CN104348887B - Resource allocation methods and device in cloud management platform - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
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- H04L67/10—Protocols in which an application is distributed across nodes in the network
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Abstract
This disclosure relates to resource allocation methods and device in a kind of cloud management platform.This method includes the analysis parameter of built-in each application server, based on analysis each application server of parameter monitoring different periods resource behaviour in service；Each application server monitored is compared in the resource behaviour in service of different periods with the corresponding analysis parameter threshold level of setting；Each application server is accordingly increased or reduced in the resource that different periods are used for according to comparison result determination；Corresponding resource distribution is carried out for each application server in the corresponding period based on the resource distribution of identified each application server.The disclosure can carry out optimal resource distribution for each application server in advance.
Description
Technical field
This disclosure relates to field of cloud calculation, particularly, the resource allocation methods and device being related in a kind of cloud management platform.
Background technique
IaaS(Infrastructure as a Service at present, infrastructure service) cloud management platform user Shen
Please resource open and can be carried out by two ways: one is by system prefab-form, one is customized customization resources.Its
Middle system prefab-form covers the virtual machine of different stage, storage, net source service template, and user can be according to service application
The template of demand selection different stage；And customized customization resource is then that user can be for needed for the customized selection of service application
Resource.
Since the virtual machine of different IaaS clouds management platform has not in performance with the physical server actually similarly configured
Same difference, actual services of the user before operation system deployment after the demand and operation of unclear own service amount
Amount, so usually customized is also an assessment, therefore, system prefab-form and customized customization resource can not be realized really
The optimization collocation of most users resource.
One business it is online, due to its unpredictable user volume, portfolio and data volume, can only often provide estimation
Value, therefore, resource customized in most users can not the business that is carried of accurate match itself, cause resource utilization mistake
It is low, waste of resource and investment, or resource utilization is caused to transfinite, form traffic bottleneck or even delay machine；Furthermore due to difference
Service application resource consumed by different moments is not also identical, though optimal fixed resource configuration template has been selected, but
It will cause the wasting of resources within some period.
Summary of the invention
The disclosure proposes new technical solution in view of at least one of problem above.
The disclosure provides the resource allocation methods in a kind of cloud management platform in terms of one, can be each application
Server carries out optimal resource distribution in advance.
The disclosure provides the resource allocation device in a kind of cloud management platform in its another aspect, can be each application
Server carries out optimal resource distribution in advance.
According to the disclosure, the resource allocation methods in a kind of cloud management platform are provided, comprising:
The analysis parameter of built-in each application server, based on analysis each application server of parameter monitoring different periods money
Source behaviour in service；
By each application server monitored in the resource behaviour in service of different periods and the corresponding analysis parameter door of setting
Limit value is compared；
Each application server is accordingly increased in the resource that different periods are used for according to comparison result determination
Or reduction；
It is carried out accordingly in the corresponding period for each application server based on the resource distribution of identified each application server
Resource distribution.
In some embodiments of the present disclosure, the analysis parameter of each application server includes cpu busy percentage, memory utilization
In rate, network interface card rate, storage network rate, system response time, packet loss, disk read-write rate and application process quantity extremely
It is one few.
In some embodiments of the present disclosure, each application server includes mail server, Website server, database clothes
Business device and application server.
In some embodiments of the present disclosure, the corresponding analysis parameter threshold level of setting includes the highest of corresponding analysis parameter
Threshold value and minimum threshold.
According to the disclosure, the resource allocation device in a kind of cloud management platform is additionally provided, comprising:
Analytical parameter setup unit is respectively answered for the analysis parameter of built-in each application server based on analysis parameter monitoring
With server different periods resource behaviour in service；
Comparing unit, for by each application server monitored different periods resource behaviour in service and the phase of setting
Parameter threshold level should be analyzed to be compared；
Resource control unit, for determining the money being used for each application server in different periods according to comparison result
Source is accordingly increased or is reduced；
Resource configuration unit, for the resource distribution based on identified each application server the corresponding period be each application
Server carries out corresponding resource distribution.
In some embodiments of the present disclosure, the analysis parameter of each application server includes cpu busy percentage, memory utilization
In rate, network interface card rate, storage network rate, system response time, packet loss, disk read-write rate and application process quantity extremely
It is one few.
In some embodiments of the present disclosure, each application server includes mail server, Website server, database clothes
Business device and application server.
In some embodiments of the present disclosure, the corresponding analysis parameter threshold level of setting includes the highest of corresponding analysis parameter
Threshold value and minimum threshold.
