do an audit, which includes a review of the solution’s approach and any attached costs. In the past, the people who built and deployed cloud solutions were reluctant to have their choices questioned. These days, because we want the most value from the cloud solutions, many have changed their minds about questions and oversight—or more often, company leadership changed their minds for them. Many of the projects I take on these days focus on review and improvement audits rather than on build and migrate deployments. Once everything works in cloud architectures, you can deploy and operationalize. Just because it works does not mean that it’s optimized. If you look at the differences between your architecture and one that’s optimized, you could have a “working” solution that costs you millions of dollars each week. To present this visually, see the figure below. Note that positions 1 and 37 are the least optimized. They cost more money and are the most inefficient. When we look at each side, note that cloud solutions can be under-utilized or overutilized, such as with containers and serverless computing. Those on the left of the chart might not have included enough containers, whereas those scoring on the right have used containers too much. The point of optimization is to be in position 19, where we use the right number of containers to make the most of costs and solution efficiency. IDG Of course, you can leverage this metric for any holistic architecture or all configured technology. You could even apply it to microarchitectures, such as a few applications moving to serverless or containers, for instance. Note that including polynomial views of the data for smoothing creates a curve that’s more likely to reflect typical real-life behavior. In the real world, this data never actually runs in a straight line. What does a cloud architecture audit mean? If you’re just starting your cloud journey, it’s a way to test and track the optimization of your proposed solutions. You want to get as close to the middle of the chart as you can. If you’re mid-journey, which most of us are, an audit is a way to evaluate solutions already in place as well as solutions that are currently under development. The hard part will be to allow others to evaluate your decisions. In many instances, they will second-guess the initial architecture using a forced ranking process which will put your solution at some point between 1 and 37 on this chart. Hopefully you land in the middle, but it’s unlikely. It’s not a sign of weakness to have others check your work. People who call for architecture audits on themselves should be praised. The alternative is to spend millions of unnecessary dollars or even do irreparable damage to the business. The stakes are just too high to let egos get in the way. Let’s be smart. Related content analysis Strategies to navigate the pitfalls of cloud costs Cloud providers waste a lot of their customers’ cloud dollars, but enterprises can take action. By David Linthicum Nov 15, 2024 6 mins Cloud Architecture Cloud Management Cloud Computing analysis Understanding Hyperlight, Microsoft’s minimal VM manager Microsoft is making its Rust-based, functions-focused VM tool available on Azure at last, ready to help event-driven applications at scale. By Simon Bisson Nov 14, 2024 8 mins Microsoft Azure Rust Serverless Computing how-to Docker tutorial: Get started with Docker volumes Learn the ins, outs, and limits of Docker's native technology for integrating containers with local file systems. By Serdar Yegulalp Nov 13, 2024 8 mins Devops Cloud Computing Software Development news Red Hat OpenShift AI unveils model registry, data drift detection Cloud-based AI and machine learning platform also adds support for Nvidia NIM, AMD GPUs, the vLLM runtime for KServe, KServe Modelcars, and LoRA fine-tuning. By Paul Krill Nov 12, 2024 3 mins Generative AI PaaS Artificial Intelligence Resources Videos