CN100356729C - Method and system for monitoring network service performance - Google Patents
Method and system for monitoring network service performance Download PDFInfo
- Publication number
- CN100356729C CN100356729C CNB2004100320792A CN200410032079A CN100356729C CN 100356729 C CN100356729 C CN 100356729C CN B2004100320792 A CNB2004100320792 A CN B2004100320792A CN 200410032079 A CN200410032079 A CN 200410032079A CN 100356729 C CN100356729 C CN 100356729C
- Authority
- CN
- China
- Prior art keywords
- baseline
- data
- network
- performance
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Images
Landscapes
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The present invention discloses a method and a system for monitoring the performance of network services. In the method, parameters are analyzed by a baseline configured by a network management system so as to determine the range of service data and the type of the baseline; the network management system obtains the average value, the maximum value and/or the minimum value of the service data from the service data according to configured sampling service data so that one or a plurality of baselines reflecting the performance of the service are formed by using the average value, the maximum value and/or the minimum value as a reference point; the current service data is obtained from a network, the service data of the current sampling point is compared with the baselines, and when the service data values of continuous a plurality of sampling points are offset from one baseline at the same side of the baseline, the performance of the network services is determined to have a fixed variation tendency.
Description
Technical field
The present invention relates to communication network technology, relate in particular to a kind of method and system of monitor network service feature.
Background technology
In communication network, operator need hold network ruuning situation in each in period, judge by analyzing the performance data of gathering whether operation degenerates or anomaly trend, as the foundation that the network optimization, plan of operation, failure diagnosis, viral source are found, black Internet bar is followed the trail of.
At present in the network performance analysis, particularly in the flow analysis, operator only obtains the performance data of being correlated with, and adds up according to time shaft and management object collection, certain statistical information constantly can only be obtained, the long-time running trend or the cycling service trend of network can't be obtained.
Summary of the invention
The object of the present invention is to provide a kind of method and system of monitor network service feature, exist in the prior art can't be for a long time or the problem of periodicity monitor network service feature variation tendency to solve.
Realize technical method of the present invention:
A kind of method of monitor network service feature, but described network has the network management system of collection network business datum, and the method comprising the steps of:
By network management system configuration baseline analysis parameter, if simple baseline, then definite baseline type and historical data are counted, if the periodicity baseline is then determined period type, historical data periodicity and distributed points as analysed basis our unit;
According to disposing the business datum of from network, sampling;
If simple baseline, then from described business datum, obtain mean value, maximum and/or the minimum value that historical data is counted, and be one or more horizontal base line that datum mark forms the reflection service feature with this value, if periodicity baseline, then according to historical data periodicity mean value, maximum and/or minimum value of each distributed points as analysed basis our unit in the acquisition cycle from described business datum, and with this value is one or more baseline that datum mark forms the reflection service feature;
With described baseline is reference, from network, obtain the current business data value and the value corresponding with current time point on the baseline compares, when the business datum value that continuous a plurality of sampled points occur all was greater than or less than the value of corresponding sampled point on the baseline, then there was fixing variation tendency in the service feature of decision network.
When there is fixing variation tendency in the service feature of decision network, produces warning information or/and carry out pre-configured control strategy network is carried out optimal control in dynamic.
Described baseline is for existing the simple baseline of relatively stable characteristic in the expression traffic data is during a time period, this baseline analysis parameter comprises baseline type, historical data point data, the superseded mode of abnormal data and confidential interval.
There is the period of performance baseline of cyclic variation trend in described baseline within a certain period of time for representing business datum, and this baseline analysis parameter comprises cycle, historical data periodicity, distributional analysis point base unit, the superseded mode of abnormal data and confidential interval.
