CN102004671B - Resource management method of data center based on statistic model in cloud computing environment - Google Patents
Resource management method of data center based on statistic model in cloud computing environment Download PDFInfo
- Publication number
- CN102004671B CN102004671B CN 201010543864 CN201010543864A CN102004671B CN 102004671 B CN102004671 B CN 102004671B CN 201010543864 CN201010543864 CN 201010543864 CN 201010543864 A CN201010543864 A CN 201010543864A CN 102004671 B CN102004671 B CN 102004671B
- Authority
- CN
- China
- Prior art keywords
- resource
- data
- data center
- cloud computing
- application program
- 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.)
- Expired - Fee Related
Links
- 238000007726 management method Methods 0.000 title abstract description 20
- 238000004458 analytical method Methods 0.000 claims abstract description 14
- 238000009826 distribution Methods 0.000 claims abstract description 10
- 238000012544 monitoring process Methods 0.000 claims abstract description 5
- 238000000034 method Methods 0.000 claims description 27
- 238000013179 statistical model Methods 0.000 claims description 27
- 238000013468 resource allocation Methods 0.000 claims description 13
- 230000006399 behavior Effects 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 8
- 238000010801 machine learning Methods 0.000 claims description 5
- 238000007405 data analysis Methods 0.000 claims description 4
- 238000005259 measurement Methods 0.000 claims description 3
- 238000004891 communication Methods 0.000 claims description 2
- 230000007423 decrease Effects 0.000 claims description 2
- 238000013461 design Methods 0.000 claims description 2
- 238000013507 mapping Methods 0.000 claims description 2
- 230000001105 regulatory effect Effects 0.000 abstract description 3
- 238000010219 correlation analysis Methods 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 5
- 230000008859 change Effects 0.000 description 4
- 238000012417 linear regression Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000012217 deletion Methods 0.000 description 3
- 230000037430 deletion Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000013508 migration Methods 0.000 description 3
- 230000005012 migration Effects 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 238000012806 monitoring device Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- RZVAJINKPMORJF-UHFFFAOYSA-N Acetaminophen Chemical compound CC(=O)NC1=CC=C(O)C=C1 RZVAJINKPMORJF-UHFFFAOYSA-N 0.000 description 1
- 208000007542 Paresis Diseases 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 208000012318 pareses Diseases 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Images
Abstract
The invention relates to a resource management method cluster of a data center based on the statistics in cloud computing environment, which comprises four steps of: 1. collecting work loads, application problem performance and resource use condition information; 2. inputting the information into a model and carrying out model analysis by using a statistic analysis method KCCA (Kernel Canonical Correlation Analysis) and remote-distance relative algorithm; 3. classifying work modes according to the current environment, and regulating the resource distribution according to control parameters; and 4. regulating the resource distribution according to the controller output and simultaneously updating the resource states. The resource management method of the invention firstly considers the characteristic that the work loads of the novel data center in the cloud computing environment are continuously changed, the real-time monitoring and the elastic management are carried out on the resources of the data center according to user requirements and the resource use conditions to ensure that the integral resource consumption can reach the minimum under the condition that the system performance is not influenced. The resource management method has wide practical value and application prospects in the technical field of elastic resource management of the cloud computing data center.
Description
(1) technical field
The method for managing resource of Statistical learning model and machine learning is adopted at the new types of data center under a kind of cloud computing environment of the present invention relates to, relate in particular under a kind of cloud computing environment data center based on the method for managing resource of statistical model, its online management load and resource distribution aspect, the elevator system performance belongs to cloud computing flexible resource management domain.
(2) background technology
At present, so that the demand of computing power is constantly increased, existing data center server quantity constantly increases along with the develop rapidly of network application, and how managing huge server cluster becomes the focus that everybody pays close attention to.Popular along with cloud computing, increasing network (Web) service and commercial the application are deployed in the cloud computing environment, server for magnanimity in the cloud computing data center, the resource of how effectively to manage cloud computing data center promotes resource utilization while use cost and reaches the minimum focus that becomes current cloud computing area research.
Except carrying out the experiment simulation from use benchmark (benchmark), one of method at present commonly used by setting up the relational model between resource and performance and by model resource being managed, the data of namely utilizing small-sized benchmark or part server to collect are carried out statistical study and are weighed relation between resource and performance with this, come the data center resources is managed.The data of collecting under this mode can not represent the overall data in the production run environment, can't process the operating load that constantly changes under the cloud computing environment simultaneously.This mode is dumb to the management of resource, can not resource and performance dynamically be adjusted according to the user's of data center actual demand, might be in the situation that the operating load surge occurs, and system performance significantly descends.
If employing dynamic control technology, by online collection strategy and statistical model, variation according to operating load manages distribution to resource automatically, the shortcoming that can avoid above-mentioned static management to bring, and can be to make system satisfy the demand of flexible resource management, have very important significance at cloud computing new types of data centre sphere tool.
(3) summary of the invention
1, purpose: in view of this, the purpose of this invention is to provide under a kind of cloud computing environment data center based on the method for managing resource of statistical model, it at first collects application program capacity parameter and resource behaviour in service, in the situation that satisfies resource requirement and performance SLA, data center applications program feature is dynamically controlled, used minimum resource to reach the maximum such target of performance thereby reach.