The technical solution of the disclosure compared with system prefab-form in the prior art and customized customization resource scheme, by
It is adjusted in it according to actual use situation of each application server monitored to various physical resources come dynamic and each application is taken
Therefore the resource distribution of business device can enable each application server in each period all in optimal operational condition, neither
System resource is wasted it is also possible that each application server obtains required system resource.
Detailed description of the invention
Attached drawing described herein is used to provide further understanding of the disclosure, constitutes part of this application.Attached
In figure:
Fig. 1 is the flow diagram of the resource allocation methods in the cloud management platform of an embodiment of the present disclosure.
Fig. 2 is the structural schematic diagram of the resource allocation device in the cloud management platform of an embodiment of the present disclosure.
Specific embodiment
The disclosure is described below with reference to accompanying drawings.It should be noted that description below is only explanatory in itself and shows
Example property, never as to the disclosure and its application or any restrictions used.Unless stated otherwise, otherwise, implementing
Component described in example and the positioned opposite and numerical expression and numerical value of step are not intended to limit the scope of the present disclosure.In addition,
Technology well known by persons skilled in the art, method and apparatus may not be discussed in detail, but be meant as in appropriate circumstances
Part of specification.
The following embodiments of the disclosure devise a kind of fast resource distribution method based on memory analysis, by IaaS cloud
The analysis parameter for managing built-in each application server in platform, monitors the performance parameters of each application server, according to point of setting
Analysis condition ruling obtain each application server of user corresponding to different periods best resource configuration, and according to it is determining not
The resource distributions such as virtual machine for modifying user automatically with the best resource configuration result of period, so that the related application of user is each
Optimal resource distribution is used in a period, avoids the wasting of resources or forms traffic bottleneck.
Fig. 1 is the flow diagram of the resource allocation methods in the cloud management platform of an embodiment of the present disclosure.
As shown in Figure 1, the embodiment may comprise steps of:
S102, the analysis parameter of built-in each application server, based on analysis each application server of parameter monitoring when different
The resource behaviour in service of section；
Specifically, the analysis parameter of each application server can include but is not limited to cpu busy percentage, memory usage, net
At least one in card rate, storage network rate, system response time, packet loss, disk read-write rate and application process quantity
It is a.
Since the bottleneck point of each application server is different, for example, the bottleneck point of some application servers may be CPU,
And the bottleneck point of other application server may be memory, the bottleneck point of also some application servers may include simultaneously
CPU and packet loss etc..It therefore, can not be all itself is respectively set in each application server analysis parameter according to application.
Due to period difference, the resource behaviour in service of same application may be different, the resource behaviour in service of different application
May be different, therefore, monitoring device obtains each application server when each according to the parameter of every kind of required monitoring of application in real time
The resource behaviour in service of section.
S104 joins each application server monitored in the resource behaviour in service of different periods and the corresponding analysis of setting
Number threshold value is compared；
Wherein, the corresponding analysis parameter threshold level of setting may include corresponding analysis parameter highest threshold value and minimum door
Limit value.
Specifically, analysis parameter threshold level may include: cpu busy percentage highest threshold value and cpu busy percentage it is minimum
Threshold value；The highest threshold value of memory usage and the minimum threshold of memory usage；The highest threshold value of network interface card rate with
The minimum threshold of network interface card rate；Store the highest threshold value of network rate and the minimum threshold of storage network rate；System
The highest threshold value of the speed of response and the minimum threshold of the system speed of response；The highest threshold value and packet loss of packet loss are most
Threshold ones；The highest threshold value of disk read-write rate and the minimum threshold of disk read-write rate；Application process quantity is most
The minimum threshold of high threshold and application process quantity.
S106 is determined according to comparison result and is carried out accordingly to each application server in the resource that different periods are used for
Increase or reduces；
Under normal circumstances, when the resource behaviour in service monitored in real time between corresponding analysis parameter highest threshold value with most
When between threshold ones, it is believed that the corresponding resource of analysis parameter is in optimal use state, that is, its utilization rate etc. reaches best
Value.
If some analyzes the corresponding resource behaviour in service of parameter not between its highest threshold value and minimum threshold,
Then think that the behaviour in service of respective resources is not up to optimum state, it can be according between the real time status and each threshold value monitored
Relationship corresponding resource is increased or is reduced.
