CN103607300B - Method and system for risk processing within cloud application operation period - Google Patents

Abstract

The invention discloses a method and system for risk processing within a cloud application operation period. The method comprises: first indicator data of a first monitoring indicator related to cloud application operation are obtained in the cloud application operation period; if the first indicator data meet a preset warning condition, all first association resources influencing changing of the first indicator data and/or all second association resources influencing changing of second indicator data are determined in a cloud computing environment, wherein the second indicator data are indicator data of a second monitoring indicator influencing changing of the first indicator data in the cloud computing environment; an adjustment task for first association resource adjustment and/or an adjustment task for second association resource adjustment are/is determined, wherein the adjustment task indicates an adjustment way enabling the first indicator data not to meet the preset warning condition; and one adjustment task is selected from all the adjustment tasks and the association resources corresponding to the selected adjustment task are adjusted by using the selected adjustment task.

Description

A kind of risk processing method of cloud application runtime and system

Technical field

The present invention relates to communication technical field, more particularly, to a kind of risk processing method of cloud application runtime and system.

Background technology

Cloud data center construct on typical data center the network virtualization layer with functions such as calculating, storages and Cloud service layer, can more intelligent more hommization equipment is managed, more dynamically transfer data resource, and with The mode that need to take is paid user and is used.This method of service of cloud data center significantly improves cloud data center resource Utilization ratio and the convenience of resource use, but meanwhile, the complexity of cloud data center also greatly increases, and increases cloud The risk management difficulty of application run-time.

Traditional cloud data center is mainly to the risk management measures of cloud application runtime: is related to cloud application operation The monitoring resource index definition max-thresholds of connection and/or minimum threshold values, just trigger warning strategies after beyond preset threshold range. Illustrate: assume that running related correlated resources to application app-a includes server a, server b, switch C, data base d, application server e etc., for ensure operation risk within the runtime for the app-a be found at any time and It is processed at any time, then need the monitor control index of above-mentioned all correlated resources is defined maximum and/or minimum threshold values and corresponding Triggering warning strategies, specifically it is assumed that monitoring resource index includes: the cpu utilization rate of server a, application server e Active threads number etc., for server a, if the cpu utilization rate of server a be more than 90% and the persistent period reach By 20 minutes, then the not enough alarm of triggering computing resource, if less than 5% and the persistent period reached the cpu utilization rate of server a By 24 hours, then the remaining alarm of triggering computing resource.

After alarm is triggered, traditional cloud data center can be notified by modes such as mail, note or web-based management ends Correlated resources (such as, server a) and indication information (such as, the server carrying Risk Content of operation maintenance personnel current risk The cpu utilization rate of a be more than 90% and the persistent period reach 20 minutes), then by manually investigation being carried out to current risk Reason, and how to select in troubleshooting procedure effectively to operate to eliminate the experience that risk needs by operation maintenance personnel, but, this artificial Investigate the mode of risk so that investigation speed is slow, investigation difficulty is greatly it is impossible to fast and accurately release the operation of cloud application runtime Risk.

Content of the invention

In view of this, the main purpose of the embodiment of the present invention is to provide a kind of risk processing method of cloud application runtime And system, to realize fast and accurately releasing the purpose of the operation risk of cloud application runtime.

For achieving the above object, embodiments provide a kind of risk processing method of cloud application runtime, comprising:

Obtain the first achievement data of the first supervision index relevant with cloud application operation in the cloud application runtime；

If described first achievement data meets default alarm conditions, cloud computing environment determines impact described first Each first correlated resources that achievement data changes and/or each second association that impact the second achievement data changes Resource, described second achievement data is to affect the second supervision index that described first achievement data changes in cloud computing environment Achievement data；

Determine the adjustment task that described first correlated resources are adjusted and/or described second correlated resources are adjusted Whole adjustment task, described adjustment task is the adjustment side making described first achievement data be unsatisfactory for described default alarm conditions Formula；

An adjustment task is chosen from each adjustment task determining, and using selection adjustment task adjustment and described choosing Take the corresponding correlated resources of adjustment task.

Preferably, in the above-mentioned methods, described determine adjustment task that described first correlated resources are adjusted and/or The adjustment task that described second correlated resources are adjusted, specifically includes:

Determine the adjustment target making described first achievement data be unsatisfactory for described default alarm conditions；

When described adjustment target is to reduce described first achievement data, obtains and described first correlated resources are adjusted The adjustment task for reducing described first achievement data, and/or, obtain use that described second correlated resources are adjusted In the adjustment task reducing described first achievement data；

When described adjustment target is to raise described first achievement data, obtains and described first correlated resources are adjusted The adjustment task for raising described first achievement data, and/or, obtain use that described second correlated resources are adjusted In the adjustment task raising described first achievement data.

Preferably, in the above-mentioned methods, the adjustment task described first correlated resources being adjusted is that the first adjustment is appointed Business, the adjustment task that described second correlated resources are adjusted is the second adjustment task, and described adjustment from each determining is appointed Choose an adjustment task in business, specifically include:

The first condition calculating described first adjustment task executes probability and/or the second condition of described second adjustment task Execution probability, wherein, described first condition execution probability is under conditions of the described first supervision index is the first achievement data Described first achievement data is made to be unsatisfactory for the execution probability of the adjustment task of described default alarm conditions, described second condition execution Probability is in the condition that the described first supervision index is the first achievement data and described second supervision index is the second achievement data Under make described first achievement data be unsatisfactory for the execution probability of the adjustment task of described default alarm conditions；

Execute from described calculated all conditions and probability, choose maximum execution probability, and choose described maximum execution Probability corresponding adjustment task, with using selection adjustment task adjustment and the described selection corresponding correlated resources of adjustment task.

Preferably, in the above-mentioned methods, using selection adjustment task adjustment and the described selection corresponding pass of adjustment task After connection resource, also include:

Reacquire the first achievement data of described first supervision index, if the first achievement data of described reacquisition Still meet described default alarm conditions, then reduce the probit of described maximum execution probability, continue executing with and be calculated from described All conditions execute the step choosing maximum execution probability in probability, until described first achievement data be unsatisfactory for described default Till alarm conditions.

Preferably, said method also includes: builds monitor control index related reasoning model in advance；

The method for building up of described monitor control index related reasoning model, specifically includes:

Determine at least one first supervision index relevant with cloud application operation；

Determine each correlated resources and/or impact second supervision affecting that the data of described first supervision index changes Each correlated resources that the data of index changes, described second supervision index is to affect described first prison in cloud computing environment The supervision index changing depending on the data of index；

The adjustment of each correlated resources described in the determination of adjustment target of the achievement data according to the described first supervision index is appointed Business, described adjustment target is to raise the achievement data of described first supervision index or the index number reducing described first supervision index According to；

Setting probability distribution table, described probability distribution table includes first condition probability, second condition probability and described tune The preset value of effective execution probability of whole task；

Wherein, described effective execution probability makes the achievement data of the first supervision index be discontented with after being carried out described adjustment task The probit of the default alarm conditions of foot, described first condition probability is when described first monitors that index is interval in the first preset data When interior, described effective execution probability is the probability of preset value, and described second condition probability is when described effective execution probability is default Described in during value, second monitors probability in the second preset data interval for the index.

