A method for monitoring the operating fault status of a cloud computing server

Abstract

The present invention relates to the field of digital data processing technology, and proposes a method for monitoring the operation fault state of a cloud computing server, comprising: obtaining a monitoring data curve of a monitoring element; obtaining coordinate points and clustering clusters of coordinate points according to monitoring data in the monitoring data curve; obtaining longitudinal point density and longitudinal distance characteristic index according to the clustering clusters of coordinate points; obtaining clustering partition function values ​​according to the longitudinal point density and longitudinal distance characteristic index; obtaining clustering sets using the clustering partition function values; obtaining master-slave feature discrimination values ​​and clustering deviations according to the clustering sets; obtaining deviation range intervals and master-slave discrimination range intervals according to the master-slave feature discrimination values ​​and clustering deviations; obtaining a decision tree after dimension reduction using the deviation range intervals and master-slave discrimination range intervals, and obtaining the operation fault monitoring result of the cloud server through the decision tree after dimension reduction. The present invention improves the accuracy of operation fault state monitoring by improving the accuracy of the decision tree pruning algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of digital data processing, and in particular to a method for monitoring the operating fault status of a cloud computing server. Background Art

[0002] Cloud computing servers, also known as cloud servers, are an efficient, shared computing resource service platform. Due to the large number and variety of cloud server components, robustness issues are taken into consideration when building cloud servers, so that the entire cloud server will not fail to operate due to the damage of a single component. Therefore, in cloud server fault detection, the failure of multiple components simultaneously causing the cloud server to fail to operate is often considered a fault combination.

[0003] In the monitoring of cloud computing server operation failures, due to the large number and variety of cloud server components, the failure mode generated by the combination of failures presents a high complexity of abnormal conditions, resulting in low accuracy of fault status monitoring due to excessive computational effort when analyzing cloud server failures. Therefore, it is often necessary to use decision tree pruning algorithms to reduce the dimensionality of data.

[0004] In the traditional decision tree dimensionality reduction algorithm, there are two main methods: pre-pruning and post-pruning. In the process of using decision tree pruning algorithm to reduce the dimension of data, it is usually decided whether to prune by estimating the fault detection effect of the decision tree before and after pruning. However, in the estimation process, in order to reduce the amount of calculation, not all possible fault combinations under the branch are calculated, which will cause some fault combinations to be mistakenly pruned, and some fault combinations that may cause faults are lost, resulting in low accuracy of the final fault state monitoring. Summary of the invention

[0005] The present invention provides a method for monitoring the operating fault status of a cloud computing server to solve the problem of low accuracy of fault status monitoring. The technical solution adopted is as follows:

[0006] An embodiment of the present invention provides a method for monitoring the operating fault status of a cloud computing server, the method comprising the following steps:

[0007] Acquire monitoring information data of the cloud server, wherein the monitoring information data includes the number of monitoring elements, monitoring data of each monitoring element, and a monitoring data curve;

[0008] Obtain the differential vector between any two monitoring elements according to the monitoring data curves of any two monitoring elements; obtain the coordinate point of each monitoring element according to the differential vector between any two monitoring elements, and obtain the clustering result of the coordinate point of each monitoring element according to the coordinate point; obtain each sliding window of each cluster in the clustering result of the coordinate point of each monitoring element according to the shape of each cluster in the clustering result of the coordinate point of each monitoring element, and obtain the clustering partition function value of each sliding window of each cluster in the clustering result of the coordinate point of each monitoring element according to the sliding window; obtain the clustering set of the coordinate point of each monitoring element by using the clustering partition function values ​​of all sliding windows of each cluster in the clustering result of the coordinate point of each monitoring element;

[0009] According to each cluster set of the coordinate points of each monitoring element, a cluster followability index of each cluster set of the coordinate points of each monitoring element is obtained; according to the cluster followability index, a master-slave feature discrimination value of each cluster set of the coordinate points of each monitoring element is obtained; according to each cluster set of the coordinate points of each monitoring element, a cluster deviation degree of each cluster set of the coordinate points of each monitoring element is obtained; according to the master-slave feature discrimination value and the cluster deviation degree, a deviation range interval and a master-slave discrimination range interval of each cluster set of the coordinate points of each monitoring element are obtained;

[0010] The deviation range interval and the master-slave discrimination range interval are used to obtain a decision tree after dimensionality reduction, and the operation fault monitoring result of the cloud server is obtained through the decision tree after dimensionality reduction.

[0011] Preferably, the method for obtaining the differential vector between any two monitoring elements according to the monitoring data curves of any two monitoring elements is:

[0012] Sampling the monitoring data curve of each monitoring element at a preset sampling frequency to obtain sampling data of each monitoring element, and using a sequence composed of the sampling data of each monitoring element in ascending time order as a sampling vector of each monitoring element;

[0013] One of any two monitoring elements is taken as the first target monitoring element, and the other monitoring element is taken as the first marked monitoring element. The difference between the sampling data at the same acquisition time in the sampling vectors of the first target monitoring element and the first marked monitoring element is calculated, and a vector composed of all the differences in ascending time order is taken as the differential vector of the first marked monitoring element relative to the first target monitoring element.

[0014] Preferably, the method of obtaining the coordinate point of each monitoring element according to the differential vector between any two monitoring elements and obtaining the clustering result of the coordinate point of each monitoring element according to the coordinate point is:

[0015] For a differential vector between any two monitoring elements, one of the two monitoring elements is used as a second target monitoring element, an element at each acquisition time in the differential vector is used as a horizontal coordinate, an element at each acquisition time in the sampling vector of the second target monitoring element is used as a vertical coordinate, and a data point determined by the horizontal and vertical coordinates is used as a coordinate point of the second target monitoring element;

[0016] All coordinate points of the second target monitoring element are used as input of the DBSCAN clustering algorithm, and the output of the DBSCAN clustering algorithm is used as the clustering result of all coordinate points of the second target monitoring element.