It is illustrated by taking cpu busy percentage as an example:
If monitoring that the current cpu busy percentage of some application server is higher than the most wealthy family for application server setting
Limit value, then the CPU for being shown to be application server distribution is insufficient, therefore, can be before the identical period reaches, for the application clothes
Business device configuration increases the configuration granularity of CPU, for example, can increase CPU to particle one by one, it can also be real-time according to detecting
The difference of cpu busy percentage and highest threshold value determines that the dominant frequency of selected CPU increases a dominant frequency if difference is larger
Higher CPU increases a lower CPU of dominant frequency, if difference is smaller to avoid the waste of resource.
Still by taking cpu busy percentage as an example, it is assumed that the current cpu busy percentage of some application server monitored is answered less than this
With the minimum threshold of server settings, then the CPU for being shown to be application server distribution is superfluous, therefore, can be when identical
Before section reaches, it is reduced to the CPU granularity of application server distribution, it, can be with for example, CPU can be reduced to particle one by one
The dominant frequency of reduced CPU is determined according to the difference of the real-time cpu busy percentage and minimum threshold that detect, if difference compared with
Greatly, then the higher CPU of wherein dominant frequency is reduced, if difference is smaller, the lower CPU of wherein dominant frequency is reduced, to keep away
Exempt from the waste of resource.
Example is saved as within again to be illustrated:
If monitoring that the current memory utilization rate of some application server is higher than the highest for application server setting
Threshold value is then shown to be the low memory of application server distribution, therefore, can answer before the identical period reaches for this
The configuration granularity for increasing memory is configured with server, for example, particle memory can be increased one by one, can also basis detect
The difference of real-time utilization rate and highest threshold value determines that the size of selected memory increases by one if difference is larger
A biggish memory close to difference increases a lesser memory close to difference, if difference is smaller to avoid resource
Waste.
For example, monitoring the memory usage of some application virtual machine of user, 3 points to 4 periods have been more than to set in the afternoon
Fixed highest threshold value 60%, monitoring system provides alarm at this time, and IaaS cloud management platform is redefined to the virtual machine at this
The resource distribution of period determines to carry out memory dilatation, later logical in the daily period to meet the growth requirement of portfolio
Crossing memory analysis method in advance is the virtual machine pre-configuration resource, to solve the problems, such as traffic bottleneck.
Still by taking memory usage as an example, it is assumed that the current memory utilization rate of some application server monitored is less than should
The minimum threshold of application server setting, then the memory for being shown to be application server distribution is superfluous, therefore, can be identical
Before period reaches, it is reduced to the memory granularity of application server distribution, for example, memory can be reduced to particle one by one, also
The size of reduced memory can be determined according to the difference of the real-time utilization rate and minimum threshold that detect, for example,
A memory bar being wherein closer to difference can be reduced, to avoid the waste of resource.
Wherein, which can be as unit of certain several hour in one day, or middle of the month it is a few
It is unit, can also be that certain some months or certain days in 1 year are unit, period setting is carried according to each server
The difference of application and be separately provided.
Other analysis parameters are similar, are just no longer illustrated one by one herein.
S108 carries out phase in the corresponding period based on the resource distribution of identified each application server for each application server
The resource distribution answered, so that the resource of each application server can reach optimum rate of utilization in each period.
In this embodiment, due to it according to each application server for monitoring to the actual use shape of various physical resources
Therefore condition, which carrys out the resource distribution that dynamic is adjusted to each application server, can enable each application server in each period
All in optimal operational condition, neither waste system resource it is also possible that each application server obtains required system resource.
Wherein, each application server in above-described embodiment can include but is not limited to mail server, Website server,
Database server and application server.
Illustrate, it is assumed that IaaS cloud manage platform passage capacity data monitoring probe to all virtual machines of later A into
Row performance monitoring, the CPU of the virtual machine X of A and memory usage reach 90% after finding, far beyond the upper of CPU and memory
Limit, it is thus determined that being the CPU computing capability for being further added by 1GHz of the virtual machine X of user A before the identical period in future arrives
And the memory size of 2G.Then, configuration result is transmitted to money therein by the resource configuration unit in IaaS cloud management platform
Unit is opened in source, and resource opens unit and forms the resource distribution template optimized according to configuration result, and is automatically applied to user A
Virtual machine X on, user can be obtained on interface virtual machine X change information.
It should be pointed out that the resource behaviour in service of each application server not only at any time the difference of section and change, and
And the resource behaviour in service of each application server is also possible to change with the difference in monitoring place.
For example, there may be differences for behaviour in service of the same application in each province, and hence it is also possible to according to different location
Different threshold values is set, that is, period and/or locality factors can be considered in the threshold value of setting.
