CN102111307B - Method and device for monitoring and controlling network risks - Google Patents
Method and device for monitoring and controlling network risks Download PDFInfo
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Abstract
The invention provides a method and device for monitoring and controlling network risks. The method comprises the following steps: step one, selecting the sample space of network objectives of some network element (NE), carrying out statistic analysis on the sample point data of the sample space, and calculating a dynamic baseline, wherein the network objectives comply with a normal distribution character; step two, according to preset tolerability and the dynamic baseline, determining the upper tolerance limit and the lower tolerance limit of the network objectives as early warning thresholds for triggering an early warning generating mechanism of the predictive network objectives; and step three, judging whether values of the predictive network objectives which are monitored and controlled in real time exceed the early warning thresholds; and if the values exceed the early warning thresholds, triggering the early warning generating mechanism. By realizing a dynamic threshold method, the threshold values can be set more reasonably and accurately, multiple early warning grades can be triggered, accurate monitoring and control for networks is reached, hidden failures in the networks are found, and finally after-event problem analysis of the prior art for the network failures is changed into initiative monitoring and controlling of the network objectives before problems generate, thus effectively ensuring the normal operation of the networks.
Description
Technical field
The present invention relates to the network index monitoring technique, especially, relate to a kind of network risks method for supervising and device.
Background technology
Network technology is used more and more widely at present, and the routine work that people are a lot of and recreation all be unable to do without network.The service that provides along with network grows with each passing day, and user's perception requires also to improve constantly.The index that a series of reflection network capacitys, quality and coverage condition are arranged in mobile wireless network, can characterize overall network performance, can make operator fully control the overall operation situation of network, the important evidence that provides reference for lasting construction and planning, the optimization of network.Therefore, examination and the monitoring to network index especially performance index and the network operation situation during great festivals or holidays seems very important.
Network index supervisory control system of the prior art is to monitoring and the early warning of network index, and the general method that static benchmark thresholding is set of using is analyzed the abnormal conditions that occur in the network, produces when surpassing default thresholding and reports to the police.So-called static benchmark gate method, namely, performance index or other network indexes are arranged a fixing threshold value, the threshold value of current network achievement data and setting is compared, if exceeded above-mentioned threshold value then produced alarm, if do not exceed above-mentioned threshold value, it is normal then to be considered as network.
But prior art adopts above-mentioned method for monitoring network to have following shortcoming:
One, index threshold value of the prior art generally is that these professional personnel rule of thumb manually arrange, thus there is very big error, inaccurate.
Two, because the own characteristic of mobile wireless network and user's flowability, its performance index are fluctuation situation, and adopt static benchmark gate method, for the index with wave characteristic, as a hour telephone traffic, data traffic, every line telephone traffic, paging load etc., can not reflect the unusual condition of network in time, exactly.
Three, can not directly embody the otherness when the different alarm level of reflection between the equipment network element.
Summary of the invention
Technical problem to be solved by this invention provides a kind of network risks method for supervising and device, uses the dynamic benchmark gate method, seeks the feature of professional sudden change, finds that prior to client perception network is unusual, breaks through the monitoring blind area of traditional absolute index.
In order to address the above problem, the invention discloses a kind of network risks method for supervising, comprise: step 1, choose the sample space of network index of the Normal Distribution characteristic of a certain network element, sample points certificate to above-mentioned sample space is carried out statistical analysis, calculates the dynamic baseline of above-mentioned network index; The line of tolerance up and down of the network index that the tolerance that step 2, basis are preset and above-mentioned dynamic baseline are determined said network element is as the early warning thresholding that triggers prediction network index early warning generation mechanism; Whether step 3, the judgement value of the described prediction network index of monitoring in real time exceed above-mentioned early warning thresholding, in this way, then trigger above-mentioned early warning generation mechanism; Above-mentioned steps one specifically comprises: choose the corresponding sample space of network index actual monitored demand with said network element; Sample points certificate to this sample space is carried out preliminary treatment, obtains effective historical data; Utilization is calculated the fluctuation range of above-mentioned effective historical data based on the dynamic baseline algorithm of historical statistical data, determines the dynamic baseline of above-mentioned network index according to the above-mentioned fluctuation range that obtains, and above-mentioned dynamic baseline comprises baseline and following baseline; Above-mentioned dynamic baseline algorithm based on historical statistical data is: utilize pretreated network index in effective historical data of granularity sometime, calculate this network index in the index average value mu of this time granularity; Utilize pretreated network index in effective historical data of granularity sometime and the index average value mu of this time granularity, calculate this network index at the meansquaredeviation of this time granularity; Utilize the index average value mu of above-mentioned network index and the meansquaredeviation of above-mentioned network index, determine that this network index is at the base value up and down of each time granularity.
Preferably, above-mentioned steps two also comprises, utilizes several above-mentioned default tolerances that several grades of tolerance lines are set, and the index early warning is divided into urgent early warning, serious early warning, important early warning and general early warning.
