Aligning Kubernetes Resources with Business Demand and Innovation

Today’s enterprises are adopting new technology resources such as Kubernetes, containers, and Pods at a rapid pace to deliver modern applications and service assurance; onboard new workloads; and keep pace with business demand. However, Kubernetes administrators, service owners, and site reliability engineers (SREs) face challenges with right-sizing deployments to meet service level agreements (SLAs) and control costs against increasingly tight budgets. How do you analyze existing capacity, identify potential risks and bottlenecks, and accurately predict the resources required to support future changes in business demand? Where do you start?

Visualize resources you have to meet business and customer expectations

Do you have the right resources to satisfy business and customer expectations? Without insights into risk, efficiency, and cost, delivering quality services that meet your SLAs could be a challenge.

BMC Helix Continuous Optimization provides Kubernetes administrators, service owners, and SREs with the insights to understand the overall health and status of business services and gain visibility across all current enterprise resources to help maximize their performance, availability, and service. It also helps quickly identify overallocated Pods and containers, while automating optimizations to reduce service slowdowns and deliver service assurance.

Optimize existing infrastructure resources to meet business key performance indicators (KPIs) and SLAs

One of the primary challenges is determining how to make the most of the investments that run your business today.

BMC Helix Continuous Optimization provides artificial intelligence (AI), predictive analytics, machine learning (ML), and automation to help you dynamically optimize modern application resources based on Kubernetes, microservices, containers, and Pods. Predictive insight and recommendations for prescriptive actions can help you increase efficiency and lower costs.

AI- and ML-automated recommendations are based on historical resource use data, providing insight into unused or underutilized resources that can be terminated or reclaimed. Predictive saturation alerts and notifications help you adjust and optimize resource configurations to increase performance and efficiency while resolving resource constraints to prevent business service disruption.

Accurately plan to support changes in business demand

increasingly complex IT service and operations management (ITSM/ITOM) environments make it difficult to accurately predict and align resources to support business demand and innovation.

BMC’s Helix Continuous Optimization makes it easy to run “what if” simulations and reports to accurately predict resource requirements with before and after usage models. “What if” modeling provides insights into the best resource configurations, location, and cost so you can make informed decisions and prevent application performance slowdowns or failures.

To learn more about aligning Kubernetes resources to support business demand and innovation download the e-book, Service Assurance and Optimization with AIOps

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These postings are my own and do not necessarily represent BMC's position, strategies, or opinion.

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