A kind of Network performance monitoring system comprises:
The performance data library module is used to store History Performance Data;
The base-line data library module is used to store baseline configuration data and base-line data;
The service data acquisition module is used for collecting from communication network the real data of relevant performance operational indicator, and stores described performance data library module into;
The baseline analysis configuration module, be used to receive the baseline analysis configuration parameter of customization, if simple baseline, then definite baseline type and historical data are counted, store configuration data into the base-line data library module, if the periodicity baseline is then determined period type, historical data periodicity and distributed points as analysed basis our unit store configuration data into the base-line data library module;
The base-line data analysis module, read History Performance Data and baseline analysis configuration parameter formation baseline from described performance data library module and base-line data library module respectively, if simple baseline, the mean value of counting then according to the historical data that obtains, maximum and/or minimum value, form one or more horizontal base line of reflection service feature for datum mark, and be saved in the baseline database, if periodicity baseline, then according to the mean value of each distributed points as analysed basis our unit in the cycle that obtains, maximum and/or minimum value connect one or more baseline that the back forms the reflection service feature as datum mark, and are saved in the baseline database;
Network optimization analysis module is used for judging the network performance quality according to the business configuration of base-line data, the network equipment and the current performance data that collects, and network is carried out corresponding Optimizing operation.
The present invention has following beneficial effect:
1, can monitor abnormal flow in the communication network, automatically unusual port be positioned and isolated controlling, be particularly useful in the black Internet bar of control virus and control.
2, the basic data by providing service feature to change is convenient to the O﹠M personnel and is carried out the network optimization, the network planning, failure diagnosis, viral source discovery, the tracking of black Internet bar.
3, by confidential interval rejecting abnormalities data, when avoiding carrying out the baseline analysis prediction because sample point seldom causes a deviation bigger situation.
4, by the baseline interval mode, provide an acceptable service feature of user constant interval.
5, the trend that the baseline by continuous consistent variation tendency relatively can early detection performance change (degenerating/improve).
Description of drawings
Fig. 1 is a telecommunications metropolitan area network schematic diagram;
Fig. 2 is a network optimization baseline analysis system configuration schematic diagram of the present invention;
Fig. 3 A is the simple baseline schematic diagrames of two district office uplink ports;
Fig. 3 B is the simple baseline schematic diagrames of eight district office uplink ports;
Fig. 4 A is two district office current business data and baseline comparison diagrams;
Fig. 4 B is eight district office current business data and baseline comparison diagrams;
Fig. 5 is two district office uplink port date periodicity baseline schematic diagrames;
Fig. 6 is that two district office uplink port actual traffic data and date periodicity baseline compare schematic diagram.
Embodiment
Embodiment one
Present embodiment describes performance simple radical line analysis of the present invention.Consult the network of telecommunications metropolitan area network shown in Figure 1, in metropolitan area, the broadband Access Network that such classification is converged, be divided into two areas, He Ba district, two districts, all adopt MA5 100 that ADSL is provided service access, all traffic aggregations enter the metropolitan area network transmission behind ISN8850.Working procedure carries out the collection of data on flows on webmaster.The scope of gathering is the per day speed of all ISN8850 uplink ports.
Consult shown in Figure 2ly, the Network performance monitoring system comprises:
Performance database: preserve History Performance Data.
Baseline database: preserve baseline configuration data and base-line data.
Service data acquisition module: be responsible for collecting the real data of relevant performance operational indicator, and be saved in the performance database from communication network.
Baseline analysis configuration template: be responsible for receiving the baseline analysis configuration parameter of customization, configuration data is saved in database, be used for baseline analysis.
Base-line data analysis module: read History Performance Data and baseline analysis configuration parameter from described performance data library module and base-line data library module respectively, form the required baseline of user based on the baseline analysis method, and be saved in the baseline database.
Network optimization analysis module: be responsible for according to base-line data, in conjunction with the business configuration of the network equipment and the current performance data that collects, carry out baseline relatively, judge the network performance quality, output network is optimized analysis report, and carries out the automatic or manual Optimizing operation of network configuration.
Performance simple radical line analysis comprises: the configuration of simple radical line analysis, abnormal data rejecting, simple baseline data analysis, baseline are relatively.
1, simple radical line analysis configuration: baseline historical data scope is set up in definition, needs the following relevant parameter of configuration:
Baseline type: day baseline, all baselines, month baseline, year baseline.
