17933343. CLOUD COMPUTING QOS METRIC ESTIMATION USING MODELS simplified abstract (Dell Products L.P.)
Contents
- 1 CLOUD COMPUTING QOS METRIC ESTIMATION USING MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CLOUD COMPUTING QOS METRIC ESTIMATION USING MODELS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does this technology handle real-time data processing for predicting response time accurately?
- 1.11 What are the specific machine learning algorithms used to train the models for predicting response time?
- 1.12 Original Abstract Submitted
CLOUD COMPUTING QOS METRIC ESTIMATION USING MODELS
Organization Name
Inventor(s)
Miguel Paredes Quinones of Campinas (BR)
Romulo Teixeira de Abreu Pinho of Niteroi (BR)
CLOUD COMPUTING QOS METRIC ESTIMATION USING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17933343 titled 'CLOUD COMPUTING QOS METRIC ESTIMATION USING MODELS
Simplified Explanation
The abstract of the patent application describes models used to predict quality of service metrics, specifically focusing on predicting response time based on infrastructure occupancy status and trained models. This allows for adjustments to be made to better meet quality of service requirements.
- Predicting response time using occupancy status and trained models
- Estimating metrics to adjust infrastructure for better quality of service
- Focusing on response time to improve service satisfaction
Potential Applications
The technology described in the patent application could be applied in various industries and sectors, including:
- Telecommunications
- Information technology
- Transportation systems
- Healthcare facilities
Problems Solved
This technology helps address several issues related to quality of service metrics, such as:
- Improving response time for better customer satisfaction
- Enhancing overall performance of infrastructure
- Meeting quality of service requirements more effectively
Benefits
The use of predictive models for quality of service metrics offers several benefits, including:
- Optimizing resource allocation based on occupancy status
- Proactively addressing potential service issues
- Enhancing overall user experience and satisfaction
Potential Commercial Applications
The technology could be utilized in various commercial settings, such as:
- Service providers
- Network operators
- Data centers
- Cloud computing companies
Possible Prior Art
One possible prior art for this technology could be predictive maintenance models used in industrial settings to anticipate equipment failures and optimize maintenance schedules.
Unanswered Questions
How does this technology handle real-time data processing for predicting response time accurately?
The patent application does not provide details on the real-time data processing capabilities of the predictive models.
What are the specific machine learning algorithms used to train the models for predicting response time?
The patent application does not specify the machine learning algorithms employed in training the models for predicting response time.
Original Abstract Submitted
Models to predict quality of service metrics are disclosed. A response time is predicted using an occupancy status of an infrastructure and models that have been trained to predict a response time. Estimating a metric, such as the response time, allows the infrastructure to adjust to issues such that requests better satisfy quality of service requirements.
