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How to Prevent CI/CD from Sidetracking Optimization

Asaf Ezra

Co-Founder and GM, Intel Granulate

In a market hungry for new features and functionality, delivering bug-free code at high velocity has become imperative for businesses and engineering teams. Often times, this comes at the cost of infrastructure performance optimization.

Continuous integration, delivery, and deployment enable rapid product improvements through fast feature introduction and fast turn-around on feature changes. Testability is better, with simpler and quicker fault isolation, and time to resolution is shorter due to the smaller code changes. Gone are the days of deployment-windows scheduled weeks in advance—and no one is looking back. CI/CD has taken off like a house on fire, with many companies now operating a smooth CI/CD toolchain in an agile DevOps setting to rapidly deliver features, fixes and updates.

Over time, the CI/CD pipeline created an adverse effect on system performance and optimization. Traditional deployment workflows supported manual point in time performance/cost analysis by performance engineers and cloud consultants, enabling sysadmins to balance performance and cost. The velocity of code changes introduced by continuous processes undermines this logic, as the manual point in time system optimization recommendations quickly become stale and irrelevant.

CI/CD best practices call for the integration of performance tests with the pipeline, using preset benchmarks to pass or fail pipelines. But developers are under even greater pressure to push business logic as quickly as possible. Typically, they don’t have the bandwidth to figure out optimal infrastructure settings including OS and kernel resource allocation.

Integrating real-time infrastructure optimization into DevOps processes is an intense and difficult undertaking. With SLA’s hanging overhead and to maintain high QoS, there is no time to constantly optimize infrastructure settings, not to mention OS and kernel resource allocation. To mitigate the risk, businesses run on relatively low utilization. This means paying for way more compute than is actually required at any given time in an optimized world. This overlooked price tag can take a toll on the business at large.

Shift it to the Left

One way to improve performance in a constant sprint environment is to introduce testing in the early stages of development cycles. As opposed to the traditional approach of executing tests at the latest stages of dev, this practice, called Shift Left Performance Testing, allows smaller, ad hoc performance tests to be performed against individual components as they are developed.

Aligning performance with functional and unit tests means that teams need to begin performance testing when functionality is implemented, and configure those performance tests to run automatically and alert about decreases in performance. Performance testing is integrated as a part of the CI/CD process and executed in local environments after each code commit.

Shift it to Autonomous

Another approach to performance optimization for changing environments involves continuous autonomous optimization. The growing system architectural complexity has given rise to this new generation of tools, geared at bridging the gap between what is humanly possible to optimize and what can actually be achieved by unlocking the vast potential of the machine.

Autonomous performance optimization uses ML to configure the infrastructure, including OS and kernel resource allocation in real-time. Balancing cost and performance, these solutions tune the infrastructure on the fly precisely to the workload and goals of the application. Because autonomous performance optimization technology can employ continuous, seamless system adaptations and optimizations to create a streamlined app environment.

Without automated infrastructure optimization, CI/CD would ultimately mean you’ll continue to accumulate excessive compute costs. Taking a real-time, automated approach to infrastructure optimization is the simplest and most cost-effective solution to this problem, which isn’t going anywhere any time soon. As is often the case, innovative technologies create problems that only other innovative technologies can solve.

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