Historical data is counted: carry out the required historical data of simple baseline data analysis and count out.Count many more long-term trend that reflect more, the nearest variation tendency of the fine reflection of less energy of counting are set.
Abnormal data is eliminated mode: eliminate abnormal data; Do not eliminate abnormal data.Do not eliminate the mode of abnormal data, all data will be used for baseline analysis.
Confidential interval: eliminate abnormal data and adopt [mean value-mean value * n%, mean value+mean value * n%], wherein n% is a confidence level, and the n value is 30, and the control of n can be by being provided with realization.
The scope and the baseline type of initial data determined in the configuration of simple radical line analysis.Eliminate the abnormal data mode for adopting, just need carry out the preliminary treatment of data.Entered for the 3rd step on the contrary: simple base-line data distributional analysis.Setting up a day baseline below is that example illustrates.
For example: set up simple day baseline with reference to the historical data in past 7 days altogether, the confidence level of employing 30% is eliminated abnormal data, and parameter is:
Baseline type: day baseline
Historical data is counted: 7
Abnormal data is eliminated mode: eliminate abnormal data
Confidential interval: [mean value-mean value * 30%, mean value+mean value * 30%]
The initial data of analyzing following (seven days):
The ISN 8850 uplink port actual speed rate (Gb/s) in two districts
1 2 3 4 5 6 7
0.7 0.45 0.65 0.5 0.60 0.55 0.85
The ISN 8850 uplink port actual speed rate (Gb/s) in eight districts
1 2 3 4 5 6 7
0.8 0.69 0.9 0.76 0.42 0.85 0.94
2, abnormal data is rejected
The data that abnormal data is meant and all initial data mean deviations are too big.Because the data of sample point are few, the calculating of inapplicable normal distribution adopts simple confidential interval [mean value-mean value * n%, mean value+mean value * n%] mode to come the rejecting abnormalities data.This initial average output value is different from follow-up baseline average.We are calculated as follows to the data in the example:
The ISN 8850 uplink port actual speed rate (Gb/s) in two districts
Mean value=(0.7+0.45+0.65+0.5+0.60+0.55+0.85)/7=0.614
Confidential interval: [0.614 * 0.7,0.614*1.3]=[0.43,0.80]
The 7th data 0.85 are disallowable.
The ISN 8850 uplink port actual speed rate (Gb/s) in eight districts
Mean value=(0.8+0.69+0.9+0.76+0.42+0.85+0.94)/7=0.766
Confidential interval: [0.766 * 0.7,0.766*1.3]=[0.54,0.99]
The 5th data 0.42 are disallowable.
3, simple baseline data analysis
Simple baseline data analysis is that historical data sampled point valid data are calculated its mean value, maximum, minimum value, so just forms three points, extends along x direction of principal axis horizontal-extending horizontal direction and has just formed baseline.To the data in the example, calculated data is as follows, extends and has just formed a day baseline.
The ISN 8850 uplink port actual speed rate (Gb/s) in two districts:
Maximum=MAX[0.7,0.45,0.65,0.5,0.60,0.55]=0.7
Minimum value=MIN[0.7,0.45,0.65,0.5,0.60,0.55]=0.45
Mean value=AVG[0.7,0.45,0.65,0.5,0.60,0.55]=0.575
The simple baseline of the performance in two districts is consulted shown in Fig. 3 A.
The ISN 8850 uplink port actual speed rate (Gb/s) in eight districts:
Maximum=MAX[0.8,0.69,0.9,0.76,0.85,0.94]=0.94
Minimum value=MIN[0.8,0.69,0.9,0.76,0.85,0.94]=0.69
Mean value=AVG[0.8,0.69,0.9,0.76,0.85,0.94]=0.82
The simple baseline of the performance in eight districts is consulted shown in Fig. 3 B.
4, baseline relatively
Network optimization analysis module, the current performance data from network obtains compares these performance datas and base-line data, relatively adopts baseline interval graph mode.Exceed the baseline interval according to current sampling point and do understanding current business performance change.