[0017] Preferably, the method of obtaining each sliding window of each cluster in the clustering result of the coordinate points of each monitoring element according to the shape of each cluster in the clustering result of the coordinate points of each monitoring element, and obtaining the clustering partition function value of each sliding window of each cluster in the clustering result of the coordinate points of each monitoring element according to the sliding window is:

[0018] For each cluster in the clustering result of the coordinate point of each monitoring element, a minimum bounding rectangle is made for the shape of the cluster using the minimum bounding rectangle technique, the maximum height of the minimum bounding rectangle on the vertical axis is used as the vertical length of the window, the first preset parameters are used as the horizontal width and the sliding step of the window respectively, and the result of each sliding of the window on the cluster is used as each sliding window of the cluster;

[0019] For each sliding window of the cluster, the number of data points in the sliding window is used as the numerator, the horizontal width of the sliding window is used as the denominator, and the ratio of the numerator to the denominator is used as the vertical point density of the sliding window;

[0020] The average height of the local cluster shape in the longitudinal direction in the sliding window is used as the local longitudinal distance difference of the sliding window, and the inverse of the ratio of the local longitudinal distance difference to the longitudinal length of the sliding window and the sum of the second preset parameter are used as the first component factor; the ratio of the variance of the longitudinal coordinate values ​​of all elements in the cluster to the variance of the longitudinal coordinate values ​​of all elements in the sliding window of the cluster is used as the second component factor; the product of the first component factor and the second component factor is used as the longitudinal distance characteristic index of the sliding window;

[0021] The product of the longitudinal point density and the longitudinal distance characteristic index is used as the clustering segmentation function value of the sliding window.

[0022] Preferably, the method of obtaining the clustering set of the coordinate points of each monitoring element by using the clustering partition function values ​​of all sliding windows of each cluster cluster in the clustering result of the coordinate points of each monitoring element is:

[0023] For each clustering cluster in the clustering results of the coordinate points of each monitoring element, the minimum value of the clustering partitioning function of all sliding windows of the clustering cluster is obtained, the sliding window corresponding to the minimum value is used as the target sliding window, the center point of the target sliding window is used as the longitudinal partitioning point, the clustering cluster is partitioned in the longitudinal direction along the longitudinal partitioning point, and the partitioning results of the clustering cluster are used as two clustering sets of the coordinate points of the monitoring element.

[0024] Preferably, the method of obtaining the clustering followability index of each clustering set of coordinate points of each monitoring element according to each clustering set of coordinate points of each monitoring element is:

[0025] For each cluster of coordinate points of each monitoring element, a rectangular coordinate system is established with the center coordinate of the cluster set as the coordinate origin. , , , The straight lines are taken as each directrix;

[0026] For each directrix, the Euclidean distance between the projection point of each element in the cluster set on the directrix and the coordinate origin is calculated, and the standard deviation of all the Euclidean distances is taken as the standard deviation of the directrix;

[0027] In the rectangular coordinate system The standard deviation of the directrix is The ratio of the standard deviations of the directrix is ​​used as the first multiplication factor;

[0028] In the rectangular coordinate system , The maximum value of the standard deviation of the directrix is ​​taken as the numerator, and the rectangular coordinate system , The minimum value of the standard deviation of the directrix is ​​used as the denominator, and the ratio of the numerator to the denominator is used as the second multiplication factor;

[0029] The product of the first multiplication factor and the second multiplication factor is used as the clustering followability index of the cluster set.

[0030] Preferably, the method for obtaining the master-slave feature discrimination value of each cluster set of the coordinate points of each monitoring element according to the cluster followability index is:

[0031] For each cluster set of the coordinate points of each monitoring element, the number of elements in the cluster set is taken as the numerator, the area of ​​the minimum circumscribed rectangle of the shape of the cluster set is taken as the denominator, and the ratio of the numerator to the denominator is taken as the cluster density of the cluster set;

[0032] The product of the cluster followability index and the cluster density of the cluster set is used as the master-slave feature discrimination value of the cluster set.

[0033] Preferably, the method of obtaining the cluster deviation of each cluster set of coordinate points of each monitoring element according to each cluster set of coordinate points of each monitoring element is:

[0034]

[0035] In the formula, Indicates The coordinate point of the monitoring element The cluster deviation of the cluster set is Indicates The monitoring element corresponds to The number of sliding windows on the cluster sets, Indicates The monitoring element corresponds to The horizontal coordinate of the center point of the nth sliding window on the cluster set, Indicates The monitoring element corresponds to The horizontal coordinate of the center point of the cluster set, Indicates The monitoring element corresponds to The length of the horizontal axis range of the cluster sets, Indicates the first monitoring element corresponding to The cluster set The clustering partition function value of the sliding window, Indicates the first monitoring element corresponding to The mean of the clustering partition function values ​​of all sliding windows on the cluster set.

[0036] Preferably, the method for obtaining the deviation range interval and the master-slave discrimination range interval of each cluster set of the coordinate points of each monitoring element according to the master-slave feature discrimination value and the cluster deviation degree is:

[0037]

[0038]

[0039]

[0040]

[0041] In the formula, Indicates The coordinate point of the monitoring element The deviation range of cluster sets is Indicates The monitoring element corresponds to The horizontal coordinate of the center point of the cluster set, Indicates The coordinate point of the monitoring element In the rectangular coordinate system of cluster sets The standard deviation of the directrix, Indicates The coordinate point of the monitoring element The cluster deviation of the cluster sets; Indicates The coordinate point of the monitoring element The lower limit of the master-slave discrimination range of cluster sets, Indicates The coordinate point of the monitoring element The upper limit of the master-slave discrimination range of cluster sets, Indicates The coordinate point of the monitoring element The master-slave discrimination range interval of cluster sets, Indicates The coordinate point of the monitoring element The master-slave feature discriminant value of the cluster set, Indicates The mean of the master-slave feature discrimination values ​​of all cluster sets of the coordinate points of the monitoring elements.

