CN104519082A - Expansion method and device of cloud computation - Google Patents
Expansion method and device of cloud computation Download PDFInfo
- Publication number
- CN104519082A CN104519082A CN201310450238.XA CN201310450238A CN104519082A CN 104519082 A CN104519082 A CN 104519082A CN 201310450238 A CN201310450238 A CN 201310450238A CN 104519082 A CN104519082 A CN 104519082A
- Authority
- CN
- China
- Prior art keywords
- computational resource
- time point
- utilization rate
- resource utilization
- computing unit
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides an expansion method and device of cloud computation. A computing resource of the cloud computation is distributed to a plurality of computing units, and the method comprises the following steps: sampling a computing resource usage rate of each application in a cloud platform on which the cloud computation is performed at first N time points; using time as X-axis and the computing resource usage rate as Y-axis, computing slope and intercept of a straight line formed by fitting N coordinate points constructed by the N time points and the computing resource usage rates applied to the N time points; computing the computing resource usage rate applied to the N+1-th time point according to the slope and the intercept, if the computing resource usage rate applied to the N+1-th time point is greater than a first preset threshold value, determining number of the computing units required for expanding according to the first preset threshold value, the computing resource usage rates applied to the N-th time point and the N+1-th time point; and expanding the application according to the number of the computing units required for expanding. Through the adoption of the method and device provided by the invention, the resource waste can be reduced.
Description
Technical field
The application relates to field of cloud computer technology, the expansion method of particularly a kind of cloud computing and device.
Background technology
Cloud computing is a kind of business computation model, is distributed in by calculation task on the resource pool of a large amount of computer formation, enables various application system obtain computing power, memory space and information service as required.
Along with the development of cloud computing technology, increasing Web service all accesses cloud computing place cloud platform, greatly reduces O&M cost and operation threshold.Then the visit capacity of Web service is surged along with the appearance of focus, at this moment just needs the computational resource to user (comprising CPU, Memory etc.) to carry out capacity expansion and upgrading, otherwise certain customers can be caused to access.
Amazon relatively early adopts automatic capacity-enlargement technology, and realize automatic dilatation by creating new virtual machine, implementation method is fairly simple, but, automatic capacity-enlargement technology due to Amazon be for virtual machine-level other, the granularity of its scheduling resource is too large, can cause the wasting of resources.
Summary of the invention
In view of this, the object of the present invention is to provide a kind of expansion method of cloud computing, the method can reduce scheduling of resource granularity, reduces the wasting of resources.
For achieving the above object, technical scheme provided by the invention is:
An expansion method for cloud computing, the computational resource of described cloud computing is by graduation to multiple computing unit, and the computational resource of each computing unit is less than the computational resource that virtual machine takies; The method comprises:
Each computational resource utilization rate being applied in top n time point in the cloud platform of sampling cloud computing place;
Take time as abscissa, computational resource utilization rate is ordinate, calculate the slope of the straight line of the N number of coordinate points matching be made up of described N number of time point and this computational resource utilization rate being applied in described N number of time point and cut a square;
The computational resource utilization rate that this is applied in N+1 time point is calculated according to described slope and intercept, if this computational resource utilization rate being applied in N+1 time point is greater than the first predetermined threshold value, then determine according to described first predetermined threshold value, this computational resource utilization rate being applied in N+1 time point the computing unit number needing dilatation;
The computing unit number of dilatation carries out dilatation to this application as required.
A flash chamber for cloud computing, the computational resource of described cloud computing is by graduation to multiple computing unit, and the computational resource of each computing unit is less than the computational resource that virtual machine takies; This device comprises: sampling unit, fitting unit, computing unit, dilatation unit;
Described sampling unit, for each computational resource utilization rate being applied in top n time point in described cloud computing place cloud platform of sampling;
Described fitting unit, for take time as abscissa, computational resource utilization rate is ordinate, calculate the slope of the straight line of the N number of coordinate points matching be made up of described N number of time point and this computational resource utilization rate being applied in described N number of time point and cut a square;
Described computing unit, for calculating according to described slope and intercept the computational resource utilization rate that this is applied in N+1 time point, if this computational resource utilization rate being applied in N+1 time point is greater than the first predetermined threshold value, then determine according to described first predetermined threshold value, this computational resource utilization rate being applied in N+1 time point the computing unit number needing dilatation;
Described dilatation unit, the computing unit number for dilatation as required carries out dilatation to this application.