The speed data of the current collection of two district ISN8850 uplink ports:
0.6?0.58?0.65?0.49?0.52?0.56?0.65
Two district's current datas and base-line data are relatively shown in Fig. 4 A.
The speed data of the current collection of eight district ISN8850 uplink ports:
0.96?0.98?0.95?0.92?0.8
Eight district's current datas and base-line data are relatively shown in Fig. 4 B.
In relatively judging, use for reference the quality sampling analysis method in the quality management system, the general employing has continuous 7 times to surpass the deviation that is in identical direction, can decision-making system have fixing variation tendency.Native system exceeds a certain baseline interval according to continuous several times in same direction and judges whether network performance the performance change on certain trend has taken place.Its number of times is by the decision of different business field, and communication network generally adopts the 4-7 point to calculate, and also availablely adjusts (zooming in or out) according to the current business actual conditions.
5, when there is fixing variation tendency in the service feature of decision network, carries out pre-configured control strategy network is carried out optimal control in dynamic.
Judge performance change trend when getting 3 as quality sampling bias number of times.By last two figure as can be seen, the current uplink port actual flow of the convergence device in two districts is in the baseline scope, temporarily big variation can not appear, the demand that does not also have dilatation, and continuous three baseline intervals above baseline maximum and minimum value have appearred in the current uplink port actual flow of the convergence device in eight districts, represent that this Local Area Network traffic carrying capacity is excessive, be badly in need of carrying out the dilatation measure.In actual implementation process, the access point in eight intervals is cut to the ISN 8850 times in two intervals, realize the network optimization that no dilatation is invested like this.
Embodiment two:
Present embodiment describes period of performance baseline analysis of the present invention.Equally with the ISN8850 uplink port of network Zhong Er shown in Figure 1 district office every day 8:00-9:00AM the port actual speed rate be example.The Network performance monitoring system as shown in Figure 2.
The period of performance baseline analysis comprises: periodically baseline analysis configuration, abnormal data rejecting, periodicity base-line data analysis and baseline are relatively.
1, periodically the baseline analysis configuration comprises:
Cycle: according to service feature, the general cycle comprises diurnal periodicity, cycle, month cycle and annual period.
The historical data periodicity: the initial data of carrying out baseline analysis is the historical data in a plurality of cycles, historical data is many more to reflect general trend more, but increase/subtract the business of trend for having for a long time, the baseline that is comprehensively formed by too much historical data can not reflect nearest service feature well.Very few historical data causes data deviation bigger again easily, and individual influence is bigger, can not be as effective performance baseline.The number of cycles of suitable historical data is 3-8 cycle.
Distributional analysis point base unit: be exactly in the cycle, carry out the unit of the time point of distributional analysis.Generally in diurnal periodicity, adopt hour as distributional analysis point base unit; Adopt day as distributional analysis point base unit in the cycle; Adopt day as distributional analysis point base unit in the cycle moon; Adopt the moon as distributional analysis point base unit in annual period.
Abnormal data is eliminated mode: eliminate abnormal data; Do not eliminate abnormal data.Do not eliminate the mode of abnormal data, all data will be used for baseline analysis.
Confidential interval: eliminate abnormal data and adopt [mean value-mean value * n%, mean value+mean value * n%], wherein n% is a confidence level, and n gets 30, and the control of n can be by being provided with realization.
Periodically the scope and the baseline type of initial data determined in the baseline analysis configuration.Eliminate the abnormal data mode for adopting, just need carry out the preliminary treatment of data.Entered for the 3rd step on the contrary: periodically base-line data distributional analysis.Setting up a day baseline below is that example illustrates:
For example: set up a day baseline with reference to the port actual speed rate historical data in past 7 days altogether, distributional analysis in 24 hours point is arranged every day.The confidence level of employing 30% is eliminated abnormal data, and parameter is:
Cycle: diurnal periodicity
Historical data periodicity: 7
Distributed points as analysed basis our unit: hour
Abnormal data is eliminated mode: eliminate abnormal data
Confidential interval: [mean value-mean value * 30%, mean value+mean value * 30%]
Get 7 days simulation initial data of one of them distributional analysis point 8:00-9:00AM, as follows:
Unit: Gb/s
1 2 3 4 5 6 7
0.66 0.68 0.72 0.61 0.70 0.79 0.64
In order to set up periodically day baseline, need carry out following processing to each distributional analysis point.To situation in the example, need carry out following steps 2-3 respectively to 24 hours sections and handle.