Preferably, carry out preliminary treatment in above-mentioned sample points certificate to sample space, obtain in the step of effective historical data, the pretreatment strategy that adopts is: in conjunction with relevant situations such as the fault of finding during the O﹠M, festivals or holidays, major events, determine and eliminating exceptional sample point data, the typical data that keeps the above-mentioned network index Changing Pattern of reflection is as above-mentioned effective historical data; Perhaps, account for the ratio of whole data volume according to abnormal data in history, judge and delete minimum and maximum numerical value, remaining data is as above-mentioned effective historical data; Perhaps, according to probabilistic algorithm, based on the normal data ratio that historical statistical data is determined, select and concentrate the data that distribute as above-mentioned effective historical data the most.
Preferably, above-mentioned steps one further comprises: according to the normal distribution characteristic of above-mentioned network index, with [μ+2 σ, μ-2 σ] of each time granularity up and down base value the dynamic baseline of general early warning thresholding is set; According to the normal distribution characteristic of above-mentioned network index, with [μ+3 σ, μ-3 σ] of each time granularity up and down base value the dynamic baseline of important early warning thresholding is set.
Preferably, in above-mentioned steps two, static benchmark thresholding is set to serious early warning thresholding.
Preferably, in above-mentioned arbitrary network risks method for supervising embodiment, above-mentioned network index is the performance index of network.
Corresponding network risks method for supervising embodiment of the present invention, the invention also discloses a kind of network risks supervising device, comprise: dynamic baseline determination module, be used for choosing the sample space of network index of the Normal Distribution characteristic of a certain network element, sample points certificate to this sample space is carried out statistical analysis, calculates the dynamic baseline of above-mentioned network index; Early warning thresholding determination module is used for determining according to the dynamic baseline that default tolerance and above-mentioned dynamic baseline determination module obtain the line of tolerance up and down of above-mentioned network index, as the early warning thresholding that triggers prediction network index early warning generation mechanism; The early warning trigger module is used for judging whether the value of the above-mentioned prediction network index of monitoring exceeds the early warning thresholding that above-mentioned early warning thresholding determination module is set in real time, if then trigger the early warning generation mechanism; Above-mentioned dynamic baseline determination module specifically comprises: the sample space acquiring unit is used for choosing the corresponding sample space data of network index actual monitored demand with described network element; Pretreatment unit carries out preliminary treatment for the sample points certificate of the sample space that above-mentioned sample space acquiring unit is obtained, and obtains effective historical data; Dynamic baseline computing unit, the fluctuation range of above-mentioned effective historical data of above-mentioned pretreatment unit acquisition is calculated in utilization based on the dynamic baseline algorithm of historical statistical data, determine the dynamic baseline of above-mentioned network index according to the above-mentioned fluctuation range that obtains, above-mentioned dynamic baseline comprises baseline and following baseline; The dynamic baseline algorithm based on historical statistical data that above-mentioned dynamic baseline computing unit adopts is: utilize network index that above-mentioned pretreatment unit obtains in effective historical data of granularity sometime, calculate this network index in the index average value mu of this time granularity; Utilize above-mentioned network index that above-mentioned pretreatment unit obtains in effective historical data of granularity and the index average value mu of this time granularity sometime, calculate this network index at the meansquaredeviation of this time granularity; Utilize the index average value mu of above-mentioned network index and the meansquaredeviation of above-mentioned network index, determine that this network index is at the base value up and down of each time granularity.
Preferably, above-mentioned early warning thresholding determination module also comprises early warning threshold level setup unit, utilizes several above-mentioned default tolerances that several grades of tolerance lines are set, and the index early warning is divided into urgent early warning, serious early warning, important early warning and general early warning.
Preferably, when above-mentioned pretreatment unit carries out preliminary treatment to the sample points certificate of sample space, the pretreatment strategy that adopts is: in conjunction with relevant situations such as the fault of finding during the O﹠M, festivals or holidays, major events, determine and eliminating exceptional sample point data, the typical data that keeps reflection network index Changing Pattern is as above-mentioned effective historical data; Perhaps, account for the ratio of whole data volume according to abnormal data in history, judge and delete minimum and maximum numerical value, remaining data is as above-mentioned effective historical data; Perhaps, according to probabilistic algorithm, based on the normal data ratio that historical statistical data is determined, select and concentrate the data that distribute as above-mentioned effective historical data the most.
Preferably, above-mentioned dynamic baseline determination module further comprises: early warning thresholding baseline rank setup unit, be used for the normal distribution characteristic according to above-mentioned network index, with [μ+2 σ, μ-2 σ] of each time granularity up and down base value the dynamic baseline of general early warning thresholding is set; With [μ+3 σ, μ-3 σ] of each time granularity up and down base value the dynamic baseline of important early warning thresholding is set.
Preferably, the static benchmark thresholding of above-mentioned early warning threshold level setup unit is set to serious early warning thresholding.
Preferably, in above-mentioned arbitrary network risks supervising device, above-mentioned network index is the performance index of network.
