For most organizations, the journey to the cloud is well underway. Unfortunately, many cloud migration decisions are made without options evaluation and realistic performance and financial expectations.
In this paper, we review the approach and case studies that show the value of modeling and optimization for any organization, no matter where they are on their journey to the cloud.
If you are evaluating options for migrating your Data Warehouse workloads to the cloud and are concerned with the cloud data platform selection, optimizing cloud migration decisions, organizing dynamic performance and financial management of your Data Warehouse workloads, optimizing DevOps decisions, and predicting the electrical power and carbon footprint in a Hybrid Multi-Cloud environment, THIS PRESENTATION IS FOR YOU!
Unrealistic performance and financial expectations inevitably lead to unwelcome surprises. Financial stakeholders are worried about the annual budgeting process that might need unplanned revisions. IT stakeholders are concerned with selecting the right cloud data platforms and resource allocation to meet business service level goals (SLG) continuously.
We utilize modeling and optimization to estimate the minimum configuration and cost needed to support an organization’s workload SLGs. Our approach does not require months of work, and we typically deliver the results in two to three weeks. We apply the same technology to optimize cloud migration and dynamic capacity management decisions. The actual measurement data are compared with the predicted results. When significant anomalies and their root causes are determined, the model provides new recommendations to meet SLGs with the lowest cost continuously.
In this presentation, we review this approach and case studies that show the value of modeling and optimization for any organization, no matter where they are on their journey to the cloud.
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