2, abnormal data is rejected
The data that abnormal data is meant and all initial data mean deviations are too big.Because the sample data of distributional analysis point is few, the calculating of inapplicable normal distribution adopts simple confidential interval [mean value-mean value * n%, mean value+mean value * n%] mode (n=30) to come the rejecting abnormalities data.This initial average output value is different from follow-up baseline average.As follows to the data computation in the example:
Mean value=(0.66+0.68+0.95+0.61+0.70+0.79+0.64)/7=0.72
Confidential interval: [0.72 * 0.7,0.72*1.3]=[0.50,0.94]
The 3rd data 0.95 are disallowable.
3, periodically base-line data analysis
Periodically the base-line data analysis is to adopt valid data to calculate its mean value, maximum, minimum value to each distributional analysis point in the cycle, and a plurality of distributional analysis points in the cycle just form three lines like this, as the periodicity baseline.
Data to above-mentioned distributional analysis point are calculated as follows:
Maximum=MAX[0.66,0.68,0.61,0.70,0.79,0.64]=0.79
Minimum value=MIN[0.66,0.68,0.61,0.70,0.79,0.64]=0.61
Mean value=AVG[0.66,0.68,0.61,0.70,0.79,0.64]=0.68
24 hours sections are carried out following steps 2-3 respectively handle, three baselines of totally 24 distributional analysis point formation as shown in Figure 5.
4, baseline relatively
The performance data of the current period that obtains from network compares these data and current base-line data.Relatively adopt curve interval graph mode, as shown in Figure 6.Exceed the service feature variation that the specific distribution analysis site is understood in the curve interval according to certain distributional analysis point.
Use for reference the quality sampling analysis method in the quality management system, the general employing has continuous 7 times to surpass the deviation that is in equidirectional, can decision-making system have fixing variation tendency.Native system exceeds the baseline interval according to the continuous several times in the cycle in same direction and judges whether network performance the performance change on certain trend has taken place.Described continuous several times adopts the 15%-30% of distributed points number to calculate by the decision of the distributed points in the cycle.
For the data that adopt the output of performance simple radical line analysis and period of performance baseline analysis, can carry out the network optimization, ensure the telecommunications network service quality.For example: have the fixing big trend that becomes for the network interface flow, system-computed goes out according to current rate of change, and what will reach the heap(ed) capacity of the current configuration of network in the future.When the network interface flow reach heap(ed) capacity 60% the time, system sends alarm, when the network interface flow reach heap(ed) capacity 80% the time, system provides high severity alarm, and whether need to increase the band width configuration of PVC, and record is carried out in user's operation by modal dialog mandatory requirement user decision.If the user need increase the PVC band width configuration, the network interface PVC flow configuration template that then provides the user to select required configuration.
Use this baseline analysis method early warning degradation or the busy situation of Internet resources effectively, thereby control is to the optimization of network configuration.If the user has disposed strategic server, can (for example: the network optimization control strategy top network interface flow of mentioning etc.) customize some key network performance index based on strategic server, among the SLA that network performance index limits and the user signs with key, thereby realize automatic optimal control in dynamic to network.
Adopt the dateout of this baseline analysis method to can be used as the reference data that the network service efficiency improves in operator.For example: to the data in the example, 24 hours distributed points adopt 4 continuous distribution point deviations to change as serious trend and judge sign.Can see that port flow was on the low side, is lower than baseline value since 0 o'clock to 4 o'clock, operator can provide some preferential policies at this period like this, makes the user use in this period and obtains certain discount, makes full use of network.
