BOSTON, Aug. 18, 2020 /PRNewswire/ -- (KubeCon 2020) -- Carbon Relay today announced a free and self-service edition of Red Sky Ops, an AIOps platform for automatically configuring and continuously optimizing containerized applications. Launched in July 2019, Red Sky Ops is the first AIOps solution for application management in Kubernetes environments that enhances application performance, reduces infrastructure costs, and eliminates the need for manual optimization by DevOps, networking, and IT professionals.
Red Sky Ops uses machine learning to study, replicate and stress-test application environments, and then proactively learn and deploy optimal configurations, schedules, and resource allocations. It works with the Kubernetes scheduler to take into account service requirements, policy constraints around hardware or software use, workload-specific issues and deadlines—all with minimal engineering involvement.
"Kubernetes has quickly become the container orchestration platform of choice for many organizations due to its power and versatility. But frequent, manual tuning and excessive alerting are impeding Kubernetes initiatives with DevOps teams struggling to ensure reliable and optimal application performance without over-provisioning resources," said Matt Provo, co-founder and CEO of Carbon Relay. "Launching a free and self-service version of our AIOps solution will make the promise of Kubernetes far more accessible, help more organizations accelerate and automate optimized application performance, and free their most talented technologists for more strategic initiatives."
With Carbon Relay's free edition of Red Sky Ops, IT teams gain access to:
Break Through the Kubernetes Complexity Barrier
Until now, managing and maintaining consistent and high application performance and reliability in Kubernetes environments has proven to be complicated and difficult. First-generation AIOps management tools are too imprecise and not automated, often missing opportunities for big performance gains and generating too many alerts. Red Sky Ops provides an intelligent and automated way to address Kubernetes and container complexity, boosting application performance, reducing costs, and stopping alert floods for DevOps teams.
Cut Costs and increase uptime with Automated Optimization
Red Sky Ops uses machine learning to drive major application performance gains and cost reductions in complex Kubernetes application environments. It proactively assesses all relevant factors to determine the best set of deployment choices and then automatically implement them, and recalculates on-the-fly to maintain top performance as conditions change. The result is the highest and most reliable performance possible at the lowest costs, all without the constant inundation of unnecessary alerts. With Red Sky Ops, organizations can automatically double application performance while reducing operational costs associated with cloud consumption by up to 40 percent.
Carbon Relay released Red Sky Ops as open source under the Apache v2 license. It includes the Red Sky Ops Kubernetes load balancer, controller, API services, and authentication services. In February 2020, Carbon Relay announced new funding from Insight Partners to accelerate the growth of its Red Sky Ops solution.
Click here to learn more about how to get started with Red Sky Ops.
About Carbon Relay
Carbon Relay brings together world-class data scientists and software engineers to enable businesses to drive breakthrough IT and operations efficiency. Carbon Relay is set apart by its unwavering focus on building AI-powered software products that are designed to help people, not replace them. The Company's solutions uniquely occupy a middle ground between machine and human intelligence where they leverage the strengths of both for maximum effectiveness. The Red Sky Ops platform uses machine learning to drive major application performance gains and cost reductions in complex environments. Carbon Relay was founded in 2015 and is based in Boston and Washington, DC. Learn more at https://www.carbonrelay.com.
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