CN105183627A - Server performance prediction method and system - Google Patents
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- CN105183627A CN105183627A CN201510684298.7A CN201510684298A CN105183627A CN 105183627 A CN105183627 A CN 105183627A CN 201510684298 A CN201510684298 A CN 201510684298A CN 105183627 A CN105183627 A CN 105183627A
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Abstract
The invention discloses a server performance prediction method and system. The method comprises the following steps: collecting server disk size data, server CPU utilization ratio data and server internal memory utilization ratio data; conducting fitting analysis by utilizing the disk size data, comparing the analysis result with actual disk size data, and judging whether a server magnetic disk is abnormal or not according to the comparative result; according to the CPU utilization ratio data, respectively conducting statistics on the CPU utilization ratio data in each time period, and comparing the statistical result in each time period with the actual corresponding CPU utilization ratio data, and judging whether an internal memory is abnormal or not according to the comparative result. Remedial measures can be adopted even if the server is not in failure, and stress of operation and maintenance personnel can be relieved.
Description
Technical field
The present invention relates to server operation management field, particularly the method and system predicted of a kind of server performance.
Background technology
Along with develop rapidly that is scientific and technical and infotech, the application of high performance computer system is also more and more extensive, server operation maintenance also develops into supermatic O&M mode from manual detection, and wherein the monitoring of server performance is an important monitoring project.Present stage, existing performance monitoring can be supplied to operation maintenance personnel real-time performance state and historical performance curve, helped operation maintenance personnel to carry out real-time understanding to server health degree, to carry out corresponding maintenance work according to current state to server.But, present stage, the business that carries of large-scale machine room was all high real-time and high concurrency, carry out again when a failure occurs safeguarding the normal operation that will certainly affect original business, therefore, how the variation tendency of predictive server performance, just adopts remedial measures when catastrophic failure not yet appears in server, prevents trouble before it happens, alleviating the on-stream pressure of operation maintenance personnel, is those skilled in the art's technical issues that need to address.
Summary of the invention
The object of this invention is to provide the method and system of a kind of server performance prediction, the method just can adopt remedial measures when catastrophic failure not yet appears in server, prevents trouble before it happens, and alleviates the on-stream pressure of operation maintenance personnel.
For solving the problems of the technologies described above, the invention provides the method for a kind of server performance prediction, comprising:
Gather predetermined amount of time server disk size data, server cpu busy percentage data and server memory availability data;
Utilize described server disk capacity data to carry out Fitting Analysis, analysis result and real server disk size data are compared, and whether abnormal according to comparative result determining server disk;
According to described server cpu busy percentage data, respectively the described server cpu busy percentage data of each period are added up, the server cpu busy percentage data of each period corresponding with reality for the statistics of each period are compared, and whether abnormal according to comparative result determining server CPU;
According to described server memory availability data, respectively the described server memory availability data of each period is added up, the server memory availability data of each period corresponding with reality for the statistics of each period is compared, and whether abnormal according to comparative result determining server internal memory.
Wherein, utilize described server disk capacity data to carry out Fitting Analysis, analysis result and real server disk size data compared, and whether extremely comprises according to comparative result determining server disk:
Utilize described server disk capacity data to carry out linear curve fit analysis, determine matched curve;
Obtain the predicted value of real server disk size data according to described matched curve, and determine the first fiducial interval according to described predicted value;
Real server disk size data and described first fiducial interval are compared;
When described real server disk size data are in described first fiducial interval range, then described server disk is normal;
When described real server disk size data are not in described first fiducial interval range, then described server disk is abnormal.
Wherein, according to described server cpu busy percentage data, respectively the described server cpu busy percentage data of each period are added up, the server cpu busy percentage data of each period corresponding with reality for the statistics of each period are compared, and whether extremely comprise according to comparative result determining server CPU:
According to described server cpu busy percentage data, respectively described server cpu busy percentage data corresponding for each predetermined point of time are added up, determine the mean values that each predetermined point of time is corresponding;
Described mean values is utilized to determine the second fiducial interval that each predetermined point of time is corresponding;
The server cpu busy percentage data of each time point actual are compared with the second fiducial interval of corresponding time point;
When in described second fiducial interval range, then described server CPU is normal;
When not in described first fiducial interval range, then described server CPU is abnormal.
