This paper explores the complex task of optimizing and managing performance and costs in a Hybrid Multi-Cloud environment. We introduce our advanced cloud performance and cost optimization methodology and technology, highlighting the critical role of workload characterization, observability, performance monitoring, and prediction in mastering cloud complexities.
Through a selection of practical examples, we illustrate how our performance and cost prediction models effectively refine decision-making throughout the Hybrid Multi-Cloud adoption journey. The interplay of performance and cost is intricate, shaped by the selection of cloud providers, cloud data platforms, and the nature of applications. Our discussion includes how performance monitoring and prediction models shed light on the consequences of increased user and volumes of data and the introduction of new applications on both performance and cost.
At the heart of this paper, we delve into how workload characterization, observability, and the analysis of performance and cost anomalies are used by modeling to optimize allocation of the cloud resources, adjustment of workload management rules and the establishment of ongoing performance and cost governance.
Additionally, we venture into the rapidly evolving domain of generative AI application development, presenting how our cloud performance and cost optimization technology reduces the uncertainty and risk of not achieving performance and cost objectives in the intricate landscape of generative AI in the cloud.