CN108009077A - A kind of service operation status assessment algorithm and system based on big data environment - Google Patents
A kind of service operation status assessment algorithm and system based on big data environment Download PDFInfo
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- CN108009077A CN108009077A CN201711239817.4A CN201711239817A CN108009077A CN 108009077 A CN108009077 A CN 108009077A CN 201711239817 A CN201711239817 A CN 201711239817A CN 108009077 A CN108009077 A CN 108009077A
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- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
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Abstract
The invention discloses a kind of service operation status assessment algorithm based on big data environment, including：S1, gathers the parameter index of all fictitious host computers；S2, the parameter index belonged on same fictitious host computer is classified, and obtains the achievement data of each subclassification；S3, is respectively calculated the achievement data of each subclassification according to algorithm, obtains the numerical value of each subclassification；S4, the corresponding operating status of the numerical value of the numerical value of each subclassification and each subclassification is shown；S5, the numerical value of the similar subclassification of all virtual machines of weighted calculation, and the corresponding operating status of the numerical value of the numerical value of similar subclassification and similar subclassification is shown.The invention also discloses a kind of service operation status assessing system based on big data environment.Using the present invention, it is possible to reduce the workload of hand inspection big data business system running state, and assist administrator to be more visually known the situation of big data business system operation, reduce the time of malfunction elimination.
Description
Technical field
The present invention relates to information technology field, more particularly to a kind of service operation status assessment based on big data environment to calculate
Method and a kind of service operation status assessing system based on big data environment.
Background technology
Big data is a new era of Information Technology Development, represents volatile data message to traditional calculating skill
The technological challenge and difficulty that art and information technology are brought, represent the new technology and method needed for big data processing, also represent
The big data analysis and caused new invention of application, new demand servicing and new opportunity to develop.
Current big data is attention rate highest in IT industries, with fastest developing speed, one of most vigorous technology of the market demand.According to
Research report shows that the year two thousand twenty whole world is newly-built and the information content of duplication alreadys exceed 40ZB；And the data volume of China then can be
The year two thousand twenty is more than 8ZB.Except the growth of data volume, data type also becomes increasingly complex.Sent out according to Chinese information Communication Studies institute
Cloth《Chinese big data development investigation report in 2015》It has been shown that, Chinese big data market scale is up to 115.9 hundred million within 2015
Member, speedup is up to 38%.Furthermore it is contemplated that 2016 to 2018 years Chinese big data market scales also increase the high speed for maintaining 40% or so
It is long.
All carry out the relevant service construction of big data in industry-by-industry successively at present, for example in colleges and universities, utilize big data
Analyze the action trail of student, there is provided student's early warning business., can be by entirely using big data technology again such as in transportation industry
Traffic conditions in the range of city perform an analysis, there is provided accident-prone road section and period relationship analysis early warning business.Big data conduct
The new technology risen after cloud computing, the big data business more than 80% are deployed in cloud computing platform, this business and technology newly risen
With reference to mode have huge challenge to business and technology operation maintenance personnel.
Under cloud computing environment, a big data business system is usually made of multiple fictitious host computers, any one host
Operating status can all influence the availability of big data business, so to fully understand the operation shape of big data business system
Condition, it is necessary to understand the operating condition of all fictitious host computers and the indices of fictitious host computer operation, comprehensive indices obtain
The situation of big data business system overall operation.
Big data, which is deployed on cloud computing platform, can have the problem of more, except each big number mentioned in the preceding paragraph
All it is made of according to business many fictitious host computers.Big data business system running state be subject to these fictitious host computers except that can be influenced it
Outside, the influence of other virtual machines in the physical machine and the physical machine where these virtual machines is also suffered from.Certainly, when
When physical machine breaks down, all virtual machines above physical machine including big data business can all be affected.
Another situation, the resource of all public physical server of these virtual machines, such as CPU, memory, storage, network, once
The virtual machine of other business occupies excessive resource, causes big data business virtual machine not work normally, it will causes big
The traffic failures such as the loss of data of data service.We assume that a big data business by 5 virtual robot arms into this 5 virtual
Machine is on 5 physical servers, and in addition to the virtual machine of big data business, all also there are other of 10 for every physical machine
Business virtual machine.So, operation maintenance personnel is managed in daily work, and the scope of a big data business management is：5 physics
55 virtual machines above machine and physical machine.If administrative staff need to be responsible for 10 big data business, then need to safeguard
The scope of concern is also just sufficiently large.