Compared with prior art, the present invention has the following advantages:
The present invention is by implementing the realization of dynamic threshold method for early warning to the network index that meets the normal distribution characteristic, the network index data that collect and the early warning tolerance line (early warning activation threshold value) that calculates based on historical data are compared, trigger the early warning generation mechanism when surpassing the tolerance line.Realize the active monitoring of network index, by the early warning thresholding of the every index of different network elements is set, reach the accurate monitoring to network, network element or zone that discovery index promptly and accurately is unusual, excavate the hidden failure in the network, the final realization turns to the case study afterwards of prior art the network index before problem produces initiatively to monitor.Simultaneously, the present invention is in conjunction with the normal distribution characteristic of network index, by the average of the network index of historical data, the computing of mean square deviation are directly obtained dynamic baseline, than more simple and efficient by the algorithm that the historical data modeling method is obtained dynamic baseline, and have the accuracy rate suitable with it, so practicality is very strong.The present invention also utilizes the characteristic of network index normal distribution, and the tolerance line is set to different warning levels, and the user's that fits more fine-grained management demand realizes with different levels early warning.Make Virtual network operator have one in time and omnibearing control to network index and the network operation situation during great festivals or holidays etc., real-time analysis is also grasped every index situation of change in the network operation, guarantees that network normally runs.
Description of drawings
Fig. 1 is the flow chart of network risks method for supervising embodiment of the present invention;
Fig. 2 is the flow chart that the present invention calculates dynamic baseline embodiment;
Fig. 3 is the flow chart that the present invention is based on the dynamic baseline algorithm of historical statistical data;
Fig. 4 is the dynamic baseline of the present invention and the schematic diagram that concerns of tolerating line up and down;
Fig. 5 is the structured flowchart of network risks supervising device of the present invention;
Fig. 6 is the structured flowchart of the dynamic baseline determination module of the present invention embodiment.
Embodiment
For above-mentioned purpose of the present invention, feature and advantage can be become apparent more, the present invention is further detailed explanation below in conjunction with the drawings and specific embodiments.
One of core idea of the embodiment of the invention is, the thought that adopts a kind of active to monitor is come capture network index hidden danger early, normal distribution characteristic according to network index, realization by the dynamic threshold method, the more reasonable threshold value that obtains automatically accurately generates warning level automatically, reaches the accurate monitoring to network, excavate the hidden failure in the network, the final realization turns to prior art the index before problem produces initiatively to monitor to the case study afterwards of network failure.
Through checking, because network performance index also meets the normal distribution characteristic, so the method and apparatus among the present invention is particularly useful for monitoring and early warning to network performance index.
With reference to Fig. 1, show the flow chart of network risks method for supervising embodiment of the present invention, embodiment is complete by computer for this method, comprising:
Step 1, choose the sample space of network index of the Normal Distribution of a certain network element, to the sample points of sample space according to carrying out statistical analysis, the dynamic baseline of computing network index;
This step has illustrated that the effective object of network risks method for supervising embodiment of the present invention is the network index of different network elements, and the distribution Normal Distribution characteristic of the desired value of this network index.The network index of said network element can be as a hour telephone traffic, data traffic, every line telephone traffic, paging load, network performance index that drop rate equal time sensitiveness is strong, also can be other network index as the data business.
The sample points of the sample space that this step is chosen is according to the historical normal value that is the prediction network index.
The line of tolerance up and down of the network index that the tolerance that step 2, basis are preset and above-mentioned dynamic baseline are determined said network element is as the early warning thresholding that triggers prediction network index early warning generation mechanism.
Wherein, above-mentioned tolerance can be the empirical value that those skilled in the art rationally choose according to different network elements, different network indexes.
Whether step 3, the judgement value of the above-mentioned prediction network index of monitoring in real time exceed above-mentioned early warning thresholding, if then trigger above-mentioned early warning generation mechanism.
In another embodiment of network risks method for supervising of the present invention, above-mentioned steps 1 can further be subdivided into a plurality of steps, specifically referring to the flow chart of the dynamic baseline embodiment of calculating shown in Figure 2, comprising:
In theory, sample space is more big, and the distortion factor of base-line data is more low; But sample space is more big, and original data volume will be more big, so just to data obtain, storage, computing all bring bigger expense and difficulty.Therefore need select the sample space of appropriate scale according to the actual monitored needs.In practical operation, for example predict that when hour being the network index of time granularity in principle, each time point is minimum will choose over one month historical data as the sample points certificate.
In this step, to the sample points of sample space when carrying out preliminary treatment, the pretreatment strategy of employing can for:
Strategy one: in conjunction with relevant situations such as the fault of finding during the O﹠M, festivals or holidays, major events, determine and eliminating exceptional sample point data, keep the typical data of reflection network index Changing Pattern, as effective historical data.
Strategy two: account for the ratio of whole data volume according to abnormal data in history, judge and delete minimum and maximum numerical value, remaining data is as effective historical data.
Strategy three: according to probabilistic algorithm, based on the normal data ratio that historical statistical data is determined, select effectively historical data of the most concentrated data conduct that distributes.
Above-mentioned strategy can independently use according to character and the different network elements of network index, also can be used in combination.Certainly, according to the characteristic difference of prediction network index, can also adopt other pretreatment strategy that the sample points certificate of above-mentioned sample space is handled.The embodiment of the invention does not limit and adopts which kind of pretreatment strategy.
So-called dynamic baseline algorithm based on historical statistical data refers to based on historical statistical data, sets index in the method in the reasonable change zone of different periods.It has embodied the trend of index in the regularity variation of different periods, also can be described as " trend algorithm ".
