Cloud software service resource allocation method based on QoS model self-correction
US12273279B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 30, 2019 |
| Grant date | Apr 8, 2025 |
| Priority date | — |
| Expiry date | Sep 3, 2041 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L43/20
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
The present invention relates to a cloud software service resource allocation method based on QoS model self-correction, the method comprising: step S1: performing online self-learning to obtain a QoS prediction model; step S2: collecting runtime data under a certain workload, and improving the accuracy of the QoS prediction model under a current workload through self-correction control; step S3: constructing a fitness function in combination with the quality of service (QoS) and the cloud resource cost (Cost), and searching for a target resource allocation scheme by using an improved particle swarm optimization algorithm; step S4: comparing the current resource allocation situation with the searched target resource allocation scheme to obtain a difference therebetween, and then adjusting resources according to a certain proportion; and step S5: repeating steps S2 to S4 until the current resource allocation situation is the same as the target resource allocation scheme which means that resource adjustment is completed. The present invention can realize a best resource allocation when the QoS prediction model is inaccurate.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.