CN114138625A - Method and system for evaluating health state of server, electronic device and storage medium - Google Patents
Method and system for evaluating health state of server, electronic device and storage medium Download PDFInfo
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Abstract
The disclosure provides an evaluation method of a server health state, which can be applied to the technical field of big data. The evaluation method for the health state of the server comprises the following steps: acquiring running characteristic data of a target server in multiple dimensions in a preset historical time period; calculating to obtain an operation predicted value of each dimension according to the operation characteristic data of each dimension in a preset historical time period; acquiring an operation actual value of each dimension of a target server at the current moment; calculating to obtain an operation revision value of each dimension according to the operation predicted value and the operation actual value, wherein the operation revision value of each dimension is an operation revision value based on the operation predicted value and the operation actual value of each dimension; and determining the current health assessment score of the target server according to the operation revision value of each dimension, wherein the health assessment score is used for representing the current health state of the target server. The disclosure also provides an evaluation system of the server health state, an electronic device and a storage medium.
Description
Technical Field
The disclosure relates to the technical field of big data, in particular to a method and a system for evaluating the health state of a server, electronic equipment and a storage medium.
Background
In the period of equipment explosion growth, whether a server is healthy or not restricts all applications running on the server, so that the evaluation of the health degree of the server is very important for monitoring whether the applications run normally or not.
The health degree evaluation of the current server mainly comprises the steps of obtaining various monitoring dimensions of the server, manually analyzing the health degree of the server, or detecting the monitoring dimensions by using a fixed threshold, and setting unhealthy monitoring dimensions of the server to be unhealthy when the monitoring dimensions of the server exceed the fixed threshold.
The method needs to manually configure various dimension thresholds, and on one hand, the method depends on expert experience and needs to consume more manpower; on the other hand, the threshold configured in this way is difficult to meet the changing requirements of different environments and different time periods, for example, in busy time periods or some servers, the dimension exceeding the threshold is healthy, and in other time periods or other servers, the dimension exceeding the threshold is unhealthy, if the threshold is used for evaluation, the effect is a knife cut, and as long as the dimension exceeding the threshold is unhealthy, the health value cannot be adjusted in a self-adaptive manner, so that the evaluation is inaccurate.
Disclosure of Invention
Technical problem to be solved
In view of the above problems, the present disclosure provides a method, a system, an electronic device, and a storage medium for evaluating a health status of a server, which are used to at least partially solve the technical problems of inaccurate evaluation caused by the fact that the conventional method relies on historical experience and is difficult to meet the changing requirements of different environments and time periods.
(II) technical scheme
One aspect of the present disclosure provides a method for evaluating a health status of a server, including: acquiring running characteristic data of a target server in multiple dimensions in a preset historical time period; calculating to obtain an operation predicted value of each dimension according to the operation characteristic data of each dimension in a preset historical time period; acquiring an operation actual value of each dimension of a target server at the current moment; calculating to obtain an operation revision value of each dimension according to the operation predicted value and the operation actual value, wherein the operation revision value of each dimension is an operation revision value based on the operation predicted value and the operation actual value of each dimension; and determining the current health assessment score of the target server according to the operation revision value of each dimension, wherein the health assessment score is used for representing the current health state of the target server.
Further, calculating the operation revision value of each dimension according to the operation predicted value and the operation actual value comprises: when the actual operation value is larger than the predicted operation value, calculating according to the baseline value and the loss reduction coefficient to obtain a loss reduction score, and obtaining a loss reduction revision value according to a preset initial score and the loss reduction score; when the actual operation value is smaller than the predicted operation value, calculating according to the baseline value and the gain coefficient to obtain a gain score, and obtaining a gain revision value according to a preset initial score and the gain score; when the operation actual value is equal to the operation predicted value, the preset initial score is the operation revised value.
Further, when the operation actual value is greater than the operation predicted value, obtaining the impairment revision value includes: when the operation actual value is larger than the operation predicted value and is smaller than the baseline value, calculating a loss reduction fraction M1 according to the following formula:
wherein X is an operation predicted value, Y is an operation actual value, Z is a baseline value, and a is a first loss reduction coefficient; the first impairment revision value N1 ═ L-M1, where L is the initial score;
when the operation actual value is larger than the operation predicted value and is larger than the baseline value, calculating a loss reduction fraction M2 according to the following formula:
M2＝(Y-Z)×b
wherein b is a second impairment coefficient; the second impairment revision value N2 is P-M2, where P is the baseline score for the baseline value.
Further, when the operation actual value is smaller than the operation predicted value, obtaining the gain revised value includes: the gain fraction M3 is calculated according to the following equation:
wherein X is an operation predicted value, Y is an operation actual value, Z is a baseline value, and c is a gain coefficient; the gain revision value N3 ═ L + M3, where L is the initial score.