As the preferred embodiments of the present invention, with reference to Fig. 3, show the flow chart of the dynamic baseline algorithm that the present invention is based on historical statistical data, the dynamic baseline algorithm based on historical statistical data that above-mentioned steps 13 is utilized can specifically may further comprise the steps:
Time granularity in this step can be according to actual needs determines arbitrarily, can be one hour, several hours, one day or a week etc.
Determine that namely network index is in the reasonable fluctuation range of the desired value of this time granularity.The maximum of this network index in above-mentioned reasonable fluctuation range is base value, and in like manner, the minimum value of this network index in above-mentioned reasonable fluctuation range is base value down.
Employing determines that according to index average value mu and meansquaredeviation this network index at the base value up and down of granularity sometime, obtains the method for dynamic baseline, makes the acquisition of dynamic baseline break away from complicated modeling process, becomes simple and convenient, is easy to more realize.
In the above-mentioned preferred embodiment that has adopted dynamic baseline algorithm based on historical statistical data shown in Figure 3, above-mentioned steps 1 can further include:
It is the dynamic baseline of general early warning thresholding that this step specifically can arrange [μ+2 σ, μ-2 σ];
[μ+3 σ, μ-3 σ] is set is the dynamic baseline of important early warning thresholding.
According to the normal distribution characteristic of network index, the early warning thresholding of different stage is set, can make the user according to network actual demand and monitoring demand, warning level is set flexibly, improved accuracy and the practicality of early warning more.
In the above-mentioned steps 13, determine that the process of dynamic baseline is: will utilize the base value up and down of each time granularity that the dynamic baseline algorithm based on historical statistical data obtains to be connected to up and down dynamically baseline.Comprise: connect the maximum base value of each time granularity of above-mentioned network index, be plotted as dynamic baseline.In conjunction with above-mentioned warning level step 14 explanation is set, utilizes the μ+2 σ values of each time granularity to connect into the last dynamic baseline of above-mentioned general early warning thresholding; Utilize the μ+3 σ values of each time granularity to connect into the last dynamic baseline of above-mentioned important early warning thresholding.
Connect the minimum base value of each time granularity of above-mentioned network index, be plotted as down dynamic baseline.In conjunction with the explanation of above-mentioned warning level step 14, utilize the μ-2 σ value of each time granularity to connect into the following dynamic baseline of above-mentioned general early warning thresholding; Utilize the μ-3 σ value of each time granularity to connect into the following dynamic baseline of above-mentioned important early warning thresholding.
Fig. 2 and Fig. 3 have illustrated definite process of dynamic baseline, and in real work, we generally will add a tolerance according to the dynamic baseline that calculates, and determine the tolerance line, finally tolerating that line is as the early warning thresholding.
Below in conjunction with the dynamic baseline of above-described embodiment acquisition, describe the line of tolerance up and down that the default tolerance of above-mentioned steps 2 bases and above-mentioned dynamic baseline are determined network index in detail, predict the execution mode of the early warning thresholding of network index early warning generation mechanism as triggering:
At first, according to the difference of network index, choose rational tolerance according to experience.
Tolerance is typically chosen between the 5%-10%, according to actual conditions, can the elasticity adjustment on tolerance between baseline and the last tolerance line.
Secondly, calculate the tolerance line according to following formula:
Last tolerance line=last baseline * (1+ tolerance) * 100%............... formula (1)
Under tolerate line=following baseline * (1-tolerance) * 100%............... formula (2).
According to above-mentioned the execution mode dynamic baseline of determining and the relation of tolerating line as shown in Figure 4.
The step 2 of other method embodiment of the present invention can also arrange multistage tolerance line at different specialties, network index and corresponding monitoring demand, disposes early warning flexibly.Generally, the index warning level is divided into promptly (red early warning), serious (orange early warning), important (yellow early warning) and general (blue early warning) four ranks just can satisfies the monitoring needs, and than more comprehensively distinguishing the potential hazard that index departs from.Wherein, the representative meaning of each warning level can be defined as follows:
Urgent early warning: the prompting network index exceeded tolerable scope certain long-time after, recover normal yet, each business has been subjected to and has seriously influenced, and needs all departments' high concentration to solve;
Serious early warning: the prompting network index has significantly exceeded tolerable scope, and business may be had a strong impact on solution immediately;
Important early warning: the prompting network index has exceeded tolerable scope to a certain extent or has exceeded a period of time, and business may be under some influence, and needs the prompting related personnel to take adequate measures to alleviate or solution;
General early warning: the prompting network index has exceeded tolerable scope, but the time is not long, departs from not quite, needs the prompting related personnel follow the tracks of concern.
It is bigger in different time period crests, trough difference that above-mentioned each the method embodiment of the present invention is primarily aimed at the index normal value, and the network index of Normal Distribution rule.This normal value must be set different early warning thresholdings at the different periods at the bigger network index of different time period fluctuation ranges, determines the reasonable distributed areas of desired value in the different periods, unusual distributed areas.