Therefore under current technological development trend, it is trend of the times that big data business, which is deployed on cloud computing platform, and
Current main trend.This goes retrieval to go to safeguard for supervisory engineering staff, it is necessary to expend substantial amounts of manpower.
There are many traditional cloud computing management platforms on existing market, they can provide the unified pipe of virtualized environment
Reason and O＆M monitoring, including the physics such as bottom physical machine, storage, interchanger are set and carry out capacity monitor, or have some product
The cloud computing management platform of board can monitor the sensor of physical server, monitor in real time they humiture, power supply, fan etc.
State.Virtual machine is directed at the same time, can monitor the utilization rate of each resource of virtual machine, as CPU, memory, storage, network make
With rate etc..
Although existing cloud computing management platform monitors physical machine and virtual machine, but the granularity monitored is thicker
Rough, in summary, the shortcomings that existing technology, is as follows：
1st, many because being known as of big data business system are influenced, data are disperseed, and the data shown are not complete, it is necessary to manually
The content of switching displaying, which is not supported either to switch, to be caused complicated or can not obtain detailed failure cause.
2nd, these management softwares lack the concept of operation system, the related prison that can not be unified to big data business
Control analysis.They treat fictitious host computer as a single individual, each fictitious host computer be individually monitored and
Management, ignores the part as an operation system, the contact between fictitious host computer, when other components break down,
Single virtual operational state of mainframe is good again, and operation system can not also access.
The content of the invention
The technical problems to be solved by the invention are, there is provided a kind of service operation status assessment based on big data environment
Algorithm, can reduce the workload of hand inspection big data business system running state, and assist administrator to be more visually known
The situation of big data business system operation.
The technical problems to be solved by the invention also reside in, there is provided it is a kind of it is simple in structure, accuracy is high based on big data
The service operation status assessing system of environment, can reduce the time of malfunction elimination.
In order to solve the above technical problem, the present invention provides a kind of service operation status assessment based on big data environment
Algorithm, including：S1, gathers the parameter index of all fictitious host computers；S2, by the parameter index belonged on same fictitious host computer into
Row classification, obtains the achievement data of each subclassification；S3, the achievement data of each subclassification is calculated, obtained according to algorithm respectively
Obtain the numerical value of each subclassification；S4, the corresponding operating status of the numerical value of the numerical value of each subclassification and each subclassification is shown；
S5, the numerical value of the similar subclassification of all virtual machines of weighted calculation, and by the numerical value of similar subclassification and the number of similar subclassification
It is worth corresponding operating status to be shown.
As the improvement of such scheme, the parameter index includes CPU parameters, memory parameters, storage parameter, network parameter
And read-write I/O parameter.
As the improvement of such scheme, when the parameter index belonged on same fictitious host computer is classified, described point
Class Type includes healthy class, risk class and efficiency class.
As the improvement of such scheme, the step S3 includes：Subitem calculating is carried out to the achievement data of each subclassification, is obtained
Obtain the numerical value of each subitem；Cum rights calculating is carried out to the numerical value of each subitem according to the calculating ratio of each subclassification, obtains each subitem
Weighted Coefficients；The numerical value of each subclassification is obtained according to the Weighted Coefficients of each subitem.
Correspondingly, present invention also offers a kind of service operation status assessing system based on big data environment, including：Adopt
Collect module, for gathering the parameter index of all fictitious host computers；Sort module, for the ginseng that will belong on same fictitious host computer
Number index is classified, and obtains the achievement data of each subclassification；Computing module, for respectively pressing the achievement data of each subclassification
Calculated according to algorithm to obtain the numerical value of each subclassification, and the number of the similar subclassification for all virtual machines of weighted calculation
Value；Display module, for the corresponding operating status of the numerical value of the numerical value of each subclassification and each subclassification to be shown, and is used for
The corresponding operating status of the numerical value of the numerical value of similar subclassification and similar subclassification is shown.
As the improvement of such scheme, the computing module includes：First calculating sub module, for respectively by each subclassification
Achievement data calculated according to algorithm to obtain the numerical value of each subclassification；Second calculating sub module, for weighted calculation institute
There is the numerical value of the similar subclassification of virtual machine.
As the improvement of such scheme, first computing module includes：Subitem computing unit, for each subclassification
Achievement data carries out subitem calculating, obtains the numerical value of each subitem；Cum rights computing unit, for the calculating ratio according to each subclassification
Cum rights calculating is carried out to the numerical value of each subitem, obtains the Weighted Coefficients of each subitem；Numerical calculation unit, for the band according to each subitem
Weights obtain the numerical value of each subclassification.