For achieving the above object, the present invention proposes cloud computing new types of data central elastic resource management implementation method, data center's structure as shown in Figure 1.Comprise: main controlled node is interconnected by network adapter and data network, monitor and control a plurality of nodes and virtual machine, master controller is responsible for borrowing the point control device to carry out the unified allocation of resources management to the downstream, comprise the operations such as the phisical drive of any amount of readable data of interpolation, deletion and Migration Control System and storage medium, administrative model is responsible for load and the resource using information of collecting carried out analyzing and processing, then transfers to controller and controls.Computing node comprises the virtual machine of any amount, each intra-node comprises the resources of virtual machine control of a Node Controller responsible node inside, the resource driver is responsible for the allocation manager of resource, and a plurality of application resource usage monitoring device and Network Performance Monitors of comprising are at interior virtual machine and monitor of virtual machine.
2, technical scheme: for achieving the above object, technical scheme of the present invention is such:
As shown in Figure 2, data center is based on the method for managing resource of statistical model under a kind of cloud computing environment, it adopts real-time load and monitoring resource mode, by statistical model analyzing and processing monitor data, generate the ideal Distribution data, process in conjunction with actual conditions by controller, output actual allocated data-guiding system resource allocation, it is characterized in that: the method may further comprise the steps:
Step 1: dynamic collection application program load information and data center's resource using information;
Step 2: the information of collecting is inputed to statistical model as the input data, and main body adopts KCCA algorithm and far away
The Range-based algorithm distributes according to the output adjustresources data analysis and prediction;
Step 3: according to four kinds of mode of operations that design in the model controller, definition and adjustment control parameter use the method adjustresources quantity allotted of machine learning to satisfy SLA;
Step 4: data are analyzed in output, and guidance system carries out resource and distributes, and the while is new data more.
Wherein, the described application program load information of step 1 mainly is the per second number of request, namely uses the number of this application program in the current period; Difference according to different application program resource requirements, also need to collect the resource using information of CPU, I/O and internal memory, max cap. according to behaviour in service and surplus resources assessment virtual machine, to satisfy the application program capacity demand, avoid dividing timing because of resource and surpass the hydraulic performance decline that the virtual machine max cap. causes, be the best between the collection cycle of application program load information and data center's resource using information take 0.8 second～1.2 seconds.
Wherein, the described KCCA algorithm of step 2 has the superperformance of processing nonlinear data, mainly according to the load state of input data prediction in 5 minutes futures, consider node location, the resource behaviour in service generates application program capacity and resource model, further calculate desirable resource allocation conditions, remote related algorithm is by the degree of correlation between correlation parameter computing application program resource demand, choose least correlativing coefficient, guarantee that the resource of distributing has minimum relatedness, avoid resource contention, realize the effective supply of resource elasticity, the data analysis algorithm in the statistical model is done corresponding adjustment by the keeper according to system's actual conditions.
Wherein, the described mode of operation of step 3 is according to existing web application load variations rule, be divided into round the clock pattern, the working day/weekend pattern, great holiday pattern and four kinds of patterns of user's autonomous mode; Controller has reflected that controlled variable is that performance parameter and the processed variable that current measurement is exported namely pass by and current reference input the dynamic relationship between the various resources of distribution; Under the different mode of operations collection cycle of needed application program load information and data center's resource using information and since controller shake caused control parameter all can be different, thereby optimize the mapping relations between virtual resource and the application service, satisfy the application service rank target SLO of application service; Above-mentioned mode of operation is done corresponding adjustment by the keeper according to system's actual conditions.
Wherein, the described analysis data of step 4 refer to by resulting resource allocation data behind statistical model and the controller strategy; This data communication device is crossed access control parameter after the statistical model analysis, analyze in conjunction with current actual conditions, avoid underestimating the generation that the resource contention that causes or some application can't obtain enough this situations of resource because of system situation, after resource has assigned, upgrade the surplus resources data, realize that one takes turns the resource allocation flow.
3, advantage and effect: under a kind of cloud computing environment of the present invention data center based on the method for managing resource of statistical model, it compared with the prior art, its major advantage is: (1) promotes original data center resource provisioning efficient, pares down expenses.Original data center takes excessive supply mechanism for avoiding violating service level agreement (SLA) to user's Litis aestimatio, for data center brings very large expense.This method is dynamically adjusted according to data-center applications load and performance, has further guaranteed the accuracy that resource is distributed by control theory, has guaranteed that the performance of application program goes to ask; (2) with statistical machine learning method and control theory in the resource management of virtual computing system, make up the adaptive problem that new framework, model and method change for application load and system environments to solve computational resource.(3) propose the typical control model of controller, for the different regulating and controlling parameter of different pattern usings, realized using minimum resource to reach optimum performance this purpose.
(4) description of drawings
New types of data division center model synoptic diagram under Fig. 1 cloud computing environment of the present invention
Fig. 2 is based on the model structure synoptic diagram of statistical machine learning
Fig. 3 is based on the resource management schematic flow sheet of model
Fig. 4 controller control resource is distributed synoptic diagram
Fig. 5 carries out the schematic flow sheet of Dynamic Resource Allocation for Multimedia according to system load, performance and resource behaviour in service
Fig. 6 statistical model analysis module synoptic diagram
Wherein symbol description is as follows among Fig. 4:
Reference input (x): the resource of virtual machine is distributed in representative, such as CPU, internal memory, network I/O etc.