In sum, the present invention passes through the computational resource graduation of cloud computing to multiple computing unit, and determine that this is applied in the computational resource utilization rate of N+1 time point according to a certain computational resource utilization rate being applied in top n time point obtained of sampling, when this is applied in that the computational resource utilization rate of N+1 time point is too high needs dilatation, be that basic dilatation unit carries out dilatation with computing unit.The computational resource had due to computing unit is less, thus can reduce scheduling of resource granularity, reduces the wasting of resources.
Accompanying drawing explanation
Fig. 1 is the expansion method flow chart of embodiment of the present invention cloud computing;
Fig. 2 is the structural representation of the flash chamber of embodiment of the present invention cloud computing.
Embodiment
For making object of the present invention, technical scheme and advantage clearly understand, to develop simultaneously embodiment referring to accompanying drawing, scheme of the present invention is described in further detail.
In the embodiment of the present invention, in order to reduce scheduling of resource granularity, reduce the wasting of resources, by the computational resource graduation of cloud computing to multiple computing unit, the computational resource that each computing unit has is less than computational resource that virtual machine takies (computational resource that virtual machine takies for: the summation of the computational resource needed for the computational resource for application all in virtual machine of virtual machine configuration and virtual machine self-operating).When transporting a certain application calculated on the cloud platform of place and needing dilatation, be that basic dilatation unit carries out dilatation with computing unit, compare relative to the automatic capacity-enlargement technology of Amazon being basic dilatation unit with virtual machine of the prior art, because the resource transfer granularity of computing unit is less, thus the wasting of resources can be reduced.
The number of the computational resource that above-mentioned computing unit has can preset, such as be less than the computational resource that virtual machine self-operating needs take, after setting computational resource that a computing unit has and be how many, just can determine to determine how many computing units can be divided, such as, suppose that the computational resource of cloud computing is 10, the computational resource that each computing unit has is set as 2, then can by the computational resource graduation of cloud computing to (computational resource that computational resource/each computing unit of computing unit number=cloud computing has) in 5 computing units.
See the expansion method flow chart that Fig. 1, Fig. 1 are embodiment of the present invention cloud computings, wherein, the computational resource of cloud computing by graduation to multiple computing unit, the computational resource that each computing unit has is less than the computational resource of virtual machine, and as shown in Figure 1, the method mainly comprises the following steps:
Each computational resource utilization rate being applied in top n time point in step 101, sampling cloud computing place cloud platform.
Every one section of Preset Time, to the computational resource utilization rate of this application, sampling should be carried out, finally can obtain the computational resource utilization rate that this is applied in the 1st time point, the 2nd time point ... and N number of time point.
Step 102, be abscissa with time, computational resource utilization rate is ordinate, calculate the slope of the straight line of the N number of coordinate points matching be made up of described N number of time point and this computational resource utilization rate being applied in described N number of time point and cut a square.
In the present embodiment, when with time be abscissa, computational resource utilization rate for ordinate time, the computational resource utilization rate of top n time point can be applied according to this sampling, obtain (T
1, C
1), (T
2, C
2) ..., (T
i, C
i) ..., (T
n, C
n) be total to N number of coordinate points, wherein, T
irepresent i-th time point in top n time point, C
irepresent that this is applied in time point T
icomputational resource utilization rate.
Sampling obtains after this is applied in the computational resource utilization rate of top n time point, in order to predict the computational resource utilization rate of this N+1 time, straight line can be simulated according to above-mentioned N number of coordinate points, and calculate slope and the intercept of this straight line, predict that this is applied in the computational resource utilization rate of N+1 time point according to the slope of this straight line and intercept.