Wherein, also comprise:
To the described server disk capacity data gathered, described server cpu busy percentage data and described server memory availability data upgrade.
Wherein, also comprise:
When described server disk, server CPU and/or server memory abnormal time, send warning message.
The invention provides the system of a kind of server performance prediction, comprising:
Acquisition module, for gathering predetermined amount of time server disk size data, server cpu busy percentage data and server memory availability data;
Whether server disk determination module, for utilizing described server disk capacity data to carry out Fitting Analysis, compares analysis result and real server disk size data, and abnormal according to comparative result determining server disk;
Server CPU determination module, for according to described server cpu busy percentage data, respectively the described server cpu busy percentage data of each period are added up, the server cpu busy percentage data of each period corresponding with reality for the statistics of each period are compared, and whether abnormal according to comparative result determining server CPU;
Server memory determination module, for according to described server memory availability data, respectively the described server memory availability data of each period is added up, the server memory availability data of each period corresponding with reality for the statistics of each period is compared, and whether abnormal according to comparative result determining server internal memory.
Wherein, described server disk determination module comprises:
Fitting unit, for utilizing described server disk capacity data to carry out linear curve fit analysis, determines matched curve;
First predicting unit, for obtaining the predicted value of real server disk size data according to described matched curve, and determines the first fiducial interval according to described predicted value;
First comparing unit, for comparing real server disk size data and described first fiducial interval; When described real server disk size data are in described first fiducial interval range, then described server disk is normal; When described real server disk size data are not in described first fiducial interval range, then described server disk is abnormal.
Wherein, described server CPU determination module comprises:
Described server cpu busy percentage data corresponding for each predetermined point of time, for according to described server cpu busy percentage data, are added up, are determined the mean values that each predetermined point of time is corresponding by averaging unit respectively;
Second predicting unit, determines for utilizing described mean values the second fiducial interval that each predetermined point of time is corresponding;
Second comparing unit, for comparing the server cpu busy percentage data of each time point actual with the second fiducial interval of corresponding time point; When in described second fiducial interval range, then described server CPU is normal; When not in described first fiducial interval range, then described server CPU is abnormal.
Wherein, also comprise:
Update module, for the described server disk capacity data to collection, described server cpu busy percentage data and described server memory availability data upgrade.
Wherein, also comprise:
Alarm module, for when described server disk, server CPU and/or server memory abnormal time, send warning message.
Method and the system of server performance prediction provided by the present invention comprise: gather predetermined amount of time server disk size data, server cpu busy percentage data and server memory availability data; Utilize described server disk capacity data to carry out Fitting Analysis, analysis result and real server disk size data are compared, and whether abnormal according to comparative result determining server disk; According to described server cpu busy percentage data, respectively the described server cpu busy percentage data of each period are added up, the server cpu busy percentage data of each period corresponding with reality for the statistics of each period are compared, and whether abnormal according to comparative result determining server CPU; According to described server memory availability data, respectively the described server memory availability data of each period is added up, the server memory availability data of each period corresponding with reality for the statistics of each period is compared, and whether abnormal according to comparative result determining server internal memory;
The method is passed through the server disk capacity in predetermined amount of time, and the historical data of server cpu busy percentage and server memory utilization factor is added up; And the disposal route corresponding according to each class data separate carries out statistical study, and according to analysis result predictive server performance, compare according to predicted data and measured data, whether can there is fault by determining server corresponding site according to comparative result.Namely the method just can adopt remedial measures when catastrophic failure not yet appears in server, prevents trouble before it happens, and alleviates the on-stream pressure of operation maintenance personnel.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only embodiments of the invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to the accompanying drawing provided.
The process flow diagram of the method for the server performance prediction that Fig. 1 provides for the embodiment of the present invention;
The history curve schematic diagram of one day server cpu busy percentage data that Fig. 2 provides for the embodiment of the present invention;
Fig. 3 provides for the embodiment of the present invention morning 10 in September point server cpu busy percentage data history curve schematic diagram;
Fig. 4 provides for the embodiment of the present invention September server disk capacity data history curve schematic diagram;
The structured flowchart of the system of the server performance prediction that Fig. 5 provides for the embodiment of the present invention.
Embodiment
Core of the present invention is to provide the method and system of a kind of server performance prediction, and the method just can adopt remedial measures when catastrophic failure not yet appears in server, prevents trouble before it happens, and alleviates the on-stream pressure of operation maintenance personnel.