Measure output (y): representative system is wished the performance parameter that reaches, such as the parameter index of defined among the SLA such as handling capacity, response time
Departure (e): being a kind of tolerance of control accuracy (accuracy), is the steady-state behaviour index of control system.It depends on reference input (x) and measures output (y)
Lag parameter (α, β): be used for that decision systems increases or the speed of deletion resource, its concrete numerical value is relevant with above-mentioned four kinds of patterns, and the α value is larger, shows that the speed of adding resource is faster; The β value is larger, shows that the speed that removes resource is faster.
(5) embodiment
For making the purpose, technical solutions and advantages of the present invention express clearlyer, the present invention is further described in more detail below in conjunction with drawings and the specific embodiments.
The present invention requires each node to support simultaneously to share storage aspect hardware condition.Aspect software condition, if that the operating system employing is Linux, require its kernel version more than 2.6.18, to avoid the lowest version kernel in the defective aspect the power management.
Satisfied appointed condition required for the present invention is seen Fig. 1, this data center infrastructure comprises: main controlled node is interconnected by network adapter and data network, monitor and control a plurality of virtual machines in a plurality of nodes and the node, master controller is responsible for the downstream node controller is carried out the unified allocation of resources management, comprise the operations such as the phisical drive of any amount of readable data of interpolation, deletion and Migration Control System and storage medium, administrative model is responsible for load and the resource using information of collecting carried out analyzing and processing, then transfers to controller and controls.Computing node comprises the virtual machine of any amount, a kind of application program (for example Web2.0) is installed on each virtual machine, each intra-node comprises the resources of virtual machine control of a Node Controller responsible node inside, the resource driver is responsible for the allocation manager of resource, and a plurality of application resource usage monitoring device and Network Performance Monitors of comprising are at interior virtual machine and monitor of virtual machine.
Resource Management Model based on statistics is seen Fig. 2, the present invention adopts real-time load and monitoring resource mode, by statistical model analyzing and processing monitor data, generate the ideal Distribution data, process in conjunction with actual conditions by controller, output actual allocated data-guiding system resource allocation is to guarantee saving simultaneously system overhead in the performance of using realization system maximum under the less resource situation.
The information that data center collects comprises: the corresponding application program load information of data center, and take the per second number of request as example, analysis user is for the demand of this application program; Collect involved user's request index among user's service level agreement SLA, such as throughput and response time etc., with this as the system performance measurement index; Data center's resource operating position such as data such as CPU usage, I/O and the network bandwidths, is used for weighing system's behaviour in service and entire system capacity.Need the above-mentioned information of real-time collecting, the data that upgrade in time guarantee the data center services quality.
The below describes with an example, as shown in Figure 5, may further comprise the steps:
Step 501: the load of real-time collecting application program, performance and resource using information system.Record simultaneously the information of obtaining, the update cycle is made as 1.0 seconds.
Step 502: the information that above-mentioned steps is collected is input in the statistical model module and analyzes as input, and the relation between the load of Main Analysis application program, performance and resource are used is with the input of its output as step 503.Specifically shown in Fig. 6 statistical model analysis module.
Step 503: director mode analysis.According to the needed resources of virtual machine of user, such as CPU, internal memory etc., the Resource Allocation Formula that utilizes above-mentioned statistical model to provide is considered the interference (such as the demand that administers and maintains of system) that external environment produces system, according to the mode of operation of present analysis, take different control models.Under different patterns, the controller lag parameter is different, for example in great holiday pattern, α=0.9 is set carries out rapid reaction to system exception etc., add fast resource, β=0.01 assurance system can more conservative estimation when needs remove resource, does not need rapid reaction.This group parameter need to be applicable to the rapid-action pattern.Under other patterns, suitably adjust this group parameter, its system is met consumers' demand to the reacting condition of operating load does not need to consume simultaneously too much resource.
Step 504: control the distribution of resource according to the parameter of controller output.At first by the master controller allocating task to Node Controller, Node Controller distributes the virtual machine that meets the demands according to concrete application demand.
Wherein statistical model analysis module flow process may further comprise the steps as shown in Figure 6:
Step 601: in service in system, according to the collected information of Fig. 5 step 501, the curve that the real-time monitoring system parameter generates is observed the exceptional value appearance whether curve has catastrophe point or do not meet matched curve.If exist, illustrate that then Rush Hour appears in this application of current data center, then should take steps 602 to process; If do not exist, then illustrative system is stable, then carry out step 603.
Step 602: outlier appears in system's curve, and the proof system resource requirement need to carry out larger change.Need this moment fast the system resource behaviour in service to be analyzed, when system resource reaches max cap., whether can satisfy resource requirement.Because be outlier, the current parameter that obtains can not represent relational model between whole load, performance and resource, if but untimely processing meeting the performance of system is had a great impact, so need in time carry out analyzing and processing to outlier.If the system resource Pooled resources satisfies current demand, then directly give controller and process, the current outlier of quick solution; If also can't satisfy the application program capacity demand when current system resource reaches max cap., need in time take Intel Virtualization Technology, such as quick resources that adds such as virtual machine (vm) migrations, satisfy the user resources demand.Namely system resource overhead is saved in as required supply.The resource that adds is put into the system resource pond, by the unified control of controller.