Wherein, the slope calculating the straight line of the N number of coordinate points matching be made up of top n time point and this computational resource utilization rate being applied in top n time point is as follows with the method for cutting square:
First calculate the mean value Tavg of top n time point, and this is applied in the mean value Cavg of the computational resource utilization rate of top n time point;
Then slope k described in following formulae discovery is adopted:
Finally adopt intercept b described in following formulae discovery:
b＝Cavg-k×Tavg。
Step 103, calculate according to described slope and intercept the computational resource utilization rate that this is applied in N+1 time point, if this computational resource utilization rate being applied in N+1 time point is greater than the first predetermined threshold value, then determine to need the computing unit number of dilatation according to described first predetermined threshold value, this computational resource utilization rate being applied in N number of time point and N+1 time point.
Calculate the straight line of the N number of coordinate points matching be made up of top n time point and this computational resource utilization rate being applied in top n time point slope and after cutting square, can calculate according to described slope and intercept the computational resource utilization rate that this is applied in N+1 time point, concrete grammar is for adopting following formula:
C
n+1=k × T
n+1+ b, wherein, T
n+1represent N+1 time point, C
n+1represent that this is applied in the computational resource utilization rate of N+1 time point, k is the slope of straight line described in step 102, and b is the intercept of straight line described in step 102.
In actual applications, when the computational resource utilization rate of this application is too high, just need to carry out dilatation, for this reason, a threshold value (being assumed to be the first predetermined threshold value Co) can be preset, when then only having computational resource utilization rate to N+1 time point higher than this first predetermined threshold value, just need to calculate the computing unit number needing dilatation.
Can determine to need according to described first predetermined threshold value, this computational resource utilization rate being applied in N number of time point and N+1 time point the computing unit number of dilatation, concrete grammar is for adopting following formula:
Wherein, U is the computing unit number needing dilatation; Cb is the second predetermined threshold value, represents the optimal computed resource utilization of this application, can rule of thumb preset; Num represents the current computing unit number taken of this application.
The computing unit number of step 104, as required dilatation carries out dilatation to this application.
After calculating this application and needing the computing unit number of dilatation, just can perform dilatation to this application according to the computing unit data calculated, this application can normally be run.
In the embodiment of the present invention shown in Fig. 1, described computational resource is: CPU and/or storage resources.
Above the expansion method of embodiment of the present invention cloud computing is described in detail, has present invention also offers a kind of flash chamber of cloud computing, be described below in conjunction with Fig. 2.
Be the structural representation of the flash chamber of embodiment of the present invention cloud computing see Fig. 2, Fig. 2, wherein, the computational resource of described cloud computing is by graduation to multiple computing unit, and the computational resource that each computing unit has is less than the computational resource of virtual machine; As shown in Figure 2, this device comprises: sampling unit 201, fitting unit 202, computing unit 203, dilatation unit 204; Wherein,
Sampling unit 201, for each computational resource utilization rate being applied in top n time point in described cloud computing place cloud platform of sampling;
Fitting unit 202, for take time as abscissa, computational resource utilization rate is ordinate, calculate the slope of the straight line of the N number of coordinate points matching be made up of described N number of time point and this computational resource utilization rate being applied in described N number of time point and cut a square;
Computing unit 203, for calculating according to described slope and intercept the computational resource utilization rate that this is applied in N+1 time point, if this computational resource utilization rate being applied in N+1 time point is greater than the first predetermined threshold value, then determine according to described first predetermined threshold value, this computational resource utilization rate being applied in N+1 time point the computing unit number needing dilatation;
Dilatation unit 204, the computing unit number for dilatation as required carries out dilatation to this application.
In Fig. 2 shown device, described fitting unit 202 when calculating the slope of straight line and the intercept of the N number of coordinate points matching be made up of described N number of time point and this computational resource utilization rate being applied in described N number of time point, for:
Calculate the mean value Tavg of described N number of time point, and this is applied in the mean value Cavg of the computational resource utilization rate of described N number of time point;
Adopt slope k described in following formulae discovery:
wherein, i=1,2 ..., N, T
irepresent the computational resource utilization rate of i-th time point; C
irepresent that this is applied in the computational resource utilization rate of i-th time point;
Adopt intercept b described in following formulae discovery:
b＝Cavg-k×Tavg。
In Fig. 2 shown device, described computing unit 203 is calculating according to described slope and intercept the computational resource utilization rate C that this is applied in N+1 time point
n+1time, adopt following formulae discovery:
C
N+1＝k×T
N+1+b。
In Fig. 2 shown device, described computing unit 203, when determining according to described first predetermined threshold value, this computational resource utilization rate being applied in N+1 time point the computing unit number U needing dilatation, adopts following formulae discovery:
wherein, Cb is the second predetermined threshold value, and Cb is less than the first predetermined threshold value; Num is the current computing unit number taken of this application.