For making the object of the embodiment of the present invention, technical scheme and advantage clearly, below in conjunction with the accompanying drawing in the embodiment of the present invention, technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
Please refer to Fig. 1, the method for server performance prediction, comprising:
Due to the variation tendency of server different performance supplemental characteristic, adopt different predicting strategies, a preliminary prediction is carried out to server performance.
S100, collection predetermined amount of time server disk size data, server cpu busy percentage data and server memory availability data;
Wherein, want that one is carried out to server and predict accurately, first will collect the historical data of server, namely gather predetermined amount of time server disk size data, server cpu busy percentage data and server memory availability data; Wherein, the size of described predetermined amount of time can be determined according to actual conditions.Such as, by nearest one week, January or more over a long time in the collection of disk size data; Such as want data prediction more accurate, then image data needs a lot, then long in predetermined amount of time; And the schedule time segment length of image data is consistent, also different on the impact predicted the outcome, because the levels of precision of performance prediction also has certain relation with the newness degree of data.Such as the closer to data representativeness may be better.The consistent level of business corresponding with server can also there be some relations.Utilizing the data being in the stage of stable development to carry out prediction effect can be relatively good.
S110, utilize described server disk capacity data to carry out Fitting Analysis, analysis result and real server disk size data are compared, and whether abnormal according to comparative result determining server disk;
Wherein, for the prediction of server disk capacity, due to the generation of various journal file, the growth of database, the data stored in server disk increase gradually, can select suitable fit approach, such as, after business enters the stage of stable development according to service conditions, the data of server process every day are roughly in an equilibrium level, therefore the growth of disk size is also linear, and linearity curve can be adopted to go matching, thus the change of predictive server disk size; Be about to actual server disk capacitance values compare with numerical value predict according to linearity curve, then can determining server disk whether exception.
Namely can by nearest one week, January or more over a long time in disk size change curve, linear fit carried out to this curve, finally the disk size in certain moment following is predicted, and whether can arrange threshold determination server disk abnormal.
S120, according to described server cpu busy percentage data, respectively the described server cpu busy percentage data of each period are added up, the server cpu busy percentage data of each period corresponding with reality for the statistics of each period are compared, and whether abnormal according to comparative result determining server CPU;
Wherein, can by nearest one week, January or more over a long time in the cpu busy percentage data of same time period server, certain time period cpu busy percentage following is predicted, and according to predict the outcome with actual result whether compare to determine server CPU abnormal.
Wherein, for server cpu busy percentage, because this parameter is relevant to server process business, during heavy traffic, cpu busy percentage is high, and when business is idle, cpu busy percentage is low, therefore cpu busy percentage curve has obvious peak period, low ebb phase and the stage of stable development, such as 6:00 ~ 18:00 process on working day business is more, and CPU is in higher utilization factor, working day, other times then processed less business with when having a holiday, and CPU is in idle state.In addition, cpu busy percentage presents cyclical variation in units of sky, therefore will consider this two aspects when predicting; Therefore respectively the described server cpu busy percentage data of each period are added up, the server cpu busy percentage data of each period corresponding with reality for the statistics of each period are compared, and whether abnormal according to comparative result determining server CPU; Such as, the matching of curve can be carried out by the utilization factor of every day same time period.In addition the data predicted according to the same time period compare with real data, increase/the minimizing of business burst can be obtained, thus determine whether server CPU such as can carry out predicting namely because server cpu busy percentage is different from server disk capacity according to the curve obtained shown in Fig. 2 with Fig. 3 extremely, not cumulative, therefore can not direct linear fit.Due to cpu busy percentage and intensity of traffic close relation, thus cpu busy percentage to present daytime high, the trend that night is low, as shown in Figure 2.Therefore, adopt the data analysis of every day same time period when prediction cpu busy percentage, as shown in Figure 3, according to this month the morning 10 time cpu busy percentage, infer that cpu busy percentage is approximately 51%; Compared by historical data predicted value and actual measured value, if current server cpu busy percentage is far above this month historical data average, then illustrate that exception has appearred in server service, operation maintenance personnel should be investigated, in order to avoid there is the error that cannot retrieve.Also can add up according to the utilization factor of every day same time period.In addition the same time data utilizing statistics to predict compares with real data, can obtain the increase/minimizing of business burst, thus determines that whether server CPU is abnormal.
