CN105607948A - Virtual machine migration prediction method based on SLA - Google Patents
Virtual machine migration prediction method based on SLA Download PDFInfo
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- CN105607948A CN105607948A CN201510961787.2A CN201510961787A CN105607948A CN 105607948 A CN105607948 A CN 105607948A CN 201510961787 A CN201510961787 A CN 201510961787A CN 105607948 A CN105607948 A CN 105607948A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
- G06F9/485—Task life-cycle, e.g. stopping, restarting, resuming execution
- G06F9/4856—Task life-cycle, e.g. stopping, restarting, resuming execution resumption being on a different machine, e.g. task migration, virtual machine migration
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5083—Techniques for rebalancing the load in a distributed system
- G06F9/5088—Techniques for rebalancing the load in a distributed system involving task migration
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Abstract
The invention relates to the technical field of cloud computing, in particular to a virtual machine migration prediction method based on an SLA (Service Level Agreement). According to the method, firstly, the resource use condition of each virtual machine is monitored once every a period of time; then, after the curve fitting by a mathematical method on the basis of monitoring data, an equation of the curve is obtained; then, the resource quantity used by the virtual machines at the next time interval can be predicted; next, the resource use quantity at the next time interval is compared to the threshold value specified in the SLA; if the resource use quantity at the next time interval exceeds the threshold value, the virtual machine is likely to be about to overload; otherwise, the coming of the next monitoring time interval is waited; and finally, if the overload condition occurs, the virtual machine is migrated to an idle host. The virtual machine migration prediction method based on the SLA provided by the invention has the advantages that an active virtual machine migration strategy is realized through actively predicting the resource use trend of the virtual machine; the problem that the SLA cannot be met after the migration by a conventional migration strategy is solved; and the method can be used for virtual machine migration.
Description
Technical field
The present invention relates to cloud computing technology field, particularly a kind of virtual machine (vm) migration Forecasting Methodology based on SLA.
Background technology
In cloud computing environment, in order to make full use of resource, may occur that the virtual machine that multiple application distribute according to SLA is positioned at the situation on same station server. And the summation that may also there will be resources of virtual machine exceeded the physical resource upper limit case of server, in the time that the load of certain application increases, the resource of other application is just preempted. At this time cloud service business just cannot meet the promise of the resource of in SLA, user being made. At this time often adopt the method for virtual machine (vm) migration to alleviate.
Conventional method is the method for the virtual machine performance isolation based on alarm, and first this method carries out resource monitoring to every main frame, with the interior example that saves as, when the memory usage of certain main frame is during higher than a threshold value, gives a warning, and automatically carries out virtual machine (vm) migration. The benefit of this method is that the operation of migration must be correct, because the situation that resource is seized has occurred really. But the shortcoming of this method is, in the time need to moving, the resource of main frame is very in short supply, the virtual machine that cannot meet SLA can cause load to increase owing to cannot obtaining more resource, and the resource load of all virtual machines in this physical machine will level off to certain constant separately. At this moment cannot know which platform virtual machine need to be moved. And if the virtual machine of migration is not the virtual machine that cannot meet SLA, the virtual machine that load is surged may also be stayed on this main frame, still likely continues preempting resources, the situation that cannot meet SLA also may continue to occur.
Summary of the invention
The technical problem that the present invention solves is a kind of virtual machine (vm) migration Forecasting Methodology based on SLA; Solve the problem that aforementioned prior art exists.
The technical scheme that the present invention solves the problems of the technologies described above is:
Described method comprises the following steps:
Step 1: the resource service condition to every virtual machine is once monitored at set intervals;
Step 2: after carrying out curve fitting by the method for mathematics based on monitor data, obtain the equation of this curve, then dope the resource quantity that next time interval virtual machine uses;
Step 3: the size of the threshold value that relatively next time interval resource use amount and SLA specify, if exceeded threshold value, this virtual machine is probably will load excessive, perform step so 4, otherwise wait for a period of time, while arrival at upper once monitoring period interval, execution step 1;
Step 4: this virtual machine (vm) migration is gone to comparatively idle main frame.
Described curve refers to monitor data is saved as to historical data, choosing suitable curve carries out curve fitting again, then select to meet the equation of curvilinear characteristic, the conventional curve that can select has logarithmic function, exponential function, quadratic function, more than secondary polynomial function, trigonometric function etc.
Described resource service condition refers to the performance indications of virtual machine, and as CPU usage, memory usage etc., concrete index can be selected according to business demand.
Method of the present invention can produce following beneficial effect:
1, the inventive method is a kind of virtual machine (vm) migration strategy of active, is which platform virtual machine load is surged and moves also not occurring to have doped before the situation that host resource is in short supply. This method can ensure certainly can not occur resources of virtual machine deficiency.
2, the inventive method is a kind of migration strategy of Cost And Performance equilibrium, can ensure first to use idle physical machine resource, in the situation that load too high can not meet SLA, just moves.
Brief description of the drawings
Below in conjunction with accompanying drawing, the present invention is further described:
Fig. 1 is flow chart of the present invention;
Detailed description of the invention
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out to clear, complete description, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiment. Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtaining under creative work prerequisite, belong to the scope of protection of the invention.
Algorithm can be virtual machine according to it demand to resource distinguish important degree, this can analogize to many different treasures that are worth. Just can know the quantity of the idling-resource of every main frame by monitor data. The virtual machine that larger idling-resource can hold is also more, and this is similar to several empty knapsacks. So the problem of virtual machine (vm) migration strategy has just become 0-1 knapsack problem.
