CN111598390B - Method, device, equipment and readable storage medium for evaluating high availability of server - Google Patents
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Abstract
The application relates to a server high availability evaluation method, a device, a computer readable storage medium and a computer apparatus, wherein the method comprises the following steps: acquiring a high-availability service directory; selecting a target feature vector from the high-availability service directory as an evaluation index; distributing corresponding resources in the server to the target user according to the evaluation index; acquiring a historical use value of the target user using the resources in the server in a historical period; predicting a predicted value of the target user using the resources in the server in a preset period according to the historical use value; acquiring an actual use value of the resources in the server used by the target user within the preset period; calculating user satisfaction according to the actual use value and the predicted value; and determining the availability of the server according to the user satisfaction. The scheme provided by the application can realize the evaluation of the high availability of the server.
Description
The present application claims priority from chinese patent office, application number 2019109841043, entitled "method for high availability assessment applying SLA", filed on day 10 and 16 of 2019, the entire contents of which are incorporated herein by reference.
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a readable storage medium for evaluating high availability of a server.
Background
With the development of computer technology, the computerized requirements of daily business of commercial and social institutions have reached an unprecedented level. In order to solve the serious value loss caused by the server shutdown, a high-availability server cluster appears, and the high-availability server cluster mainly provides continuous service as far as possible to users. The traditional high availability evaluation is mainly based on the functions and performances of the high availability service cluster, and the evaluation result often cannot effectively meet the requirements of users on high availability service quality.
Disclosure of Invention
Based on this, it is necessary to provide a server high availability evaluation method, apparatus, device and readable storage medium for solving the technical problem that the conventional high availability evaluation cannot meet the requirement of the user on the high availability service quality.
A server high availability assessment method, comprising:
acquiring a high-availability service directory;
selecting a target feature vector from the high-availability service directory as an evaluation index;
distributing corresponding resources in the server to the target user according to the evaluation index;
Acquiring a historical use value of the target user using the resources in the server in a historical period;
predicting a predicted value of the target user using the resources in the server in a preset period according to the historical use value;
acquiring an actual use value of the resources in the server used by the target user within the preset period;
calculating user satisfaction according to the actual use value and the predicted value;
and determining the availability of the server according to the user satisfaction.
In one embodiment, the selecting the target feature vector from the high availability service directory as the evaluation index includes:
and selecting a target feature vector from the high-availability service catalogue by a machine learning algorithm based on a decision tree algorithm as an evaluation index.
In one embodiment, the allocating the corresponding resources in the server to the target user according to the evaluation index includes:
analyzing the user data by adopting the consistency analysis of the alpha algorithm to obtain the matching degree of the user requirement and the actual process and the fitness of the user requirement;
and distributing corresponding resources in a server for the target user according to the evaluation index, the matching degree and the adaptability.
In one embodiment, the predicting, according to the historical usage value, the predicted value of the target user using the resource in the server in a preset period of time includes:
acquiring a preset period;
inputting the preset time period into a prediction model; the predictive model is a time series model based on the historical use value;
and predicting a predicted value of the target user using the resources in the server in the preset period through the prediction model.
In one embodiment, the method further comprises:
judging whether the actual use value of the resources in the server used by the target user in a preset period meets the user requirement of the target user or not;
if not, the corresponding resources in the server are allocated again to the target user.
In one embodiment, said calculating user satisfaction from said actual usage value and said predicted value comprises:
constructing an evaluation model according to the actual use value and the predicted value;
carrying out usability evaluation through the evaluation model to obtain usability evaluation results;
and calculating the user satisfaction according to the usability evaluation result.
In one embodiment, the calculating the user satisfaction according to the usability evaluation result includes:
Inputting the predicted value and the usability evaluation result into a user satisfaction formula to obtain user satisfaction; the user satisfaction formula is as follows:
wherein Q is user satisfaction, Y P And Y is the availability evaluation result for the predicted value.