S130, according to described server memory availability data, respectively the described server memory availability data of each period is added up, the server memory availability data of each period corresponding with reality for the statistics of each period is compared, and whether abnormal according to comparative result determining server internal memory.
For server memory utilization factor, similar to server cpu busy percentage, identical method can be used to predict.
Wherein, can by nearest one week, January or more over a long time in same time period server memory availability data, certain time period memory usage following is predicted, and according to predict the outcome with actual result whether compare to determine server memory abnormal.
Based on technique scheme, the method for the server performance prediction that the embodiment of the present invention provides, the method can be passed through the server disk capacity in predetermined amount of time, and the historical data of server cpu busy percentage and server memory utilization factor is added up; And the disposal route corresponding according to each class data separate carries out statistical study, and according to analysis result predictive server performance, compare according to predicted data and measured data, whether can there is fault by determining server corresponding site according to comparative result.Namely the method just can adopt remedial measures when catastrophic failure not yet appears in server, prevents trouble before it happens, and alleviates the on-stream pressure of operation maintenance personnel.
Optionally, in above-described embodiment, utilize described server disk capacity data to carry out Fitting Analysis, analysis result and real server disk size data compared, and whether extremely can comprise according to comparative result determining server disk:
Utilize described server disk capacity data to carry out linear curve fit analysis, determine matched curve;
Obtain the predicted value of real server disk size data according to described matched curve, and determine the first fiducial interval according to described predicted value;
Wherein, the setting of the first fiducial interval will require to set according to realistic accuracy.
Real server disk size data and described first fiducial interval are compared;
When described real server disk size data are in described first fiducial interval range, then described server disk is normal;
When described real server disk size data are not in described first fiducial interval range, then described server disk is abnormal.
Such as, as shown in Figure 4.Record is carried out to this month server disk size, and obtains a data growth curve by linear fit.Can calculate following disk increase by this.Server disk capacity will reach 550G can to predict September 29 from Fig. 4.
Optionally, according to described server cpu busy percentage data in above-described embodiment, respectively the described server cpu busy percentage data of each period are added up, the server cpu busy percentage data of each period corresponding with reality for the statistics of each period are compared, and whether extremely comprise according to comparative result determining server CPU:
According to described server cpu busy percentage data, respectively described server cpu busy percentage data corresponding for each predetermined point of time are added up, determine the mean values that each predetermined point of time is corresponding;
Described mean values is utilized to determine the second fiducial interval that each predetermined point of time is corresponding;
Wherein, the setting of the second fiducial interval will require to set according to realistic accuracy.
The server cpu busy percentage data of each time point actual are compared with the second fiducial interval of corresponding time point;
When in described second fiducial interval range, then described server CPU is normal;
When not in described first fiducial interval range, then described server CPU is abnormal.
Optionally, the similar method to server cpu busy percentage also can be utilized to calculate for server memory utilization factor.
Based on above-mentioned any technical scheme, the method can also comprise:
To the described server disk capacity data gathered, described server cpu busy percentage data and described server memory availability data upgrade.
Wherein, the renewal of data can ensure forecasting reliability and accuracy.
Based on above-mentioned any technical scheme, the method can also comprise:
When described server disk, server CPU and/or server memory abnormal time, send warning message.
Wherein, user can be pointed out to carry out malfunction elimination by warning message, prevent because user's carelessness causes fault to occur, the reliability of raising method, thus improve Consumer's Experience.
Based on technique scheme, the method of the server performance prediction that the embodiment of the present invention provides, the method is according to recorded server performance historical data, render history performance data curve, carry out matching with linear function, finally utilize the performance data of function prediction server within following a period of time, operation maintenance personnel is protected in advance for predicting the outcome, decrease the pressure of operation maintenance personnel, improve the stability of server service.Utilized historical data can also be upgraded, and when a failure occurs it, fault handling can be carried out by reminding user; Thus improve reliability and accuracy.
Embodiments provide the method for server performance prediction, just can be adopted remedial measures when catastrophic failure not yet appears in server by said method.
Be introduced the system of the server performance prediction that the embodiment of the present invention provides below, the system of server performance prediction described below can mutual corresponding reference with the method that above-described server performance is predicted.