Further, the step of calculating an operation predicted value of each dimension according to the operation characteristic data of each dimension in the preset historical time period includes: generating time sequence data according to the operation characteristic data of each dimension in a preset historical time period and the corresponding time data; preprocessing time series data; and inputting the time series of the preprocessed time series data into a pre-constructed Prophet model, and predicting to obtain the operation predicted value of each dimension of the target server at the current moment.
Further, the process of constructing the pre-constructed Prophet model comprises the following steps: calculating the growth trend change according to the time sequence data to obtain a trend item; calculating periodic variation according to the time sequence data to obtain periodic items; calculating holiday effect change according to the time sequence data to obtain holiday items; calculating abnormal change occurring during model training according to the time sequence data to obtain an error item; and constructing a Prophet model according to the trend term, the period term, the holiday term and the error term.
Further, determining the target server's current health assessment score according to the running revision value of each dimension comprises: determining a weight coefficient of each dimension based on a preset weight distribution strategy; based on the weight coefficients and the running revision values for each dimension, a target server current health assessment score is determined.
Further, determining the target server's current health assessment score according to the running revision value of each dimension comprises: and adjusting the operation strategy of the target server according to the health evaluation score and the operation revision value of each dimension.
Furthermore, the multiple dimensions in the running characteristic data of the multiple dimensions of the target server in the preset historical time period are acquired to be at least two dimensions of CPU utilization rate, memory utilization rate, disk I/O (input/output), disk utilization rate, file opening number and connection number.
Another aspect of the present disclosure provides a system for evaluating a health status of a server, including: the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring the multi-dimensional operation characteristic data of a target server in a preset historical time period; the first calculation module is used for calculating to obtain an operation predicted value of each dimension according to the operation characteristic data of each dimension in a preset historical time period; the second acquisition module is used for acquiring the actual operation value of each dimension of the target server at the current moment; the second calculation module is used for calculating to obtain an operation revision value of each dimension according to the operation predicted value and the operation actual value, and the operation revision value of each dimension is an operation revision value based on the operation predicted value and the operation actual value of each dimension; and the determining module is used for determining the current health assessment score of the target server according to the operation revision value of each dimension, and the health assessment score is used for representing the current health state of the target server.
Yet another aspect of the present disclosure provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the computer program to cause the processor to perform the method for assessing the health status of a server as described above.
A further aspect of the present disclosure provides a computer-readable storage medium on which a computer program is stored, which, when executed by a processor, implements a method for assessing a health status of a server as described above.
A further aspect of the disclosure provides a computer program product comprising a computer program which, when executed by a processor, implements a method of assessing a health status of a server as described above.
(III) advantageous effects
According to the evaluation method, the evaluation system, the electronic device and the storage medium for the health state of the server, all dimensions on the server are predicted based on a machine learning algorithm, an actual operation value and a predicted operation value are compared, and the health degree of the server is set according to a comparison result; the method considers the difference of each dimension on each server, and the prediction of each dimension by using a machine learning algorithm can be self-adaptive to the change of different time periods, so that the evaluation precision of the health degree of the server is improved.
Drawings
For a more complete understanding of the present disclosure and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
fig. 1 schematically illustrates an application scenario of an evaluation method of a server health status according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a method of assessing server health status according to an embodiment of the present disclosure;
FIG. 3 schematically illustrates a flowchart of a method of calculating a running revision value for each dimension based on a running predicted value and a running actual value, according to an embodiment of the present disclosure;
FIG. 4 is a flow chart that schematically illustrates a method for calculating an operation prediction value for each dimension according to operation feature data of each dimension in a preset historical time period, according to an embodiment of the present disclosure;
FIG. 5 schematically illustrates a method flow diagram of a construction process of a pre-constructed Prophet model according to an embodiment of the present disclosure;
FIG. 6 schematically illustrates a flow chart of a method for determining a target server's current health assessment score based on the running revision value of each dimension, according to an embodiment of the present disclosure;
FIG. 7 schematically illustrates a block diagram of a system for assessing server health status according to an embodiment of the present disclosure;
fig. 8 schematically shows a block diagram of an electronic device adapted to implement the above described method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
Some block diagrams and/or flow diagrams are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations thereof, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the instructions, which execute via the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks. The techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). In addition, the techniques of this disclosure may take the form of a computer program product on a computer-readable storage medium having instructions stored thereon for use by or in connection with an instruction execution system.