[0042] Preferably, the method of obtaining the decision tree after dimension reduction by using the deviation range interval and the master-slave discrimination range interval is:

[0043] Taking the master-slave discrimination range interval of each cluster set of the coordinate point of each monitoring element as the master-slave discrimination range interval of the target cluster set, calculating the intersection interval between the master-slave discrimination range interval of the target cluster set and the deviation range interval of each other cluster set, and calculating the ratio of the length of the intersection interval to the length of the master-slave discrimination range interval of the target cluster set, if the ratio is lower than a third preset parameter, it is considered that the target cluster set is independent;

[0044] The ratio of the length of the union interval of the deviation range intervals of all independent cluster sets of the coordinate points of each monitoring element to the length of the union interval of the deviation range intervals of all cluster sets of the coordinate points of each monitoring element is used as the master-slave relationship measurement index between any two monitoring elements;

[0045] A decision tree is constructed through all monitoring elements, and a decision tree pruning algorithm is used to obtain a reduced-dimensional decision tree based on the master-slave relationship metric index.

[0046] The beneficial effect of the present invention is that the present invention converts the time series data into cluster data by analyzing the correlation characteristics between the monitoring data of the monitoring elements, performing pairwise calculations between the monitoring data of the monitoring elements, and each cluster represents a certain data range, and the monitoring elements represented by the monitoring data may show strong correlation characteristics. According to the principle, the distribution law of the coordinate points in the cluster set is characterized by the ratio of the variance of the coordinate points, and the master-slave feature discrimination value is constructed in combination with the cluster density, which is used to characterize the strength of the correlation characteristics between the monitoring data of the monitoring elements represented by the coordinate points in each cluster set. At the same time, according to the density distribution of the cluster set along the horizontal axis, the cluster deviation between the coordinate points of the cluster and the geometric center of the cluster is calculated, and the cluster deviation is used as the weight and combined with Principle, the deviation range of each cluster set is obtained, compared with the traditional The principle takes into account the distribution of data, and the range is more accurate. Then, combined with the master-slave feature discrimination value of each cluster set, it is determined whether the cluster shows the characteristics of strong correlation between components, and finally the master-slave discrimination range interval is obtained, and the decision tree pruning algorithm is used according to the deviation range interval and the master-slave discrimination range interval to complete the dimensionality reduction of the monitoring data of the monitoring element, and then the operation fault monitoring result of the cloud server is obtained. Compared with the traditional decision tree pruning algorithm, the present invention avoids the neglect of certain fault combinations due to pruning while fully pruning, which leads to incomplete consideration of fault combinations and the risk of fault monitoring failure. Therefore, the present invention improves the accuracy of operation fault state monitoring by improving the accuracy of the decision tree pruning algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0048] Figure 1 A schematic diagram of a process for monitoring a cloud computing server operation fault state provided by an embodiment of the present invention;

[0049] Figure 2 A schematic diagram of a sliding window on a cluster provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] See also Figure 1 , which shows a flow chart of a method for monitoring a running fault state of a cloud computing server provided by an embodiment of the present invention, the method comprising the following steps:

[0052] Step S001, obtaining monitoring data and monitoring data curves of monitoring elements of a cloud server.

[0053] The present invention uses a distributed cloud server as an example to monitor its operating failure. The monitoring data of each monitoring element of the distributed cloud server is obtained through the cloud server monitoring platform, and the monitoring data of each monitoring element is the continuous monitoring data of each monitoring element of the previous day, and the number of monitoring elements of the distributed cloud server is obtained. In order to avoid the influence of different dimensions on the subsequent analysis results, the monitoring data of each monitoring element is z-score normalized to obtain the monitoring data of each monitoring element after normalization. The z-score normalization is a well-known technology, and the specific process is not repeated. For the monitoring data of each monitoring element after normalization, the monitoring data curve of the monitoring element is drawn by MATLAB software, and the horizontal axis of the monitoring data curve is time, and the vertical axis is the size of the monitoring data.

[0054] At this point, the number of monitoring elements, monitoring data of the monitoring elements and monitoring data curves are obtained.

[0055] Step S002, obtaining clusters of coordinate points according to the monitoring data curve, obtaining longitudinal point density and longitudinal distance characteristic index according to the clusters of coordinate points, obtaining cluster partitioning function values ​​according to the longitudinal point density and longitudinal distance characteristic index, and obtaining cluster sets using the cluster partitioning function values.

[0056] In cloud servers, because different monitoring components form an operational server through a certain structural combination, the monitoring data between monitoring components often have a strong correlation. For example, when a router is powered off, the load size of the router must be fixed. If these two situations are regarded as a fault combination, the simulation calculation of the fault combination is invalid.

[0057] Furthermore, the number of monitoring elements is recorded as M. Since the monitoring data under different monitoring elements are likely to be correlated, in order to explain how to calculate the correlation between the monitoring data between two monitoring elements, take the first The data monitoring curve of the monitoring element and the The monitoring data curve of a monitoring element is used as an example to illustrate. .

[0058] The present invention expresses the correlation between two faults in a master-slave relationship. The fault monitoring parameters of each monitoring element are When The fault monitoring parameters of each monitoring element must be , indicating that when The fault monitoring parameters of each monitoring element are There is no need to The fault parameters of the monitoring element are monitored, which can be expressed as: The fault monitoring parameters of each monitoring element are At that time, The monitoring element is The main component of the monitoring component.

[0059] Respectively Monitoring element, The monitoring data curve of each monitoring element is sampled to obtain the Monitoring element, The sampling data of the monitoring element is collected at a sampling frequency of 10000 Hz. Monitoring element, The sampling data of the monitoring elements are arranged in ascending time order as the vectors of Monitoring element, The sampling vector of the monitoring element is Monitoring element, The sampling vectors of the monitoring elements are recorded as , ,in and The elements at the same position in represent the collected data at the same collection time.