Adopt the network management system of this baseline analysis method and telecommunications OSS system to set up interface and transmit some utilization rate performance index, can be to the utilization rate that baseline analysis comes out the low time period, adopts different rate sections to divide, the expenses ajustment strategy of formation science.
Adopt periodically baseline analysis method, can be used for monitoring the abnormal flow in the communication network, unusual port is positioned, be even more important aspect control virus and the black Internet bar of control.For example: will adopt the network management system of baseline analysis and virus monitoring software to set up interface, can pass through the flow baseline analysis, find out the port of abnormal flow, further analyze it then and transmit message or other the viral forwarding servers that bag can find whether to carry out the dos attack request, thereby carry out port isolation control, prevent that it from jeopardizing the operation of whole network.
Claims (9)
1, a kind of method of monitor network service feature, but described network has the network management system of collection network business datum, it is characterized in that comprising step:
By network management system configuration baseline analysis parameter, if simple baseline, then definite baseline type and historical data are counted, if the periodicity baseline is then determined period type, historical data periodicity and distributed points as analysed basis our unit;
According to disposing the business datum of from network, sampling;
If simple baseline, then from described business datum, obtain mean value, maximum and/or the minimum value that historical data is counted, and be one or more horizontal base line that datum mark forms the reflection service feature with this value, if periodicity baseline, then according to historical data periodicity mean value, maximum and/or minimum value of each distributed points as analysed basis our unit in the acquisition cycle from described business datum, and with this value is one or more baseline that datum mark forms the reflection service feature;
With described baseline is reference, from network, obtain the current business data value and the value corresponding with current time point on the baseline compares, when the business datum value that continuous a plurality of sampled points occur all was greater than or less than the value of corresponding sampled point on the baseline, then there was fixing variation tendency in the service feature of decision network.
2, the method for claim 1 is characterized in that, when there is fixing variation tendency in the service feature of decision network, produces warning information or/and carry out pre-configured control strategy network is carried out optimal control in dynamic.
3, the method for claim 1 is characterized in that, before the maximum and/or minimum value of determining business datum, rejects the abnormal data bigger with the mean deviation of all business datums in the business datum.
4, method as claimed in claim 3 is characterized in that, adopts [mean value-mean value * n%, mean value+mean value * n%] confidential interval come the rejecting abnormalities data, promptly be not regarded as abnormal data in this interval business datum and reject, wherein n% is a confidence level, n=20～30.
5, method as claimed in claim 3 is characterized in that, adopts the mean value of method of weighting computing service data, gives weights near sampled point of current time and is higher than weights from current time sampled point far away.
6, as the arbitrary described method of claim 1 to 5, it is characterized in that, described baseline is for existing the simple baseline of relatively stable characteristic in the expression traffic data is during a time period, this baseline analysis parameter comprises baseline type, historical data point data, the superseded mode of abnormal data and confidential interval.
7, method as claimed in claim 6 is characterized in that, is that datum mark extends with mean value, maximum and/or the minimum value that obtains, and forms the simple baseline of performance perpendicular to the axis of representing the business datum value.
8, as the arbitrary described method of claim 1 to 5, it is characterized in that, there is the period of performance baseline of cyclic variation trend in described baseline within a certain period of time for representing business datum, and this baseline analysis parameter comprises cycle, historical data periodicity, distributional analysis point base unit, the superseded mode of abnormal data and confidential interval.