For the sake of facilitating an understanding of the present application, the following partial terms related to the present application are explained:
evaluation of health degree: the health evaluation means that the state of the equipment or application is represented by a numerical value, and generally, less than 60 points in percentage represent unhealthy, and the lower the score, the unhealthy.
Prophet algorithm: prophet is a time series prediction algorithm of Facebook open source, is based on an algorithm of a decomposable (trend, season, holiday) model, supports influences of self-defined seasons and holidays, and has more flexible parameter configuration compared with a Holt-Winters algorithm and an ARIMA algorithm.
In the cloud computing era, due to the characteristics of high efficiency, simplicity and convenience in cloud deployment and the like, a large number of services and services are deployed on a cloud platform, and the deployment of the cloud platform needs a large number of servers for supporting, so that monitoring of the servers is an important part of monitoring of the whole cloud platform. Currently, the monitoring of the server mainly comprises CPU utilization rate, memory utilization rate, disk I/O, disk utilization rate, file opening number, CONNTRACK and the like, operation and maintenance personnel set a threshold value for each monitoring dimension according to expert experience, and judge that the server is unhealthy if the threshold value is greater than or less than the threshold value, the mode is too dependent on the expert experience, and the personalized difference of the server is ignored by setting a fixed threshold value, so that the judgment is easy to be inaccurate. Based on this, the present disclosure proposes a method, a system, an electronic device, and a storage medium for evaluating a health status of a server.
Fig. 1 schematically illustrates an exemplary system architecture 100 that may be applied to a method of assessing server health status according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have installed thereon various communication client applications, such as a web browser application, a search-type application, an instant messaging tool, social platform software, etc. (by way of example only).
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the terminal devices 101, 102, 103. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.
It should be noted that the evaluation method for the health status of the server provided by the embodiment of the present disclosure may be generally executed by the terminal devices 101, 102, 103 and the server 105. Accordingly, the system for evaluating the health status of the server provided by the embodiment of the present disclosure may be generally disposed in the terminal devices 101, 102, 103 and the server 105. The method for evaluating the health status of the server provided by the embodiment of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the system for evaluating the health status of the server provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Fig. 2 schematically shows a flow chart of a method of assessing a health status of a server according to an embodiment of the present disclosure.
As shown in FIG. 2, the method 200 for assessing the health status of a server may include operations S210-S250.
In operation S210, operation feature data of a target server in multiple dimensions within a preset historical time period is acquired.
The preset historical time period may be configured in advance according to user needs, for example, the preset historical time period may be: if the current value of one minute is predicted, the operation characteristic data is a statistical value of operation characteristic data of each dimension per minute in a preset historical time period, and the preset historical time period is historical data used for one month. The operation characteristic data of multiple dimensions includes, for example, CPU usage, memory usage, and the like.
For example, in an embodiment of the present application, the server general-purpose portrait feature data may be obtained from the log database, for example, through identification information of the target server, where the identification information may be, for example, according to an Identity Document (ID) of the target server; the running characteristic data of the target server in multiple dimensions within a preset historical time period can be inquired and obtained from the log database according to the ID of the target server, so that the running characteristic data of the target server is quantized; wherein, the log database can store all historical operating characteristic data of the target server according to the day, for example; it should be understood that the above embodiments are only exemplary, and the manner of acquiring the running characteristic data of a specific plurality of dimensions and the data content stored in the log database may be flexibly adjusted according to the user's needs, and are not limited to the above embodiments.
In operation S220, an operation prediction value of each dimension is calculated according to the operation feature data of each dimension in the preset historical time period.
The operation characteristics of all dimensions on the server are predicted based on a machine learning algorithm, and the time series prediction algorithm is mainly adopted to predict the operation characteristics of all dimensions, so that the change of different time periods can be self-adapted, and the evaluation precision of the health degree of the server is improved. For example, in an embodiment of the present application, the manner of calculating the operation predicted value corresponding to different dimensions is not necessarily the same, and for example, the operation predicted value of each dimension may be calculated according to the feature data of each dimension and the manner of calculating the operation predicted value corresponding to the feature data of each dimension.
In operation S230, an operation actual value of each dimension of the target server at the current time is obtained.
In operation S240, a running revision value for each dimension is calculated based on the running predicted value and the running actual value, and the running revision value for each dimension is based on the running predicted value and the running actual value.
And correcting the health degree of the dimension according to the actual operation value and the predicted operation value to obtain an operation revised value of the dimension, wherein the operation revised value is used for representing the health degree of the target server in the dimension. According to the method and the system, the health degree is corrected through the operation actual value and the operation predicted value, the difference of monitoring dimensions on each server is considered, and the evaluation precision of the health degree of the server is improved.
In operation S250, a current health assessment score of the target server is determined according to the running revision value of each dimension, and the health assessment score is used to represent the current health status of the target server.