The input parameter of algorithm is the demand of virtual machine to resource, the sum of virtual machine, the idling-resource amount of main frame. SS is virtual machine (vm) migration strategy stack. The idling-resource composition knapsack collection BagSet that calculates respectively m main frame goes forward side by side after line ordering, and the resource of every virtual machine is used and predicted by PolyFitForecast.
What needs were moved puts into treasure collection TreasureSet to virtual machine. Concentrate each knapsack solution 0-1 knapsack problem to show that the virtual machine set that need to move to current main frame puts into tactful stack to knapsack.
In knapsack collection (BagSet), the algorithm of each knapsack solution 0-1 knapsack problem is as follows:
Solve 0-1 knapsack problem, can use the algorithm of Dynamic Programming, because the method for separating 0-1 knapsack problem is a classical algorithm, the Xie Weiyi obtaining is decided to be optimal solution, so just can obtain best virtual machine (vm) migration strategy.
。
Claims (3)
1. the virtual machine (vm) migration Forecasting Methodology based on SLA, is characterized in that, described method comprise withLower step:
Step 1: the resource service condition to every virtual machine is once monitored at set intervals;
Step 2: after carrying out curve fitting by the method for mathematics based on monitor data, obtain the side of this curveJourney, then dopes the resource quantity that next time interval virtual machine uses;
Step 3: the size of the threshold value that relatively next time interval resource use amount and SLA specify, if exceededThreshold value, this virtual machine is probably will load excessive, performs step so 4, otherwise waits for one sectionTime, while arrival at upper once monitoring period interval, execution step 1;
Step 4: this virtual machine (vm) migration is gone to comparatively idle main frame.
2. method according to claim 1, is characterized in that, described curve refers to monitoringData save as historical data, then choose suitable curve and carry out curve fitting, and then select to meet curve spyThe equation of levying, the conventional curve that can select has logarithmic function, exponential function, quadratic function, more than secondaryPolynomial function, trigonometric function etc.
3. method according to claim 1 and 2, is characterized in that, described resource service condition refers toBe the performance indications of virtual machine, as CPU usage, memory usage etc., concrete index can be according to businessDemand is selected.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106020936A (en) * | 2016-06-07 | 2016-10-12 | 深圳证券通信有限公司 | Virtual machine dispatching method and device for financial cloud platform on basis of operating loads |
| CN106899660A (en) * | 2017-01-26 | 2017-06-27 | 华南理工大学 | Cloud data center energy-saving distribution implementation method based on trundle gray forecast model |
| CN107391230A (en) * | 2017-07-27 | 2017-11-24 | 郑州云海信息技术有限公司 | A kind of implementation method and device for determining virtual machine load |
| CN107579852A (en) * | 2017-09-15 | 2018-01-12 | 郑州云海信息技术有限公司 | Virtual network performance isolation system and method based on historical models in Cloud Server |
| WO2018076791A1 (en) * | 2016-10-31 | 2018-05-03 | 华为技术有限公司 | Resource load balancing control method and cluster scheduler |
| CN108255581A (en) * | 2018-01-15 | 2018-07-06 | 郑州云海信息技术有限公司 | A kind of load based on neural network model determines method, apparatus and system |
| CN108268321A (en) * | 2016-12-30 | 2018-07-10 | 三星电子株式会社 | For migrating the method for workload and machine frame system |
| CN108519919A (en) * | 2018-03-19 | 2018-09-11 | 山东超越数控电子股份有限公司 | A method of realizing server resource dynamic dispatching under virtual cluster environment |
| CN110275773A (en) * | 2018-10-30 | 2019-09-24 | 湖北省农村信用社联合社网络信息中心 | Paas resource circulation utilization index system based on truthful data models fitting |
| WO2020134364A1 (en) * | 2018-12-28 | 2020-07-02 | 中兴通讯股份有限公司 | Virtual machine migration method, cloud computing management platform, and storage medium |
| US11301307B2 (en) | 2019-07-24 | 2022-04-12 | Red Hat, Inc. | Predictive analysis for migration schedulers |
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| CN108268321B (en) * | 2016-12-30 | 2023-11-07 | 三星电子株式会社 | Method and rack system for migrating workload |
| CN108268321A (en) * | 2016-12-30 | 2018-07-10 | 三星电子株式会社 | For migrating the method for workload and machine frame system |
| CN106899660A (en) * | 2017-01-26 | 2017-06-27 | 华南理工大学 | Cloud data center energy-saving distribution implementation method based on trundle gray forecast model |
| CN107391230A (en) * | 2017-07-27 | 2017-11-24 | 郑州云海信息技术有限公司 | A kind of implementation method and device for determining virtual machine load |
| CN107391230B (en) * | 2017-07-27 | 2021-03-09 | 苏州浪潮智能科技有限公司 | Implementation method and device for determining load of virtual machine |
| CN107579852A (en) * | 2017-09-15 | 2018-01-12 | 郑州云海信息技术有限公司 | Virtual network performance isolation system and method based on historical models in Cloud Server |
| CN108255581A (en) * | 2018-01-15 | 2018-07-06 | 郑州云海信息技术有限公司 | A kind of load based on neural network model determines method, apparatus and system |
| CN108519919A (en) * | 2018-03-19 | 2018-09-11 | 山东超越数控电子股份有限公司 | A method of realizing server resource dynamic dispatching under virtual cluster environment |
| CN110275773B (en) * | 2018-10-30 | 2020-08-28 | 湖北省农村信用社联合社网络信息中心 | Paas resource recycling index system based on real data model fitting |
| CN110275773A (en) * | 2018-10-30 | 2019-09-24 | 湖北省农村信用社联合社网络信息中心 | Paas resource circulation utilization index system based on truthful data models fitting |
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Application publication date: 20160525 |