A server high availability assessment device, the device comprising:
the high-availability service catalog acquisition module is used for acquiring the high-availability service catalog;
the evaluation index determining module is used for selecting a target feature vector from the high-availability service catalogue as an evaluation index;
the server resource allocation module is used for allocating corresponding resources in the server to the target user according to the evaluation index;
a resource use value acquisition module, configured to acquire a historical use value of the target user using the resource in the server in a historical period;
the prediction module is used for predicting a predicted value of the target user using the resources in the server in a preset period according to the historical use value;
the resource use value acquisition module is further used for acquiring an actual use value of the resources in the server used by the target user in the preset period;
the user satisfaction calculating module is used for calculating user satisfaction according to the actual use value and the predicted value;
And the availability determination module is used for determining the availability of the server according to the user satisfaction.
In one embodiment, the evaluation index determination module is further configured to:
and selecting a target feature vector from the high-availability service catalogue by a machine learning algorithm based on a decision tree algorithm as an evaluation index.
In one embodiment, the server resource allocation module is further configured to:
analyzing the user data by adopting the consistency analysis of the alpha algorithm to obtain the matching degree of the user requirement and the actual process and the fitness of the user requirement;
and distributing corresponding resources in a server for the target user according to the evaluation index, the matching degree and the adaptability.
In one embodiment, the prediction module is further configured to:
acquiring a preset period;
inputting the preset time period into a prediction model; the predictive model is a time series model based on the historical use value;
and predicting a predicted value of the target user using the resources in the server in the preset period through the prediction model.
In one embodiment, the apparatus further comprises:
the user demand judging module is used for judging whether the actual use value of the resources in the server used by the target user in a preset period meets the user demand of the target user or not;
The server resource allocation module is further configured to, if the actual usage value of the resources in the server used by the target user in the preset period of time does not meet the user requirement of the target user, re-allocate the corresponding resources in the server to the target user.
In one embodiment, the user satisfaction calculation module is further configured to:
constructing an evaluation model according to the actual use value and the predicted value;
carrying out usability evaluation through the evaluation model to obtain usability evaluation results;
and calculating the user satisfaction according to the usability evaluation result.
In one embodiment, the user satisfaction calculation module is further configured to:
inputting the predicted value and the usability evaluation result into a user satisfaction formula to obtain user satisfaction; the user satisfaction formula is as follows:
wherein Q is user satisfaction, Y P And Y is the availability evaluation result for the predicted value.
A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the method described above.
A computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of the method described above.
According to the method, the device, the equipment and the readable storage medium for evaluating the high availability of the server, after the high availability service catalog is acquired, the target feature vector is selected from the high availability service catalog to serve as an evaluation index, corresponding resources in the server are allocated to target users according to the evaluation index, historical use values of the resources in the server used by the target users in a historical period are acquired, predicted values of the resources in the server used by the target users in a preset period are predicted according to the historical use values, actual use values of the resources in the server used by the target users in the preset period are acquired, accordingly user satisfaction is calculated according to the actual use values and the predicted values, and the availability of the server is determined according to the user satisfaction. Therefore, the high availability of the server can be evaluated on the premise of meeting the requirement of the user on the high available service quality.
Drawings
FIG. 1 is a diagram of an application environment for a server high availability assessment method in one embodiment;
FIG. 2 is a model diagram of a server high availability assessment method in one embodiment;
FIG. 3 is a flow chart of a server high availability assessment method in one embodiment;
FIG. 4 is a flowchart of a server high availability assessment method according to another embodiment;
FIG. 5 is a schematic diagram of an analog system using a VMware workstation in one embodiment;
FIG. 6 is a block diagram of a server high availability assessment device in one embodiment;
FIG. 7 is a block diagram of a server high availability evaluation device in another embodiment;
FIG. 8 is a block diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The server high availability evaluation method provided by the application can be applied to an application environment shown in figure 1. Wherein the terminal 102 is in network communication with a high availability server 106 via a proxy server 104. Taking the above-mentioned server high availability evaluation method as an example to be executed on the proxy server 104, and describing with reference to the server high availability evaluation model diagram shown in fig. 2, the proxy server 104 obtains a high availability service directory; selecting a target feature vector from the high-availability service catalogue as an evaluation index; distributing corresponding resources in the server for the target user according to the evaluation index; acquiring a historical use value of a target user for using resources in a server in a historical period; predicting a predicted value of resources in a target user use server in a preset period according to the historical use value; acquiring an actual use value of resources in a target user use server within a preset period; and calculating the user satisfaction according to the actual use value and the predicted value.