[0060] Calculate the The monitoring element is relative to the The differential vector of the monitoring elements :

[0061]

[0062]

[0063] In the formula, Indicates The monitoring element is relative to the The differential vector of the monitoring elements The gth value in and Respectively represent Monitoring element, The g-th collected data in the sampling vector of the monitoring element; Indicates The monitoring element is relative to the The differential vector of the monitoring element, G represents the The monitoring element is relative to the The number of values ​​in the differential vector of the monitoring elements, , , Respectively represent The monitoring element is relative to the The first value, the second value, and the last value in the differential vector of the monitoring elements.

[0064] It should be noted that and Elements in the same position represent data at the same acquisition time. The difference vector represents the The monitoring data of the first monitoring element is relative to the The change of the monitoring data of each monitoring element. The more stable the change of the differential vector is within a period of time, the more stable the difference vector is within the period of time. The more likely a monitoring element is to belong to components.

[0065] Furthermore, the differential vector at a certain acquisition time The values ​​in Axis coordinates, acquisition vector The sample data in Axis coordinates, you can get a coordinate point. The differential vector of all acquisition moments The values ​​in Axis coordinates, acquisition vector The sample data in Axis coordinates are obtained to obtain all coordinate points. All coordinate points are used as input of DBSCAN clustering algorithm (Density-Based Spatial Clustering of Applications with Noise, DBSCAN), and the output of DBSCAN clustering algorithm is used as the clustering result of all coordinate points. In the clustering result, each cluster contains multiple coordinate points. DBSCAN clustering algorithm is a well-known technology, and the specific process is not repeated here.

[0066] The vertical coordinate range of each cluster in the clustering result is The data range of the monitoring data of each monitoring element, the horizontal axis range corresponding to each cluster is when the When the monitoring data range of a monitoring element is The fluctuation range of the monitoring data of each monitoring element. If the clustering result is relatively dense within a certain range of the horizontal axis, it means that the The monitoring element is When the monitoring data of the monitoring element is within the horizontal axis range of the cluster, The monitoring data of each monitoring element must be within a fixed range. The monitoring data of the monitoring element is The monitoring data of each monitoring element has a strong follow-up feature. The more likely a monitoring element is to belong to the A monitoring element.

[0067] When the clustering results obtained by the DBSCAN clustering algorithm are divided into a cluster as long as the density threshold of the coordinate points is met, this will cause the horizontal coordinate range of each cluster to be defined too large when classifying the master-slave relationship of the components. Therefore, the obtained clustering results are divided as follows:

[0068] Specifically, the minimum bounding rectangle technique is used to make the minimum bounding rectangle of each cluster in the clustering result, and the height of the minimum bounding rectangle of the kth cluster in the vertical direction is recorded as The minimum bounding rectangle technique is a well-known technique, and the specific process is not described in detail. Taking the kth cluster in the clustering result as an example, the vertical length is set to , the horizontal width is The window size is , sliding from left to right in turn, traversing all elements of the kth cluster. In order to facilitate subsequent calculations, the window formed by the first sliding is recorded as the first sliding window, the window formed by the second sliding is recorded as the second sliding window, and so on. The schematic diagram of the sliding window on the cluster is as follows Figure 2 shown.

[0069] Calculate the first monitoring element corresponding to the The first The vertical point density of a sliding window:

[0070]

[0071] In the formula, Indicates the first monitoring element corresponding to The first The vertical point density of the sliding window, Indicates the first monitoring element corresponding to The first The number of data points in the sliding window, Indicates the first monitoring element corresponding to The horizontal width of the sliding window for clusters.

[0072] The smaller the number of data points in the sliding window, the smaller the vertical density relative to the horizontal axis, and the smaller the vertical point density, that is, the lower the density of data points in the sliding window at this position, the more it needs to be separated as a separate cluster. In addition, the larger the vertical point density, the smaller the vertical point density within the cluster range. The monitoring data of the monitoring element is The stronger the following characteristics of the monitoring data of the first monitoring element, the The more likely a monitoring element is to belong to A monitoring element.

[0073] Furthermore, for the ith monitoring element corresponding to the The first The average height of the local cluster shape in the sliding window in the vertical direction is recorded as the local vertical distance difference. The vertical length of the sliding window is used as the cluster vertical distance difference of the sliding window.

[0074] Calculate the first monitoring element corresponding to the The first The vertical feature index of the sliding window:

[0075]

[0076] In the formula, Indicates the first monitoring element corresponding to The first The vertical feature index of the sliding window, Indicates the first monitoring element corresponding to The first The local vertical distance difference of the sliding window, Indicates the first monitoring element corresponding to The cluster longitudinal distance difference of clusters, Indicates the first monitoring element corresponding to The variance of the ordinate values ​​of all elements in a cluster, Indicates the first monitoring element corresponding to The first The variance of the ordinate values ​​of all elements in a sliding window.

[0077] The smaller the ratio of the local vertical distance difference to the cluster vertical distance difference, the smaller the height of the local cluster shape in the sliding window in the vertical direction. To a certain extent, it reflects that the cluster points in the local area of ​​the cluster cluster are denser in the vertical direction. At the same time, the i-th monitoring element corresponding to the i-th monitoring element is The first The smaller the variance of the ordinate values ​​of all elements in a sliding window is relative to the variance of the ordinate values ​​of all elements in the cluster, that is, The larger it is, the smaller the discreteness of the cluster points in the local area of ​​the cluster is, that is, the higher the density is, and the larger the longitudinal characteristic index is.

[0078] Furthermore, the obtained vertical distance feature index is used as the weight of the vertical point density. The larger its value is, the more clustered the points in the sliding window are. If the clustering degree of the clustering points in the sliding window is lower, the part is more likely to have a point that can characterize the first The monitoring element belongs to The more clusters of monitoring elements there are, the more they need to be separated into separate clusters.