In Fig. 2 shown device, described computational resource is: CPU and/or storage resources.
The above, be only preferred embodiment of the present invention, be not intended to limit protection scope of the present invention.Within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (10)
1. an expansion method for cloud computing, is characterized in that, the computational resource of described cloud computing is by graduation to multiple computing unit, and the computational resource of each computing unit is less than the computational resource that virtual machine takies; The method comprises:
Each computational resource utilization rate being applied in top n time point in the cloud platform of sampling cloud computing place;
Take time as abscissa, computational resource utilization rate is ordinate, calculate the slope of the straight line of the N number of coordinate points matching be made up of described N number of time point and this computational resource utilization rate being applied in described N number of time point and cut a square;
The computational resource utilization rate that this is applied in N+1 time point is calculated according to described slope and intercept, if this computational resource utilization rate being applied in N+1 time point is greater than the first predetermined threshold value, then determine according to described first predetermined threshold value, this computational resource utilization rate being applied in N+1 time point the computing unit number needing dilatation;
The computing unit number of dilatation carries out dilatation to this application as required.
2. method according to claim 1, is characterized in that,
The slope of straight line and the method for intercept that calculate the N number of coordinate points matching be made up of described N number of time point and this computational resource utilization rate being applied in described N number of time point are:
Calculate the mean value Tavg of described N number of time point, and this is applied in the mean value Cavg of the computational resource utilization rate of described N number of time point, and adopts slope k described in following formulae discovery:
wherein, i=1,2 ..., N, T
irepresent the computational resource utilization rate of i-th time point; C
irepresent that this is applied in the computational resource utilization rate of i-th time point;
Adopt intercept b described in following formulae discovery:
b＝Cavg-k×Tavg。
3. method according to claim 2, is characterized in that,
The computational resource utilization rate C that this is applied in N+1 time point is calculated according to described slope and intercept
n+1method for adopt following formula:
C
N+1＝k×T
N+1+b。
4. method according to claim 3, is characterized in that,
Determine that the method for the computing unit number U needing dilatation is for adopting following formula according to described first predetermined threshold value, this computational resource utilization rate being applied in N+1 time point:
wherein, Cb is the second predetermined threshold value, and Cb is less than the first predetermined threshold value; Num is the current computing unit number taken of this application.
5. the claim according to claim 1,2,3 or 4, is characterized in that,
Described computational resource is: CPU and/or storage resources.
6. a flash chamber for cloud computing, is characterized in that, the computational resource of described cloud computing is by graduation to multiple computing unit, and the computational resource of each computing unit is less than the computational resource that virtual machine takies; This device comprises: sampling unit, fitting unit, computing unit, dilatation unit;
Described sampling unit, for each computational resource utilization rate being applied in top n time point in described cloud computing place cloud platform of sampling;
Described fitting unit, for take time as abscissa, computational resource utilization rate is ordinate, calculate the slope of the straight line of the N number of coordinate points matching be made up of described N number of time point and this computational resource utilization rate being applied in described N number of time point and cut a square;
Described computing unit, for calculating according to described slope and intercept the computational resource utilization rate that this is applied in N+1 time point, if this computational resource utilization rate being applied in N+1 time point is greater than the first predetermined threshold value, then determine according to described first predetermined threshold value, this computational resource utilization rate being applied in N+1 time point the computing unit number needing dilatation;
Described dilatation unit, the computing unit number for dilatation as required carries out dilatation to this application.
