FinOps-driven optimization of cloud resource usage for high-performance computing using machine learning

Nawrocki, Piotr; Smendowski, Mateusz

doi:10.1016/j.jocs.2024.102292

Journal of Computational Science

2024

DOI: 10.1016/j.jocs.2024.102292

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FinOps-driven optimization of cloud resource usage for high-performance computing using machine learning

Piotr Nawrocki,

Mateusz Smendowski

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2024

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Cited by 2 publications

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Optimizing multi-time series forecasting for enhanced cloud resource utilization based on machine learning

Smendowski,

Nawrocki

2024

Knowledge-Based Systems

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Optimizing multi-time series forecasting for enhanced cloud resource utilization based on machine learning

Smendowski,

Nawrocki

2024

Knowledge-Based Systems

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Cloud PricingOps: A Decision Support Framework to Explore Pricing Policies of Cloud Services

Fragiadakis,

Tsadimas,

Filiopoulou

et al. 2024

Applied Sciences

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To maximize the business value of the cloud, the cost of cloud solutions is explored alongside technical quality and performance. To enable this form of exploration engineering, finance and business teams collaborate in the context of FinOps, the operational framework that provides the required decision-making. Prominent providers, such as Google and Microsoft, provide FinOps to their customers, integrating cost factors when designing a cloud solution. However, different providers apply different pricing policies for their products, and these policies also change through time. Therefore, there are numerous efforts to explore price evolution through time for different cloud products applying different decision-making methods using different datasets. In an effort to establish a systematic approach to support decision-making on alternative pricing policies for cloud services and compare them across services and providers, the CloudPricingOps framework is proposed in this paper. It constitutes a decision support system that provides alternative decision-making methods, such as hedonic models, time series, and clustering and machine learning techniques, to deal with problems related to cloud product pricing policy analysis, comparison and prediction. It also constitutes a systematic method of discrete steps to integrate additional decision-making methods to deal with these problems and input datasets to be used regardless of the method or the cloud pricing problem that needs to be solved. Two discrete examples based on real data are also presented in this paper to demonstrate the usefulness of the CloudPricingOps framework for cloud engineering and business teams.

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FinOps-driven optimization of cloud resource usage for high-performance computing using machine learning

Cited by 2 publications

References 13 publications

Optimizing multi-time series forecasting for enhanced cloud resource utilization based on machine learning

Optimizing multi-time series forecasting for enhanced cloud resource utilization based on machine learning

Cloud PricingOps: A Decision Support Framework to Explore Pricing Policies of Cloud Services

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