The terminal 102 may be, but not limited to, various personal computers, notebook computers, smartphones, tablet computers, and portable wearable devices, the proxy server 104 may be a service level proxy, a service level agreement is stored, and the high availability server 106 is a high availability server proxy server 104 for providing service for the terminal, and the high availability server 106 may be implemented by a stand-alone server or a server cluster composed of a plurality of servers.
As shown in fig. 3, in one embodiment, a server high availability assessment method is provided. The present embodiment is mainly exemplified by the application of the method to the proxy server 104 in fig. 1. Referring to fig. 3, the server high availability evaluation method specifically includes the steps of:
s302, obtaining a high-availability service catalog.
The high-availability service catalog comprises information such as service types and service contents provided by the high-availability server for different users.
In one embodiment, the proxy server receives a service level agreement (Service Level Agreement, SLA) sent by the high availability server before acquiring the high availability service directory, then creates the high availability service directory according to the service level agreement, and stores the high availability service directory in a database. When evaluating the high availability of the high availability server, the high availability service directory is read from the database. The service level agreement is an agreement or contract which is determined by the negotiation of the service provider and the user and related to the service quality level, wherein the purpose of making the agreement or contract is to make the service provider and the user agree on the service quality, level, performance, priority, responsibility and the like, and the service level agreement comprises relevant parameters of the service level agreement, the parameters and the corresponding descriptions thereof are shown in the following table.
TABLE 1 service level agreement parameter table
| Parameters (parameters) | Description of the invention |
| CPU capacity | CPU running speed of high-availability system virtual machine |
| Memory capacity | Flash memory space size of high-availability system virtual machine |
| Lead-in time | Preparation and import time before formal use |
| Storage of | Storage space size required for user data |
| Availability of | Availability of SLA services subscribed by a user in a given time interval |
| Service response time | Time for service provider to respond to and process high availability service requests for users to sign up for SLAs |
S304, selecting the target feature vector from the high-availability service catalogue as an evaluation index.
The target feature vector is a feature vector capable of showing individual features of users corresponding to different terminals, and the feature vector may specifically be at least one of a Memory capacity (Memory), a response Time (Time) and a Storage (Storage), and the high-availability service directory contains all feature vectors that can be measured.
In one embodiment, after the proxy server obtains the high available service directory, a machine learning algorithm is adopted to select the target feature vector from the high available service directory, wherein the adopted machine learning algorithm may be a machine learning algorithm based on a decision tree, specifically may be an ID3 algorithm, and the decision tree is a tree established by relying on a decision. In machine learning, a decision tree is a predictive model representing a mapping between object attributes and object values, each node representing an object, each bifurcation path in the tree representing a possible attribute value, and each leaf node corresponding to the value of the object represented by the path traversed from the root node to the leaf node. The decision tree has only a single output, and if there are multiple outputs, independent decision trees can be built to handle the different outputs, respectively, and the ID3 algorithm is a greedy algorithm used to construct the decision tree.
The above embodiment is described as an example. Firstly, setting a given training set as TR, wherein the elements of TR are represented by feature vectors and classification results thereof, and an attribute table Attrlist of a classification object is [ A ] l ，A 2 ，...A n ]The set Class composed of all classification results is { C l ，C 2 ...C m Generally, n.gtoreq.1 and m.gtoreq.2. For each attribute A i The value range is ValueType (A i ) The value range is discrete. Thus, an element of TR can be expressed as<X，C>In which x= (a) 1 ，...a n )，a i The value of the ith attribute corresponding to the example X, C epsilon Class is the classification result of the example X; then randomly selecting a subset of training examples to form a training window, and repeatedly executing the following steps:
(1) constructing a decision tree for the set of instances within the window;
(2) searching a counter example of the decision tree;
(3) if the counterexample exists, adding the counterexample to a training window, and executing the step (1); if the counterexample does not exist, returning to the obtained decision tree. And selecting the attribute with the maximum information quantity as the expansion attribute. This heuristic is also known as the principle of minimum entropy, which maximizes the amount of information obtained is equivalent to minimizing uncertainty, even though entropy is minimized. Given a subset of positive and negative instances S, a training window is constructed. When the decision has k different outputs, then the entropy of S is:
Wherein P is i To take the probability of the ith value in k different outputs.
