But, to be fair, tuning cloud applications while they run has grown beyond human scale--we need machines to do it better. The trillions of possible resources, middleware, and application parameter configurations can be integrated effectively—after identifying them correctly—into the CI/CD cycle by leveraging AI, and that’s where Opsani’s value proposition lies. Through its AI-driven Continuous Optimization application performance solution, the company delivers real-time, autonomous application tuning integrated into their clients’ CI/CD toolchain.
Using machine learning, Opsani automatically tunes the run-time parameters of its clients’ cloud applications in response to code, middleware, or environmental changes. Opsani takes the guesswork out of balancing requirements, cost, and performance by delivering a Continuous Optimization system that increases application performance and reduces cost. Further, Opsani’s system leverages machine learning to automatically adjust resource assignments and configuration settings. In the following conversation, Opsani’s CEO, Ross Schibler, shares insights on how the company empowers developers and operations with tools and services purpose-built for modern enterprise IT infrastructures.
Who are Opsani’s typical clients?
We work with clients in all industries, especially those companies with cloud-based apps that are running their service in either a VM or container-based environment. Our customers have a mature CI/CD pipeline in place and are frequently releasing new code. Most importantly, our typical clients have a desire to retain reliability and improve performance of their application while reducing their cloud spend.
Please elaborate on the underlying principles of the Opsani AI.
With our solution’s deployment in the cloud adoption pipeline, we can go beyond just the code and application layers as the Opsani neural net AI optimizes resource choices (CPU, memory), middleware configurations (JVM GC type/parameters, pool sizes), kernel parameters (page sizes, jumbo packet sizes), as well as application parameters (thread pools, cache timeouts, write delays), to find the best among the vast number of potential combinations.
We perform the initial optimization and then monitor the application performance and perform continuous optimization in response to changes in the application, middleware, or its environment. Further, we have internal APIs that help us to adjust parameters and measure the results.
How does Opsani AI continuously search for optimal settings and implement solutions? What are the benefits achieved by the clients?
The Opsani AI prioritizes the client’s KPIs and balances their optimization target between the highest performance and lowest cost. The efficiencies we find deliver a faster and better user experience while reducing resource costs. We built the Opsani AI to always fine-tune application performance that delivers our customers top and bottom line growth.
In fact, the neural network can find combinations of settings that humans may not even think of. In addition, we integrate with Kubernetes, Azure, Google Cloud Platform, and AWS and provide Plug-ins for Jenkins, Spinnaker, AppDynamics, Wavefront, Datadog, SignalFX, New Relic, Prometheus, and Terraform.
Please cite a case study where Opsani AI has enabled clients to overcome hurdles and attain desired outcomes.
One of the global leaders in family history and consumer genomics moved its operations to the cloud to enable it to scale with its customer base, and implemented CI/CD processes to facilitate faster feature rollout. However, with the rapid growth in both its user base and the range of products it provided, the company was hard-pressed to ensure that it was achieving optimum performance, efficiency, and customer experience via its cloud applications. Through the integration of Opsani AI, a new best practice was added to the client’s CI/CD toolkit—Continuous Optimization--which resulted in 230 percent performance per dollar gains and an average of more than 50 percent in cloud cost reduction.
What are some of the milestones that Opsani has achieved over the years?
The big moment came when we realized that the AI would tune applications not only faster, but way better than a human. The idea for Opsani’s AI came from watching autonomous cars navigate their environments. Autonomous cars use a neural network of sensors to ingest the ever-changing conditions around them, and then make the necessary corrections to keep the car moving safely. All of this happens autonomously.
We applied that concept to application optimization and tested our prototype AI on an in-house application. Opsani takes a little time to study the environment in which it is working, so we set the application and left for the night. The next day we expected to see maybe a 20 to 40 percent optimization, but the tool produced a 170 percent improvement. That was a ‘Eureka’ moment and the point at which we knew we had a business. Ever since then we’ve been saying to our customers, “come for the performance and stay for the cost-savings.” Approximately three-fourths of our clients achieve positive ROI within the same quarter they implement Opsani.
What does the future hold for Opsani?
Just like AI is enabling cars to function without human interference, using AI to autonomously tune applications will reduce human dependency and bring remarkable gains in cloud-based application performance and efficiency. Engineers’ time will be freed up to create new application features.
DevOps is changing, growing and becoming a strategic advantage. CI and CD will be joined by CO (Continuous Optimization) as the recognized best practices trifecta of DevOps. In the near future, we will be positioning Opsani to revolutionize the cloud optimization landscape, and inspire a transformation in DevOps culture.