As the improvement of such scheme, the service operation status assessing system based on big data environment further includes storage
Module, for storing the parameter index of all fictitious host computers.
As the improvement of such scheme, the parameter index includes CPU parameters, memory parameters, storage parameter, network parameter
And read-write I/O parameter.
As the improvement of such scheme, when the parameter index belonged on same fictitious host computer is classified, described point
Class Type includes healthy class, risk class and efficiency class.
Implement the present invention, have the advantages that：
The present invention can reduce the workload of hand inspection big data business system running state, and assist administrator more can
The situation of big data business system operation is visually known, reduces the time of malfunction elimination.Specifically, the present invention has with following
Beneficial effect：
1st, big data business is managed by building service management pattern, it is comprehensive strong；
2nd, in virtual machine aspect, according to the health value, value-at-risk, efficiency value of algorithm evaluation virtual machine, more fully；
3rd, in service layer, the operating condition of big data business is fully assessed according to algorithm.Scope of assessment includes business
The weighted calculation of virtual machine and time associated virtual machine is directly linked, calculating content includes health value, value-at-risk, efficiency value；
4th, involved in algorithm to weight index value be all can be customized, can be made by oneself according to user's actual need
Justice, flexibility are strong.
Brief description of the drawings
Fig. 1 is the first embodiment flow chart of the service operation status assessment algorithm of the invention based on big data environment；
Fig. 2 is the second embodiment flow chart of the service operation status assessment algorithm of the invention based on big data environment；
Fig. 3 is the structure diagram of HDFS operation systems；
Fig. 4 is the first embodiment structural representation of the service operation status assessing system of the invention based on big data environment
Figure；
Fig. 5 is the second embodiment structural representation of the service operation status assessing system of the invention based on big data environment
Figure；
Fig. 6 is the 3rd embodiment structural representation of the service operation status assessing system of the invention based on big data environment
Figure.
Embodiment
To make the object, technical solutions and advantages of the present invention clearer, the present invention is made into one below in conjunction with attached drawing
It is described in detail on step ground.Only this is stated, appearance in the text of the invention or the side such as the up, down, left, right, before and after that will appear from, inside and outside
Position word, only on the basis of the attached drawing of the present invention, it is not that the specific of the present invention is limited.
Referring to Fig. 1, Fig. 1 shows that the first of the service operation status assessment algorithm of the invention based on big data environment implements
Example, it includes：
S101, gathers the parameter index of all fictitious host computers；
The parameter index include CPU parameters, memory parameters, storage parameter, network parameter and read-write I/O parameter, but not with
This is limitation.
S102, the parameter index belonged on same fictitious host computer is classified, and obtains the achievement data of each subclassification；
When the parameter index belonged on same fictitious host computer is classified, the classification type includes healthy class, wind
Dangerous class and efficiency class, but it is not limited system.
S103, is respectively calculated the achievement data of each subclassification according to algorithm, obtains the numerical value of each subclassification；
S104, the corresponding operating status of the numerical value of the numerical value of each subclassification and each subclassification is shown；
S105, the numerical value of the similar subclassification of all virtual machines of weighted calculation, and by the numerical value of similar subclassification and similar
The corresponding operating status of numerical value of subclassification is shown.
Referring to Fig. 2, Fig. 2 shows that the first of the service operation status assessment algorithm of the invention based on big data environment implements
Example, it includes：
S201, gathers the parameter index of all fictitious host computers；
The parameter index include CPU parameters, memory parameters, storage parameter, network parameter and read-write I/O parameter, but not with
This is limitation.
S202, the parameter index belonged on same fictitious host computer is classified, and obtains the achievement data of each subclassification；
When the parameter index belonged on same fictitious host computer is classified, the classification type includes healthy class, wind
Dangerous class and efficiency class, but it is not limited system.
S203, carries out subitem calculating to the achievement data of each subclassification, obtains the numerical value of each subitem；
S204, carries out cum rights calculating to the numerical value of each subitem according to the calculating ratio of each subclassification, obtains the band of each subitem
Weights；
S205, the numerical value of each subclassification is obtained according to the Weighted Coefficients of each subitem.
S206, the corresponding operating status of the numerical value of the numerical value of each subclassification and each subclassification is shown；
S207, the numerical value of the similar subclassification of all virtual machines of weighted calculation, and by the numerical value of similar subclassification and similar
The corresponding operating status of numerical value of subclassification is shown.