Step 603: use next 5 minutes operating load of simple linear regression model (LRM) prediction, i.e. per second application requests number.Simple linear regression model (LRM) can effectively catch the operating load change with time, even more complicated historical data also can be concluded its load of prediction easily.
Step 604: the operating load of prediction is assessed the performance that the required resource requirement of existing workload and system can reach as the input of model.The factor of many complexity all can affect the performance of application program, for example change of mixed load, application code etc., adopt KCCA algorithm and remote related algorithm to realize the multivariate statistical analysis modeling, analyze simultaneously the impact that a plurality of influence factors are brought system performance, the adjustment model parameter generates desirable resource allocation data in real time.At last, with the input of its output as Fig. 5 step 503, jump to the director mode analysis.
Fig. 3 is the resource management schematic flow sheet that the present invention is based on model; Fig. 4 is that controller control resource is distributed synoptic diagram.
Check in this example that resource is used and the parameter such as operating load and carry out in resource allocation process, carrying out according to the collection of resources loop cycle when corresponding parameter is upgraded.Adopt above-mentioned flexible resource management method can accomplish to guarantee that system uses less resource to reach maximum performance at any time, to satisfy user's demand as far as possible.
It should be noted last that: above embodiment is the unrestricted technical scheme of the present invention in order to explanation only, although with reference to above-described embodiment the present invention is had been described in detail, those of ordinary skill in the art is to be understood that: still can make amendment or be equal to replacement the present invention, and not breaking away from any modification or partial replacement of the spirit and scope of the present invention, it all should be encompassed in the middle of the claim scope of the present invention.
Claims (5)
- Under the cloud computing environment data center based on the method for managing resource of statistical model, it adopts real-time load and monitoring resource mode, by statistical model analyzing and processing monitor data, generate the ideal Distribution data, process in conjunction with actual conditions by controller, output actual allocated data-guiding system resource allocation, it is characterized in that: the method may further comprise the steps:Step 1: dynamic collection application program load information and data center's resource using information;Step 2: the information of collecting is inputed to statistical model as the input data, and main body adopts KCCAAlgorithm and remote related algorithm distribute according to the output adjustresources data analysis and prediction;Step 3: according to four kinds of mode of operations that design in the model controller, definition and adjustment control parameter,Use the method adjustresources quantity allotted of machine learning to satisfy SLA;Step 4: data are analyzed in output, and guidance system carries out resource and distributes, and the while is new data more.
- Under a kind of cloud computing environment according to claim 1 data center based on the method for managing resource of statistical model, it is characterized in that: the described application program load information of step 1 mainly is the per second number of request, namely uses the number of this application program in the current period; Difference according to different application program resource requirements, also need to collect the resource using information of CPU, I/O and internal memory, max cap. according to behaviour in service and surplus resources assessment virtual machine, to satisfy the application program capacity demand, avoid dividing timing because of resource and surpass the hydraulic performance decline that the virtual machine max cap. causes, be the best between the collection cycle of application program load information and data center's resource using information take 0.8 second～1.2 seconds.
- Under a kind of cloud computing environment according to claim 1 data center based on the method for managing resource of statistical model, it is characterized in that: the described KCCA algorithm of step 2 has the superperformance of processing nonlinear data, mainly according to the load state of input data prediction in 5 minutes futures, consider node location, the resource behaviour in service generates application program capacity and resource model, further calculate desirable resource allocation conditions, remote related algorithm is by the degree of correlation between correlation parameter computing application program resource demand, choose least correlativing coefficient, guarantee that the resource of distributing has minimum relatedness, avoid resource contention, realize the effective supply of resource elasticity, the data analysis algorithm in the statistical model is done corresponding adjustment by the keeper according to system's actual conditions.
- Under a kind of cloud computing environment according to claim 1 data center based on the method for managing resource of statistical model, it is characterized in that: the described mode of operation of step 3 is according to existing web application load variations rule, be divided into round the clock pattern, the working day/weekend pattern, great holiday pattern and four kinds of patterns of user's autonomous mode; Controller has reflected that controlled variable is that performance parameter and the processed variable that current measurement is exported namely pass by and current reference input the dynamic relationship between the various resources of distribution; Under the different mode of operations collection cycle of needed application program load information and data center's resource using information and since controller shake caused control parameter all can be different, thereby optimize the mapping relations between virtual resource and the application service, satisfy the application service rank target SLO of application service; Above-mentioned mode of operation is done corresponding adjustment by the keeper according to system's actual conditions.