[0079] Calculate the first monitoring element corresponding to the The first The clustering partition function value of sliding windows:

[0080]

[0081] In the formula, Indicates the first monitoring element corresponding to The first The clustering partition function value of the sliding window, Indicates the first monitoring element corresponding to The first The vertical feature index of the sliding window, Indicates the first monitoring element corresponding to The first The vertical point density of a sliding window.

[0082] The vertical point density and vertical distance feature index both represent the degree of clustering of cluster points in the sliding window. The lower the degree of clustering in the sliding window, the more likely it is that there is a characteristic The monitoring element belongs to The larger the value, the higher the clustering value. The monitoring data of the monitoring element is The stronger the following characteristics of the monitoring data of the first monitoring element, the The more likely a monitoring element is to belong to A monitoring element.

[0083] For the i-th monitoring element corresponding to the A cluster is selected, and the sliding window corresponding to the minimum value of the clustering partition function of all sliding windows of the cluster is taken as the target sliding window, and the center point of the target sliding window is taken as the vertical partition point. The kth cluster is partitioned into two cluster sets along the vertical direction of the vertical partition point, thereby avoiding poor accuracy due to over-definition of the horizontal axis range of the cluster when classifying the master-slave relationship of the elements.

[0084] At this point, based on the above analysis, each cluster corresponding to the i-th monitoring element is segmented according to the same method as above to obtain all cluster sets corresponding to the i-th monitoring element.

[0085] Step S003, obtaining the cluster followability index and cluster density according to the cluster set, obtaining the master-slave feature discrimination value and cluster deviation according to the cluster followability index and cluster density, and obtaining the deviation range interval and the master-slave discrimination range interval according to the master-slave feature discrimination value and cluster deviation.

[0086] Based on the above analysis, the transformation of monitoring data from time series to cluster sets is completed. Next, the monitoring data range represented by each cluster set is analyzed. Is the first element subordinate to the components.

[0087] according to in principle, The data points fall within the range of three standard deviations of the mean, and in order to avoid the clustering element points causing some data abnormalities due to statistical errors, the three standard deviations of the mean of each cluster set are used as the statistical feature range of the cluster set.

[0088] However, due to the different distributions of different cluster sets, the data of cluster sets are less likely to follow the normal distribution in the actual process, and there is a greater possibility of deviation. Therefore, it is necessary to analyze the deviation of cluster sets.

[0089] Specifically, with the central coordinates of each cluster set as the center, the standard deviations in four directions are calculated to express the statistical feature range of the cluster. Specifically, a coordinate system is established with the central coordinates of the cluster set as the origin of the coordinates. , , , As each directrix, obtain the projection points of all cluster points in the cluster set on the directrix, calculate the Euclidean distance between each projection point on each directrix and the center coordinate, calculate the standard deviation of all Euclidean distances corresponding to each directrix, and use , , , The standard deviation of the straight line is recorded as , , , .

[0090] Furthermore, calculate the The monitoring element corresponds to Cluster following index of cluster sets:

[0091]

[0092] In the formula, Indicates The monitoring element corresponds to The clustering followability index of the cluster set, , , , Respectively represent The monitoring element corresponds to The cluster sets correspond to , , , The standard deviation of the straight line is Indicates The monitoring element corresponds to The cluster sets correspond to , The maximum value of the standard deviation of the straight line is Indicates The monitoring element corresponds to The cluster sets correspond to , The straight line is the minimum value of the standard deviation of the directrix.

[0093] It is a cluster set along The standard deviation of the direction is divided by the The standard deviation of the direction, along Standard deviation of direction The smaller the value, the closer the cluster is. The smaller the data range of the direction, the The monitoring element is The smaller the data difference of the monitoring elements, the better the following effect; Standard deviation of direction The larger the value, the closer the cluster is. The larger the data range of the direction, the The monitoring element is The larger the following range of a monitoring element, the better the following effect. The bigger it is, the better the following effect.

[0094] like Greater than , indicating that the clustering set is along The range in the direction is greater than along The ratio of the two represents the difference in the range of the two. That is, the larger the ratio, the greater the difference in the range, which means that the points in the cluster are more likely to be along the Direction arrangement trend, representing the The data of each monitoring element has the same The trend is increasing with the increase of data of monitoring components. Represents the The data of the monitoring element and the The data of the monitoring element follows the trend in changes. The larger the ratio, the stronger the trend. Component pair The better the component following effect.

[0095] Finally, the two are multiplied to get the clustering followability index The larger the value, the Among the monitoring elements Within the data range represented by the cluster sets, The monitoring element is The more monitoring elements follow the better.

[0096] Further, based on the clustering followability index obtained above, calculate the The monitoring element corresponds to The master-slave feature discriminant value of a cluster set:

[0097]

[0098]

[0099] In the formula, Indicates The monitoring element corresponds to The cluster density of the cluster sets, Indicates The monitoring element corresponds to The number of cluster points in a cluster set, Indicates The monitoring element corresponds to The area of ​​the minimum bounding rectangle of a cluster set; Indicates The monitoring element corresponds to The master-slave feature discriminant value of the cluster set, Indicates The monitoring element corresponds to The cluster following index of a cluster set.

[0100] No. The monitoring element corresponds to Cluster following index of cluster sets The larger the The monitoring element corresponds to The cluster density of cluster sets The larger the The monitoring element is The better the following effect of the monitoring element, the better the following effect. The more likely a monitoring element is to belong to monitoring elements; the final master-slave feature discrimination value The larger the value, the The more likely a monitoring element is to belong to A monitoring element.