Preferably, in the above-mentioned methods, the described first condition execution probability calculating described first adjustment task and/or institute State the second condition execution probability of the second adjustment task, specifically include:

Monitor the current of index using in the described first achievement data described monitor control index related reasoning model of renewal first Data, inquires about the probability distribution table in described monitor control index related reasoning model according to the current data after described renewal, and will The first condition probability inquiring is as the first condition execution probability of the described first adjustment task；

And/or,

Monitor the current of index using in the described first achievement data described monitor control index related reasoning model of renewal first Data, and the current number of the second supervision index in described monitor control index related reasoning model is updated using described second achievement data According to；Inquire about the probability distribution table in described monitor control index related reasoning model according to the current data after described renewal, and according to Inquire about the second condition probability obtaining and the preset value of described effective execution probability calculates the described second Article 2 adjusting task Part executes probability.

The embodiment of the present invention additionally provides a kind of risk processing system of cloud application runtime, comprising:

Achievement data acquisition module, for obtaining the first supervision index relevant with cloud application operation in the cloud application runtime The first achievement data；

Correlated resources determining module, for when described first achievement data meets default alarm conditions, in cloud computing ring In border, each first correlated resources that described first achievement data of determination impact changes and/or impact the second achievement data are sent out Each second correlated resources of changing, described second achievement data is to affect described first achievement data in cloud computing environment to send out The achievement data of the second supervision index of changing；

Adjustment task determining module, for determining adjustment task that described first correlated resources are adjusted and/or right The adjustment task that described second correlated resources are adjusted, described adjustment task is described for making described first achievement data be unsatisfactory for The adjustment mode of default alarm conditions；

Adjustment task chooses module, for choosing an adjustment task from each adjustment task determining；

Risk processing module, for using choosing, the adjustment of adjustment task is corresponding with described selection adjustment task to associate money Source.

Preferably, in said system, described adjustment task determining module, specifically include:

Adjustment target determination unit, for determining the tune making described first achievement data be unsatisfactory for described default alarm conditions Whole target；

First adjustment task determining unit, for when described adjustment target is to reduce described first achievement data, obtaining The adjustment task for reducing described first achievement data that described first correlated resources are adjusted, and/or, obtain to institute State the adjustment task for reducing described first achievement data that the second correlated resources are adjusted；

Second adjustment task determining unit, for when described adjustment target is to raise described first achievement data, obtaining The adjustment task for raising described first achievement data that described first correlated resources are adjusted, and/or, obtain to institute State the adjustment task for raising described first achievement data that the second correlated resources are adjusted.

Preferably, in said system, the adjustment task that described first correlated resources are adjusted is that the first adjustment is appointed Business, the adjustment task that described second correlated resources are adjusted is the second adjustment task, and described adjustment task chooses module, tool Body includes:

Execution probability calculation unit, for calculating the first condition execution probability and/or described of described first adjustment task The second condition execution probability of the second adjustment task, wherein, described first condition execution probability is to monitor index described first For making described first achievement data be unsatisfactory for holding of the adjustment task of described default alarm conditions under conditions of the first achievement data Row probability, described second condition execution probability is to monitor for the first achievement data and described second in the described first supervision index to refer to Described first achievement data is made to be unsatisfactory for the adjustment task of described default alarm conditions under conditions of being designated as the second achievement data Execution probability；

Adjustment task chooses unit, general for executing the maximum execution of selection in probability from described calculated all conditions Rate, and choose described maximum execution probability corresponding adjustment task, to choose adjustment using selection adjustment task adjustment with described The corresponding correlated resources of task.

Preferably, in said system,

Described risk processing module, is additionally operable to corresponding with described selection adjustment task using choosing the adjustment of adjustment task After correlated resources, reacquire the first achievement data of described first supervision index, if the first index of described reacquisition Data still meets described default alarm conditions, then reduce the probit of described maximum execution probability, continue executing with from described calculating The all conditions that obtain execute the step choosing maximum execution probability in probability, until described first achievement data be unsatisfactory for described Till default alarm conditions.

Preferably, said system also includes: model building module, for building monitor control index related reasoning model in advance；

Described model building module, specifically includes:

Monitor index determining unit, for determining at least one first supervision index relevant with cloud application operation；

Correlated resources determining unit, each association that the data for determining the described first supervision index of impact changes Each correlated resources that the data of resource and/or impact the second supervision index changes, described second monitors that index is cloud meter Calculate the supervision index affecting in environment that the data of described first supervision index changes；

Adjustment task determining unit, for described in the adjustment target determination of the achievement data according to the described first supervision index The adjustment task of each correlated resources, described adjustment target is to raise the achievement data of described first supervision index or reduce described The achievement data of the first supervision index；

Probability tables arranging unit, for arranging probability distribution table, described probability distribution table include first condition probability, second The preset value of effective execution probability of conditional probability and described adjustment task；Wherein, described effective execution probability is carried out institute The achievement data making the first supervision index after stating adjustment task is unsatisfactory for presetting the probit of alarm conditions, and described first condition is general Rate is that described effective execution probability is the probability of preset value when described first monitors that index is in the first preset data interval, institute Stating second condition probability is the second supervision index described in when described effective execution probability is preset value in the second preset data area Interior probability.

Preferably, in said system,

Described execution probability calculation unit, specifically for updating described monitor control index association using described first achievement data The current data of the first supervision index in inference pattern, according to the current data inquiry described monitor control index association after described renewal Probability distribution table in inference pattern, and using the first condition inquiring probability as the described first first condition adjusting task Execution probability；And/or, update the first supervision index in described monitor control index related reasoning model using described first achievement data Current data, and update the second supervision index in described monitor control index related reasoning model using described second achievement data Current data；Inquire about the probability distribution table in described monitor control index related reasoning model according to the current data after described renewal, And described second adjustment task is calculated according to the preset value inquiring about the second condition probability obtaining and described effective execution probability Second condition executes probability.

The risk processing method of cloud application runtime provided in an embodiment of the present invention and system, when the first supervision obtaining refers to After target first achievement data meets default alarm conditions, determine that described first achievement data of impact occurs in cloud computing environment Each first correlated resources of change and/or impact each second correlated resources of changing of the second achievement data, described the Two achievement datas are the index number affecting the second supervision index that described first achievement data changes in cloud computing environment According to；Then, it is determined that the adjustment task that described first correlated resources are adjusted and/or described second correlated resources are adjusted Whole adjustment task, finally chooses an adjustment task from each adjustment task determining, and is adjusted using choosing adjustment task The whole and described selection corresponding correlated resources of adjustment task, to make the first achievement data no longer after correlated resources are adjusted Meet default alarm conditions.The embodiment of the present invention can be automatically adjusted to the monitor control index meeting default alarm conditions, from And be automatically obtained risk investigation, overcome that the brought investigation speed of artificial investigation is slow, the difficult defect such as big of investigation it is achieved that Fast and accurately release the purpose of the operation risk of cloud application runtime.