In order to detect the importance of each attribute, the importance thereof may be evaluated by the information Gain of each attribute. For attribute a, assume that its value range is (V l ，V 2 ，...V n ) The information gain of attribute a in training instance S may be defined as follows:
wherein S is i Representing the value of attribute A in training instance S as V i Is, |S i The l represents the potential of the collection. In practical cases, the attribute a with the largest Gain (S, a) includes three feature vectors, which are: memory capacity, response time, and storage.
S306, distributing corresponding resources in the server to the target user according to the evaluation index.
The corresponding resources in the server are resources of a high-availability server, wherein the resources of the server comprise computing resources, storage resources and network resources of the server.
In one embodiment, after selecting a target feature vector from a high-availability service directory as an evaluation index, the proxy server analyzes user data by adopting consistency analysis of an alpha algorithm to obtain matching degree of user requirements and actual processes and adaptation degree of the user requirements, and then allocates corresponding resources in the server for the target user according to the evaluation index, the matching degree and the adaptation degree.
The function of the alpha algorithm is described: matching the event log L to a Petri net, the Petri net becomes the behavior of the event log. The input of the alpha algorithm is an event log L based on a task T, the output is a Petfi network, and the service provider is limited by our SLA, so that the quality is ensured in the whole service process of the user.
Since the participant may access some states not included in the desired model when completing the task or the desired model may include events not occurring in the log file, a variable (log coverage C) is defined to evaluate the matching of the desired model (user demand) and the actual process.
Where |E| represents all event numbers, and |e ε E| represents the number of events actually occurring.
The fitness refers to the degree of association of the actual operation trajectory with the desired model. The consistency analysis of the actual instance process is carried out through the expected model, so that the fitness (based on Petri net) of each instance and the whole task relative to the expected model can be obtained. The fitness of the desired model for each example process is defined as follows:
wherein: m is the number of tokens (token) lost when the actual process is reproduced on the Petri net model; c is the number of tokens consumed; r is the number of tokens still existing after the task is completed; p is the number of tokens generated.
S308, acquiring a historical use value of the resources in the use server of the target user in a historical period.
S310, predicting a predicted value of the resources in the server used by the target user in a preset period according to the historical use value.
In one embodiment, the proxy server monitors the practical performance of the application program operated by the corresponding terminal for the target application and the use condition of the resource in real time after distributing the corresponding resource in the high availability server for the target user according to the evaluation index, and stores the corresponding use value of the resource in the database.
In one embodiment, the proxy server obtains from the database a historical usage value of the resource in the server used by the target user during the historical period, and then constructs a prediction model according to the obtained historical usage value, where the created prediction model is used to predict the predicted value of the resource in the server used by the target user during the preset period, and the prediction model may be a time sequence prediction model.
In one embodiment, the proxy server obtains a preset time period to be predicted, inputs the preset time period into a prediction model, and predicts a predicted value of a resource in the high-availability server used by a target user in the preset time period through the prediction model. For example, when the evaluation index is the memory capacity, the obtained historical use value of the memory capacity of the server used by the target user in the historical period is the predicted value of the memory capacity of the server used by the target user in the preset period; when the evaluation index is the response time, the historical response time of the target user in the historical period is obtained, and the predicted response time of the target user for responding to the target user request by using the resources in the server in the preset period is predicted.
S312, obtaining the actual use value of the resources in the server used by the target user in a preset period.
S314, calculating the user satisfaction according to the actual use value and the predicted value.
In one embodiment, the proxy server obtains the predicted actual use value of the resource in the server used by the target user in the preset period after predicting the predicted value of the resource in the server used by the target user in the preset period, and calculates the user satisfaction according to the actual use value and the predicted value.
In one embodiment, the proxy server constructs an evaluation model according to the actual use value and the predicted value after predicting the predicted value of the resource in the server used by the target user in the preset period; and carrying out usability evaluation through an evaluation model to obtain a usability evaluation result, and then calculating satisfaction according to the usability evaluation result, wherein the predicted value and the usability evaluation result are input into a user satisfaction formula to calculate the user satisfaction.
S316, determining the availability of the server according to the user satisfaction.