[0101] Furthermore, based on the above analysis, Each cluster set corresponding to the monitoring element is obtained by setting a sliding window to segment the cluster clusters, which means that there are multiple sliding windows on the cluster set. The horizontal coordinate of the center point of each sliding window of the cluster set is obtained, and the first The monitoring element corresponds to The coordinate of the center point of the nth sliding window of the cluster set is marked as ; At the same time, obtain the horizontal coordinate of the center point of each cluster set, and The monitoring element corresponds to The coordinates of the center points of the cluster sets are marked as .

[0102] Calculate the The monitoring element corresponds to The cluster deviation of cluster sets:

[0103]

[0104] In the formula, Indicates The monitoring element corresponds to The cluster deviation of the cluster set is Indicates The monitoring element corresponds to The number of sliding windows on the cluster sets, Indicates The monitoring element corresponds to The horizontal coordinate of the center point of the nth sliding window of the cluster set, Indicates The monitoring element corresponds to The horizontal coordinate of the center point of the cluster set, Indicates The monitoring element corresponds to The length of the horizontal axis range of the cluster sets, Indicates the first monitoring element corresponding to The first cluster set The clustering partition function value of the sliding window, Indicates the first monitoring element corresponding to The mean of the clustering partition function values ​​of all sliding windows of the cluster set.

[0105] The clustering partition function value represents the clustering points along the clustering The density in the axis direction represents the density at the position of the sliding window; Represents the distance between the center of the sliding window and the center of the cluster set, divided by The purpose is to convert distance into ratio; It is the density of each sliding window position relative to the density of the entire cluster set. The larger its value, the greater the density of the sliding window position. The larger the The larger the value, the cluster deviation The bigger.

[0106] A positive value indicates that the dense area of ​​cluster points in the cluster is biased towards the center. Positive direction; negative value indicates that the dense area of ​​cluster points in the cluster is biased towards the center. Negative direction. The larger the value, the more serious the deviation. When dividing the data range of the cluster, you should divide the distance longer in the direction with high density of coordinate points to make the data division range more reasonable.

[0107] Furthermore, according to The deviation range interval and master-slave discrimination range interval of each cluster set corresponding to the monitoring element:

[0108]

[0109]

[0110]

[0111]

[0112] In the formula, Indicates The coordinate point of the monitoring element The deviation range of cluster sets is Indicates The monitoring element corresponds to The horizontal coordinate of the center point of the cluster set, Indicates The coordinate point of the monitoring element In the rectangular coordinate system of cluster sets The standard deviation of the directrix, Indicates The coordinate point of the monitoring element The cluster deviation of the cluster sets; Indicates The coordinate point of the monitoring element The lower limit of the master-slave discrimination range of cluster sets, Indicates The coordinate point of the monitoring element The upper limit of the master-slave discrimination range of cluster sets, Indicates The coordinate point of the monitoring element The master-slave discrimination range interval of cluster sets, Indicates The coordinate point of the monitoring element The master-slave feature discriminant value of the cluster set, Indicates The mean of the master-slave feature discrimination values ​​of all cluster sets of the coordinate points of the monitoring elements.

[0113] In the first formula here, multiplying by 3 is based on in principle, The data points fall within three standard deviations of the mean. or The cluster set range is shifted left or right according to the cluster deviation, and the final interval range is taken as the range of the mth cluster set.

[0114] The parameter The standardized mathematical method will not be described here; this formula is obtained by multiplying the interval of the first formula by Obtained because of The larger the master-slave feature discrimination value of a cluster set is, the more likely it is that the jth element belongs to the interval of the i-th element.

[0115] At this point, the deviation range interval and the master-slave discrimination range interval of the cluster set are obtained.

[0116] Step S004, using the deviation range interval and the master-slave discrimination range interval to obtain a decision tree after dimensionality reduction, and obtaining the operation fault monitoring result of the cloud server through the decision tree after dimensionality reduction.

[0117] The master-slave discrimination range interval of each cluster set of the coordinate points of each monitoring element is used as the master-slave discrimination range interval of the target cluster set, and the intersection interval between the master-slave discrimination range interval of the target cluster set and the deviation range interval of each other cluster set is calculated. The ratio of the length of the intersection interval to the length of the master-slave discrimination range interval of the target cluster set is calculated. If the ratio is lower than 10%, the target cluster set is considered to be independent.

[0118] The ratio of the length of the union of the deviation range intervals of all independent cluster sets of the coordinate points of each monitoring element to the length of the union of the deviation range intervals of all cluster sets is used as the master-slave relationship measurement index between any two monitoring elements.

[0119] Furthermore, the mean value of all monitoring data of each monitoring element is taken as the judgment value of each monitoring element, the sequence composed of the judgment values ​​of all monitoring elements in ascending order of numerical values ​​is taken as the judgment value sequence, and the value of the zth element in the judgment value sequence is recorded as A decision tree is constructed by judging the value sequence, and each node in the decision tree represents a monitoring element.

[0120] Specifically, a monitoring element corresponding to the minimum element value in the judgment value sequence is used as a decision node. A corresponding monitoring element is used as a decision node; the value sequence greater than and less than or equal to A monitoring element corresponding to the element value of is used as the first opportunity node of the decision node, and the value greater than and less than or equal to A monitoring element corresponding to the element value of is used as the second opportunity node of the decision node, and the value greater than and less than or equal to A monitoring element corresponding to the element value of is taken as the third opportunity node of the decision node, and so on; for any opportunity node, let’s take the uth opportunity node as an example and take the value greater than and less than or equal to A monitoring element corresponding to the element value of is used as the first result node of the u-th opportunity node, and the value greater than and less than or equal to A monitoring element corresponding to the element value of is used as the second result node of the u-th opportunity node, and the value greater than and less than or equal to A monitoring element corresponding to the element value of is used as the third result node of the uth opportunity node; and so on. The decision tree is constructed in the above manner, and the number of layers of the decision tree is 3, wherein the first layer is the decision layer, the second layer is the opportunity layer, and the third layer is the result layer, and each node represents a monitoring element.