Brief description

In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing Have technology description in required use accompanying drawing be briefly described it should be apparent that, drawings in the following description are the present invention Some embodiments, for those of ordinary skill in the art, on the premise of not paying creative work, can also basis These accompanying drawings obtain other accompanying drawings.

Fig. 1 is one of schematic flow sheet of risk processing method of embodiment of the present invention cloud application runtime；

Fig. 2 is the two of the schematic flow sheet of risk processing method of embodiment of the present invention cloud application runtime；

Fig. 3 is the node definition schematic diagram of embodiment of the present invention monitor control index related reasoning model；

Fig. 4 is the first directed acyclic graph of embodiment of the present invention monitor control index related reasoning model；

Fig. 5 is the second directed acyclic graph of embodiment of the present invention monitor control index related reasoning model；

Fig. 6 is the schematic flow sheet of embodiment of the present invention related reasoning method for establishing model；

Fig. 7 is a kind of structural representation of the risk processing system of embodiment of the present invention cloud application runtime；

Fig. 8 is another kind of structural representation of the risk processing system of embodiment of the present invention cloud application runtime.

Specific embodiment

Purpose, technical scheme and advantage for making the embodiment of the present invention are clearer, below in conjunction with the embodiment of the present invention In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described it is clear that described embodiment is The a part of embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art The every other embodiment being obtained under the premise of not making creative work, broadly falls into the scope of protection of the invention.

In the cloud computing environment that cloud application is run, the resource related to cloud application running status except physical server, Outside switch, data base and intermediate equipment, also virtual server (vm server), virtual switch etc., and virtual resource Also can dynamically increase or decrease according to the actual requirements, the mapping relations between virtual resource and physical resource also can dynamically occur Change, etc..Based on this, when the virtual unit in physical equipment or cloud computing environment or cloud application itself etc. break down all By the normal operation of impact cloud application, so, the present invention implements the risk processing method and processing device of the cloud application runtime of offer, Mainly when the cloud application runtime abnormal operating condition occurring, in time the related resource in cloud computing environment is adjusted, To realize automatically processing the purpose of operation risk.For realizing this purpose, below each embodiment of the present invention is specifically situated between Continue.

Embodiment one

Referring to Fig. 1, the schematic flow sheet of the risk processing method of the cloud application runtime providing for the embodiment of the present invention one, Specifically include:

Step 101: obtain the first index number of the first supervision index relevant with cloud application operation in the cloud application runtime According to.

Wherein, described first monitor that index can be the service request response time or user puts down in line number or service request The monitor control index that all operation maintenance personnel such as process time is concerned about.Furthermore it is possible to preset multiple first monitor control indexs, and in cloud application fortune The departure date is acquired to the achievement data of this multiple first supervision index.

Step 102: if described first achievement data meets default alarm conditions, determine impact in cloud computing environment Each first correlated resources that described first achievement data changes and/or impact the second achievement data change each Second correlated resources, described second achievement data is affect that described first achievement data changes in cloud computing environment second Monitor the achievement data of index.

In embodiments of the present invention, the resource in cloud computing environment is divided into two class resources, a class is the first correlated resources, Another kind of is the second correlated resources.The change of described first correlated resources will result directly in described first achievement data and becomes Change；The change of described second correlated resources will result directly in described second achievement data and changes and described second index number According to change directly result in described first achievement data again and change, i.e. the change of described second correlated resources leads to institute indirectly State the first achievement data to change.So, in actual applications, the correlated resources that impact the first achievement data changes can Only to include the first correlated resources, or only include the second correlated resources, or include the first correlated resources and the second association money simultaneously Source.

Wherein, described first correlated resources or described second correlated resources can be: physical host, fictitious host computer, net Network, operating system, etc.；Described second supervision index can be: vm cpu utilization rate or disk io(input and output) Flow, or vm memory usage, or network bandwidth utilization rate, or network io delay, etc..

Step 103: determine the adjustment task that described first correlated resources are adjusted and/or to the described second association money The adjustment task that source is adjusted, described adjustment task is to make described first achievement data be unsatisfactory for described default alarm conditions Adjustment mode.

It is assumed that " fictitious host computer " is the first correlated resources or the second correlated resources having determined in step 103, then right The adjustment task that fictitious host computer is adjusted can be: migration virtual machine or lifting virtual machine cpu quota, or restarts virtual Machine etc..

Step 104: choose an adjustment task from each adjustment task determining, and using selection adjustment task adjustment With the described selection corresponding correlated resources of adjustment task.

In order to more easily understand the embodiment of the present invention one, it is exemplified below:

Assume that the currently get first supervision index is: the service request response time, corresponding first achievement data is: 10 seconds, default alarm conditions were: the service request response time can trigger alarm more than 5 seconds.Due to current the first finger obtaining Mark data meets default alarm conditions, so triggering alarm, after triggering alarm, makes the service request response time when only existing Change one or more second monitor index when if it is determined that second supervision index be vm cpu utilization rate (other second supervision indexs can also be included), then further determine that the second pass making vm cpu utilization rate change Connection resource is fictitious host computer, in order that the service request response time is less than 5 seconds, just need fictitious host computer is adjusted (such as: Lifting virtual machine cpu quota), the purpose of adjustment is so that vm cpu utilization rate is changed, and final purpose is in v After the change of m cpu utilization rate, the service request response time is made to be less than 5 seconds.

Embodiment two

Referring to Fig. 2, the schematic flow sheet of the risk processing method of the cloud application runtime providing for the embodiment of the present invention two, Specifically include:

Step 201: obtain the first index number of the first supervision index relevant with cloud application operation in the cloud application runtime According to.

Step 202: if described first achievement data meets default alarm conditions, determine impact in cloud computing environment It is each that each first correlated resources that described first achievement data changes and/or impact the second achievement data change Individual second correlated resources, described second achievement data is affect in cloud computing environment that described first achievement data changes the The achievement data of two supervision indexs.

Step 203: determine the adjustment target making described first achievement data be unsatisfactory for described default alarm conditions.

Step 204: when described adjustment target is to reduce described first achievement data, obtain to described first correlated resources The adjustment task for reducing described first achievement data being adjusted, and/or, obtain and described second correlated resources are carried out The adjustment task for reducing described first achievement data of adjustment, execution step 206.

Step 205: when described adjustment target is to raise described first achievement data, obtain to described first correlated resources The adjustment task for raising described first achievement data being adjusted, and/or, obtain and described second correlated resources are carried out The adjustment task for raising described first achievement data of adjustment.

If the correlated resources that step 202 determines are only the first correlated resources, determine in step 204 or step 205 Adjustment task to the first correlated resources；If the correlated resources that step 202 determines are only the second correlated resources, in step 204 or step 205 in determine the adjustment task to the second correlated resources；If the correlated resources that step 202 determines both had included the One correlated resources include the second correlated resources again, then determine respectively in step 204 or step 205 to the first correlated resources and The adjustment task of two correlated resources.