With reference to specific example, the present invention will be further described：
As shown in figure 3, in big data HDFS operation systems, there are 4 virtual machines to carry the business, these three virtual machines point
It is not 1 Name node, 3 Data node.Name node are located at Physical server one, also have in the physical machine
Two virtual machine VM；Data node one are located at Physical server two, also have two virtual machine VM in the physical machine；
Data node one are located at Physical server three, also have two virtual machine VM in the physical machine；Data node
One is located at Physical server four, also has two virtual machine VM in the physical machine.It is so related to HDFS operation systems
The physical server of connection has 3, and business is directly linked virtual machine 4, attached associated virtual machine 8.
Step 1, gathers the parameter index of all fictitious host computers in HDFS operation systems；Specific parameter index such as following table
It is shown：
Step 2, healthy class, wind are divided into by the parameter index belonged in HDFS operation systems on same fictitious host computer
Dangerous three big subclassification of class and efficiency class, and obtain the achievement data of each subclassification；Wherein, the weight for presetting healthy class is 50, wind
The weight of dangerous class is 30, the weight of efficiency class is 20.
Specifically, the achievement data of healthy class is as shown in the table：
The achievement data of risk class is as shown in the table：
| Sequence number | Index name | Unit | Trigger condition |
| 1 | The CPU stand-by period | Millisecond | In the section of definition |
| 2 | CPU ready times | Millisecond | In the section of definition |
| 3 | Memory sharing amount | KB | It is not " 0 " |
| 4 | Memory exchange capacity | KB | It is not " 0 " |
| 5 | Memory recycle amount | KB | It is not " 0 " |
| 6 | Memory warrant quantity | KB | In the section of definition |
| 7 | Memory compression amount | KB | It is not " 0 " |
| 8 | Disk reads lag time | Millisecond | In the section of definition |
| 9 | Cpu demand value | Mhz | In the section of definition |
| 10 | CPU limits | Mhz | It is not " 0 " |
| 11 | Network data speed uplink | KBps | In the section of definition |
| 12 | Network Packet Loss number | Numeral | It is not " 0 " |
| 13 | Memory quota | Million, MB | It is not " 0 " |
| 14 | Store IO limits | Numeral | It is not " 0 " |
| 15 | Fictitious host computer snapshot space | MB | In the section of definition |
The achievement data of efficiency class is as shown in the table：
Step 3, is respectively calculated the achievement data of each subclassification according to algorithm, obtains the numerical value of each subclassification；
Healthy class：
The utilization rate of CPU is divided into by 0%-10,10%-50%, 50%- according to the Mhz absolute values of physical server
70%th, 5 fraction sections such as 70%-90%, 90%-100%.When CPU usage is in some section, calculate corresponding
Cpu load score value.
Likewise, memory usage is divided into 0%-30%, 30%-80%, 80%-100%3 sections, according to area
Between calculate corresponding memory usage score value.The activity of fictitious host computer memory is actual in total memory shared by fictitious host computer
The amount of activity.Memory warrant quantity runs the amount of fictitious host computer actual use for underlying operating system, and calculating is combined with activity.Root
Overall score is calculated according to memory usage scoring and memory actual activity stock number two subitem cum rights of scoring, is obtained final
Memory load scoring.
The calculating of storage score value mainly uses：Disk is taken to read in lag time and disk write-in lag time index most
Big value is used as and refers to index.4 sections are divided into, are respectively：0-10ms、10-50ms、50-100ms、>100ms, according to reality
Duration, calculates storage load score value.
Using network interface card overall transmission rate, the percentage of network usage is calculated.Percentage is converted into network again to bear
Carry scoring.
Risk class：
The significance level of self-defined each risk item, for example red risk item is set for the superlative degree, yellow risk Xiang Weici
It is advanced, and so on.And be the risk item setup measures score value of classification, once alarm triggered, just deducts points.Then according to resource
Overall load scoring subtract alarm produce deduction of points value, obtain overall operating condition.One red risk item triggering button 15
Point, 5 points of a yellow risk item triggering button.
Efficiency class：
In week age, collection calculates point, calculates CPU, memory, the vacancy rate of disk of virtual machine respectively, judges this
Whether a little collection points are idle point.Within the defined period, if the big Mr. Yu's numerical value in collection point, virtual machine are considered as at this
Between be idle in section.