[0121] Furthermore, the master-slave relationship measurement index between the two monitoring elements represented by each two adjacent nodes in the decision tree is calculated. If the master-slave relationship measurement index is higher than 90%, it means that the two monitoring elements represented by the adjacent nodes have a strong subordinate relationship, and the branches between the adjacent nodes need to be pruned. Pruning all nodes in the decision tree obtains the pruned decision tree, which is the decision tree after dimensionality reduction.

[0122] Based on the above analysis, a decision tree after dimensionality reduction is obtained based on the master-slave relationship measurement index through a decision tree pruning algorithm. The decision tree pruning algorithm is a well-known technology, and the specific process is not repeated here. In the decision tree after dimensionality reduction, the leaf node traverses back along the decision tree to obtain multiple nodes. Each traversal trajectory is a group of node combinations, called a fault combination. Each fault combination is judged by a technician, and each fault combination is classified into a fault mode, and a maintenance strategy is formulated for each fault mode.

[0123] When performing fault detection on a running cloud server, when the various detection data of the cloud server simultaneously meet the abnormal monitoring data range corresponding to each node of one of the fault combinations, it is determined that the cloud server has the fault mode of the fault combination and maintenance is performed according to the formulated strategy.

[0124] At this point, a method for monitoring the operating fault status of a cloud computing server is completed.

[0125] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for monitoring the operating fault status of a cloud computing server, characterized in that: The method comprises the following steps: Acquire monitoring information data of the cloud server, wherein the monitoring information data includes the number of monitoring elements, monitoring data of each monitoring element, and a monitoring data curve; Obtain the differential vector between any two monitoring elements according to the monitoring data curves of any two monitoring elements; obtain the coordinate point of each monitoring element according to the differential vector between any two monitoring elements, and obtain the clustering result of the coordinate point of each monitoring element according to the coordinate point; obtain each sliding window of each cluster in the clustering result of the coordinate point of each monitoring element according to the shape of each cluster in the clustering result of the coordinate point of each monitoring element, and obtain the clustering partition function value of each sliding window of each cluster in the clustering result of the coordinate point of each monitoring element according to the sliding window; obtain the clustering set of the coordinate point of each monitoring element by using the clustering partition function values ​​of all sliding windows of each cluster in the clustering result of the coordinate point of each monitoring element; According to each cluster set of the coordinate points of each monitoring element, a cluster followability index of each cluster set of the coordinate points of each monitoring element is obtained; according to the cluster followability index, a master-slave feature discrimination value of each cluster set of the coordinate points of each monitoring element is obtained; according to each cluster set of the coordinate points of each monitoring element, a cluster deviation degree of each cluster set of the coordinate points of each monitoring element is obtained; according to the master-slave feature discrimination value and the cluster deviation degree, a deviation range interval and a master-slave discrimination range interval of each cluster set of the coordinate points of each monitoring element are obtained; The deviation range interval and the master-slave discrimination range interval are used to obtain a decision tree after dimensionality reduction, and the operation fault monitoring result of the cloud server is obtained through the decision tree after dimensionality reduction.

2. A method for monitoring the operating fault status of a cloud computing server according to claim 1, characterized in that: The method for obtaining the differential vector between any two monitoring elements according to the monitoring data curves of any two monitoring elements is: Sampling the monitoring data curve of each monitoring element at a preset sampling frequency to obtain sampling data of each monitoring element, and using a sequence composed of the sampling data of each monitoring element in ascending time order as a sampling vector of each monitoring element; One of any two monitoring elements is taken as the first target monitoring element, and the other monitoring element is taken as the first marked monitoring element. The difference between the sampling data at the same acquisition time in the sampling vectors of the first target monitoring element and the first marked monitoring element is calculated, and a vector composed of all the differences in ascending time order is taken as the differential vector of the first marked monitoring element relative to the first target monitoring element.

3. A method for monitoring the operating fault status of a cloud computing server according to claim 1, characterized in that: The method of obtaining the coordinate point of each monitoring element according to the differential vector between any two monitoring elements and obtaining the clustering result of the coordinate point of each monitoring element according to the coordinate point is: For a differential vector between any two monitoring elements, one of the two monitoring elements is used as a second target monitoring element, an element at each acquisition time in the differential vector is used as a horizontal coordinate, an element at each acquisition time in the sampling vector of the second target monitoring element is used as a vertical coordinate, and a data point determined by the horizontal and vertical coordinates is used as a coordinate point of the second target monitoring element; All coordinate points of the second target monitoring element are used as input of the DBSCAN clustering algorithm, and the output of the DBSCAN clustering algorithm is used as the clustering result of all coordinate points of the second target monitoring element.

4. A method for monitoring the operating fault status of a cloud computing server according to claim 1, characterized in that: The method of obtaining each sliding window of each cluster in the clustering result of each monitoring element's coordinate point according to the shape of each cluster in the clustering result of each monitoring element's coordinate point, and obtaining the clustering partition function value of each sliding window of each cluster in the clustering result of each monitoring element's coordinate point according to the sliding window is: For each cluster in the clustering result of the coordinate point of each monitoring element, a minimum bounding rectangle is made for the shape of the cluster using the minimum bounding rectangle technique, the maximum height of the minimum bounding rectangle on the vertical axis is used as the vertical length of the window, the first preset parameters are used as the horizontal width and the sliding step of the window respectively, and the result of each sliding of the window on the cluster is used as each sliding window of the cluster; For each sliding window of the cluster, the number of data points in the sliding window is used as the numerator, the horizontal width of the sliding window is used as the denominator, and the ratio of the numerator to the denominator is used as the vertical point density of the sliding window; The average height of the local cluster shape in the longitudinal direction in the sliding window is used as the local longitudinal distance difference of the sliding window, and the inverse of the ratio of the local longitudinal distance difference to the longitudinal length of the sliding window and the sum of the second preset parameter are used as the first component factor; the ratio of the variance of the longitudinal coordinate values ​​of all elements in the cluster to the variance of the longitudinal coordinate values ​​of all elements in the sliding window of the cluster is used as the second component factor; the product of the first component factor and the second component factor is used as the longitudinal distance characteristic index of the sliding window; The product of the longitudinal point density and the longitudinal distance characteristic index is used as the clustering segmentation function value of the sliding window.