- 5. data center is characterized in that based on the method for managing resource of statistical model under a kind of cloud computing environment according to claim 1: the described analysis data of step 4 refer to by resulting resource allocation data behind statistical model and the controller strategy; This data communication device is crossed access control parameter after the statistical model analysis, analyze in conjunction with current actual conditions, avoid underestimating the generation that the resource contention that causes or some application can't obtain enough this situations of resource because of system situation, after resource has assigned, upgrade the surplus resources data, realize that one takes turns the resource allocation flow.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201010543864 CN102004671B (en) | 2010-11-15 | 2010-11-15 | Resource management method of data center based on statistic model in cloud computing environment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201010543864 CN102004671B (en) | 2010-11-15 | 2010-11-15 | Resource management method of data center based on statistic model in cloud computing environment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102004671A CN102004671A (en) | 2011-04-06 |
| CN102004671B true CN102004671B (en) | 2013-03-13 |
Family
ID=43812049
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 201010543864 Expired - Fee Related CN102004671B (en) | 2010-11-15 | 2010-11-15 | Resource management method of data center based on statistic model in cloud computing environment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102004671B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10084665B1 (en) | 2017-07-25 | 2018-09-25 | Cisco Technology, Inc. | Resource selection using quality prediction |
| US10091070B2 (en) | 2016-06-01 | 2018-10-02 | Cisco Technology, Inc. | System and method of using a machine learning algorithm to meet SLA requirements |
| US11892933B2 (en) | 2018-11-28 | 2024-02-06 | Oracle International Corporation | Predicting application performance from resource statistics |
Families Citing this family (86)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102170474A (en) * | 2011-04-22 | 2011-08-31 | 广州杰赛科技股份有限公司 | Method and system for dynamic scheduling of virtual resources in cloud computing network |
| CN102164184A (en) * | 2011-04-22 | 2011-08-24 | 广州杰赛科技股份有限公司 | Computer entity access and management method for cloud computing network and cloud computing network |
| US20130019015A1 (en) * | 2011-07-12 | 2013-01-17 | International Business Machines Corporation | Application Resource Manager over a Cloud |
| CN102958166B (en) * | 2011-08-29 | 2017-07-21 | 华为技术有限公司 | A kind of resource allocation methods and resource management platform |
| CN102331948A (en) * | 2011-09-01 | 2012-01-25 | 杭州湾云计算技术有限公司 | Resource state-based virtual machine structure adjustment method and adjustment system |
| CN102279771B (en) * | 2011-09-02 | 2013-07-10 | 北京航空航天大学 | Method and system for adaptively allocating resources as required in virtualization environment |
| US8612599B2 (en) * | 2011-09-07 | 2013-12-17 | Accenture Global Services Limited | Cloud service monitoring system |
| CN103024825B (en) | 2011-09-26 | 2015-07-01 | ***通信集团公司 | Method and device of distributing network source among multiple applications of terminal |
| US8826277B2 (en) * | 2011-11-29 | 2014-09-02 | International Business Machines Corporation | Cloud provisioning accelerator |
| CN102420869A (en) * | 2011-12-02 | 2012-04-18 | 浪潮集团有限公司 | Security monitoring method for cloud data center |
| CN103178990A (en) * | 2011-12-20 | 2013-06-26 | ***通信集团青海有限公司 | Network device performance monitoring method and network management system |
| CN102902344A (en) * | 2011-12-23 | 2013-01-30 | 同济大学 | Method for optimizing energy consumption of cloud computing system based on random tasks |
| CN102520785B (en) * | 2011-12-27 | 2015-04-15 | 东软集团股份有限公司 | Energy consumption management method and system for cloud data center |
| CN102521715B (en) * | 2011-12-29 | 2016-04-13 | 上海华东电脑股份有限公司 | A kind of method and system controlling application system Resourse Distribute |
| US8756609B2 (en) | 2011-12-30 | 2014-06-17 | International Business Machines Corporation | Dynamically scaling multi-tier applications vertically and horizontally in a cloud environment |
| CN102567080B (en) * | 2012-01-04 | 2015-03-04 | 北京航空航天大学 | Virtual machine position selection system facing load balance in cloud computation environment |
| US8732291B2 (en) * | 2012-01-13 | 2014-05-20 | Accenture Global Services Limited | Performance interference model for managing consolidated workloads in QOS-aware clouds |
| US9509632B2 (en) | 2012-04-25 | 2016-11-29 | Empire Technology Development Llc | Workload prediction for network-based computing |
| CN102724298A (en) * | 2012-05-25 | 2012-10-10 | 清华大学 | Method for configuring storage parameter under cloud environment |
| US9635134B2 (en) * | 2012-07-03 | 2017-04-25 | Empire Technology Development Llc | Resource management in a cloud computing environment |
