18178613. DATA LOADING BASED ON WORKLOAD PREDICTIONS FOR IMPROVED PERFORMANCE OF CLOUD-BASED SYSTEMS simplified abstract (SAP SE)
Contents
- 1 DATA LOADING BASED ON WORKLOAD PREDICTIONS FOR IMPROVED PERFORMANCE OF CLOUD-BASED SYSTEMS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DATA LOADING BASED ON WORKLOAD PREDICTIONS FOR IMPROVED PERFORMANCE OF CLOUD-BASED SYSTEMS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Database Performance Optimization
- 1.13 Original Abstract Submitted
DATA LOADING BASED ON WORKLOAD PREDICTIONS FOR IMPROVED PERFORMANCE OF CLOUD-BASED SYSTEMS
Organization Name
Inventor(s)
DATA LOADING BASED ON WORKLOAD PREDICTIONS FOR IMPROVED PERFORMANCE OF CLOUD-BASED SYSTEMS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18178613 titled 'DATA LOADING BASED ON WORKLOAD PREDICTIONS FOR IMPROVED PERFORMANCE OF CLOUD-BASED SYSTEMS
Simplified Explanation
The patent application describes a method for optimizing database performance by using machine learning models to predict workload patterns and preloading data into memory.
Key Features and Innovation
- Utilizes machine learning models to predict cluster arrival rate curves (cARC) for workloads.
- Determines column visiting times for database tables based on workload predictions.
- Generates a column list for preloading data into low-latency memory.
- Improves database performance by loading necessary data before workload execution.
Potential Applications
This technology can be applied in various industries where database performance optimization is crucial, such as finance, healthcare, e-commerce, and more.
Problems Solved
- Enhances database performance by predicting workload patterns.
- Reduces latency by preloading data into memory based on workload predictions.
- Improves overall efficiency of database systems.
Benefits
- Increased database performance and efficiency.
- Reduced latency and improved response times.
- Enhanced user experience and system reliability.
Commercial Applications
Optimizing database performance is essential for businesses that rely on data processing and storage. This technology can be valuable for companies in sectors like finance, healthcare, e-commerce, and more, where efficient database operations are critical for success.
Prior Art
Readers interested in prior art related to this technology can explore research papers, patents, and industry publications on database optimization, machine learning in database management, and predictive data loading techniques.
Frequently Updated Research
Stay informed about the latest advancements in machine learning for database optimization, predictive data loading strategies, and performance enhancement techniques in database systems.
Questions about Database Performance Optimization
How does predictive data loading improve database performance?
Predictive data loading anticipates the data needs of upcoming workloads, reducing latency by preloading necessary information into memory before it is requested.
What are the key benefits of using machine learning models for workload prediction in database systems?
Machine learning models can accurately forecast workload patterns, allowing for proactive data management strategies that optimize performance and efficiency.
Original Abstract Submitted
Methods, systems, and computer-readable storage media for receiving a workload period, during which a workload is applied to a database system, providing a set of ML models based on historical data representative of historical executions of the workload over the workload period, each ML model configured to predict a cluster arrival rate curve (cARC), and during execution of the workload period and, for each timeslice of a plurality of timeslice of the workload period: providing a predicted cARC from each ML model, the predicted cARC representative of a predicted workload, determining column visiting times for each of a plurality of columns of each of a plurality of tables stored in the database system, generating a column list based on the column visiting times, and loading column data representative of columns included in the column list into low-latency memory prior to execution of a workload during the respective timeslice.