5. The method for monitoring the operating fault status of a cloud computing server according to claim 1, characterized in that: The method for obtaining the clustering set of the coordinate points of each monitoring element by using the clustering partition function values ​​of all sliding windows of each cluster cluster in the clustering result of the coordinate points of each monitoring element is: For each clustering cluster in the clustering results of the coordinate points of each monitoring element, the minimum value of the clustering partitioning function of all sliding windows of the clustering cluster is obtained, the sliding window corresponding to the minimum value is used as the target sliding window, the center point of the target sliding window is used as the longitudinal partitioning point, the clustering cluster is partitioned in the longitudinal direction along the longitudinal partitioning point, and the partitioning results of the clustering cluster are used as two clustering sets of the coordinate points of the monitoring element.

6. A method for monitoring the operating fault status of a cloud computing server according to claim 1, characterized in that: The method for obtaining the clustering followability index of each clustering set of coordinate points of each monitoring element according to each clustering set of coordinate points of each monitoring element is: For each cluster of coordinate points of each monitoring element, a rectangular coordinate system is established with the center coordinate of the cluster set as the coordinate origin. , , , The straight lines are taken as each directrix; For each directrix, the Euclidean distance between the projection point of each element in the cluster set on the directrix and the coordinate origin is calculated, and the standard deviation of all the Euclidean distances is taken as the standard deviation of the directrix; In the rectangular coordinate system The standard deviation of the directrix is The ratio of the standard deviations of the directrix is ​​used as the first multiplication factor; In the rectangular coordinate system , The maximum value of the standard deviation of the directrix is ​​taken as the numerator, and the rectangular coordinate system , The minimum value of the standard deviation of the directrix is ​​used as the denominator, and the ratio of the numerator to the denominator is used as the second multiplication factor; The product of the first multiplication factor and the second multiplication factor is used as the clustering followability index of the cluster set.

7. A method for monitoring the operating fault status of a cloud computing server according to claim 1, characterized in that: The method for obtaining the master-slave feature discrimination value of each cluster set of the coordinate point of each monitoring element according to the cluster followability index is: For each cluster set of the coordinate points of each monitoring element, the number of elements in the cluster set is taken as the numerator, the area of ​​the minimum circumscribed rectangle of the shape of the cluster set is taken as the denominator, and the ratio of the numerator to the denominator is taken as the cluster density of the cluster set; The product of the cluster followability index and the cluster density of the cluster set is used as the master-slave feature discrimination value of the cluster set.

8. The method for monitoring the operating fault status of a cloud computing server according to claim 1, characterized in that: The method for obtaining the cluster deviation of each cluster set of coordinate points of each monitoring element according to each cluster set of coordinate points of each monitoring element is: In the formula, Indicates The coordinate point of the monitoring element The cluster deviation of the cluster set is Indicates The monitoring element corresponds to The number of sliding windows on the cluster set, Indicates The monitoring element corresponds to The horizontal coordinate of the center point of the nth sliding window on the cluster set, Indicates The monitoring element corresponds to The horizontal coordinate of the center point of the cluster set, Indicates The monitoring element corresponds to The length of the horizontal axis range of the cluster sets, Indicates the first monitoring element corresponding to The cluster set The clustering partition function value of the sliding window, Indicates the first monitoring element corresponding to The mean of the clustering partition function values ​​of all sliding windows on the cluster set.

9. A method for monitoring the operating fault status of a cloud computing server according to claim 1, characterized in that: The method for obtaining the deviation range interval and the master-slave discrimination range interval of each cluster set of the coordinate point of each monitoring element according to the master-slave feature discrimination value and the cluster deviation degree is: In the formula, Indicates The coordinate point of the monitoring element The deviation range of cluster sets is Indicates The monitoring element corresponds to The horizontal coordinate of the center point of the cluster set, Indicates The coordinate point of the monitoring element In the rectangular coordinate system of cluster sets The standard deviation of the directrix, Indicates The coordinate point of the monitoring element The cluster deviation of the cluster sets; Indicates The coordinate point of the monitoring element The lower limit of the master-slave discrimination range of cluster sets, Indicates The coordinate point of the monitoring element The upper limit of the master-slave discrimination range of cluster sets, Indicates The coordinate point of the monitoring element The master-slave discrimination range interval of cluster sets, Indicates The coordinate point of the monitoring element The master-slave feature discriminant value of the cluster set, Indicates The mean of the master-slave feature discrimination values ​​of all cluster sets of the coordinate points of the monitoring elements.

10. A method for monitoring the operating fault status of a cloud computing server according to claim 1, characterized in that: The method of obtaining the decision tree after dimension reduction by using the deviation range interval and the master-slave discrimination range interval is: Taking the master-slave discrimination range interval of each cluster set of the coordinate point of each monitoring element as the master-slave discrimination range interval of the target cluster set, calculating the intersection interval between the master-slave discrimination range interval of the target cluster set and the deviation range interval of each other cluster set, and calculating the ratio of the length of the intersection interval to the length of the master-slave discrimination range interval of the target cluster set, if the ratio is lower than a third preset parameter, it is considered that the target cluster set is independent; The ratio of the length of the union interval of the deviation range intervals of all independent cluster sets of the coordinate points of each monitoring element to the length of the union interval of the deviation range intervals of all cluster sets of the coordinate points of each monitoring element is used as the master-slave relationship measurement index between any two monitoring elements; A decision tree is constructed through all monitoring elements, and a decision tree pruning algorithm is used to obtain a reduced-dimensional decision tree based on the master-slave relationship metric index.

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