9, a kind of Network performance monitoring system is characterized in that comprising:
The performance data library module is used to store History Performance Data;
The base-line data library module is used to store baseline configuration data and base-line data;
The service data acquisition module is used for collecting from communication network the real data of relevant performance operational indicator, and stores described performance data library module into;
The baseline analysis configuration module, be used to receive the baseline analysis configuration parameter of customization, if simple baseline, then definite baseline type and historical data are counted, store configuration data into the base-line data library module, if the periodicity baseline is then determined period type, historical data periodicity and distributed points as analysed basis our unit store configuration data into the base-line data library module;
The base-line data analysis module, read History Performance Data and baseline analysis configuration parameter formation baseline from described performance data library module and base-line data library module respectively, if simple baseline, the mean value of counting then according to the historical data that obtains, maximum and/or minimum value, form one or more horizontal base line of reflection service feature for datum mark, and be saved in the baseline database, if periodicity baseline, then according to the mean value of each distributed points as analysed basis our unit in the cycle that obtains, maximum and/or minimum value connect one or more baseline that the back forms the reflection service feature as datum mark, and are saved in the baseline database;
Network optimization analysis module is used for judging the network performance quality according to the business configuration of base-line data, the network equipment and the current performance data that collects, and network is carried out corresponding Optimizing operation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2004100320792A CN100356729C (en) | 2004-03-31 | 2004-03-31 | Method and system for monitoring network service performance |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2004100320792A CN100356729C (en) | 2004-03-31 | 2004-03-31 | Method and system for monitoring network service performance |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN1677934A CN1677934A (en) | 2005-10-05 |
| CN100356729C true CN100356729C (en) | 2007-12-19 |
Family
ID=35050250
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNB2004100320792A Expired - Fee Related CN100356729C (en) | 2004-03-31 | 2004-03-31 | Method and system for monitoring network service performance |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN100356729C (en) |
Families Citing this family (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN100384153C (en) * | 2005-12-26 | 2008-04-23 | 北京交通大学 | Network performance analysis report system based on IPv6 and its implementing method |
| CN101141759B (en) * | 2007-02-12 | 2010-12-29 | 中兴通讯股份有限公司 | Call behavior statistical and analytical method and device |
| CN101291177B (en) * | 2008-06-16 | 2012-05-23 | 中兴通讯股份有限公司 | Diagnosis method and device for historical performance data of network management |
| CN101770419B (en) * | 2008-12-31 | 2013-03-20 | ***股份有限公司 | System robustness analyzer and analysis method |
| CN102035666A (en) * | 2009-09-24 | 2011-04-27 | 中兴通讯股份有限公司 | Method of administration of networks for business single plate and network administration system |
| CN102111307B (en) * | 2009-12-29 | 2013-09-04 | 亿阳信通股份有限公司 | Method and device for monitoring and controlling network risks |
| CN102480381B (en) * | 2010-11-29 | 2015-04-15 | 腾讯科技（深圳）有限公司 | Method and device for checking network service operational data |
| CN102026230A (en) * | 2010-12-20 | 2011-04-20 | 中兴通讯股份有限公司 | Method and device for monitoring data service quality of code division multiple access (CDMA) network |
| CN102495808B (en) * | 2011-11-24 | 2014-12-17 | 深圳市亚特尔科技有限公司 | Method for saving memory space |
| CN103139007A (en) * | 2011-12-05 | 2013-06-05 | 阿里巴巴集团控股有限公司 | Method and system for detecting application server performance |
| CN103793309B (en) * | 2012-10-29 | 2017-11-21 | ***通信集团浙江有限公司 | A kind of batch service method for early warning and device |
| CN103036741B (en) * | 2012-12-19 | 2016-02-03 | 北京神州绿盟信息安全科技股份有限公司 | The defining method of flow monitoring baseline and device |
| CN103905227B (en) * | 2012-12-26 | 2018-05-22 | ***通信集团辽宁有限公司 | A kind of server energy consumption control method and system |
| CN104767651B (en) * | 2014-01-07 | 2019-02-26 | ***通信集团黑龙江有限公司 | A kind of multi-service merger processing method, system and device |
| CN103856965A (en) * | 2014-03-24 | 2014-06-11 | 宁夏信友通信监理咨询有限责任公司 | WLAN (Wireless Local Area Network) comprehensive analysis system and method |
