As cloud usage grows, so does cloud “waste”. In fact, devops.com calculates that cloud waste will cost businesses $17.6 billion in 2020. Waste comes from idle and over-provisioned resource, therefore, it is essential to know more about where you execute your workloads to avoid waste being built in from the outset. Key insights include knowing the predicted runtime to understand when and how long to provision resource, and being proactive if workloads fail – especially if they fail “silently”.
In addition, determining how to optimally fit workloads of different priority and SLA types helps reduce wait times, priority conflicts and delays.
Learn MoreYellowDog Optima leverages advanced machine learning so you can understand the predictability of your workloads and adapt to any complex variations that may occur from time to time. This enables you to schedule workloads with greater insight, avoiding potential conflicts and ensuring SLAs are met and exceeded.
Improve workload execution efficiency by running more workloads on the same resources.
Reduce priority conflict between workload tasks with optimal scheduling.
Mitigate risk and drive performance by planning workload execution costs and overheads in advance.
Reduce opportunity cost and priority conflict with optimal workload scheduling.
Improve adherence to SLAs with automated proactive alerting so that issues are highlighted and resolved quicker.
Accurately predict the cost and operational impact of expanding workload processing through precise, predictive workload scheduling.
Read how models were able to deliver predicted time for workloads with 96% confidence for CPU Time. Reducing costs, boosting end-client satisfaction, and optimising hardware utilisation.
Learn more about the challenges businesses face in avoiding cloud waste and the business impact of using YellowDog Optima.
Book DemoTo book a demo of YellowDog Optima, or to ask our team for further information, you're just a click away!
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