For the little index of network desired value fluctuation in the different time sections, for example, the little index of range of normal value fluctuation in 24 hours monitoring periods, when this network index is carried out risk profile, use static benchmark gate method to get final product, namely get a fixed fixed numeric values as the early warning thresholding of this network index.In the step 2 of the various embodiments described above, can be set to serious early warning thresholding by static benchmark thresholding.
Be example with " traffic call drop ratio " performance index of predicting a certain regional network element of in August, 2008 below, elaborate network risks method for supervising of the present invention.
At first, calculate the early warning thresholding of " the traffic call drop ratio " that be used for prediction in August, 2008 this area.Comprise:
Step 1, from " traffic call drop than " index historical data base of this area's network element, " traffic call drop than " index of choosing that in July, 2008 should 5 o'clock～21 o'clock every day is as sample space.
Because the data of 5 o'clock～21 o'clock every day are historical normal value, and from 22 o'clock to 24 o'clock data with from 0 o'clock to 4 o'clock data, because the data of above-mentioned time range has a unsteadiness, be difficult to formulate effective and reasonable fluctuation range, so the data of choosing 5 o'clock～21 o'clock every day are as sample space.
Granularity writing time of supposing above-mentioned " traffic call drop ratio " achievement data is 1 hour, i.e. " traffic call drop ratio " achievement data of each hour record.Because recorded 30 days data July, so this sample space comprises 30 days sample data, comprise 17 sample points certificates in the sample data of every day again, these 17 sample point data record from 5 o'clock to 21 o'clock " traffic call drop than " desired value of each time granularity.
This step is specially: " traffic call drop ratio " desired value, totally 30 sample points certificates of choosing this time granularity record 9 o'clock every days of 1 day to No. 30 July in 2008 (that is 8:00～9:00 point).Calculate the average value mu of above-mentioned 30 sample points certificates then.
Step 4, utilize the mean square deviation computing formula to calculate the meansquaredeviation of above-mentioned effective historical data;
The computing formula of above-mentioned meansquaredeviation is:
Wherein, x
iRepresent the numerical value through each " the traffic call drop ratio " index after the step 3 processing, the number of N representative " traffic call drop ratio " achievement data,
Equal that above-mentioned steps two obtains in granularity sometime " traffic call drop than " index average value mu.
Step 4, definite dynamic baseline up and down;
Because it is the normal distribution of μ, σ that parameter is obeyed in the distribution of " traffic call drop ratio " performance index of above-mentioned this area network element.Definition according to normal distyribution function:
Wherein, the distribution function of the desired value of f (x) expression network index, x is the desired value of network index, and μ is that the mathematic expectaion of x is average, and σ is the mean square deviation of x.
Can draw according to formula (4):
The network index value is fallen [μ-1 σ, μ+1 σ] interval probability:
P{μ-σ＜X≤μ+σ}＝Φ(1)-Φ(-1)＝2Φ(1)-1＝0.6826
Wherein, P represents that the network index value falls into certain interval probability, and Φ represents Standard Normal Distribution numerical value.
The network index value is fallen [μ-2 σ, μ+2 σ] interval probability:
P{μ-2σ＜X≤μ+2σ}＝Φ(2)-Φ(-2)＝2Φ(2)-1＝0.9544
The network index value is fallen [μ-3 σ, μ+3 σ] interval probability:
P{μ-3σ＜X≤μ+3σ}＝Φ(3)-Φ(-3)＝2Φ(3)-1＝0.9974
This shows that for normal random variable X, it almost is sure thing that its value drops in the interval [μ+3 σ, μ-3 σ], Here it is so-called " 3 σ rule ".Because the regularity of distribution of " traffic call drop ratio " desired value also is normal distribution.Thereby, can calculate μ+2 σ, μ-2 σ values of 9 o'clock, as the dynamic baseline up and down of " traffic call drop ratio " general early warning 9 o'clock every days of in August, 2008; Calculate μ+3 σ, μ-3 σ values of 9 o'clock, as the dynamic baseline up and down of " traffic call drop ratio " important early warning 9 o'clock every days of in August, 2008.Result of calculation is referring to table one.The rest may be inferred, the dynamic baseline up and down of each warning level of each time granularity such as calculated at 10 o'clock, 11 o'clock, and the result is referring to table two.
Step 5, drafting be dynamic baseline up and down;
Selection 5 connects into the last dynamic baseline of this warning level up to the last dynamic baseline of a certain warning level of 21 o'clock each time granularities;
In like manner, selection 5 connects into the following dynamic baseline of this warning level up to the following dynamic baseline of the warning level of 21 o'clock each time granularities.
Be specially: July 5 in 2008 was connected into the last dynamic baseline of general early warning thresholding up to 17 μ+2 σ values of 21 o'clock; July 5 in 2008 was connected into the following dynamic baseline of general early warning thresholding up to 17 μ-2 σ values of 21 o'clock; Obtain the dynamic baseline up and down of in August, 2008 " traffic call drop ratio " general early warning thresholding.
In like manner, July 5 in 2008 was connected into the last dynamic baseline of important early warning thresholding up to 17 μ+3 σ values of 21 o'clock; July 5 in 2008 was connected into the following dynamic baseline of important early warning thresholding up to 17 μ-3 σ values of 21 o'clock; Obtain the dynamic baseline up and down of in August, 2008 " traffic call drop ratio " important early warning thresholding.