After the health evaluation score of the target server in the preset historical time period is obtained, some necessary decisions such as capacity expansion decisions can be made according to the health degree, and better service is provided for the user.
For example, in some possible embodiments, in order to improve the reliability of the health assessment score calculation result, the operation revision value of each dimension may have a corresponding weight, for example, the health assessment score of the target server in a preset historical time period may be calculated according to the operation revision values of multiple dimensions and the corresponding weights, so that the final health assessment score may be more accurate by setting the weights corresponding to some important dimensions to be larger and setting the weights corresponding to other dimensions to be smaller, and the health state of the target server in the analysis period may be more truly reflected.
The method for evaluating the health state of the server predicts each monitoring dimension on the server based on a machine learning algorithm, compares an actual value with a predicted value, and sets the health degree of the server according to a comparison result; due to the fact that the differences of the monitoring dimensions on the servers are considered, the dimensions can be predicted by the aid of a machine learning algorithm to be adaptive to changes of different time periods, and therefore the obtained health degree result is more accurate.
FIG. 3 schematically illustrates a flowchart of a method for calculating a running revision value for each dimension according to a running predicted value and a running actual value, according to an embodiment of the present disclosure.
As shown in fig. 3, the method for calculating the operation revision value of each dimension according to the operation predicted value and the operation actual value includes:
in operation S241, when the operation actual value is greater than the operation predicted value, an impairment score is calculated according to the baseline value and the impairment coefficient, and an impairment revised value is obtained according to a preset initial score and the impairment score.
In operation S242, when the operation actual value is smaller than the operation predicted value, a gain score is calculated according to the baseline value and the gain coefficient, and a gain revised value is obtained according to a preset initial score and the gain score.
In operation S243, when the operation actual value is equal to the operation predicted value, the preset initial score is the operation revised value.
The actual operation value and the predicted operation value are compared, and when the magnitude relation of the actual operation value and the predicted operation value is different, the calculation modes are different. Specifically, when the actual operation value is greater than the predicted operation value, which indicates that the current operation load exceeds the historical average load level, the health degree in the dimension is lower than the historical average value, so that the impairment score is calculated through a first gradient, and the impairment revised value is obtained through the initial score and the impairment score; when the actual operating value is greater than the predicted operating value and greater than the preset threshold, it is indicated that the current operating load may cause the risk of operating failure, and special prompt is needed, so that a second gradient can be set to calculate the impairment score, and the obtained impairment revised value is obviously lower than the impairment revised value obtained through the first gradient, so as to cause warning. Similarly, when the actual operation value is smaller than the predicted operation value, it is indicated that the current operation load is lower than the historical average load level, and the health degree in the dimension is higher than the historical average value, so that the gain fraction is calculated through a certain gradient, and the gain revised value is obtained through the initial fraction and the gain fraction. And when the actual operation value is equal to the predicted operation value, the current operation load is consistent with the historical average load level, and the preset initial score is the operation revision value.
On the basis of the above embodiment, when the operation actual value is greater than the operation predicted value, obtaining the impairment revision value includes: when the operation actual value is larger than the operation predicted value and is smaller than the baseline value, calculating a loss reduction fraction M1 according to the following formula:
wherein X is an operation predicted value, Y is an operation actual value, Z is a baseline value, and a is a first loss reduction coefficient; the first impairment revision value N1 ═ L-M1, where L is the initial score;
when the operation actual value is larger than the operation predicted value and is larger than the baseline value, calculating a loss reduction fraction M2 according to the following formula:
M2＝(Y-Z)×b
wherein b is a second impairment coefficient; the second impairment revision value N2 is P-M2, where P is the baseline score for the baseline value.
For example, the CPU utilization is predicted, for example, a time series prediction algorithm is used to predict that the operation predicted value of the current CPU utilization in one minute is 70%, and the operation actual value of the current actual CPU utilization in one minute is 80%, and the operation actual value is greater than the operation predicted value (Y > X), if the actual value of the actual utilization is 70%, the preset initial score is 80 minutes, taking the CPU utilization as an example, if the set baseline value is 90%, the preset initial score is 40 minutes when the utilization reaches 90%, and if the operation actual value exceeds 90%, the score of health is decreased by b minutes every 1 percent higher, and the lowest score is 0 minutes; if the actual value of the operation is between the predicted value of the operation and 90 percent, the score is reduced (90-X)/a every 1 percent higher, and X is the predicted value. As a further example, if the actual operating value is 85%, the actual operating value is greater than the predicted operating value and less than the baseline value, and according to the historical experience a being 60, the loss reduction score M1:
the first impairment revision value N1-80-5-75; and 75 is the health of the CPU utilization dimension.
