Kyndryl, Inc. (20240249220). COST FORECASTING AND MONITORING FOR CLOUD INFRASTRUCTURE simplified abstract

COST FORECASTING AND MONITORING FOR CLOUD INFRASTRUCTURE

Organization Name

Kyndryl, Inc.

Inventor(s)

Omar Odibat of Cedar Park TX (US)

Mouleswara Reddy Chintakunta of Allagadda (IN)

Manish Mitruka of Pune (IN)

Reagan Mitchell of Austin TX (US)

Umar Mohamed Iyoob Umar of Pflugerville TX (US)

Anand Bandaru of Bangalore (IN)

Gail Camille Guerrero of Plano TX (US)

Divya Devaraj of Coimbatore (IN)

COST FORECASTING AND MONITORING FOR CLOUD INFRASTRUCTURE - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240249220 titled 'COST FORECASTING AND MONITORING FOR CLOUD INFRASTRUCTURE

The abstract describes a computer-implemented method for forecasting cloud computing infrastructure costs by utilizing resource profiles, scheduled pattern detection, and similar consumer detection processes to input data into a neural network for cost forecasting.

• Simplified Explanation:

- The method uses resource profiles and consumer patterns to forecast cloud computing costs through a neural network.

• Key Features and Innovation:

- Computation of resource profiles for cloud resources. - Detection of periodic behaviors and similar consumer patterns. - Inputting data into a neural network for cost forecasting.

• Potential Applications:

- Cloud service providers can use this technology to accurately forecast infrastructure costs. - Businesses can optimize their cloud resource usage based on cost forecasts.

• Problems Solved:

- Inaccurate cost forecasting for cloud computing infrastructure. - Lack of efficient methods to predict future costs based on resource usage patterns.

• Benefits:

- Improved cost forecasting accuracy. - Optimization of cloud resource allocation. - Cost savings for businesses utilizing cloud services.

• Commercial Applications:

- "Cloud Computing Infrastructure Cost Forecasting Technology: Market Implications and Commercial Uses"

• Questions about Cloud Computing Infrastructure Cost Forecasting:

1. How does this technology improve cost forecasting accuracy? - This technology improves cost forecasting accuracy by analyzing resource profiles and consumer patterns to input data into a neural network for more precise predictions.

2. What are the potential cost-saving benefits for businesses using this technology? - Businesses can save costs by optimizing their cloud resource allocation based on accurate cost forecasts generated by this technology.

Original Abstract Submitted

disclosed embodiments provide a computer-implemented method for cloud computing infrastructure cost forecasting. resource profiles are computed for one or more cloud resources. a scheduled pattern detection process is performed for each of the one or more cloud resources to check for periodic behaviors. a similar consumer detection process is performed for each of the one or more cloud resources to identify other entities that have a similar cloud computing resource usage pattern, which can serve as supervised learning data for neural networks of disclosed embodiments. data is input to a neural network, where the input data includes the resource profile, an operational maturity score, and/or one or more similar consumer patterns, in order to obtain a cost forecast from the neural network, based on the input data.