Step 6, choose tolerance, according to above-mentioned formula (1) and (2), calculate the tolerance value of certain warning level correspondence, draw and tolerate line up and down.
So far, in August, 2008, the early warning thresholding calculating of " traffic call drop ratio " index finished, and the Risk-warning model is set up.
Then, can utilize this model that " the traffic call drop ratio " desired value in August, 2008 is carried out early warning:
When the desired value of certain time granularity has exceeded the corresponding line of tolerance up and down of baseline [μ+2 σ, μ-2 σ], but when not surpassing the corresponding line of tolerance up and down of baseline [μ+3 σ, μ-3 σ], generation " minor alarm "; with
When the desired value of certain time granularity has surpassed the line of tolerance up and down of baseline [μ+3 σ, μ-3 σ] correspondence, produce " significant alarm ".
For aforesaid each method embodiment, for simple description, so it all is expressed as a series of combination of actions, but those skilled in the art should know, the present invention is not subjected to the restriction of described sequence of movement, because according to the present invention, some step can adopt other orders or carry out simultaneously.Secondly, those skilled in the art also should know, the embodiment described in the specification all belongs to preferred embodiment, and related action and module might not be that the present invention is necessary.
Corresponding above-mentioned network risks method for supervising embodiment, the present invention also provides a kind of network risks supervising device, and the structured flowchart referring to network risks supervising device shown in Figure 5 comprises:
Dynamic baseline determination module 51 is used for choosing the sample space of network index of the Normal Distribution characteristic of a certain network element, and the sample points of above-mentioned sample space according to carrying out statistical analysis, is calculated the dynamic baseline of above-mentioned network index;
The effective object of this dynamic baseline determination module 51 is the network index of different network elements, and the distribution Normal Distribution characteristic of the desired value of this network index.The network index of said network element can be as a hour telephone traffic, data traffic, every line telephone traffic, paging load, network performance index that drop rate equal time sensitiveness is strong, also can be other network index as the data business.
Early warning thresholding determination module 52 is used for the line of tolerance up and down that the dynamic baseline that obtains according to dynamic baseline determination module 51 and the tolerance of presetting are determined above-mentioned network index, as the early warning thresholding that triggers prediction network index early warning generation mechanism;
Early warning trigger module 53 is used for judging whether the value of the above-mentioned prediction network index of monitoring exceeds the early warning thresholding that above-mentioned early warning thresholding determination module 52 is set in real time, if then trigger the early warning generation mechanism.
As the preferred embodiments of the present invention, referring to Fig. 6, show the structured flowchart of the dynamic baseline determination module of the present invention embodiment, dynamically baseline determination module 51 specifically comprises:
Sample space acquiring unit 511 is used for choosing the corresponding sample space of network index actual monitored demand with said network element;
Pretreatment unit 512 carries out preliminary treatment for the sample points certificate of the sample space that above-mentioned sample space acquiring unit 511 is obtained, and obtains effective historical data;
Dynamically baseline computing unit 513 utilizes the fluctuation range of calculating effective historical data of above-mentioned pretreatment unit 512 acquisitions based on the dynamic baseline algorithm of historical statistical data, determines the dynamic baseline of above-mentioned network index according to the above-mentioned fluctuation range that obtains.
The sample points of 512 pairs of sample spaces of above-mentioned pretreatment unit when carrying out preliminary treatment, the pretreatment strategy of employing can for:
Strategy one, in conjunction with relevant situations such as the fault of finding during the O﹠M, festivals or holidays, major events, determine and get rid of the exceptional sample point data, keep the typical data that reflects the network index Changing Pattern, as effective historical data;
Strategy two, account for the ratio of whole data volume according to abnormal data in history, judge and delete minimum and maximum numerical value, remaining data is as effective historical data;
Strategy three, according to probabilistic algorithm, based on the normal data ratio that historical statistical data is determined, select and concentrate the data that distribute as effective historical data the most.
Preferably, the dynamic baseline algorithm based on historical statistical data of above-mentioned dynamic baseline computing unit 513 employings is:
Utilize above-mentioned pretreatment unit 512 pretreated network indexes in effective historical data of granularity sometime, the computing network index is in the index average value mu of this time granularity;
Utilize above-mentioned pretreatment unit 512 pretreated network indexes in effective historical data of granularity sometime and the index average value mu of this time granularity, calculate this network index at the meansquaredeviation of this time granularity;
Utilize above-mentioned network index at index average value mu and the meansquaredeviation of granularity sometime, determine that network index is at the base value up and down of each time granularity.
In another embodiment of the present invention, above-mentioned dynamic baseline determination module 51 can also comprise:
Early warning thresholding baseline rank setup unit 514 is used for the normal distribution characteristic according to network index, with [μ-2 σ, the μ+2 σ] of each time granularity up and down base value be set to the dynamic baseline of general early warning thresholding; With [μ-3 σ, the μ+3 σ] of each time granularity up and down base value be set to the dynamic baseline of important early warning thresholding.