Step 206: the first condition calculating the first adjustment task executes probability and/or the second condition of the second adjustment task Execution probability, wherein, the adjustment task that described first correlated resources are adjusted is the first adjustment task, closes to described second The adjustment task that connection resource is adjusted is the second adjustment task.

Wherein, described first condition execution probability is to make under conditions of the described first supervision index is for the first achievement data Described first achievement data is unsatisfactory for the execution probability of the adjustment task of described default alarm conditions, and described second condition execution is general Rate is to monitor that index is under conditions of the first achievement data and described second monitors that index is the second achievement data described first Described first achievement data is made to be unsatisfactory for the execution probability of the adjustment task of described default alarm conditions.

Step 207: execute from described calculated all conditions and choose maximum execution probability probability, and choose described Maximum execution probability corresponding adjustment task, with using selection adjustment task adjustment and the described selection corresponding association of adjustment task Resource.

Wherein, described maximum execution probability corresponding adjustment task is probably what described first correlated resources were adjusted Adjustment task is it is also possible to adjustment task that described second correlated resources are adjusted.

Step 208: after correlated resources are adjusted, reacquire the first index number of described first supervision index According to.

Step 209: judge whether the first achievement data of described reacquisition meets described default alarm conditions, if It is, then execution step 210, if it is not, then execution step 211；

Step 210: reduce the probit of described maximum execution probability, continue executing with step 707.

Step 211: terminate flow process.

In step 207 to 210, using maximum execution probability corresponding adjustment task, correlated resources are being adjusted Afterwards, expected Adjustment effect may not occur, that is, the first achievement data of described first supervision index still meets described default announcement Calculated maximum execution probability is now reduced certain probit (such as: maximum execution probability is 80% by alert condition When, 80% minimizing 20% is obtained 60%, not all condition is executed the maximum execution probability in probability by 60%), then, then Obtain another one maximum execution probability from all of condition execution probability, continue with this maximum execution corresponding tune of probability Whole task carries out corresponding to the adjustment of correlated resources, so circulates, until the first achievement data is unsatisfactory for described default alarm conditions Till.

For the first condition execution probability in calculation procedure 206 and second condition execution probability, in execution, the present invention is real Further comprising the steps of before applying example: to build monitor control index related reasoning model in advance.Lower mask body place of matchmakers states monitor control index closes The method for building up of connection inference pattern, is broadly divided into three below step:

The first step: each node in monitor control index related reasoning model is defined

1st, the first supervision index node

The operation system index definition of the cloud application runtime that operation maintenance personnel is paid close attention to is the first supervision index node, than As: service request response time, user are in line number etc..In addition, each first supervision index all has its corresponding risk to alert plan Slightly, judge whether to meet default alarm conditions using the first supervision index currency, if it is, triggering alarm is to carry out Follow-up risk automatically processes.

After defining each the first supervision index node, further the codomain of each node is defined, for certain Individual first monitors index node it is assumed that its node codomain is { 1,2,3 }, wherein, each thresholding in node codomain with this first Monitor that a preset data interval (achievement data is interval) of index mutually maps.For example: assume the service request response time Node codomain is { 1,2,3 }, then the thresholding 1 in definable node codomain is corresponding with preset data interval 0-5s, defines nodal value Thresholding 2 in domain is corresponding with preset data interval 5-10s, defines the thresholding 3 in node codomain and default value interval 10-20 S corresponds to.It should be noted that the definition of node codomain is not limited to above-mentioned form, the thresholding in node codomain can also be carried out Suitable minimizing or increase etc..

2nd, the second supervision index node

The supervision index definition of cloud application runtime correlated resources is the second supervision index node, such as: vm cp U utilization rate, disk io flow, vm memory usage, network bandwidth utilization rate, network io postpones, etc..

After defining each the second supervision index node, further the codomain of each node is defined, for certain Individual second monitors index node it is assumed that its node codomain is { 1,2,3 }, wherein, each thresholding in node codomain with this second Monitor that a preset data interval (achievement data is interval) of index mutually maps.For example: assume vm cpu utilization rate Node codomain is { 1,2,3 }, then the thresholding 1 in definable node codomain is corresponding with preset data interval 0%-20%, definition section Thresholding 2 in point codomain is corresponding with preset data interval 20%-50%, defines the thresholding 3 in node codomain and default value area Between 50%-100% correspond to.It should be noted that the definition of node codomain is not limited to above-mentioned form, can also be to node codomain In thresholding carry out suitable minimizing or increase etc..

3rd, adjust task node

Will be based on the expertise predefined cloud environment control task (tune to the first correlated resources or the second correlated resources Whole task) it is defined as adjusting task node, such as: increase clustered node, lifting virtual machine density, etc..Specifically, according to Data point reuse target (reducing or raise the first supervision index) setting of one supervision index and adjustment target corresponding adjustment task； In addition, the data point reuse target of each the first supervision index, can be to should have one or more adjustment tasks.

After defining each adjustment task node, further the codomain of each node is defined, for certain tune Whole task node it is assumed that its node codomain is { t, f }, then effectively (executes adjustment task after t represents execution adjustment task After relieve current alarm, such as so that the data of current the first supervision index obtaining no longer meets default alarm conditions), f Invalid after representing execution adjustment task (do not release current alarm yet after execution adjustment task, such as so that current obtain the The data of one supervision index still meets default alarm conditions).

Second step: draw the directed acyclic graph (dag) of monitor control index related reasoning model

After node definition completes, (node definition referring to monitor control index related reasoning model as shown in Figure 3 is illustrated Figure), according to the mutual relation between each node, with directed edge, the node with dependency being connected into one by associated order has To acyclic figure (dag), wherein, out-degree be the node of o be the first supervision index node, in-degree is 0 node is that adjustment is appointed Business node, it is the second supervision index node that existing out-degree has the node of in-degree again.

For example: the first supervision index node is exemplified as " service request response time ", and actual realization also can be replaced any The operation system index node that operation maintenance personnel is concerned about, such as: " online user number ", " service request average handling time ", etc., These nodes can certainly be respectively defined as the first different supervision indexs；Second supervision index node is exemplified as " v M cpu utilization rate ", actual realization can increase or replace any software related to the first monitor control index node, equipment etc. System resource index node, such as: " disk io flow ", " vm memory usage ", " network bandwidth utilization rate ", " network i O postpones ", etc.；Adjustment task node is exemplified as " increase clustered node " and " lifting virtual machine density ", and actual realization can increase Subtract or replace any adjustment task node that can affect the second supervision achievement data change, such as: " optimization network topology ", " minimizing clustered node ", " lifting internal memory quota ", " lifting network bandwidth quota ", etc..By these be mutually related node with Directed edge is attached, and to reflect the relatedness between node with this.