Further, in the above-described embodiments, for each application server used by the resource distribution of each period
Method is adjusted according to the real-time parameter monitored.If real-time parameter is larger in the fluctuation of identical period, to each application
The resource distribution adjustment amplitude of server is also larger.But on long terms, resource of each application server in each period makes
It is general or more stable with situation, therefore each application server can be steadily adjusted by recursion method when different
The resource distribution of section.
For example, can be determined whether by following recursion methods to each application service for same period, same place
Resource used in device is increased or is reduced.
Cpu_usage (n, t1, p1)=cpu_usage ((n-1), t1, p1) * (1- α)+cpu_temp* α (1)
Wherein, α is the recurrence factor, and value range is [0,1], and cpu_temp is measured by the p1 point current t1 period
Cpu utilization rate, cpu_usage ((n-1), t1, p1) are the cpu utilization rate that a t1 period recursive calculation obtains on p1 point,
Cpu_usage (n, t1, p1) is the cpu utilization rate obtained in the current t1 period recursive calculation of p1 point.
Previous embodiment the resource for judging whether application server increased or reduced when institute according to parameter be
Cpu_temp in this example, is increased in order to enable prediction is more and more accurate in the resource for judging whether application server
Add or reduce when institute according to parameter be cpu_usage (n, t1, p1), not only embody current measured value cpu_temp,
And embody Historical Monitoring information cpu_usage ((n-1), t1, p1).
It will appreciated by the skilled person that realizing that the whole of above method embodiment and part steps can pass through
The relevant hardware of program instruction is completed, and program above-mentioned can store in a compute device readable storage medium, the journey
Sequence when being executed, executes step including the steps of the foregoing method embodiments, and storage medium above-mentioned may include ROM, RAM, magnetic disk
With the various media that can store program code such as CD.
Fig. 2 is the structural schematic diagram of the resource allocation device in the cloud management platform of an embodiment of the present disclosure.
As shown in Fig. 2, the device 20 in the embodiment may include analytical parameter setup unit 202, comparing unit 204,
Resource control unit 206 and resource configuration unit 208.Wherein,
Analytical parameter setup unit 202, it is each based on analysis parameter monitoring for the analysis parameter of built-in each application server
Resource behaviour in service of the application server in different periods；
Comparing unit 204, for each application server monitored in the resource behaviour in service of different periods and to be arranged
Corresponding analysis parameter threshold level be compared；
Resource control unit 206, for being used for each application server in different periods according to comparison result determination
Resource accordingly increased or reduced；
Resource configuration unit 208 in the corresponding period is each for the resource distribution based on identified each application server
Application server carries out corresponding resource distribution.
In this embodiment, due to it according to each application server for monitoring to the actual use shape of various physical resources
Therefore condition, which carrys out the resource distribution that dynamic is adjusted to each application server, can enable each application server in each period
All in optimal operational condition, neither waste system resource it is also possible that each application server obtains required system resource.
Further, the analysis parameter of each application server can include but is not limited to cpu busy percentage, memory usage,
In network interface card rate, storage network rate, system response time, packet loss, disk read-write rate and application process quantity at least
One.
Further, each application server can include but is not limited to mail server, Website server, database service
Device and application server.
Wherein, the corresponding analysis parameter threshold level of setting may include corresponding analysis parameter highest threshold value and minimum door
Limit value.
For example, the highest threshold value of cpu busy percentage and the minimum threshold of cpu busy percentage；The most wealthy family of memory usage
The minimum threshold of limit value and memory usage；The highest threshold value of network interface card rate and the minimum threshold of network interface card rate；Storage
The highest threshold value of network rate and the minimum threshold of storage network rate；The highest threshold value and system of the system speed of response
The minimum threshold of the speed of response；The highest threshold value of packet loss and the minimum threshold of packet loss；Disk read-write rate is most
The minimum threshold of high threshold and disk read-write rate；The highest threshold value and application process quantity of application process quantity are most
Threshold ones.
It should be pointed out that the resource behaviour in service of each application server not only at any time the difference of section and change, and
And the resource behaviour in service of each application server is also possible to change with the difference in monitoring place.
For example, there may be differences for behaviour in service of the same application in each province, and hence it is also possible to according to different location
Different threshold values is set, that is, period and/or locality factors can be considered in the threshold value of setting.
