International Business Machines Corporation (20240281303). ESTIMATING WORKLOAD ENERGY CONSUMPTION simplified abstract
Contents
ESTIMATING WORKLOAD ENERGY CONSUMPTION
Organization Name
International Business Machines Corporation
Inventor(s)
Marcelo Carneiro Do Amaral of Tokyo (JP)
Huamin Chen of Westford MA (US)
ESTIMATING WORKLOAD ENERGY CONSUMPTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240281303 titled 'ESTIMATING WORKLOAD ENERGY CONSUMPTION
Simplified Explanation: The patent application describes computer-implemented methods for estimating energy consumption of a workload in a cloud computing system. This involves collecting energy consumption data, creating models based on different durations, and calculating estimated energy consumption during specific time periods.
- **Periodically collecting energy consumption data for a workload**
- **Creating models based on different durations**
- **Calculating estimated energy consumption during specific time periods**
- **Receiving requests for estimated energy consumption**
- **Calculating combined estimated energy consumption based on different models**
Potential Applications: This technology can be applied in cloud computing environments to accurately estimate energy consumption of workloads, helping users optimize resource allocation and reduce costs.
Problems Solved: This technology addresses the challenge of accurately estimating energy consumption in cloud computing systems, enabling better resource management and cost efficiency.
Benefits: The benefits of this technology include improved resource allocation, cost savings, and enhanced sustainability in cloud computing operations.
Commercial Applications:
- Optimizing resource allocation in cloud computing environments
- Reducing energy costs for cloud service providers
- Enhancing sustainability practices in cloud computing operations
Prior Art: Researchers can explore prior art related to energy consumption estimation in cloud computing systems, such as existing methods for workload analysis and resource optimization.
Frequently Updated Research: Researchers may find updated studies on energy-efficient computing, cloud resource management, and sustainability practices in cloud environments relevant to this technology.
Questions about Energy Consumption Estimation in Cloud Computing: 1. How does this technology improve resource allocation in cloud computing systems? 2. What are the potential cost-saving benefits of accurately estimating energy consumption in cloud environments?
Original Abstract Submitted
computer-implemented methods for estimating energy consumption of a workload in a cloud computing system are provided. aspects include periodically collecting an energy consumption data for the workload, creating a first model based on the energy consumption data corresponding to a first duration, and creating a second model based on the energy consumption data corresponding to a second duration, wherein the second duration is longer than the first duration. aspects also include receiving a request for an estimated energy consumption of a workload during a time period and calculating a first estimated energy consumption of the workload during the time period based on the first model. aspects further include calculating a second estimated energy consumption of the workload during the time period based on the second model and calculating a combined estimated energy consumption of the workload based on the first estimate and the second estimate.