| US8887056B2 (en) * | 2012-08-07 | 2014-11-11 | Advanced Micro Devices, Inc. | System and method for configuring cloud computing systems |
| CN103577268B (en) * | 2012-08-07 | 2016-12-21 | 复旦大学 | Adaptive resource Supply Method based on application load |
| CN102917031A (en) * | 2012-09-25 | 2013-02-06 | 浙江图讯科技有限公司 | Data computing system of safety production cloud service platform for industrial and mining enterprises |
| CN102930394B (en) * | 2012-10-26 | 2016-12-21 | 安徽科大讯飞信息科技股份有限公司 | The monitoring method of a kind of teaching equipment and resource and device |
| EP2929717A4 (en) | 2012-12-07 | 2016-07-20 | Hewlett Packard Entpr Dev Lp | Network resource management |
| CN103049309B (en) * | 2012-12-11 | 2017-09-29 | 华为技术有限公司 | Resource management apparatus, the method and system of virtual machine |
| CN103064744B (en) * | 2012-12-27 | 2016-06-29 | 北京仿真中心 | The method for optimizing resources that a kind of oriented multilayer Web based on SLA applies |
| CN103077079B (en) * | 2012-12-28 | 2016-06-08 | 华为技术有限公司 | Virtual machine (vm) migration control method and device |
| CN103092326A (en) * | 2013-01-25 | 2013-05-08 | 浪潮电子信息产业股份有限公司 | Host energy saving method based on time |
| CN103078759B (en) * | 2013-01-25 | 2017-06-06 | 北京润通丰华科技有限公司 | The management method and device of calculate node, system |
| WO2014119719A1 (en) * | 2013-02-01 | 2014-08-07 | 日本電気株式会社 | Resource control system, control pattern generation device, control device, resource control method and program |
| CN104104551B (en) * | 2013-04-02 | 2018-03-23 | ***通信集团公司 | Cloud resource need assessment method and device |
| CN103338241B (en) * | 2013-06-19 | 2016-08-10 | 合肥工业大学 | A kind of new publicly-owned cloud framework and virtual resources adaptive configuring method thereof |
| CN103458055A (en) * | 2013-09-22 | 2013-12-18 | 广州中国科学院软件应用技术研究所 | Clout competing platform |
| CN103544063B (en) * | 2013-09-30 | 2017-02-08 | 三星电子（中国）研发中心 | Method and device for removing processes applied to Android platform |
| CN103559089B (en) * | 2013-10-30 | 2016-06-08 | 南京邮电大学 | A kind of virtual machine requirement forecasting based on service level agreement constraint realizes method |
| CN103763130B (en) * | 2013-12-31 | 2018-06-19 | 华为数字技术（苏州）有限公司 | Management method, the device and system of large-scale cluster |
| CN103685563A (en) * | 2013-12-31 | 2014-03-26 | 湖南师范大学 | Cloud calculating system based on mobile communication network and resource management method thereof |
| WO2015130643A1 (en) * | 2014-02-28 | 2015-09-03 | Intel Corporation | Technologies for cloud data center analytics |
| CN103986669B (en) * | 2014-05-07 | 2017-04-19 | 华东师范大学 | Assessment method of resource allocation strategy in cloud computing |
| US10027536B2 (en) * | 2014-06-25 | 2018-07-17 | Futurewei Technologies, Inc. | System and method for affinity-based network configuration |
| US10091058B2 (en) | 2014-11-10 | 2018-10-02 | Futurewei Technologies, Inc. | Method and apparatus for model-driven, affinity-based, network functions |
| CN105991667B (en) * | 2015-01-27 | 2019-05-28 | 华为软件技术有限公司 | The method and apparatus for correcting resources error |
| CN104793993B (en) * | 2015-04-24 | 2017-11-17 | 江南大学 | The cloud computing method for scheduling task of artificial bee colony particle cluster algorithm based on Levy flights |
| CN105607948A (en) * | 2015-12-18 | 2016-05-25 | 国云科技股份有限公司 | Virtual machine migration prediction method based on SLA |
| CN105763367A (en) * | 2016-02-02 | 2016-07-13 | 四川长虹电器股份有限公司 | Data center energy consumption management method based on virtualization |
| CN116737256A (en) * | 2016-02-22 | 2023-09-12 | 斯诺普西斯公司 | Method for automatically adjusting computing system, computing system and computer readable medium |
| CN105871618B (en) * | 2016-04-13 | 2019-05-24 | 上海交通大学 | More resource regulating methods of erratic demand are coped in cloud scheduler |
| US10454877B2 (en) | 2016-04-29 | 2019-10-22 | Cisco Technology, Inc. | Interoperability between data plane learning endpoints and control plane learning endpoints in overlay networks |
| US20170357910A1 (en) * | 2016-06-10 | 2017-12-14 | Apple Inc. | System for iteratively training an artificial intelligence using cloud-based metrics |
| EP3436929A4 (en) | 2016-06-17 | 2019-10-16 | Hewlett-Packard Development Company, L.P. | Shared machine-learning data structure |
| US10334334B2 (en) * | 2016-07-22 | 2019-06-25 | Intel Corporation | Storage sled and techniques for a data center |
| CN106250206A (en) * | 2016-07-27 | 2016-12-21 | 云南电网有限责任公司信息中心 | A kind of resource pool automatic measurement & calculation method based on virtual machine |
| CN106293872A (en) * | 2016-07-27 | 2017-01-04 | 云南电网有限责任公司信息中心 | A kind of SLA resources balance management-control method based on resource pool |
| US10942776B2 (en) | 2016-09-21 | 2021-03-09 | Accenture Global Solutions Limited | Dynamic resource allocation for application containers |
| CN108205469B (en) * | 2016-12-20 | 2023-12-15 | 华为技术有限公司 | MapReduce-based resource allocation method and server |
| US20180255122A1 (en) * | 2017-03-02 | 2018-09-06 | Futurewei Technologies, Inc. | Learning-based resource management in a data center cloud architecture |