Illustrate: the first directed acyclic graph of monitor control index related reasoning model shown in Figure 4, in the diagram, fixed Adopted " increase clustered node " and " lifting virtual machine density " is the adjustment task to the second correlated resources, and this adjustment task will affect Second data variation monitoring index " vm memory usage ", the second supervision index " vm memory usage " will affect first Monitor the data variation of index " service request response time ".Additionally, also there is the directed edge shown in Fig. 5 even in the embodiment of the present invention Connect mode, the second directed acyclic graph of monitor control index related reasoning model shown in Figure 5, in Figure 5, definition " increases collection Group node " and " lifting virtual machine density " are the adjustment tasks to the first correlated resources, and this adjustment task will directly affect first Monitor the data variation of index " service request response time ".Certainly, also there is Fig. 4 and be connected with Fig. 5 Suo Shi in the embodiment of the present invention Mode simultaneous directed edge connected mode.

3rd step: setting conditional probability distribution table

After the completion of directed acyclic graph (dag) definition, each node of in figure is required to be arranged according to expertise data One conditional probability distribution table.It is broadly divided into following two situations:

Situation one: referring to Fig. 4, based on exist between the first supervision index a, the second supervision index b and adjustment task c with Lower relation: c- ＞ b- ＞ a, that is, execute c and the achievement data leading to b changes, the achievement data of b changes The achievement data leading to a is changed, if p(a|b) it is when b is the second designated value (nodal value of the second supervision index Thresholding in domain) when a be the first designated value (first supervision index node codomain in thresholding) probability；If p(b|c) It is that b is the second designated value (the second supervision index when c is the 3rd designated value (effectively execution probability or invalid execution probability) Thresholding in node codomain) probability；P(c=t) represent the preset value effectively executing probability, p(c=f) indicate Wu The preset value of effect execution probability.Wherein, described effective execution probability makes the finger of the first supervision index after referring to execute adjustment task Mark data is unsatisfactory for the probit of default alarm conditions, and described invalid execution probability makes the first supervision after referring to execute adjustment task The achievement data of index still meets the probit of default alarm conditions.

For example: referring to table 1 to table 3, table 1 is the conditional probability distribution table of a, and table 2 is the conditional probability distribution table of b, table 3 Predetermined probabilities distribution table for c.

B	P(a=1|b)	P(a=2|b)	P(a=3|b)
				1	0.5	0.3	0.2
2	0.1	0.6	0.3
				3	0.1	0.2	0.7

Table 1 a node condition probability tables

C	P(b=1|c)	P(b=2|c)	P(b=3|c)
				T	0.3	0.6	0.1
F	0.1	0.2	0.7

Table 2 b node condition probability tables

P(c=t)	P(c=f)
		0.6	0.4

Table 3 c node predetermined probabilities table

Situation two: referring to Fig. 5, there is following relation: c- ＞ a based between the first supervision index a, adjustment task c, I.e. the achievement data leading to a is changed, if p(c=t|a by execution c) it is when a is (the first supervision of the first designated value Thresholding in the node codomain of index) when c be the 3rd designated value (effectively execution probability or invalid execution probability) probability.

For example: referring to table 4 to table 5, table 4 is the conditional probability distribution table of c, and table 5 is the predetermined probabilities distribution table of c.

A	P(c=t|a)	P(c=f|a)
			1	0.8	0.2
2	0.6	0.4
			3	0.5	0.5

Table 4 c node condition probability tables

P(c=t)	P(c=f)
		0.6	0.4

Table 5 c node predetermined probabilities table

Based on the model establishment step of above-mentioned introduction, the flow process of related reasoning method for establishing model shown in Figure 6 is shown It is intended to, the method for building up of described monitor control index related reasoning model, specifically include:

Step 601: determine at least one first supervision index relevant with cloud application operation.

Step 602: determine each correlated resources and/or the impact affecting that the data of described first supervision index changes Each correlated resources that the data of the second supervision index changes, described second monitors index for affecting institute in cloud computing environment State the supervision index that the data of the first supervision index changes.

Step 603: each correlated resources described in the determination of adjustment target of the achievement data according to the described first supervision index Adjustment task, described adjustment target be raise described first supervision index achievement data or reduce described first supervision index Achievement data.

Step 604: setting probability distribution table, described probability distribution table include first condition Probability p (c=t|a), the Two conditional probability p(b|c=t) and described adjustment task effective execution probability preset value p(c=t).

Wherein, described effective execution Probability p (c=t) makes the finger of the first supervision index after being carried out described adjustment task Mark data is unsatisfactory for the probit of default alarm conditions, and described first condition Probability p (c=t|a) is when the described first prison Regard index first preset data interval in when described effective execution probability as preset value probability, described second condition Probability p (b|c=t) it is the second supervision index described in when described effective execution probability is preset value in the second preset data interval Probability.

Based on the above-mentioned monitor control index related reasoning model pre-building, first condition execution probability in step 206 and Second condition execution probability calculates respectively in the following manner:

1st, the computing formula that described first condition executes probability is: p (c=t | a=y) (1) is obtaining the first index After effective execution probability c=t of data a=y and adjustment task c, first condition probability that just can be required in inquiry table 4 is made Execute probability for first condition.

2nd, the computing formula of described second condition execution probability is as follows:

$p(c=t|b=x,a=y)=\frac{p(c=t,b=x,a=y)}{p(b=x,a=y)}---\left(2\right)$

Wherein, p (b=x, a=y)=p (a=y | b=x) p (b=x) (3)

P (c=t, b=x, a=y)=p (a=y | b=x) p (b=x) p (b=x | c=t) p (c=t) (4)

Formula (3) and (4) are brought into formula (2), obtain final product:

P (c=t | b=x, a=y)=p (b=x | c=t) p (c=t) (5)

Obtaining the first achievement data a=y and the second achievement data b=x, and after adjustment task c, due to the Two achievement data b=x are mutually mapped with one of node codomain (the node codomain of the second supervision index node) thresholding, look into Ask probability distribution table, obtain second condition Probability p (b=x | c=t) and the default execution Probability p (c=of corresponding adjustment task T), calculate second condition execution Probability p (b=x | c=t) p (c=t) according to above-mentioned formula (5).

Understood based on above-mentioned formula, " calculating the described first first condition adjusting task and executing probability in step 206 And/or described second adjustment task second condition execution probability ", can realize in the following manner:

Monitor the current of index using in the described first achievement data described monitor control index related reasoning model of renewal first Data, inquires about the probability distribution table in described monitor control index related reasoning model according to the current data after described renewal, and will The first condition probability inquiring is as the first condition execution probability of the described first adjustment task；

And/or,

Monitor the current of index using in the described first achievement data described monitor control index related reasoning model of renewal first Data, and the current number of the second supervision index in described monitor control index related reasoning model is updated using described second achievement data According to；Inquire about the probability distribution table in described monitor control index related reasoning model according to the current data after described renewal, and according to Inquire about the second condition probability obtaining and the preset value of described effective execution probability calculates the described second Article 2 adjusting task Part executes probability.