Please refer to Fig. 5, the structured flowchart of the system of the server performance prediction that Fig. 5 provides for the embodiment of the present invention; This system can comprise:
Acquisition module 100, for gathering predetermined amount of time server disk size data, server cpu busy percentage data and server memory availability data;
Whether server disk determination module 200, for utilizing described server disk capacity data to carry out Fitting Analysis, compares analysis result and real server disk size data, and abnormal according to comparative result determining server disk;
Server CPU determination module 300, for according to described server cpu busy percentage data, respectively the described server cpu busy percentage data of each period are added up, the server cpu busy percentage data of each period corresponding with reality for the statistics of each period are compared, and whether abnormal according to comparative result determining server CPU;
Server memory determination module 400, for according to described server memory availability data, respectively the described server memory availability data of each period is added up, the server memory availability data of each period corresponding with reality for the statistics of each period is compared, and whether abnormal according to comparative result determining server internal memory.
Optionally, described server disk determination module can comprise:
Fitting unit, for utilizing described server disk capacity data to carry out linear curve fit analysis, determines matched curve;
First predicting unit, for obtaining the predicted value of real server disk size data according to described matched curve, and determines the first fiducial interval according to described predicted value;
First comparing unit, for comparing real server disk size data and described first fiducial interval; When described real server disk size data are in described first fiducial interval range, then described server disk is normal; When described real server disk size data are not in described first fiducial interval range, then described server disk is abnormal.
Optionally, described server CPU determination module can comprise:
Described server cpu busy percentage data corresponding for each predetermined point of time, for according to described server cpu busy percentage data, are added up, are determined the mean values that each predetermined point of time is corresponding by averaging unit respectively;
Second predicting unit, determines for utilizing described mean values the second fiducial interval that each predetermined point of time is corresponding;
Second comparing unit, for comparing the server cpu busy percentage data of each time point actual with the second fiducial interval of corresponding time point; When in described second fiducial interval range, then described server CPU is normal; When not in described first fiducial interval range, then described server CPU is abnormal.
Based on above-mentioned any technical scheme, this system can also comprise:
Update module, for the described server disk capacity data to collection, described server cpu busy percentage data and described server memory availability data upgrade.
Based on above-mentioned any technical scheme, this system can also comprise:
Alarm module, for when described server disk, server CPU and/or server memory abnormal time, send warning message.
Based on above-mentioned any technical scheme, this system can also comprise:
Display module, predicts the outcome for display.
In instructions, each embodiment adopts the mode of going forward one by one to describe, and what each embodiment stressed is the difference with other embodiments, between each embodiment identical similar portion mutually see.For device disclosed in embodiment, because it corresponds to the method disclosed in Example, so description is fairly simple, relevant part illustrates see method part.
Professional can also recognize further, in conjunction with unit and the algorithm steps of each example of embodiment disclosed herein description, can realize with electronic hardware, computer software or the combination of the two, in order to the interchangeability of hardware and software is clearly described, generally describe composition and the step of each example in the above description according to function.These functions perform with hardware or software mode actually, depend on application-specific and the design constraint of technical scheme.Professional and technical personnel can use distinct methods to realize described function to each specifically should being used for, but this realization should not thought and exceeds scope of the present invention.
The software module that the method described in conjunction with embodiment disclosed herein or the step of algorithm can directly use hardware, processor to perform, or the combination of the two is implemented.Software module can be placed in the storage medium of other form any known in random access memory (RAM), internal memory, ROM (read-only memory) (ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technical field.
Above the method and system that server performance provided by the present invention is predicted are described in detail.Apply specific case herein to set forth principle of the present invention and embodiment, the explanation of above embodiment just understands method of the present invention and core concept thereof for helping.It should be pointed out that for those skilled in the art, under the premise without departing from the principles of the invention, can also carry out some improvement and modification to the present invention, these improve and modify and also fall in the protection domain of the claims in the present invention.
Claims (10)
1. a method for server performance prediction, is characterized in that, comprising:
Gather predetermined amount of time server disk size data, server cpu busy percentage data and server memory availability data;
Utilize described server disk capacity data to carry out Fitting Analysis, analysis result and real server disk size data are compared, and whether abnormal according to comparative result determining server disk;
According to described server cpu busy percentage data, respectively the described server cpu busy percentage data of each period are added up, the server cpu busy percentage data of each period corresponding with reality for the statistics of each period are compared, and whether abnormal according to comparative result determining server CPU;
According to described server memory availability data, respectively the described server memory availability data of each period is added up, the server memory availability data of each period corresponding with reality for the statistics of each period is compared, and whether abnormal according to comparative result determining server internal memory.
