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

A technology for monitoring data and application performance, applied in the information field, can solve problems such as waste of server hardware resources, complicated server hardware expansion, and inconvenient server expansion, and achieve the effects of convenient operation and maintenance, reduced workload, and labor cost savings

Active Publication Date: 2015-05-06
SICHUAN ZHONGDIAN AOSTAR INFORMATION TECHNOLOGIES CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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

Method used

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

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Embodiment 1

[0076] The present invention is mainly based on the combination of cloud computing technology and its theory with the IMS (Integrated Information Operation and Maintenance Supervision System) system, and realizes the real-time monitoring and flexible expansion scheduling functions of the information system.

[0077] Elastic expansion: The concept of elastic expansion was first proposed by Amazon in the United States. In this invention, it is mainly aimed at a dynamic expansion of the hardware of the business application itself (mainly referring to virtual machines, disk space, etc. in this invention). During the operation of business applications, the number of virtual machine instances supporting business applications can be dynamically increased or decreased. The business system can automatically start more instances when the load is high, and automatically stop some instances when the load is low. Elastic expansion realizes the real on-demand allocation of resources for bus...

Embodiment 2

[0100] This embodiment is further optimized on the basis of the above embodiments. Further, in order to better realize the present invention, the step 2-4-1) includes the following specific steps:

[0101] 2-4-1-1) Query the running number of rule configurations;

[0102] 2-4-1-2) Query the virtual machines in the elastic pool in ascending order of startup and shutdown;

[0103] 2-4-1-3) Classify virtual machines into stop list and run list;

[0104] 2-4-1-4) Compare the actual number of planned runs with the number of planned runs configured by the rule. When the rule configuration is smaller than the actual number of virtual machines obtained by taking the difference between the actual and the rule configuration in reverse order from the run list, put Go to the list to be stopped; when the rule configuration is the same as the actual one, no scheduling will be performed; when the rule configuration is greater than the actual one, the number of virtual machines obtained by t...

Embodiment 3

[0107] This embodiment is further optimized on the basis of any of the above embodiments. Further, in order to better realize the present invention, the step 2-4-2) includes the following specific steps:

[0108] 2-4-2-1) Query the running number of rule configurations;

[0109] 2-4-2-2) Query the virtual machines in the elastic pool;

[0110] 2-4-2-3) Classify virtual machines into stop list and run list;

[0111]2-4-2-4) Compare the actual number of planned runs with the number of planned runs configured by the rule. When the rule configuration is smaller than the actual number of virtual machines obtained by taking the difference between the actual and the rule configuration in reverse order from the run list, in When selecting a virtual machine, first select the virtual machine in the data center and put it in the list to be stopped; when the rule configuration is the same as the actual one, it will not be scheduled; The number of virtual machines obtained by the differe...

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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...

Claims

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Application Information

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IPC IPC(8): H04L12/24H04L12/861H04L29/08
Inventor 黄荣辉朱海萍贺斌周永静杨洋余痴牟俊王绘宇赵海峰
Owner SICHUAN ZHONGDIAN AOSTAR INFORMATION TECHNOLOGIES CO LTD
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