7. device according to claim 6, is characterized in that,
Described fitting unit when calculating the slope of straight line and the intercept of the N number of coordinate points matching be made up of described N number of time point and this computational resource utilization rate being applied in described N number of time point, for:
Calculate the mean value Tavg of described N number of time point, and this is applied in the mean value Cavg of the computational resource utilization rate of described N number of time point, and adopts slope k described in following formulae discovery:
wherein, i=1,2 ..., N, T
irepresent the computational resource utilization rate of i-th time point; C
irepresent that this is applied in the computational resource utilization rate of i-th time point;
Adopt intercept b described in following formulae discovery:
b＝Cavg-k×Tavg。
8. device according to claim 7, is characterized in that,
Described computing unit is calculating according to described slope and intercept the computational resource utilization rate C that this is applied in N+1 time point
n+1time, adopt following formulae discovery:
C
N+1＝k×T
N+1+b。
9. device according to claim 8, is characterized in that,
Described computing unit, when determining according to described first predetermined threshold value, this computational resource utilization rate being applied in N+1 time point the computing unit number U needing dilatation, adopts following formulae discovery:
wherein, Cb is the second predetermined threshold value, and Cb is less than the first predetermined threshold value; Num is the current computing unit number taken of this application.
10. the device according to claim 6,7,8 or 9, is characterized in that,
Described computational resource is: CPU and/or storage resources.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310450238.XA CN104519082B (en) | 2013-09-27 | 2013-09-27 | A kind of expansion method and device of cloud computing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310450238.XA CN104519082B (en) | 2013-09-27 | 2013-09-27 | A kind of expansion method and device of cloud computing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104519082A true CN104519082A (en) | 2015-04-15 |
| CN104519082B CN104519082B (en) | 2018-11-20 |
Family
ID=52793801
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310450238.XA Active CN104519082B (en) | 2013-09-27 | 2013-09-27 | A kind of expansion method and device of cloud computing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104519082B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106020967A (en) * | 2016-05-03 | 2016-10-12 | 华中科技大学 | Hybrid cloud resource automatic expansion method |
| WO2018077079A1 (en) * | 2016-10-31 | 2018-05-03 | 阿里巴巴集团控股有限公司 | Application capacity enlargement method, apparatus and system |
| CN113254224A (en) * | 2021-07-15 | 2021-08-13 | 中电金信软件有限公司 | Computing resource capacity expansion method and device, electronic equipment and readable storage medium |
| CN113626145A (en) * | 2021-07-21 | 2021-11-09 | 济南浪潮数据技术有限公司 | Dynamic capacity expansion method and system for number of service virtual machines |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110145392A1 (en) * | 2009-12-11 | 2011-06-16 | International Business Machines Corporation | Dynamic provisioning of resources within a cloud computing environment |
| CN102193832A (en) * | 2010-03-11 | 2011-09-21 | 精英电脑股份有限公司 | Cloud computing resource scheduling method and applied system |
| CN102646062A (en) * | 2012-03-20 | 2012-08-22 | 广东电子工业研究院有限公司 | Flexible capacity enlargement method for cloud computing platform based application clusters |
| CN102681899A (en) * | 2011-03-14 | 2012-09-19 | 金剑 | Virtual computing resource dynamic management system of cloud computing service platform |
| US20130097601A1 (en) * | 2011-10-12 | 2013-04-18 | International Business Machines Corporation | Optimizing virtual machines placement in cloud computing environments |
| US20130205027A1 (en) * | 2012-02-03 | 2013-08-08 | International Business Machines Corporation | Automatic Cloud Provisioning Based on Related Internet News and Social Network Trends |
-
2013
- 2013-09-27 CN CN201310450238.XA patent/CN104519082B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110145392A1 (en) * | 2009-12-11 | 2011-06-16 | International Business Machines Corporation | Dynamic provisioning of resources within a cloud computing environment |
| CN102193832A (en) * | 2010-03-11 | 2011-09-21 | 精英电脑股份有限公司 | Cloud computing resource scheduling method and applied system |