It further, is according to being monitored for each application server method used by the resource distribution of each period
Real-time parameter be adjusted.If real-time parameter fluctuates larger resource distribution to each application server in the identical period
Adjustment amplitude is also larger.But on long terms, resource behaviour in service of each application server in each period generally still compares
More stable, therefore the resource allocation device in cloud management platform can also include recursive unit, based on through recursive algorithm
Each application server after calculating recurrence steadily adjusts each answer in the resource behaviour in service of different periods to pass through recursion method
With server different periods resource distribution.
For example, can be determined whether by following recursion methods to each application service for same period, same place
Resource used in device is increased or is reduced.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with its
The difference of his embodiment, identical and similar part can be with cross-reference between each embodiment.For Installation practice
For, since it is basically similar to the method embodiment, so being described relatively simple, related place may refer to embodiment of the method
Partial explanation.
Although describing the disclosure with reference to exemplary embodiment, it should be appreciated that the present disclosure is not limited to above-mentioned exemplary
Embodiment.It will be obvious to those skilled in the art that can be modified under conditions of without departing substantially from the scope of the present disclosure and spirit
Exemplary embodiments mentioned above.The range of the attached claims should be endowed widest explanation, such to repair comprising all
Change and equivalent structure and function.
Claims (8)
1. the resource allocation methods in a kind of cloud management platform characterized by comprising
The analysis parameter of built-in each application server, based on each application server described in the analysis parameter monitoring in different periods
Resource behaviour in service, wherein the analysis parameter of different application server is different；
By each application server monitored in the resource behaviour in service of different periods and the corresponding analysis parameter door of setting
Limit value is compared；
Each application server is accordingly increased in the resource that different periods are used for according to comparison result determination
Or reduction, wherein the resource include CPU, storage resource, in Internet resources at least one of；
It is carried out accordingly in the corresponding period for each application server based on the resource distribution of identified each application server
Resource distribution；
Wherein, described determined according to comparison result carries out phase in the resource that different periods are used for each application server
The increase or reduction answered include:
The corresponding resource behaviour in service of analysis parameter monitored not between the highest threshold value of the analysis parameter with most
In the case where between threshold ones, resource corresponding with the analysis parameter is increased or reduced.
2. the resource allocation methods in cloud management platform according to claim 1, which is characterized in that each application service
The analysis parameter of device includes cpu busy percentage, memory usage, network interface card rate, storage network rate, system response time, packet loss
At least one of rate, disk read-write rate and application process quantity.
3. the resource allocation methods in cloud management platform according to claim 1, which is characterized in that each application service
Device includes mail server, Website server, database server and application server.
4. the resource allocation methods in cloud management platform according to claim 1, which is characterized in that the setting it is corresponding
Analysis parameter threshold level includes the highest threshold value and minimum threshold of corresponding analysis parameter.
5. the resource allocation device in a kind of cloud management platform characterized by comprising
Analytical parameter setup unit, for the analysis parameter of built-in each application server, based on described in the analysis parameter monitoring
Each application server is in the resource behaviour in service of different periods, and wherein the analysis parameter of different application server is different；
Comparing unit, for by each application server monitored different periods resource behaviour in service and the phase of setting
Parameter threshold level should be analyzed to be compared；
Resource control unit, for determining the money being used for each application server in different periods according to comparison result
Source is accordingly increased or is reduced, wherein the resource include CPU, storage resource, in Internet resources at least one of；
Resource configuration unit, for the resource distribution based on identified each application server the corresponding period be each application
Server carries out corresponding resource distribution；
Wherein, the resource control unit is used in the corresponding resource behaviour in service of an analysis parameter monitored not between this
In the case where analyzing between the highest threshold value and minimum threshold of parameter, resource corresponding with the analysis parameter is increased
Add or reduces.
6. the resource allocation device in cloud management platform according to claim 5, which is characterized in that each application service
The analysis parameter of device includes cpu busy percentage, memory usage, network interface card rate, storage network rate, system response time, packet loss
At least one of rate, disk read-write rate and application process quantity.
7. the resource allocation device in cloud management platform according to claim 5, which is characterized in that each application service
Device includes mail server, Website server, database server and application server.
8. the resource allocation device in cloud management platform according to claim 5, which is characterized in that the setting it is corresponding
Analysis parameter threshold level includes the highest threshold value and minimum threshold of corresponding analysis parameter.