If the actual operating value is 95%, the actual operating value is greater than the baseline value, and according to the historical experience b being 4, the loss reduction score M2:
M2＝(95-90)×4＝20
the second impairment revision value N2-40-20; 20 is the health degree of the CPU utilization dimension, the health state is poor, and an operation failure may occur. This is disclosed through the hierarchical settlement impairment gradient, can make the impairment revision value reflect the health status of this dimension more directly perceivedly, the healthy risk of suggestion makes things convenient for the operation and maintenance personnel in time to adjust the server operation strategy.
On the basis of the above embodiment, when the operation actual value is smaller than the operation predicted value, obtaining the gain revised value includes:
the gain fraction M3 is calculated according to the following equation:
wherein X is an operation predicted value, Y is an operation actual value, Z is a baseline value, and c is a gain coefficient; the gain revision value N3 ═ L + M3, where L is the initial score.
According to the above embodiment, if the operation actual value is smaller than the operation predicted value (Y < X), the score increases by (90-X)/c every 1 percent decrease, and the full score is 100 points. If the actual operation value is 55% and the actual operation value is smaller than the predicted operation value, according to the historical experience c being 60, the gain fraction M3:
gain revision value N3-80 + 5-85; and 75 is the health degree of the CPU utilization rate dimension, which shows that the health state is good. The calculation method of the revision value is simple and convenient, the health state of each dimension of the server can be provided for operation and maintenance personnel more visually, and the operation and maintenance personnel can adjust the operation strategy of the server in time conveniently.
Fig. 4 schematically shows a flowchart of a method for calculating an operation predicted value of each dimension according to operation feature data of each dimension in a preset historical time period according to an embodiment of the present disclosure.
As shown in fig. 4, the method for calculating an operation predicted value of each dimension according to the operation feature data of each dimension in the preset historical time period includes:
in operation S221, time series data is generated according to the operation feature data of each dimension within the preset history time period and the corresponding time data thereof.
In operation S222, the time-series data is preprocessed.
In operation S223, the preprocessed time series data time series are input into the Prophet model that is constructed in advance, and the operation prediction value of the target server in each dimension at the current time is obtained through prediction.
The preset historical time period may be a period of time immediately adjacent to the current time, such as using historical operating characteristic data for the last month. Processing the operation characteristic data of multiple dimensions into operation characteristic data of a numerical class through a unified preprocessing rule, and preprocessing the operation characteristic data into data which can use a Prophet algorithm.
The Prophet model is a current more advanced time series prediction algorithm, which is essentially a decomposition of a time series, and the input of the model is a time stamp and a corresponding value of the time series, and the Prophet outputs a future time series prediction given the length of the time series to be predicted.
Fig. 5 schematically shows a method flowchart of a construction process of a Prophet model constructed in advance according to an embodiment of the present disclosure.
As shown in fig. 5, the method of the process of constructing the pre-constructed Prophet model includes:
in operation S2231, a trend change is calculated from the time-series data, resulting in a trend term.
In operation S2232, a periodic variation is calculated from the time-series data, resulting in a periodic term.
In operation S2233, a holiday effect variation is calculated from the time-series data, resulting in a holiday term.
In operation S2234, an error term is obtained from the abnormal variation occurring during the model training according to the time-series data.
In operation S2235, a Prophet model is constructed from the trend term, the period term, the holiday term, and the error term.
The method for constructing the Prophet model comprises the following steps: setting the position of a variable point on the time sequence to divide the time sequence into a plurality of sections; detecting the variation trend of each time sequence; constructing a trend model g (t) by using the variation trend, and fitting aperiodic variation; constructing a period model s (t) by using a preset period, such as a period of each week, each year, each season and the like; acquiring the number of holidays included in the time sequence, and constructing a holiday model h (t) by using the number of the holidays to express changes caused by special reasons such as holidays, holidays and the like; finally, the epsilon t is a noise term and is used for representing random unpredictable fluctuation, and the epsilon t is an error term and is subject to normal distribution; the Prophet model is constructed by utilizing the trend model, the period model and the holiday model as follows:
y(t)＝g(t)+s(s)+h(t)+∈t
the Prophet model supports influences of self-defined seasons and holidays, and has more flexible parameter configuration compared with a Holt-Winters algorithm and an ARIMA algorithm.
FIG. 6 schematically illustrates a flow chart of a method for determining a current health assessment score for a target server based on a running revision value for each dimension according to an embodiment of the present disclosure.
As shown in fig. 6, the method for determining the current health assessment score of the target server according to the operation revision value of each dimension includes:
in operation S251, a weight coefficient for each dimension is determined based on a preset weight distribution policy.