Preferably, above-mentioned early warning thresholding determination module 52 can also comprise: the early warning threshold level arranges the unit, utilizes several above-mentioned default tolerances that several grades of tolerance lines are set, and the index early warning is divided into: urgent early warning, serious early warning, important early warning and general early warning.Specifically can also be set to serious early warning thresholding by static benchmark thresholding.
In a word, the present invention adopts a kind of by carry out data mining in the communication network database, set up the method and apparatus of traffic model, index system then, the network index data that collect and the early warning tolerance line (early warning activation threshold value) that calculates based on historical data are compared, trigger the early warning generation mechanism when surpassing the tolerance line.Realize the active monitoring of network index, the early warning thresholding of the every network index by setting up different network elements, in time find unusual network element or the zone of index, excavate the hidden failure in the network, the final realization turns to prior art the network index before problem produces initiatively to monitor to the case study afterwards of network failure.Simultaneously, the present invention is in conjunction with the normal distribution characteristic of network index, by the average of the network index of historical data, the computing of mean square deviation are directly obtained dynamic baseline, than more simple and efficient by the algorithm that the historical data modeling method is obtained dynamic baseline, and have the accuracy rate suitable with it, so practicality is very strong.The present invention also utilizes the characteristic of network index normal distribution, and the tolerance line is set to different warning levels, and the user's that fits more fine-grained management demand realizes with different levels early warning.Make Virtual network operator have one in time and omnibearing control to network index and the network operation situation during great festivals or holidays etc., real-time analysis is also grasped every index situation of change in the network operation, guarantees that people's life is normally carried out.
Each embodiment in this specification all adopts the mode of going forward one by one to describe, and what each embodiment stressed is and the difference of other embodiment that identical similar part is mutually referring to getting final product between each embodiment.For device embodiment, because it is similar substantially to method embodiment, so description is fairly simple, relevant part gets final product referring to the part explanation of method embodiment.
More than to a kind of network risks method for supervising provided by the present invention and device, be described in detail, used specific case herein principle of the present invention and execution mode are set forth, the explanation of above embodiment just is used for helping to understand method of the present invention and core concept thereof; Simultaneously, for one of ordinary skill in the art, according to thought of the present invention, the part that all can change in specific embodiments and applications, in sum, this description should not be construed as limitation of the present invention.
Claims (12)
1. a network risks method for supervising is characterized in that, comprising:
Step 1, choose the sample space of network index of the Normal Distribution characteristic of a certain network element, the sample points of described sample space according to carrying out statistical analysis, is calculated the dynamic baseline of described network index;
The line of tolerance up and down of the network index that the tolerance that step 2, basis are preset and described dynamic baseline are determined described network element is as the early warning thresholding that triggers prediction network index early warning generation mechanism;
Whether step 3, the judgement value of the described prediction network index of monitoring in real time exceed described early warning thresholding, in this way, then trigger described early warning generation mechanism;
Wherein, described step 1 specifically comprises:
Choose the corresponding sample space of network index actual monitored demand with described network element;
Sample points certificate to described sample space is carried out preliminary treatment, obtains effective historical data;
Utilization is calculated the fluctuation range of described effective historical data based on the dynamic baseline algorithm of historical statistical data, determines the dynamic baseline of described network index according to the described fluctuation range that obtains, and described dynamic baseline comprises baseline and following baseline;
Described dynamic baseline algorithm based on historical statistical data is:
Utilize pretreated described network index in effective historical data of granularity sometime, calculate this network index in the index average value mu of this time granularity;
Utilize pretreated described network index in effective historical data of granularity sometime and the index average value mu of this time granularity, calculate this network index at the meansquaredeviation of this time granularity;
Utilize the index average value mu of described network index and the meansquaredeviation of described network index, determine that this network index is at the base value up and down of each time granularity.
2. network risks method for supervising according to claim 1, it is characterized in that, described step 2 also comprises: utilize several described default tolerances that several grades of tolerance lines are set, the index early warning is divided into urgent early warning, serious early warning, important early warning and general early warning.
3. network risks method for supervising according to claim 2 is characterized in that, carries out preliminary treatment in described sample points certificate to described sample space, obtains in the step of effective historical data, and the pretreatment strategy of employing is:
In conjunction with the fault of finding during the O﹠M, festivals or holidays, major event, determine and eliminating exceptional sample point data, keep the typical data of the described network index Changing Pattern of reflection, as described effective historical data; Perhaps,
Account for the ratio of whole data volume according to abnormal data in history, judge and delete minimum and maximum numerical value, remaining data is as described effective historical data; Perhaps,
According to probabilistic algorithm, based on the normal data ratio that historical statistical data is determined, select and concentrate the data that distribute as described effective historical data the most.
4. network risks method for supervising according to claim 2 is characterized in that, described step 1 further comprises:
According to the normal distribution characteristic of described network index, with [μ+2 σ, μ-2 σ] of each time granularity up and down base value the dynamic baseline of general early warning thresholding is set;
According to the normal distribution characteristic of described network index, with [μ+3 σ, μ-3 σ] of each time granularity up and down base value the dynamic baseline of important early warning thresholding is set.
5. network risks method for supervising according to claim 4 is characterized in that, in described step 2, static benchmark thresholding is set to serious early warning thresholding.