| CN105406991A (en) * | 2015-10-26 | 2016-03-16 | 上海华讯网络***有限公司 | Method and system for generating service threshold by historical data based on network monitoring indexes |
| CN106357474A (en) * | 2016-08-30 | 2017-01-25 | 成都科来软件有限公司 | Data flow baseline acquisition method and device based on link |
| CN106951351A (en) * | 2017-01-16 | 2017-07-14 | 上海新炬网络信息技术有限公司 | A kind of database loads tendency monitoring method |
| CN108965039B (en) * | 2017-05-19 | 2021-04-27 | 腾讯科技（深圳）有限公司 | Alarm method based on flow monitoring and electronic equipment |
| CN108732313A (en) * | 2018-05-31 | 2018-11-02 | 深圳市创艺工业技术有限公司 | Urban air pollution object concentration intelligence observation system |
| CN111225404B (en) * | 2018-11-23 | 2021-08-31 | 华为技术有限公司 | Network quality monitoring method and device |
| CN110377228A (en) * | 2019-06-19 | 2019-10-25 | 深圳壹账通智能科技有限公司 | Automatic expansion method, device, O&M terminal and the storage medium of block chain node |
| CN114064760B (en) * | 2021-11-18 | 2022-12-13 | 广州泰禾大数据服务有限公司 | Multi-dimensional early warning analysis and judgment method for data |
| CN114500310A (en) * | 2021-12-23 | 2022-05-13 | 中国人民解放军63921部队 | Accurate determination method for multidimensional network situation data baseline |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1267427A (en) * | 1997-06-16 | 2000-09-20 | 艾利森电话股份有限公司 | Telecommunications performance management system |
-
2004
- 2004-03-31 CN CNB2004100320792A patent/CN100356729C/en not_active Expired - Fee Related
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1267427A (en) * | 1997-06-16 | 2000-09-20 | 艾利森电话股份有限公司 | Telecommunications performance management system |
Non-Patent Citations (1)
| Title |
|---|
| 《基于策略的网络管理***的实现》 潘成胜，陈冬松，李航.《火力与指挥控制》，第28卷第3期 2003 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN1677934A (en) | 2005-10-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN100356729C (en) | Method and system for monitoring network service performance | |
| US9015312B2 (en) | Network management system and method for identifying and accessing quality of service issues within a communications network | |
| US8370193B2 (en) | Method, computer-readable media, and apparatus for determining risk scores and generating a risk scorecard | |
| CN106656627A (en) | Performance monitoring and fault positioning method based on service | |
| CN103580905B (en) | A kind of method for predicting, system and flow monitoring method, system | |
| CN102487523B (en) | User compliant analysis method and device | |
| CN101189895A (en) | Abnormality detecting method and system, and upkeep method and system | |
| CN103532776A (en) | Service flow detection method and system | |
| CN107302449A (en) | Intelligent monitoring statistics and alarm processing system and method | |
| CN108206747A (en) | Method for generating alarm and system | |
| CN110460454B (en) | Intelligent network equipment port fault prediction method based on deep learning | |
| CN101789899A (en) | Network service quality analysis method and system | |
| CN104574219A (en) | System and method for monitoring and early warning of operation conditions of power grid service information system | |
| CN108521433A (en) | A kind of monitoring of key message infrastructure security and early warning system based on artificial intelligence | |
| CN109858807B (en) | Enterprise operation monitoring method and system | |
| CN109525455B (en) | Hydrological real-time monitoring network state comprehensive evaluation method | |
| CN102387036A (en) | Method for detecting extra-large ring hidden dangers based on basic configuration data acquired by network management system of manufacturer | |
| CN101350732A (en) | Method and apparatus for analysis processing and testing network resource utilization situation of transmission network | |
| CN104809070A (en) | Method for analyzing properties based on transaction type middleware of database | |
| CN109936487A (en) | A kind of real-time analysis and monitoring method and system of Web broadcast packet | |
| CN108055152A (en) | Communication network information system method for detecting abnormality based on Distributed Services daily record | |
| CN102547789B (en) | Early warning method, device and system for quality of peer-to-peer service | |
| CN110602070A (en) | Automatic configuration management system and method for network security | |
| CN108429771A (en) | Software defined network secure state evaluating method based on catastrophe theory and device | |
| CN112699609B (en) | Diesel engine reliability model construction method based on vibration data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20071219 Termination date: 20150331 |
|
| EXPY | Termination of patent right or utility model |