| US10963813B2 (en) | 2017-04-28 | 2021-03-30 | Cisco Technology, Inc. | Data sovereignty compliant machine learning |
| US10477148B2 (en) | 2017-06-23 | 2019-11-12 | Cisco Technology, Inc. | Speaker anticipation |
| CN107480027A (en) * | 2017-07-07 | 2017-12-15 | 上海诺悦智能科技有限公司 | A kind of distributed deep learning operational system |
| US10608901B2 (en) | 2017-07-12 | 2020-03-31 | Cisco Technology, Inc. | System and method for applying machine learning algorithms to compute health scores for workload scheduling |
| US10782757B2 (en) * | 2017-08-09 | 2020-09-22 | Equinix, Inc. | Rack level power control |
| CN107515779B (en) * | 2017-09-01 | 2020-08-07 | 周口师范学院 | Virtual machine performance interference measurement system and method based on detector |
| CN110019298B (en) * | 2017-10-31 | 2021-07-30 | 北京国双科技有限公司 | Data processing method and device |
| CN107908459B (en) * | 2017-11-10 | 2020-06-09 | 山东劳动职业技术学院 | Cloud computing scheduling system |
| CN108073457B (en) * | 2017-12-28 | 2021-12-21 | 深信服科技股份有限公司 | Layered resource management method, device and system of super-fusion infrastructure |
| CN108446200B (en) * | 2018-02-07 | 2021-10-29 | 福建星瑞格软件有限公司 | Intelligent operation and maintenance method for server based on big data machine learning and computer equipment |
| CN108322541B (en) * | 2018-02-09 | 2021-04-06 | 杭州顺网科技股份有限公司 | Self-adaptive distributed system architecture |
| CN108681964B (en) * | 2018-04-19 | 2020-08-28 | 腾讯科技（深圳）有限公司 | Transaction settlement system and transaction settlement control method |
| US10867067B2 (en) | 2018-06-07 | 2020-12-15 | Cisco Technology, Inc. | Hybrid cognitive system for AI/ML data privacy |
| US10446170B1 (en) | 2018-06-19 | 2019-10-15 | Cisco Technology, Inc. | Noise mitigation using machine learning |
| CN109088747A (en) * | 2018-07-10 | 2018-12-25 | 郑州云海信息技术有限公司 | The management method and device of resource in cloud computing system |
| CN109032914A (en) * | 2018-09-06 | 2018-12-18 | 掌阅科技股份有限公司 | Resource occupation data predication method, electronic equipment, storage medium |
| CN109446020B (en) * | 2018-09-21 | 2022-05-13 | 曙光云计算集团有限公司 | Dynamic evaluation method and device of cloud storage system |
| CN111143050B (en) * | 2018-11-02 | 2023-09-19 | 中移（杭州）信息技术有限公司 | Method and equipment for dispatching container clusters |
| CN111198650A (en) * | 2018-11-16 | 2020-05-26 | 龙芯中科技术有限公司 | Control method and device of storage equipment |
| US20220004475A1 (en) * | 2018-11-28 | 2022-01-06 | 3M Innovative Properties Company | Data center infrastructure optimization method based on causal learning |
| CN109510875B (en) * | 2018-12-14 | 2021-03-09 | 北京奇艺世纪科技有限公司 | Resource allocation method and device and electronic equipment |
| CN109918170A (en) * | 2019-01-25 | 2019-06-21 | 西安电子科技大学 | A kind of cloud data center virtual machine dynamic BTS configuration method and system |
| CN110297715B (en) * | 2019-07-02 | 2021-09-14 | 北京工业大学 | Online load resource prediction method based on periodic feature analysis |
| CN111176835B (en) * | 2019-12-09 | 2023-06-16 | 南京理工大学 | Software self-adaption method based on hierarchical control |
| CN111290354B (en) * | 2020-02-21 | 2021-02-02 | 镇江香江云动力科技有限公司 | Modularized data center waste heat efficient utilization control management system based on cloud computing |
| CN113804937A (en) * | 2020-06-16 | 2021-12-17 | 普源精电科技股份有限公司 | Multifunctional measuring equipment, resource allocation method, measuring method, device and medium |
| CN112612717A (en) * | 2020-12-30 | 2021-04-06 | 吴江市景新纺织有限公司 | Intelligent device parameter debugging management system |
| CN114625544B (en) * | 2022-05-17 | 2022-08-30 | 银河麒麟软件(长沙)有限公司 | Memory balloon management method and device based on dynamic allocation of virtual machine service load |
| CN117116200B (en) * | 2023-10-23 | 2023-12-29 | 广州市惠正信息科技有限公司 | Method and system for adjusting resolution of LED display screen |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1538297A (en) * | 2003-04-16 | 2004-10-20 | ��ʿͨ��ʽ���� | Device for adjusting used of system resource and its method |
| CN101442807A (en) * | 2008-12-30 | 2009-05-27 | 北京邮电大学 | Method and system for distribution of communication system resource |
| CN101719082A (en) * | 2009-12-24 | 2010-06-02 | 中国科学院计算技术研究所 | Method and system for dispatching application requests in virtual calculation platform |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1476834A1 (en) * | 2002-02-07 | 2004-11-17 | Thinkdynamics Inc. | Method and system for managing resources in a data center |
-
2010
- 2010-11-15 CN CN 201010543864 patent/CN102004671B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1538297A (en) * | 2003-04-16 | 2004-10-20 | ��ʿͨ��ʽ���� | Device for adjusting used of system resource and its method |
| CN101442807A (en) * | 2008-12-30 | 2009-05-27 | 北京邮电大学 | Method and system for distribution of communication system resource |
| CN101719082A (en) * | 2009-12-24 | 2010-06-02 | 中国科学院计算技术研究所 | Method and system for dispatching application requests in virtual calculation platform |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10091070B2 (en) | 2016-06-01 | 2018-10-02 | Cisco Technology, Inc. | System and method of using a machine learning algorithm to meet SLA requirements |