The risk processing method of cloud application runtime provided in an embodiment of the present invention, when the first supervision index obtaining After first achievement data meets default alarm conditions, determine that affecting described first achievement data changes in cloud computing environment Each first correlated resources and/or impact each second correlated resources of changing of the second achievement data, described second finger Mark data is to affect the achievement data of the second supervision index that described first achievement data changes in cloud computing environment；So Afterwards, determine adjustment task that described first correlated resources are adjusted and/or described second correlated resources are adjusted Adjustment task, finally from determine each adjustment task choose one adjustment task, and using choose adjustment task adjustment with The described selection corresponding correlated resources of adjustment task, to make the first achievement data no longer meet after correlated resources are adjusted Default alarm conditions.The embodiment of the present invention can be automatically adjusted to the monitor control index meeting default alarm conditions, thus from Dynamic achieve risk investigation, overcome that the brought investigation speed of artificial investigation is slow, the difficult defect such as big of investigation is it is achieved that quick Accurately release the purpose of the operation risk of cloud application runtime.

Embodiment three

Referring to Fig. 7, the structural representation of the risk processing system of the cloud application runtime providing for the embodiment of the present invention three, It is characterized in that, comprising:

Achievement data acquisition module 1, refers to for obtaining the first supervision relevant with cloud application operation in the cloud application runtime Target first achievement data；

Correlated resources determining module 2, for when described first achievement data meets default alarm conditions, in cloud computing ring In border, each first correlated resources that described first achievement data of determination impact changes and/or impact the second achievement data are sent out Each second correlated resources of changing, described second achievement data is to affect described first achievement data in cloud computing environment to send out The achievement data of the second supervision index of changing；

Adjustment task determining module 3, for determining adjustment task that described first correlated resources are adjusted and/or right The adjustment task that described second correlated resources are adjusted, described adjustment task is described for making described first achievement data be unsatisfactory for The adjustment mode of default alarm conditions；

Adjustment task chooses module 4, for choosing an adjustment task from each adjustment task determining；

Risk processing module 5, for using choosing, the adjustment of adjustment task is corresponding with described selection adjustment task to associate money Source.

Wherein, described adjustment task determining module 3, specifically includes:

Adjustment target determination unit, for determining the tune making described first achievement data be unsatisfactory for described default alarm conditions Whole target；

First adjustment task determining unit, for when described adjustment target is to reduce described first achievement data, obtaining The adjustment task for reducing described first achievement data that described first correlated resources are adjusted, and/or, obtain to institute State the adjustment task for reducing described first achievement data that the second correlated resources are adjusted；

Second adjustment task determining unit, for when described adjustment target is to raise described first achievement data, obtaining The adjustment task for raising described first achievement data that described first correlated resources are adjusted, and/or, obtain to institute State the adjustment task for raising described first achievement data that the second correlated resources are adjusted.

Wherein, described adjustment task chooses module 4, specifically includes:

Execution probability calculation unit, for calculating the first condition execution probability and/or described of described first adjustment task The second condition execution probability of the second adjustment task, wherein, adjustment task that described first correlated resources are adjusted is the One adjustment task, the adjustment task that described second correlated resources are adjusted is the second adjustment task, and described first condition is held Row probability is be the first achievement data in the described first supervision index under conditions of so that described first achievement data is unsatisfactory for described The execution probability of the adjustment task of default alarm conditions, described second condition execution probability is to monitor that index is the described first One achievement data and described second monitors index for making described first achievement data be unsatisfactory for institute under conditions of the second achievement data State the execution probability of the adjustment task of default alarm conditions；

Adjustment task chooses unit, general for executing the maximum execution of selection in probability from described calculated all conditions Rate, and choose described maximum execution probability corresponding adjustment task, to choose adjustment using selection adjustment task adjustment with described The corresponding correlated resources of task.

Wherein, described risk processing module, is additionally operable to using selection adjustment task adjustment and described selection adjustment task After corresponding correlated resources, reacquire the first achievement data of described first supervision index, if the of described reacquisition One achievement data still meets described default alarm conditions, then reduce the probit of described maximum execution probability, continue executing with from institute State calculated all conditions and execute the step choosing maximum execution probability in probability, until described first achievement data is discontented with Till the described default alarm conditions of foot.

In addition, described system also includes: model building module 5, for building monitor control index related reasoning model in advance；Institute State model building module 5, specifically include:

Monitor index determining unit, for determining at least one first supervision index relevant with cloud application operation；

Correlated resources determining unit, each association that the data for determining the described first supervision index of impact changes Each correlated resources that the data of resource and/or impact the second supervision index changes, described second monitors that index is cloud meter Calculate the supervision index affecting in environment that the data of described first supervision index changes；

Adjustment task determining unit, for described in the adjustment target determination of the achievement data according to the described first supervision index The adjustment task of each correlated resources, described adjustment target is to raise the achievement data of described first supervision index or reduce described The achievement data of the first supervision index；

Probability tables arranging unit, for arranging probability distribution table, described probability distribution table include first condition probability, second The preset value of effective execution probability of conditional probability and described adjustment task；

Wherein, described effective execution probability makes the achievement data of the first supervision index be discontented with after being carried out described adjustment task The probit of the default alarm conditions of foot, described first condition probability is when described first monitors that index is interval in the first preset data When interior, described effective execution probability is the probability of preset value, and described second condition probability is when described effective execution probability is default Described in during value, second monitors probability in the second preset data interval for the index.

Wherein, described execution probability calculation unit, specifically for described execution probability calculation unit, specifically for utilizing State first achievement data update described monitor control index related reasoning model in first supervision index current data, according to described more Current data after new inquires about the probability distribution table in described monitor control index related reasoning model, and by the first condition inquiring Probability is as the first condition execution probability of the described first adjustment task；And/or, updated described using described first achievement data The current data of the first supervision index in monitor control index related reasoning model, and update described prison using described second achievement data The current data of the second supervision index in control index related reasoning model；Described prison is inquired about according to the current data after described renewal Probability distribution table in control index related reasoning model, and general according to the second condition probability that obtains of inquiry and described effective execution The preset value of rate calculates the second condition execution probability of described second adjustment task.

The risk processing system of cloud application runtime provided in an embodiment of the present invention, when the of the first supervision index obtaining After one achievement data meets default alarm conditions, determine in cloud computing environment and affect what described first achievement data changed Each first correlated resources and/or impact each second correlated resources of changing of the second achievement data, described second index Data is to affect the achievement data of the second supervision index that described first achievement data changes in cloud computing environment；Then, Determine the adjustment task and/or the adjustment that described second correlated resources are adjusted that described first correlated resources are adjusted Task, finally from determine each adjustment task choose one adjustment task, and using choose adjustment task adjustment with described Choose the corresponding correlated resources of adjustment task, default to make the first achievement data no longer meet after correlated resources are adjusted Alarm conditions.The embodiment of the present invention can be automatically adjusted to the monitor control index meeting default alarm conditions, thus automatically real Show risk investigation, overcome that the brought investigation speed of artificial investigation is slow, the difficult defect such as big of investigation is it is achieved that quick and precisely The operation risk of releasing cloud application runtime purpose.

The system structure diagram being given based on above-mentioned Fig. 7, the embodiment of the present invention additionally provides another system structure Schematic diagram, the structural representation of the risk processing system of cloud application runtime shown in Figure 8.

For effective supervision cloud application and cloud environment running status, automatically find risk and process risk, by this system in time Model divides application layer risk analyses subsystem and environment layer-management subsystem, and two subsystems are communicated by message mechanism.