In one embodiment, after the user satisfaction is calculated, a service level agreement corresponding to the target user is obtained, an availability threshold is extracted from the service level agreement, when the user satisfaction is greater than the availability threshold, the availability of the corresponding high-availability server is determined to be normal, and when the user satisfaction is less than the availability threshold, the availability of the corresponding high-availability server is determined to be abnormal.
The above embodiment is described as an example. Respectively f 1 、f 2 And f 3 Representing the sampled memory capacity, response time and storage, and sorting the historical data into time series and using Sn to represent the time series, respectively using f 1 、f 2 And f 3 Predicting a time sequence S 'corresponding to a preset period' n Prediction result Y p Respectively by Y 1 、Y 2 And Y 3 And (3) representing. Set S it The actual use value of the ith availability evaluation index at time t is Y it And predicting a predicted value obtained by the ith availability evaluation index for the t moment by a prediction model, wherein the predicted error is as follows:
the sum of squares of the errors is:
wherein omega i And the weight corresponding to the i-th evaluation index. Determining f with least sum of squares of prediction errors as optimization target 1 、f 2 And f 3 Is defined by the optimum weighting coefficient omega 1 、ω 2 And omega 3 The assessment model may be expressed as:
f＝f 1 ω 1 +f 2 ω 2 +f 3 ω 3 ，ω 1 +ω 2 +ω 3 ＝1
after an evaluation model is constructed, inputting a target feature vector corresponding to an evaluation index into the evaluation model, calculating to obtain an availability evaluation result Y, and then inputting a predicted value Y p And the usability evaluation result Y is input into a user satisfaction formula, so that the user satisfaction is calculated, and the user satisfaction formula is as follows:
wherein Q is user satisfaction, Y P And Y is the availability evaluation result for the predicted value.
After calculating the user satisfaction degree Q, acquiring an availability threshold Q corresponding to the target user t Then according to the user satisfaction degree Q and the sexual threshold Q t Calculating availability a of a high availability server, wherein:
wherein a=1 may indicate that the availability of the high availability server is normal, a=0 indicates that the availability of the high availability server is abnormal,
the following table shows the availability of high availability servers calculated by the simulation system (fig. 3), which selects the point in time t 1 To t 7 Two evaluation indexes of T, M determined by a decision tree are recorded to calculate user satisfaction, T is response time, corresponding units are μs, M is memory capacity rate, Q is user satisfaction and A is availability of the system. Wherein at time point t 4 At the moment, the state of the simulation system is artificially adjusted so that the time point t 4 As can be seen from the following table, when the availability of the high availability server is artificially destroyed, T, M and S correspondingly change, and when the availability of the server is lower, the server is used for the clothesThe server high availability evaluation method can accurately report the change of the server availability.
Table 2 availability of high availability servers
| Time point | t 1 | t 2 | t 3 | t 4 | t 5 | t 6 | t 7 |
| T | 121 | 120 | 121 | 507 | 120 | 119 | 121 |
| M | 17 | 18 | 17 | 42 | 19 | 18 | 19 |
| Q | 4 | 4 | 4 | 2 | 4 | 4 | 4 |
| A | 1 | 1 | 1 | 0 | 1 | 1 | 1 |
In one embodiment, the proxy server monitors the practical performance and the use condition of resources of the application program operated by the corresponding terminal of the target user, and judges whether the actual use value of the resources in the server used by the target user in a preset period meets the user requirement of the target user; if not, the corresponding resources in the server are allocated again to the target user. Specifically, a threshold F of fitness is set, when |fitness| > F, the system enters an interrupt, and automatic or manual reset is selected according to a specific example of a user.
In the above embodiment, after the proxy server obtains the high availability service directory, the proxy server selects the target feature vector from the high availability service directory as the evaluation index, allocates the corresponding resource in the server to the target user according to the evaluation index, obtains the historical use value of the resource in the server used by the target user in the historical period, predicts the predicted value of the resource in the server used by the target user in the preset period according to the historical use value, and obtains the actual use value of the resource in the server used by the target user in the preset period, thereby calculating the user satisfaction according to the actual use value and the predicted value, and determining the availability of the server according to the user satisfaction. Therefore, the high availability of the server can be evaluated on the premise of meeting the requirement of the user on the high available service quality.
In one embodiment, a server high availability evaluation method is also provided, and is applied to the proxy server 104 in fig. 1 to illustrate the method. Referring to fig. 4 and 5, the server high availability evaluation method specifically includes the steps of:
step 1, high availability service assessment, including the establishment of a high availability service directory.