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Families Citing this family (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10756968B2 (en) | 2015-01-26 | 2020-08-25 | Rapid7, Inc. | Network resource management devices methods and systems |
| CN104795072A (en) * | 2015-03-25 | 2015-07-22 | 无锡天脉聚源传媒科技有限公司 | Method and device for coding audio data |
| TWI582607B (en) * | 2015-11-02 | 2017-05-11 | 廣達電腦股份有限公司 | Dynamic resources planning mechanism based on cloud computing and smart device |
| CN105549907A (en) * | 2015-12-11 | 2016-05-04 | 国云科技股份有限公司 | Method for computing needed virtual machine disk IOPS according to business variables |
| CN105653373A (en) * | 2016-02-25 | 2016-06-08 | 腾讯科技（深圳）有限公司 | Resource distributing method and device |
| CN107070685A (en) * | 2016-12-21 | 2017-08-18 | 中电科华云信息技术有限公司 | Implementation method based on cloud platform service moulding plate |
| CN109144706A (en) * | 2017-06-15 | 2019-01-04 | 阿里巴巴集团控股有限公司 | A kind of dynamic allocation method of cpu resource, device and physical machine |
| CN107992951A (en) * | 2017-12-11 | 2018-05-04 | 上海市信息网络有限公司 | Capacity alarm method, system, memory and the electronic equipment of cloud management platform |
| CN109165045A (en) * | 2018-08-09 | 2019-01-08 | 网宿科技股份有限公司 | A kind of method and apparatus for the hardware configuration adjusting server |
| CN109308245A (en) * | 2018-09-07 | 2019-02-05 | 郑州市景安网络科技股份有限公司 | A kind of server resource method for early warning, device, equipment and readable storage medium storing program for executing |
| CN110661654B (en) * | 2019-09-19 | 2023-02-28 | 北京浪潮数据技术有限公司 | Network bandwidth resource allocation method, device, equipment and readable storage medium |
| CN111078537B (en) * | 2019-11-29 | 2023-09-22 | 珠海金山数字网络科技有限公司 | Evaluation method for Unity game bundle package resource division |
| CN111611084A (en) * | 2020-05-26 | 2020-09-01 | 杭州海康威视系统技术有限公司 | Streaming media service instance adjusting method and device and electronic equipment |
| CN113076231A (en) * | 2021-03-26 | 2021-07-06 | 山东英信计算机技术有限公司 | Server application scene setting method, system, terminal and storage medium |
| WO2023151268A1 (en) * | 2022-02-14 | 2023-08-17 | 华为云计算技术有限公司 | Service distribution method, apparatus and system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101938416A (en) * | 2010-09-01 | 2011-01-05 | 华南理工大学 | Cloud computing resource scheduling method based on dynamic reconfiguration virtual resources |
| CN102843419A (en) * | 2012-07-03 | 2012-12-26 | 广东电网公司信息中心 | Service resource allocation method and service resource allocation system |
| CN103164279A (en) * | 2011-12-13 | 2013-06-19 | 中国电信股份有限公司 | Method and system for distributing cloud computing resources |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7392314B2 (en) * | 2003-08-15 | 2008-06-24 | International Business Machines Corporation | System and method for load—balancing in a resource infrastructure running application programs |
| CN101894050B (en) * | 2010-07-28 | 2014-04-16 | 山东中创软件工程股份有限公司 | Method, device and system for flexibly scheduling JEE application resources of cloud resource pool |
| CN102546700B (en) * | 2010-12-23 | 2015-07-01 | 中国移动通信集团公司 | Resource scheduling and resource migration methods and equipment |
| CN102646062B (en) * | 2012-03-20 | 2014-04-09 | 广东电子工业研究院有限公司 | Flexible capacity enlargement method for cloud computing platform based application clusters |
| CN102868744A (en) * | 2012-09-10 | 2013-01-09 | 北京用友政务软件有限公司 | Automated integrated management method for realizing SaaS (Software as a Service) and IaaS (Infrastructure as a Service) |
| CN102868763B (en) * | 2012-10-08 | 2015-12-09 | 山东省计算中心 | The dynamic adjusting method that under a kind of cloud computing environment, virtual web application cluster is energy-conservation |
-
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- 2013-08-09 CN CN201310345031.6A patent/CN104348887B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101938416A (en) * | 2010-09-01 | 2011-01-05 | 华南理工大学 | Cloud computing resource scheduling method based on dynamic reconfiguration virtual resources |
| CN103164279A (en) * | 2011-12-13 | 2013-06-19 | 中国电信股份有限公司 | Method and system for distributing cloud computing resources |
| CN102843419A (en) * | 2012-07-03 | 2012-12-26 | 广东电网公司信息中心 | Service resource allocation method and service resource allocation system |
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