Wherein, application layer risk analyses subsystem includes following equipment:

1st, application monitor: be deployed in cloud application.

Cloud application can provide one or more service, refers in the first supervision of each service of cloud application runtime taken at regular intervals Target achievement data, and the achievement data of each the first supervision index is saved in cloud application knowledge base, for application risk Analyzer inquiry uses.

2nd, working knowledge storehouse: be deployed in cloud application.

It is responsible for the achievement data of each the first supervision index of storage application monitor taken at regular intervals.

3rd, application risk analyzer: be deployed in cloud application.

Application risk analyzer includes the following modules in embodiment three and can achieve the function of these modules: achievement data Acquisition module 1, correlated resources determining module 2, adjustment task determining module 3, adjustment task choose module 4.Wherein, achievement data Acquisition module 1 is used for obtaining the first achievement data from working knowledge storehouse, and correlated resources determining module 2 can be led to task processor Letter, to receive the second achievement data of task processor transmission, to determine the second association using the second achievement data obtaining Resource.Additionally, application risk analyzer is also responsible for safeguarding the monitor control index related reasoning model of cloud application runtime.

Wherein, environment layer-management subsystem includes following equipment:

1st, environmental monitor: be deployed in cloud computing environment.

Environmental monitor can be a physical server or virtual server, in taken at regular intervals cloud computing environment Second supervision index of each resource (physical host, fictitious host computer, network, operating system etc.), and the finger by the second supervision index Mark data is saved in cloud environment knowledge base, for task processor inquiry.

2nd, cloud environment knowledge base: be deployed in cloud application.

The achievement data of each the second supervision index of responsible storage environment monitor taken at regular intervals.

2nd, task processor: be deployed in cloud computing environment.

Each second index related to abnormal first achievement data currently getting is obtained from cloud environment knowledge base Data；Communicate with application risk analyzer, and send, to risk analyzer, each second achievement data obtaining；Receive application wind The adjustment task that dangerous analyzer sends chooses the adjustment task that module 4 is chosen.

3rd, environmental control: be deployed in cloud computing environment.

The environmental Kuznets Curves adjusting task for realizing correlated resources receiving task processor transmission instruct (such as: migration is empty Plan machine, lifting virtual machine cpu quota, restart virtual machine etc.), achievable embodiment three risk processing module 5 work( Energy.

As seen through the above description of the embodiments, those skilled in the art can be understood that above-mentioned enforcement All or part of step in example method can be realized by the mode of software plus necessary general hardware platform.Based on such Understand, what technical scheme substantially contributed to prior art in other words partly can be in the form of software product Embody, this computer software product can be stored in storage medium, such as rom/ram, magnetic disc, CD etc., bag Include some instructions with so that a computer equipment (can be the nets such as personal computer, server, or WMG Network communication equipment, etc.) execution each embodiment of the present invention or embodiment some partly described methods.

It should be noted that each embodiment is described by the way of going forward one by one in this specification, each embodiment emphasis is said Bright is all the difference with other embodiment, between each embodiment identical similar portion mutually referring to.For reality For applying system disclosed in example, because it corresponds to the method disclosed in Example, so description is fairly simple, in place of correlation Referring to method part illustration.

Also, it should be noted herein, such as first and second or the like relational terms are used merely to one Entity or operation are made a distinction with another entity or operation, and not necessarily require or imply between these entities or operation There is any this actual relation or order.And, term " inclusion ", "comprising" or its any other variant are intended to contain Comprising of lid nonexcludability, wants so that including a series of process of key elements, method, article or equipment and not only including those Element, but also include other key elements being not expressly set out, or also include for this process, method, article or equipment Intrinsic key element.In the absence of more restrictions, the key element that limited by sentence "including a ..." it is not excluded that Also there is other identical element including in the process of described key element, method, article or equipment.

Described above to the disclosed embodiments, makes professional and technical personnel in the field be capable of or uses the present invention. Multiple modifications to these embodiments will be apparent from for those skilled in the art, as defined herein General Principle can be realized without departing from the spirit or scope of the present invention in other embodiments.Therefore, the present invention It is not intended to be limited to the embodiments shown herein, and be to fit to and principles disclosed herein and features of novelty phase one The scope the widest causing.

Claims (10)

1. a kind of risk processing method of cloud application runtime is it is characterised in that include:

Obtain the first achievement data of the first supervision index relevant with cloud application operation in the cloud application runtime；

If described first achievement data meets default alarm conditions, determining in cloud computing environment affects described first index Each first correlated resources that data changes and/or each the second association money that impact the second achievement data changes Source, described second achievement data is to affect the second supervision index that described first achievement data changes in cloud computing environment Achievement data；

Determine adjustment task that described first correlated resources are adjusted and/or described second correlated resources are adjusted Adjustment task, described adjustment task is the adjustment mode making described first achievement data be unsatisfactory for described default alarm conditions；

Choose an adjustment task from each adjustment task determining, and adjusted with described selection using choosing the adjustment of adjustment task The corresponding correlated resources of whole task；

Wherein, described determine adjustment task that described first correlated resources are adjusted and/or to described second correlated resources The adjustment task being adjusted, specifically includes:

Determine the adjustment target making described first achievement data be unsatisfactory for described default alarm conditions；

When described adjustment target is to reduce described first achievement data, obtain the use that described first correlated resources are adjusted In reduce described first achievement data adjustment task, and/or, obtain described second correlated resources are adjusted for dropping The adjustment task of low described first achievement data；

When described adjustment target is to raise described first achievement data, obtain the use that described first correlated resources are adjusted In raise described first achievement data adjustment task, and/or, obtain described second correlated resources are adjusted for rising The adjustment task of high described first achievement data.

2. method according to claim 1 is it is characterised in that adjustment task that described first correlated resources are adjusted For first adjustment task, the adjustment task that described second correlated resources are adjusted be the second adjustment task, described from determination Each adjustment task in choose one adjustment task, specifically include:

The first condition calculating described first adjustment task executes probability and/or the second condition execution of described second adjustment task Probability, wherein, described first condition execution probability is to make institute under conditions of the described first supervision index is for the first achievement data State the execution probability that the first achievement data is unsatisfactory for the adjustment task of described default alarm conditions, described second condition executes probability It is to make under conditions of the described first supervision index is the first achievement data and described second supervision index is the second achievement data Described first achievement data is unsatisfactory for the execution probability of the adjustment task of described default alarm conditions；

Execute from described calculated all conditions and probability, choose maximum execution probability, and choose described maximum execution probability Corresponding adjustment task, with using selection adjustment task adjustment and the described selection corresponding correlated resources of adjustment task.

3. method according to claim 2 is it is characterised in that choosing adjustment using selection adjustment task adjustment with described After the corresponding correlated resources of task, also include:

Reacquire the first achievement data of described first supervision index, if the first achievement data of described reacquisition is still full The described default alarm conditions of foot, then reduce the probit of described maximum execution probability, continue executing with from described calculated institute Have ready conditions and execute the step choosing maximum execution probability in probability, until described first achievement data is unsatisfactory for described default alarm Till condition.