And 2, designing an SLA evaluation system, wherein the SLA evaluation system comprises an SLA static evaluation system and an implementation evaluation system.
The accuracy of the assessment is improved by machine learning algorithms from the user's corresponding assessment and modeling of the recorded data of the system.
And 3, SLA distribution.
A consistency analysis based on an alpha algorithm is developed to discover user data, and an event log L is matched with a Petri network, so that the Petri network becomes the behavior of the event log.
And 4, SLA monitoring.
The raw data should reflect the performance of the SLA after processing and analysis for the SLA target or SLA threshold. Specifically, a threshold F of fitness is set, and when |fitness| > F, the system enters an interrupt, and automatic or manual reset is selected according to a specific example of the user.
And 5, SLA evaluation.
And evaluating the high availability server according to the SLA static evaluation system and the SLA implementation evaluation system.
And 6, correcting the SLA.
Information from the SLA static evaluation system and the SLA implementation evaluation system is integrated to obtain the final value of high availability service provider trustworthiness.
It should be understood that, although the steps in the flowcharts of fig. 3 and 4 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 3 and 4 may include steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least some of the other steps or stages.
In one embodiment, as shown in fig. 6, there is provided a server high availability evaluation apparatus including: a high availability service catalog acquisition module 602, a server resource allocation module 606, a resource usage value acquisition module 608, a prediction module 610, a user satisfaction calculation module 612, and an availability determination module 614, wherein:
a high availability service directory acquisition module 602, configured to acquire a high availability service directory;
an evaluation index determining module 604, configured to select a target feature vector from the high available service directory as an evaluation index;
a server resource allocation module 606, configured to allocate, for the target user, a corresponding resource in the server according to the evaluation index;
a resource usage value obtaining module 608, configured to obtain a historical usage value of the target user using the resource in the server in a historical period;
a prediction module 610, configured to predict, according to the historical usage value, a predicted value of the target user using the resource in the server within a preset period of time;
the resource usage value obtaining module 608 is further configured to obtain an actual usage value of the resource in the server used by the target user in the preset period;
a user satisfaction calculating module 612, configured to calculate user satisfaction according to the actual usage value and the predicted value;
An availability determination module 614 is configured to determine availability of the server according to the user satisfaction.
In one embodiment, the evaluation index determination module 604 is further configured to:
and selecting a target feature vector from the high-availability service catalogue by a machine learning algorithm based on a decision tree algorithm as an evaluation index.
In one embodiment, the server resource allocation module 606 is further configured to:
analyzing the user data by adopting the consistency analysis of the alpha algorithm to obtain the matching degree of the user requirement and the actual process and the fitness of the user requirement;
and distributing corresponding resources in a server for the target user according to the evaluation index, the matching degree and the adaptability.
In one embodiment, the prediction module 610 is further configured to:
acquiring a preset period;
inputting the preset time period into a prediction model; the predictive model is a time series model based on the historical use value;
and predicting a predicted value of the target user using the resources in the server in the preset period through the prediction model.
In one embodiment, as shown in fig. 7, the apparatus further comprises: a user demand determination module 616, wherein:
A user demand judging module 616, configured to judge whether an actual usage value of the target user using the resource in the server in a preset period of time meets a user demand of the target user;
the server resource allocation module 606 is further configured to, if the actual usage value of the resources in the server used by the target user in the preset period of time does not meet the user requirement of the target user, re-allocate the corresponding resources in the server to the target user.
In one embodiment, the user satisfaction calculation module 612 is further configured to:
constructing an evaluation model according to the actual use value and the predicted value;
carrying out usability evaluation through the evaluation model to obtain usability evaluation results;
and calculating the user satisfaction according to the usability evaluation result.
In one embodiment, the user satisfaction calculation module 612 is further configured to:
inputting the predicted value and the usability evaluation result into a user satisfaction formula to obtain user satisfaction; the user satisfaction formula is as follows:
wherein Q is user satisfaction, Y P And Y is the availability evaluation result for the predicted value.