Collection point cpu idle：CPU usage is less than setting value；
Collection point memory idle：Value of the warrant quantity of memory activity/memory less than setting；
Collection point disk leaves unused：Disk utilization rate (checks read-write situation)；
Virtual machine leaves unused：In certain period, virtual machine cpu idle rate is higher than predetermined value, memory idle rate higher than predetermined value,
Disk vacancy rate is higher than predetermined value, it is possible to judges that the virtual machine is idle in this week age.
It is worth noting that, collection period (being in one week above) mentioned above, the quantity of collection point, each index are set
Definite value, idle point etc. are all can be customized.For example we can be set in one month, every 30 minutes gather a CPU,
Memory, the numerical value of disk judge whether index leaves unused under collection point.It is idle, memory as CPU usage is less than 30% CPU
Utilization rate is idle less than 10% memory, and storage utilization rate is idle less than 50% storage.Understood by calculating in this moon
Share M collection point.When above-mentioned cpu idle ratio is more than 98% (N/M*100%, 98% setting value), memory idle rate is more than
80% (N/M*100%, 80% setting value), when IO vacancy rates are more than 95% (N/M*100%, 95% setting value), the virtual machine
It is idle in this month.
Step 4, the corresponding operating status of the numerical value of the numerical value of each subclassification and each subclassification is shown；
Step 5, the numerical value of the similar subclassification of all virtual machines of weighted calculation, and by the numerical value of similar subclassification and together
The corresponding operating status of numerical value of class subclassification is shown.
Can then it be weighted with the weight of self-defined business associated virtual machine.In this example, with HDFS business system
The associated physical server of system has 3, and business is directly linked virtual machine 4, attached associated virtual machine 8.Because Name
Node4 is HDFS management nodes, more important with respect to the virtual machine in other business, so weight is 30, in other business
Virtual machine weight is 20.Similarly, secondary associated virtual machine VM7 and VM8 can directly influence the runnability of Name node4, so
Weight is 2, and the weight of other associated virtual machines is 1.
HDFS operation systems associated virtual machine and according to the weight of incidence relation set table it is as follows：
The health value of each virtual machine of weighted calculation, value-at-risk, efficiency value can obtain the related fortune of this operation system
The health value of row index, that is, operation system, value-at-risk, efficiency value, and shown.
Referring to Fig. 4, Fig. 4 shows the first of the service operation status assessing system 100 of the invention based on big data environment
Embodiment, it includes：
Acquisition module 1, for gathering the parameter index of all fictitious host computers；Wherein, the parameter index is joined including CPU
Number, memory parameters, storage parameter, network parameter and read-write I/O parameter, but it is not limited system.
Sort module 2, for the parameter index belonged on same fictitious host computer to be classified, obtains each subclassification
Achievement data；Specifically, when the parameter index belonged on same fictitious host computer is classified, the classification type includes strong
Health class, risk class and efficiency class, but it is not limited system.
Computing module 3, for respectively being calculated the achievement data of each subclassification to obtain each subclassification according to algorithm
Numerical value, and for all virtual machines of weighted calculation similar subclassification numerical value；
Display module 4, for the corresponding operating status of the numerical value of the numerical value of each subclassification and each subclassification to be shown,
And for the corresponding operating status of the numerical value of the numerical value of similar subclassification and similar subclassification to be shown.
As shown in figure 5, the computing module 3 includes：
First calculating sub module 31, for respectively being calculated the achievement data of each subclassification to obtain respectively according to algorithm
The numerical value of subclassification；
Second calculating sub module 32, the numerical value for the similar subclassification of all virtual machines of weighted calculation.
Further, first computing module 31 includes：
Subitem computing unit 311, for carrying out subitem calculating to the achievement data of each subclassification, obtains the number of each subitem
Value；
Cum rights computing unit 312, cum rights calculating is carried out for the calculating ratio according to each subclassification to the numerical value of each subitem,
Obtain the Weighted Coefficients of each subitem；
Numerical calculation unit 313, for obtaining the numerical value of each subclassification according to the Weighted Coefficients of each subitem.
Referring to Fig. 6, Fig. 6 shows that the 3rd of the service operation status assessing system of the invention based on big data environment implements
Example, further includes memory module 5 unlike the first embodiment shown in Fig. 3, in the present embodiment, all virtual main for storing
The parameter index of machine is, it can be achieved that the long-term storage of parameter index.