4. method according to claim 2 is it is characterised in that methods described also includes: builds monitor control index association in advance Inference pattern；

The method for building up of described monitor control index related reasoning model, specifically includes:

Determine at least one first supervision index relevant with cloud application operation；

Each correlated resources and/or impact the second supervision index that the data of the described first supervision index of determination impact changes Each correlated resources of changing of data, described second supervision index is to affect described first in cloud computing environment and monitor to refer to The supervision index that target data changes；

The adjustment task of each correlated resources described in the determination of adjustment target of the achievement data according to the described first supervision index, institute Stating adjustment target is to raise the achievement data of described first supervision index or the achievement data reducing described first supervision index；

Setting probability distribution table, described probability distribution table includes first condition probability, second condition probability and described adjustment and appoints The preset value of effective execution probability of business；

Wherein, after described effective execution probability is carried out described adjustment task, so that the achievement data of the first supervision index is unsatisfactory for pre- If the probit of alarm conditions, described first condition probability is when described first monitors that index is in the first preset data interval Described effective execution probability is the probability of preset value, and described second condition probability is when described effective execution probability is preset value Described second monitors probability in the second preset data interval for the index.

5. method according to claim 4 is it is characterised in that the first condition of the described first adjustment task of described calculating is held Row probability and/or the second condition execution probability of described second adjustment task, specifically include:

Update the current data of the first supervision index in described monitor control index related reasoning model using described first achievement data, Inquire about the probability distribution table in described monitor control index related reasoning model according to the current data after described renewal, and will inquire First condition probability as described first adjustment task first condition execution probability；

And/or,

Update the current data of the first supervision index in described monitor control index related reasoning model using described first achievement data, And the current data of the second supervision index in described monitor control index related reasoning model is updated using described second achievement data；Root Inquire about the probability distribution table in described monitor control index related reasoning model according to the current data after described renewal, and according to inquiring about The second condition probability arriving and the preset value of described effective execution probability calculate the described second second condition execution adjusting task Probability.

6. a kind of risk processing system of cloud application runtime is it is characterised in that include:

Achievement data acquisition module, for the cloud application runtime obtain relevant with cloud application operation first monitor index the One achievement data；

Correlated resources determining module, for when described first achievement data meets default alarm conditions, in cloud computing environment Each first correlated resources that determination described first achievement data of impact changes and/or impact the second achievement data become Each second correlated resources changed, described second achievement data is to affect described first achievement data in cloud computing environment to become The achievement data of the second supervision index changed；

Adjustment task determining module, for determining adjustment task that described first correlated resources are adjusted and/or to described The adjustment task that second correlated resources are adjusted, described adjustment task is to make described first achievement data be unsatisfactory for described presetting The adjustment mode of alarm conditions；

Adjustment task chooses module, for choosing an adjustment task from each adjustment task determining；

Risk processing module, for using selection adjustment task adjustment and the described selection corresponding correlated resources of adjustment task；

Wherein, described adjustment task determining module, specifically includes:

Adjustment target determination unit, for determining the adjustment mesh making described first achievement data be unsatisfactory for described default alarm conditions Mark；

First adjustment task determining unit, for when described adjustment target is to reduce described first achievement data, obtaining to institute State the adjustment task for reducing described first achievement data that the first correlated resources are adjusted, and/or, obtain to described the The adjustment task for reducing described first achievement data that two correlated resources are adjusted；

Second adjustment task determining unit, for when described adjustment target is to raise described first achievement data, obtaining to institute State the adjustment task for raising described first achievement data that the first correlated resources are adjusted, and/or, obtain to described the The adjustment task for raising described first achievement data that two correlated resources are adjusted.

7. system according to claim 6 is it is characterised in that adjustment task that described first correlated resources are adjusted For the first adjustment task, the adjustment task that described second correlated resources are adjusted is the second adjustment task, and described adjustment is appointed Module is chosen in business, specifically includes:

Execution probability calculation unit, for calculating the first condition execution probability and/or described second of described first adjustment task The second condition execution probability of adjustment task, wherein, described first condition execution probability is to monitor that index is the described first Make under conditions of one achievement data described first achievement data be unsatisfactory for the adjustment task of described default alarm conditions execution general Rate, described second condition execution probability is to monitor that index is in the described first supervision index for the first achievement data and described second Described first achievement data is made to be unsatisfactory for the execution of the adjustment task of described default alarm conditions under conditions of second achievement data Probability；

Adjustment task chooses unit, chooses maximum execution probability for executing from described calculated all conditions in probability, And choose described maximum execution probability corresponding adjustment task, with using selection adjustment task adjustment and described selection adjustment task Corresponding correlated resources.

8. system according to claim 7 it is characterised in that

Described risk processing module, is additionally operable to using selection adjustment task adjustment and the described selection corresponding association of adjustment task After resource, reacquire the first achievement data of described first supervision index, if the first achievement data of described reacquisition Still meet described default alarm conditions, then reduce the probit of described maximum execution probability, continue executing with and be calculated from described All conditions execute the step choosing maximum execution probability in probability, until described first achievement data be unsatisfactory for described default Till alarm conditions.

9. system according to claim 7 is it is characterised in that described system also includes: model building module, in advance Build monitor control index related reasoning model；

Described model building module, specifically includes:

Monitor index determining unit, for determining at least one first supervision index relevant with cloud application operation；

Correlated resources determining unit, each correlated resources that the data for determining the described first supervision index of impact changes And/or each correlated resources that the data of impact the second supervision index changes, described second monitors that index is cloud computing ring The supervision index that the data of described first supervision index changes is affected in border；

Adjustment task determining unit, for according to described first supervision index achievement data adjustment target determine described in each The adjustment task of correlated resources, described adjustment target is to raise the achievement data of described first supervision index or reduce described first Monitor the achievement data of index；

Probability tables arranging unit, for arranging probability distribution table, described probability distribution table includes first condition probability, second condition The preset value of effective execution probability of probability and described adjustment task；Wherein, described effective execution probability is carried out described tune The achievement data making the first supervision index after whole task is unsatisfactory for presetting the probit of alarm conditions, and described first condition probability is When described first monitors that index is in the first preset data interval, described effective execution probability is the probability of preset value, described the Two conditional probability are the second supervision indexs described in when described effective execution probability is preset value in the second preset data interval Probability.

10. system according to claim 9 it is characterised in that

Described execution probability calculation unit, specifically for updating described monitor control index related reasoning using described first achievement data In model, the current data of the first supervision index, inquires about described monitor control index related reasoning according to the current data after described renewal Probability distribution table in model, and the first condition inquiring probability is executed as the first condition of the described first adjustment task Probability；And/or, monitor working as of index using in the described first achievement data described monitor control index related reasoning model of renewal first Front data, and monitor the current of index using in the described second achievement data described monitor control index related reasoning model of renewal second Data；Inquire about the probability distribution table in described monitor control index related reasoning model according to the current data after described renewal, and root It is investigated that asking the second of the described second adjustment task of preset value calculating of the second condition probability obtaining and described effective execution probability Condition executes probability.