6. according to the arbitrary described network risks method for supervising of claim 1 ~ 5, it is characterized in that described network index is the performance index of network.
7. a network risks supervising device is characterized in that, comprising:
Dynamic baseline determination module is used for choosing the sample space of network index of the Normal Distribution characteristic of a certain network element, and the sample points of described sample space according to carrying out statistical analysis, is calculated the dynamic baseline of described network index;
Early warning thresholding determination module is used for determining according to the dynamic baseline that default tolerance and described dynamic baseline determination module obtain the line of tolerance up and down of described network index, as the early warning thresholding that triggers prediction network index early warning generation mechanism;
The early warning trigger module is used for judging whether the value of the described prediction network index of monitoring exceeds the early warning thresholding that described early warning thresholding determination module is set in real time, if then trigger the early warning generation mechanism;
Wherein, described dynamic baseline determination module specifically comprises:
The sample space acquiring unit is used for choosing the corresponding sample space of network index actual monitored demand with described network element;
Pretreatment unit carries out preliminary treatment for the sample points certificate of the sample space that described sample space acquiring unit is obtained, and obtains effective historical data;
Dynamic baseline computing unit, the fluctuation range of described effective historical data of described pretreatment unit acquisition is calculated in utilization based on the dynamic baseline algorithm of historical statistical data, determine the dynamic baseline of described network index according to the described fluctuation range that obtains, described dynamic baseline comprises baseline and following baseline;
The dynamic baseline algorithm based on historical statistical data that described dynamic baseline computing unit adopts is:
Utilize described network index that described pretreatment unit obtains in effective historical data of granularity sometime, calculate this network index in the index average value mu of this time granularity;
Utilize described network index that described pretreatment unit obtains in effective historical data of granularity and the index average value mu of this time granularity sometime, calculate this network index at the meansquaredeviation of this time granularity;
Utilize the index average value mu of described network index and the meansquaredeviation of described network index, determine that this network index is at the base value up and down of each time granularity.
8. network risks supervising device according to claim 7 is characterized in that, described early warning thresholding determination module also comprises:
Early warning threshold level setup unit utilizes several described default tolerances that several grades of tolerance lines are set, and the index early warning is divided into urgent early warning, serious early warning, important early warning and general early warning.
9. network risks supervising device according to claim 8 is characterized in that, when described pretreatment unit carried out preliminary treatment to the sample points certificate of sample space, the pretreatment strategy of employing was:
In conjunction with the fault of finding during the O﹠M, festivals or holidays, major event, determine and eliminating exceptional sample point data, keep the typical data of the described network index Changing Pattern of reflection, as described effective historical data; Perhaps,
Account for the ratio of whole data volume according to abnormal data in history, judge and delete minimum and maximum numerical value, remaining data is as described effective historical data; Perhaps,
According to probabilistic algorithm, based on the normal data ratio that historical statistical data is determined, select and concentrate the data that distribute as described effective historical data the most.
10. network risks supervising device according to claim 8 is characterized in that, described dynamic baseline determination module further comprises:
Early warning thresholding baseline rank setup unit is used for the normal distribution characteristic according to described network index, with [μ+2 σ, μ-2 σ] of each time granularity up and down base value the dynamic baseline of general early warning thresholding is set; With [μ+3 σ, μ-3 σ] of each time granularity up and down base value the dynamic baseline of important early warning thresholding is set.
11. network risks supervising device according to claim 10 is characterized in that, the static benchmark thresholding of described early warning threshold level setup unit is set to serious early warning thresholding.
12. according to the arbitrary described network risks supervising device of claim 7 ~ 11, it is characterized in that described network index is the performance index of network.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108775914A (en) * | 2018-05-07 | 2018-11-09 | 青岛海信网络科技股份有限公司 | A kind of transit equipment detection method and detection device |
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| CN116206427B (en) * | 2023-05-06 | 2023-06-30 | 安徽智寰科技有限公司 | Hierarchical alarm method based on universal index self-adaptive threshold |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1677934A (en) * | 2004-03-31 | 2005-10-05 | 华为技术有限公司 | Method and system for monitoring network service performance |
| CN101267362A (en) * | 2008-05-16 | 2008-09-17 | 亿阳信通股份有限公司 | A dynamic identification method and its device for normal fluctuation range of performance normal value |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030149648A1 (en) * | 2000-05-01 | 2003-08-07 | Olsen Richard B. | Method and system for measuring market conditions |
-
2009
- 2009-12-29 CN CN 200910265479 patent/CN102111307B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1677934A (en) * | 2004-03-31 | 2005-10-05 | 华为技术有限公司 | Method and system for monitoring network service performance |
| CN101267362A (en) * | 2008-05-16 | 2008-09-17 | 亿阳信通股份有限公司 | A dynamic identification method and its device for normal fluctuation range of performance normal value |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108775914A (en) * | 2018-05-07 | 2018-11-09 | 青岛海信网络科技股份有限公司 | A kind of transit equipment detection method and detection device |
| CN108775914B (en) * | 2018-05-07 | 2020-09-22 | 青岛海信网络科技股份有限公司 | Traffic equipment detection method and detection equipment |
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