In operation S252, a current health assessment score of the target server is determined based on the weight coefficient and the running revision value of each dimension.
The operation revision values of the dimensions may have corresponding weight values, for example, that is, the health assessment score of the target server in a preset historical time period may be calculated according to the operation revision values of the dimensions and the corresponding weight values, so that the final health assessment score may be more accurate by setting the weight values corresponding to some important dimensions to be larger and setting the weight values corresponding to other dimensions to be smaller, and the health state of the target server in the analysis period may be more truly reflected. Specifically, the preset weight distribution policy may be to assign a higher weight coefficient to one or more dimensions (e.g., CPU usage) with obvious changes, for example, the weight coefficient is 2, assign a lower weight coefficient to other hardware data, for example, the weight coefficient is 1, and the health assessment score is the sum of the running revision value and the product of the weight coefficients of each dimension, and is divided by the sum of the weight coefficients of each dimension.
Based on the above embodiments, determining the target server's current health assessment score according to the running revision value of each dimension includes: and adjusting the operation strategy of the target server according to the health evaluation score and the operation revision value of each dimension.
After the health evaluation score of the target server in the preset historical time period is obtained, some necessary decisions such as capacity expansion decisions can be made according to the health degree, and better service is provided for the user.
On the basis of the embodiment, the multiple dimensions in the running characteristic data of the multiple dimensions of the target server in the preset historical time period are acquired to be at least two dimensions of CPU utilization rate, memory utilization rate, disk I/O (input/output), disk utilization rate, file opening number and connection number.
According to the method, the health assessment score is revised according to the running actual value and the running predicted value, and the change of different time periods can be self-adapted by predicting through the Prophet algorithm, so that the accuracy of server health assessment is improved, and operation and maintenance personnel can conveniently and timely adjust the server running strategy.
The method and tool for product recommendation are further described in an embodiment below. The evaluation method of the above server health status is specifically described in the following embodiments. However, the following examples are merely illustrative of the present disclosure, and the scope of the present disclosure is not limited thereto.
Step 1): a plurality of dimensions of server health are defined. Setting evaluation dimensions according to operation and maintenance experience, selecting 6 dimensions of CPU utilization rate, memory utilization rate, disk I/O (input/output) rate, disk utilization rate, file opening number and connection number as the dimensions of server health degree evaluation, and acquiring operation characteristic data of the 6 dimensions; corresponding to the aforementioned step S210.
Step 2): and predicting each dimension by using a time series prediction algorithm prophet (predicting the current value of one minute, wherein historical data is a statistic value of dimension values per minute, and using historical data of one month) to obtain an operation predicted value of each dimension. The Prophet algorithm can better consider time period factors and correct predicted values of partial time points, and has higher prediction accuracy; corresponding to the aforementioned step S220.
Step 3): and (4) comparing the operation predicted value in the step (2) with the current operation actual value to obtain the health degree of each dimension. For example, the CPU utilization is predicted, prophet is used to predict the CPU utilization in the current minute is 70%, the actual CPU utilization in the current minute is 80%, the actual utilization exceeds the predicted utilization, if the actual utilization is 70%, the health is 80 minutes (considering that each dimension needs to set an upper limit and a lower limit, namely the actual value is a predicted value, but when the actual value exceeds a set baseline, the health is uniformly set as unhealthy), taking the CPU utilization as an example, if the set baseline is 90%, the health is 40 minutes if the utilization reaches 90%, and if the utilization exceeds 90%, the health is 4 minutes for every 1 percentage score higher, and the lowest is 0 minute; if the utilization rate is between the predicted value and 90%, the score of the health degree is reduced by (90-X)/60 every 1 higher percentage, wherein X is the predicted value and the predicted value is 80 points; if the score is smaller than the predicted value, the score is increased by (90-X)/60 every 1 percent, and the full score is 100; finally calculating to obtain an operation revision value; corresponding to the steps S230 and S240.
Step 4): on the basis of evaluating the health degree of each dimension in step 3, the influence weight of each dimension on the health degree of the server can be set. In this embodiment, the CPU usage weight is set to 2, the memory usage weight is set to 2, the disk I/O weight is set to 1, the disk usage weight is set to 1, the file open number weight is set to 1, and the connection number weight is set to 2 according to the server usage (the weights may also be trained according to historical data).
Step 5): and utilizing the weighted value of the health degree of each dimension as the health degree score of the server. For example, the CPU utilization dimension is 90 minutes, the memory utilization dimension is 85 minutes, the disk I/O dimension is 80 minutes, the disk utilization dimension is 87 minutes, the file opening dimension is 86 minutes, and the connection dimension is 81 minutes; the health of the server is (90 × 2+85 × 2+80 × 1+87 × 1+86 × 1+81 × 2)/9; corresponding to the aforementioned step S250.