From the foregoing, it will be observed that the present invention can reduce the workload of hand inspection big data business system running state, and assist
Administrator can more be visually known the situation of big data business system operation, reduce the time of malfunction elimination.Specifically, it is of the invention
Have the advantages that：
1st, big data business is managed by building service management pattern, it is comprehensive strong；
2nd, in virtual machine aspect, according to the health value, value-at-risk, efficiency value of algorithm evaluation virtual machine, more fully；
3rd, in service layer, the operating condition of big data business is fully assessed according to algorithm.Scope of assessment includes business
The weighted calculation of virtual machine and time associated virtual machine is directly linked, calculating content includes health value, value-at-risk, efficiency value；
4th, involved in algorithm to weight index value be all can be customized, can be made by oneself according to user's actual need
Justice, flexibility are strong.
The above is the preferred embodiment of the present invention, it is noted that for those skilled in the art
For, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also considered as
Protection scope of the present invention.
Claims (10)
- A kind of 1. service operation status assessment algorithm based on big data environment, it is characterised in that including：S1, gathers the parameter index of all fictitious host computers；S2, the parameter index belonged on same fictitious host computer is classified, and obtains the achievement data of each subclassification；S3, is respectively calculated the achievement data of each subclassification according to algorithm, obtains the numerical value of each subclassification；S4, the corresponding operating status of the numerical value of the numerical value of each subclassification and each subclassification is shown；S5, the numerical value of the similar subclassification of all virtual machines of weighted calculation, and by the numerical value of similar subclassification and similar subclassification The corresponding operating status of numerical value shown.
- 2. the service operation status assessment algorithm based on big data environment as claimed in claim 1, it is characterised in that the ginseng Number index includes CPU parameters, memory parameters, storage parameter, network parameter and read-write I/O parameter.
- 3. the service operation status assessment algorithm based on big data environment as claimed in claim 1, it is characterised in that will belong to When parameter index on same fictitious host computer is classified, the classification type includes healthy class, risk class and efficiency class.
- 4. the service operation status assessment algorithm based on big data environment as claimed in claim 1, it is characterised in that the step Rapid S3 includes：Subitem calculating is carried out to the achievement data of each subclassification, obtains the numerical value of each subitem；Cum rights calculating is carried out to the numerical value of each subitem according to the calculating ratio of each subclassification, obtains the Weighted Coefficients of each subitem；The numerical value of each subclassification is obtained according to the Weighted Coefficients of each subitem.
- A kind of 5. service operation status assessing system based on big data environment, it is characterised in that including：Acquisition module, for gathering the parameter index of all fictitious host computers；Sort module, for the parameter index belonged on same fictitious host computer to be classified, obtains the index of each subclassification Data；Computing module, for respectively being calculated the achievement data of each subclassification to obtain the number of each subclassification according to algorithm Value, and the numerical value of the similar subclassification for all virtual machines of weighted calculation；Display module, for the corresponding operating status of the numerical value of the numerical value of each subclassification and each subclassification to be shown, is used in combination Shown in by the corresponding operating status of the numerical value of the numerical value of similar subclassification and similar subclassification.
- 6. the service operation status assessing system based on big data environment as claimed in claim 5, it is characterised in that the meter Calculating module includes：First calculating sub module, for respectively being calculated the achievement data of each subclassification to obtain each subclassification according to algorithm Numerical value；Second calculating sub module, the numerical value for the similar subclassification of all virtual machines of weighted calculation.
- 7. the service operation status assessing system based on big data environment as claimed in claim 6, it is characterised in that described One computing module includes：Subitem computing unit, for carrying out subitem calculating to the achievement data of each subclassification, obtains the numerical value of each subitem；Cum rights computing unit, carries out cum rights calculating to the numerical value of each subitem for the calculating ratio according to each subclassification, obtains each The Weighted Coefficients of subitem；Numerical calculation unit, for obtaining the numerical value of each subclassification according to the Weighted Coefficients of each subitem.
- 8. the service operation status assessing system based on big data environment as claimed in claim 5, it is characterised in that further include Memory module, for storing the parameter index of all fictitious host computers.
- 9. the service operation status assessing system based on big data environment as claimed in claim 5, it is characterised in that the ginseng Number index includes CPU parameters, memory parameters, storage parameter, network parameter and read-write I/O parameter.
- 10. the service operation status assessing system based on big data environment as claimed in claim 5, it is characterised in that will belong to When the parameter index on same fictitious host computer is classified, the classification type includes healthy class, risk class and efficiency class.
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