Virtual resource flexible scheduling implementation method combining application performance indicator monitoring data

Abstract

The invention discloses a virtual resource flexible scheduling implementation method combining application performance indicator monitoring data. The method includes the following steps: 1), scheduling the configuration which needs elastic pool scheduling rules according to time and / or business system indicators; (2), conducting scheduling based on elastic pool scheduling rules; 2-1), declaring virtual machine queue to be started and stop queue; 2-2), inquiring the available elastic pool; 2-3), conducing circulation processing; 2-4), judging the scheduling rules, executing the time scheduling method of 2-4-1) when the rule is the time scheduling rule; executing the business system indicator scheduling method of 2-4-2) when the rule is the business system indicator scheduling rule; 2-5), starting all virtual machines of the queue to be started; 2-6), stopping all the virtual machines of the queue to be stopped; and 2-7), terminating the scheduling. The virtual resource flexible scheduling implementation method combining the application performance indicator monitoring data solves the problem that the hardware resource is wasted in the prior art, adopts the flexible scheduling rules, reduces the waste of the hardware resource by the business system, and effectively reduces the cost.

Description

technical field [0001] The invention relates to the field of information technology, specifically, a virtual resource flexible scheduling implementation method combined with application performance index monitoring data. Background technique [0002] Cloud resource management system: The cloud resource management system can centrally manage computing resources, storage resources, and network resources in cloud computing resource pools in multiple provinces and cities. The management platform adopts a first-level deployment, and resource operation and scheduling are all carried out locally in the resource pools of provinces and cities. The overall deployment architecture of the cloud resource management system is as follows: figure 1 The resource management platform and the resource pool of the provincial company are set as shown. The database server of the resource management platform and the application server of the resource management platform are set in the resource mana...

AI Technical Summary

Problems solved by technology

[0007] 1. Waste of hardware: an information system may need several servers at the peak of user access, but when the user access is at a low point, the server's cpu, memory and other resources are idle, which leads to server hardware waste of resources

[0008] 2. It is inconvenient to expand the server capacity: the general information system will make a certain plan for the server resources before the implementation, but with the promotion of the information system, when there are more and more visiting users, the expansion of the server hardware is relatively complicated, if the plan is not clear, there will be lead to greater waste of hardware resources

[0009] 3. The inability to flexibly calculate user access will lead to downtime risks: the number of user visits is not static. If the number of user visits to an information system increases due to some unexpected events, there is a certain degree of system downtime due to insufficient hardware resources. The risk of machine failure, although planning and disaster recovery have been basically done during the implementation of the project, this situation is still unavoidable under the current conditions

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