FIG. 7 schematically illustrates a block diagram of a system for product recommendation, according to an embodiment of the present disclosure.
As shown in fig. 7, the system 700 for recommending products includes: a first obtaining module 710, a first calculating module 720, a second obtaining module 730, a second calculating module 740, and a determining module 750.
A first obtaining module 710, configured to obtain operating characteristic data of a target server in multiple dimensions in a preset historical time period; according to an embodiment of the present disclosure, the first obtaining module 710 may be configured to perform the step S210 described above with reference to fig. 2, for example, and is not described herein again.
The first calculating module 720 is configured to calculate an operation predicted value of each dimension according to the operation feature data of each dimension in the preset historical time period. According to an embodiment of the disclosure, the first calculating module 720 may be configured to perform the step S220 described above with reference to fig. 2, for example, and is not described herein again.
The second obtaining module 730, configured to obtain an actual running value of each dimension of the target server at the current time; according to an embodiment of the present disclosure, the second obtaining module 730 may be configured to perform the step S230 described above with reference to fig. 2, for example, and is not described herein again.
A second calculating module 740, configured to calculate an operation revision value for each dimension according to the operation predicted value and the operation actual value, where the operation revision value for each dimension is an operation revision value based on the operation predicted value and the operation actual value; according to an embodiment of the present disclosure, the second calculating module 740 may be configured to perform the step S240 described above with reference to fig. 2, for example, and is not described herein again.
And a determining module 750, configured to determine a current health assessment score of the target server according to the running revision value of each dimension, where the health assessment score is used to represent a current health status of the target server. According to an embodiment of the present disclosure, the determining module 750 may be configured to perform the step S250 described above with reference to fig. 2, for example, and is not described herein again.
It should be noted that any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, any number of the first obtaining module 710, the first calculating module 720, the second obtaining module 730, the second calculating module 740, and the determining module 750 may be combined and implemented in one module, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first obtaining module 710, the first calculating module 720, the second obtaining module 730, the second calculating module 740, and the determining module 750 may be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware, and firmware, or any suitable combination of any several of them. Alternatively, at least one of the first obtaining module 710, the first calculating module 720, the second obtaining module 730, the second calculating module 740, the determining module 750 may be at least partially implemented as a computer program module, which when executed, may perform the corresponding functions.
Fig. 8 schematically shows a block diagram of an electronic device adapted to implement the above described method according to an embodiment of the present disclosure. The electronic device shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 8, the electronic device 800 described in this embodiment includes: a processor 801 which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. The processor 801 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 801 may also include onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flows according to embodiments of the present disclosure.
In the RAM 803, various programs and data necessary for the operation of the system 800 are stored. The processor 801, the ROM802, and the RAM 803 are connected to each other by a bus 804. The processor 801 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM802 and/or RAM 803. Note that the programs may also be stored in one or more memories other than the ROM802 and RAM 803. The processor 801 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809 and/or installed from the removable medium 811. The computer program, when executed by the processor 801, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
The embodiments of the present disclosure also provide a computer-readable storage medium, which may be included in the device/apparatus/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The above-mentioned computer-readable storage medium carries one or more programs which, when executed, implement the server health status assessment method according to an embodiment of the present disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM802 and/or RAM 803 described above and/or one or more memories other than the ROM802 and RAM 803.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flow chart. When the computer program product runs in a computer system, the program code is used for causing the computer system to realize the evaluation method of the health status of the server provided by the embodiment of the disclosure.
The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 801. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted in the form of a signal on a network medium, distributed, downloaded and installed via communication section 809, and/or installed from removable media 811. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809 and/or installed from the removable medium 811. The computer program, when executed by the processor 801, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
It should be noted that each functional module in each embodiment of the present disclosure may be integrated into one processing module, or each module may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present disclosure may be embodied in the form of software products, in part or in whole, which substantially contributes to the prior art.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
While the disclosure has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims and their equivalents. Accordingly, the scope of the present disclosure should not be limited to the above-described embodiments, but should be defined not only by the appended claims, but also by equivalents thereof.
Claims (13)
1. A method for evaluating the health status of a server, comprising:
acquiring running characteristic data of a target server in multiple dimensions in a preset historical time period;
calculating to obtain an operation predicted value of each dimension according to the operation characteristic data of each dimension in the preset historical time period;
acquiring the running actual value of each dimension of the target server at the current moment;
calculating to obtain an operation revision value of each dimension according to the operation predicted value and the operation actual value, wherein the operation revision value of each dimension is an operation revision value based on the operation predicted value and the operation actual value of each dimension;
and determining the current health assessment score of the target server according to the operation revision value of each dimension, wherein the health assessment score is used for representing the current health state of the target server.