| US10084665B1 (en) | 2017-07-25 | 2018-09-25 | Cisco Technology, Inc. | Resource selection using quality prediction |
| US10091348B1 (en) | 2017-07-25 | 2018-10-02 | Cisco Technology, Inc. | Predictive model for voice/video over IP calls |
| US10225313B2 (en) | 2017-07-25 | 2019-03-05 | Cisco Technology, Inc. | Media quality prediction for collaboration services |
| US11892933B2 (en) | 2018-11-28 | 2024-02-06 | Oracle International Corporation | Predicting application performance from resource statistics |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102004671A (en) | 2011-04-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102004671B (en) | Resource management method of data center based on statistic model in cloud computing environment | |
| Zhang et al. | Electricity bill capping for cloud-scale data centers that impact the power markets | |
| Wang et al. | Fuzzy modeling based resource management for virtualized database systems | |
| CN104991830A (en) | YARN resource allocation and energy-saving scheduling method and system based on service level agreement | |
| CN109491760B (en) | High-performance data center cloud server resource autonomous management method | |
| US20130339759A1 (en) | Method and system for automated application layer power management solution for serverside applications | |
| Guitart | Toward sustainable data centers: a comprehensive energy management strategy | |
| Masoumzadeh et al. | An intelligent and adaptive threshold-based schema for energy and performance efficient dynamic VM consolidation | |
| Banerjee et al. | Efficient resource utilization using multi-step-ahead workload prediction technique in cloud | |
| CN102339233A (en) | Cloud computing centralized management platform | |
| Werner et al. | Simulator improvements to validate the green cloud computing approach | |
| CN103581333A (en) | Runtime virtual resource dynamic allocation method and system based on feedforward and feedback control | |
| HoseinyFarahabady et al. | Q-flink: A qos-aware controller for apache flink | |
| US10054997B2 (en) | Multivariable control for power-latency management to support optimization of data centers or other systems | |
| Chen et al. | Using a task dependency job-scheduling method to make energy savings in a cloud computing environment | |
| Mehta et al. | Energy conservation in cloud infrastructures | |
| Li et al. | Dynamic virtual machine consolidation algorithm based on balancing energy consumption and quality of service | |
| Li et al. | Load prediction-based automatic scaling cloud computing | |
| Nesmachnow et al. | Controlling datacenter power consumption while maintaining temperature and QoS levels | |
| Vu et al. | Predictive hybrid autoscaling for containerized applications | |
| Wang et al. | Adaptive performance control of computing systems via distributed cooperative control: Application to power management in computing clusters | |
| CN105116987B (en) | The multiple power source and performance management system of a kind of cloud computing center | |
| CN113225994A (en) | Intelligent air conditioner control method facing data center | |
| Ali et al. | Profit-aware DVFS enabled resource management of IaaS cloud | |
| Faraji-Mehmandar et al. | A self-learning approach for proactive resource and service provisioning in fog environment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| ASS | Succession or assignment of patent right |
Owner name: SHANGHAI SHICONG INFORMATION TECHNOLOGY CO., LTD. Free format text: FORMER OWNER: BEIHANG UNIVERSITY Effective date: 20150408 |
|
| C41 | Transfer of patent application or patent right or utility model | ||
| COR | Change of bibliographic data |
Free format text: CORRECT: ADDRESS; FROM: 100191 HAIDIAN, BEIJING TO: 201401 FENGXIAN, SHANGHAI |
|
| TR01 | Transfer of patent right |
Effective date of registration: 20150408 Address after: 201401 Shanghai Fengxian District City Ring Road No. 2200 building 2128 room Patentee after: Shanghai Shi Cong network information technology Co., Ltd Address before: 100191 Beijing City, Haidian District Xueyuan Road No. 37 North College of computer Patentee before: Beihang University |
|
| C56 | Change in the name or address of the patentee | ||
| CP03 | Change of name, title or address |
Address after: 200233 room 202-35, Guiping Road, Shanghai, Xuhui District, 92 Patentee after: SHANGHAI JUNESH INFORMATION TECHNOLOGY CO., LTD. Address before: 201401 Shanghai Fengxian District City Ring Road No. 2200 building 2128 room Patentee before: Shanghai Shi Cong network information technology Co., Ltd |
|
| DD01 | Delivery of document by public notice |
Addressee: SHANGHAI JUNESH INFORMATION TECHNOLOGY CO., LTD. Document name: Notification to Pay the Fees |
|
| DD01 | Delivery of document by public notice | ||
| DD01 | Delivery of document by public notice |
Addressee: SHANGHAI JUNESH INFORMATION TECHNOLOGY CO., LTD. Document name: Notification of Termination of Patent Right |
|
| DD01 | Delivery of document by public notice | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20130313 Termination date: 20181115 |