Predictive database resource utilization and load balancing using neural network model
US8185909B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 6, 2007 |
| Grant date | May 22, 2012 |
| Priority date | — |
| Expiry date | Dec 24, 2030 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY10S707/99932
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A preemptive neural network database load balancer configured to observe, learn and predict the resource utilization that given incoming tasks utilize. Allows for efficient execution and use of system resources. Preemptively assigns incoming tasks to particular servers based on predicted CPU, memory, disk and network utilization for the incoming tasks. Direct write-based tasks to a master server and utilizes slave servers to handle read-based tasks. Read-base tasks are analyzed with a neural network to learn and predict the amount of resources that tasks will utilize. Tasks are assigned to a database server based on the predicted utilization of the incoming task and the predicted and observed resource utilization on each database server. The predicted resource utilization may be updated over time as the number of records, lookups, images, PDFs, fields, BLOBs and width of fields in the database change over time.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.