2. The method for assessing the health status of a server according to claim 1, wherein the calculating the operation revision value of each dimension according to the operation predicted value and the operation actual value comprises:
when the operation actual value is larger than the operation predicted value, calculating according to a baseline value and an impairment coefficient to obtain an impairment score, and obtaining an impairment revised value according to a preset initial score and the impairment score;
when the actual operation value is smaller than the predicted operation value, calculating according to a baseline value and a gain coefficient to obtain a gain score, and obtaining a gain revision value according to a preset initial score and the gain score;
when the operation actual value is equal to the operation predicted value, the preset initial score is the operation revision value.
3. The method for assessing the health status of a server according to claim 2, wherein the deriving a impairment revision value when the operation actual value is greater than the operation predicted value comprises:
when the operation actual value is larger than the operation predicted value and smaller than the baseline value, calculating the impairment score M1 according to the following formula:
wherein X is an operation predicted value, Y is an operation actual value, Z is a baseline value, and a is a first loss reduction coefficient; the first impairment revision value N1 ═ L-M1, where L is the initial score;
when the operation actual value is larger than the operation predicted value and is larger than the baseline value, calculating the impairment score M2 according to the following formula:
M2＝(Y-Z)×b
wherein b is a second impairment coefficient; the second impairment revision value N2 ═ P-M2, where P is the baseline score to which the baseline value corresponds.
4. The method for assessing the health status of a server according to claim 2, wherein the deriving a gain revision value when the operation actual value is smaller than the operation predicted value comprises:
calculating the gain fraction M3 according to:
wherein X is an operation predicted value, Y is an operation actual value, Z is a baseline value, and c is a gain coefficient; the gain revision value N3 ═ L + M3, where L is the initial score.
5. The method according to claim 1, wherein the calculating the operation prediction value of each dimension according to the operation feature data of each dimension in the preset historical time period comprises:
generating time sequence data according to the operation characteristic data of each dimension in the preset historical time period and the corresponding time data;
preprocessing the time-series data;
and inputting the preprocessed time series data time series into a pre-constructed Prophet model, and predicting to obtain the operation predicted value of the target server in each dimension at the current moment.
6. The method for assessing the health status of a server according to claim 5, wherein the process of constructing the pre-constructed Prophet model comprises:
calculating the growth trend change according to the time sequence data to obtain a trend item;
calculating periodic variation according to the time sequence data to obtain periodic items;
calculating holiday effect change according to the time sequence data to obtain holiday items;
calculating abnormal change occurring during model training according to the time sequence data to obtain an error item;
and constructing the Prophet model according to the trend term, the period term, the holiday term and the error term.
7. The method of assessing the health of a server of claim 1, wherein determining the current health assessment score for the target server based on the running revision value for each dimension comprises:
determining a weight coefficient of each dimension based on a preset weight distribution strategy;
determining the target server's current health assessment score based on the weight coefficient for each dimension and the running revision value.
8. The method of assessing the health of a server of claim 7, wherein determining the current health assessment score for the target server based on the running revision value for each dimension comprises:
and adjusting the operation strategy of the target server according to the health assessment score and the operation revision value of each dimension.
9. The method according to claim 1, wherein the multiple dimensions of the running characteristic data of the target server in the multiple dimensions in the preset historical time period are at least two dimensions of CPU utilization, memory utilization, disk I/O, disk utilization, file open number, and connection number.
10. A system for assessing the health of a server, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring the multi-dimensional operation characteristic data of a target server in a preset historical time period;
the first calculation module is used for calculating to obtain an operation predicted value of each dimension according to the operation characteristic data of each dimension in the preset historical time period;
the second acquisition module is used for acquiring the running actual value of each dimension of the target server at the current moment;
the second calculation module is used for calculating to obtain an operation revision value of each dimension according to the operation predicted value and the operation actual value, and the operation revision value of each dimension is an operation revision value of each dimension based on the operation predicted value and the operation actual value;
and the determining module is used for determining the current health assessment score of the target server according to the operation revision value of each dimension, and the health assessment score is used for representing the current health state of the target server.
11. An electronic device, comprising:
a processor;
a memory storing a computer executable program which, when executed by the processor, causes the processor to perform the method of assessing the health status of a server according to any one of claims 1 to 9.
12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of assessing the health of a server according to any one of claims 1 to 9.
13. A computer program product comprising a computer program which, when executed by a processor, implements a method of assessing the health of a server according to any one of claims 1 to 9.
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