| CN102681899A (en) * | 2011-03-14 | 2012-09-19 | 金剑 | Virtual computing resource dynamic management system of cloud computing service platform |
| US20130097601A1 (en) * | 2011-10-12 | 2013-04-18 | International Business Machines Corporation | Optimizing virtual machines placement in cloud computing environments |
| US20130205027A1 (en) * | 2012-02-03 | 2013-08-08 | International Business Machines Corporation | Automatic Cloud Provisioning Based on Related Internet News and Social Network Trends |
| CN102646062A (en) * | 2012-03-20 | 2012-08-22 | 广东电子工业研究院有限公司 | Flexible capacity enlargement method for cloud computing platform based application clusters |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106020967A (en) * | 2016-05-03 | 2016-10-12 | 华中科技大学 | Hybrid cloud resource automatic expansion method |
| CN106020967B (en) * | 2016-05-03 | 2019-04-12 | 华中科技大学 | A kind of automatic expansion method of hybrid cloud resource |
| WO2018077079A1 (en) * | 2016-10-31 | 2018-05-03 | 阿里巴巴集团控股有限公司 | Application capacity enlargement method, apparatus and system |
| CN113254224A (en) * | 2021-07-15 | 2021-08-13 | 中电金信软件有限公司 | Computing resource capacity expansion method and device, electronic equipment and readable storage medium |
| CN113626145A (en) * | 2021-07-21 | 2021-11-09 | 济南浪潮数据技术有限公司 | Dynamic capacity expansion method and system for number of service virtual machines |
| CN113626145B (en) * | 2021-07-21 | 2022-07-19 | 济南浪潮数据技术有限公司 | Dynamic capacity expansion method and system for number of business virtual machines |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104519082B (en) | 2018-11-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102981910B (en) | The implementation method of scheduling virtual machine and device | |
| Li et al. | Pricing and repurchasing for big data processing in multi-clouds | |
| CN103605662B (en) | Distributed computation frame parameter optimizing method, device and system | |
| US10025361B2 (en) | Power management across heterogeneous processing units | |
| CN103814358A (en) | Virtual machine placement within server farm | |
| US20140026147A1 (en) | Varying a characteristic of a job profile relating to map and reduce tasks according to a data size | |
| CN109522382A (en) | Spatial data gridding statistical method and device | |
| CN106373030B (en) | A kind of method of basin step power station Optimized Operation | |
| CN103970879B (en) | Method and system for regulating storage positions of data blocks | |
| EP2671152A1 (en) | Estimating a performance characteristic of a job using a performance model | |
| CN104519082A (en) | Expansion method and device of cloud computation | |
| CN104462755A (en) | Electronic equipment spare part configuration and calculation method based on reliability model | |
| Van Tendeloo et al. | Activity in pythonpdevs | |
| Liao et al. | A QoS-aware dynamic data replica deletion strategy for distributed storage systems under cloud computing environments | |
| Huang et al. | Cümülön: Matrix-based data analytics in the cloud with spot instances | |
| CN108205713A (en) | A kind of region wind power prediction error distribution determination method and device | |
| CN112506691A (en) | Method and system for recovering digital twin application fault of multi-energy system | |
| CN106201655B (en) | Virtual machine distribution method and virtual machine distribution system | |
| CN104579763A (en) | Network capacity expansion method and equipment | |
| Yang et al. | A queueing analytical model for service mashup in mobile cloud computing | |
| CN101901192B (en) | On-chip and off-chip data object static assignment method | |
| CN104516823A (en) | Method and device for storing data | |
| CN109995551B (en) | Service metering method and device of cloud computing system | |
| CN105306547A (en) | Data placing and node scheduling method for increasing energy efficiency of cloud computing system | |
| Vu et al. | Computational flood modeling with UPC architecture |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20190819 Address after: 518057 Nanshan District science and technology zone, Guangdong, Zhejiang Province, science and technology in the Tencent Building on the 1st floor of the 35 layer Co-patentee after: Tencent cloud computing (Beijing) limited liability company Patentee after: Tencent Technology (Shenzhen) Co., Ltd. Address before: Shenzhen Futian District City, Guangdong province 518044 Zhenxing Road, SEG Science Park 2 East Room 403 Patentee before: Tencent Technology (Shenzhen) Co., Ltd. |