2. whether the method for claim 1, is characterized in that, utilizes described server disk capacity data to carry out Fitting Analysis, analysis result and real server disk size data are compared, and extremely comprise according to comparative result determining server disk:
Utilize described server disk capacity data to carry out linear curve fit analysis, determine matched curve;
Obtain the predicted value of real server disk size data according to described matched curve, and determine the first fiducial interval according to described predicted value;
Real server disk size data and described first fiducial interval are compared;
When described real server disk size data are in described first fiducial interval range, then described server disk is normal;
When described real server disk size data are not in described first fiducial interval range, then described server disk is abnormal.
3. the method for claim 1, it is characterized in that, according to described server cpu busy percentage data, respectively the described server cpu busy percentage data of each period are added up, the server cpu busy percentage data of each period corresponding with reality for the statistics of each period are compared, and whether extremely comprise according to comparative result determining server CPU:
According to described server cpu busy percentage data, respectively described server cpu busy percentage data corresponding for each predetermined point of time are added up, determine the mean values that each predetermined point of time is corresponding;
Described mean values is utilized to determine the second fiducial interval that each predetermined point of time is corresponding;
The server cpu busy percentage data of each time point actual are compared with the second fiducial interval of corresponding time point;
When in described second fiducial interval range, then described server CPU is normal;
When not in described first fiducial interval range, then described server CPU is abnormal.
4. the method as described in any one of claims 1 to 3, is characterized in that, also comprises:
To the described server disk capacity data gathered, described server cpu busy percentage data and described server memory availability data upgrade.
5. method as claimed in claim 4, is characterized in that, also comprise:
When described server disk, server CPU and/or server memory abnormal time, send warning message.
6. a system for server performance prediction, is characterized in that, comprising:
Acquisition module, for gathering predetermined amount of time server disk size data, server cpu busy percentage data and server memory availability data;
Whether server disk determination module, for utilizing described server disk capacity data to carry out Fitting Analysis, compares analysis result and real server disk size data, and abnormal according to comparative result determining server disk;
Server CPU determination module, for according to described server cpu busy percentage data, respectively the described server cpu busy percentage data of each period are added up, the server cpu busy percentage data of each period corresponding with reality for the statistics of each period are compared, and whether abnormal according to comparative result determining server CPU;
Server memory determination module, for according to described server memory availability data, respectively the described server memory availability data of each period is added up, the server memory availability data of each period corresponding with reality for the statistics of each period is compared, and whether abnormal according to comparative result determining server internal memory.
7. system as claimed in claim 6, it is characterized in that, described server disk determination module comprises:
Fitting unit, for utilizing described server disk capacity data to carry out linear curve fit analysis, determines matched curve;
First predicting unit, for obtaining the predicted value of real server disk size data according to described matched curve, and determines the first fiducial interval according to described predicted value;
First comparing unit, for comparing real server disk size data and described first fiducial interval; When described real server disk size data are in described first fiducial interval range, then described server disk is normal; When described real server disk size data are not in described first fiducial interval range, then described server disk is abnormal.
8. system as claimed in claim 6, it is characterized in that, described server CPU determination module comprises:
Described server cpu busy percentage data corresponding for each predetermined point of time, for according to described server cpu busy percentage data, are added up, are determined the mean values that each predetermined point of time is corresponding by averaging unit respectively;
Second predicting unit, determines for utilizing described mean values the second fiducial interval that each predetermined point of time is corresponding;
Second comparing unit, for comparing the server cpu busy percentage data of each time point actual with the second fiducial interval of corresponding time point; When in described second fiducial interval range, then described server CPU is normal; When not in described first fiducial interval range, then described server CPU is abnormal.
9. the system as described in any one of claim 6 to 8, is characterized in that, also comprises:
Update module, for the described server disk capacity data to collection, described server cpu busy percentage data and described server memory availability data upgrade.
10. system as claimed in claim 9, is characterized in that, also comprise:
Alarm module, for when described server disk, server CPU and/or server memory abnormal time, send warning message.
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