In the above embodiment, after the proxy server obtains the high availability service directory, the proxy server selects the target feature vector from the high availability service directory as the evaluation index, allocates the corresponding resource in the server to the target user according to the evaluation index, obtains the historical use value of the resource in the server used by the target user in the historical period, predicts the predicted value of the resource in the server used by the target user in the preset period according to the historical use value, and obtains the actual use value of the resource in the server used by the target user in the preset period, thereby calculating the user satisfaction according to the actual use value and the predicted value, and determining the availability of the server according to the user satisfaction. Therefore, the high availability of the server can be evaluated on the premise of meeting the requirement of the user on the high available service quality.
The specific definition of the server high availability evaluation device may be referred to the definition of the server high availability evaluation method hereinabove, and will not be described in detail herein. The respective modules in the above-described server high availability evaluation device may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 8. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing resource usage value data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a server high availability assessment method.
It will be appreciated by those skilled in the art that the structure shown in FIG. 8 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of: acquiring a high-availability service directory; selecting a target feature vector from the high-availability service catalogue as an evaluation index; distributing corresponding resources in the server for the target user according to the evaluation index; acquiring a historical use value of a target user for using resources in a server in a historical period; predicting a predicted value of resources in a target user use server in a preset period according to the historical use value; acquiring an actual use value of resources in a target user use server within a preset period; and calculating user satisfaction according to the actual use value and the predicted value, and determining the availability of the server according to the user satisfaction.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the step of selecting a target feature vector from the high availability service directory as an evaluation index, to specifically perform the steps of: based on the decision tree algorithm, selecting a target feature vector from the high-availability service catalogue by a machine learning algorithm as an evaluation index.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the step of allocating the corresponding resources in the server to the target user according to the evaluation index, specifically performing the steps of: analyzing the user data by adopting the consistency analysis of the alpha algorithm to obtain the matching degree of the user requirement and the actual process and the fitness of the user requirement; and distributing corresponding resources in the server to the target user according to the evaluation index, the matching degree and the adaptability.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the step of predicting a predicted value of a target user's use of a resource in the server for a preset period of time based on the historical use value, to specifically perform the steps of: acquiring a preset period; inputting a preset period into a prediction model; the prediction model is a time series model obtained based on historical use values; and predicting a predicted value of the resource in the server used by the target user in a preset period by the prediction model.
In one embodiment, the computer program, when executed by the processor, causes the processor to further perform the steps of: judging whether the actual use value of the resources in the server used by the target user in a preset period meets the user requirement of the target user or not; if not, the corresponding resources in the server are allocated again to the target user.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the step of calculating user satisfaction from the actual usage value and the predicted value, to specifically perform the steps of: constructing an evaluation model according to the actual use value and the predicted value; carrying out usability evaluation through an evaluation model to obtain a usability evaluation result; and calculating the user satisfaction according to the usability evaluation result.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the step of calculating user satisfaction from the usability assessment results, to specifically perform the steps of: inputting the predicted value and the usability evaluation result into a user satisfaction formula to obtain user satisfaction; the user satisfaction formula is as follows:
wherein Q is user satisfaction, Y P As a predicted value, Y is an availability evaluation result.
In one embodiment, a computer readable storage medium is provided, storing a computer program which, when executed by a processor, causes the processor to perform the steps of: acquiring a high-availability service directory; selecting a target feature vector from the high-availability service catalogue as an evaluation index; distributing corresponding resources in the server for the target user according to the evaluation index; acquiring a historical use value of a target user for using resources in a server in a historical period; predicting a predicted value of resources in a target user use server in a preset period according to the historical use value; acquiring an actual use value of resources in a target user use server within a preset period; and calculating user satisfaction according to the actual use value and the predicted value, and determining the availability of the server according to the user satisfaction.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the step of selecting a target feature vector from the high availability service directory as an evaluation index, to specifically perform the steps of: based on the decision tree algorithm, selecting a target feature vector from the high-availability service catalogue by a machine learning algorithm as an evaluation index.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the step of allocating the corresponding resources in the server to the target user according to the evaluation index, specifically performing the steps of: analyzing the user data by adopting the consistency analysis of the alpha algorithm to obtain the matching degree of the user requirement and the actual process and the fitness of the user requirement; and distributing corresponding resources in the server to the target user according to the evaluation index, the matching degree and the adaptability.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the step of predicting a predicted value of a target user's use of a resource in the server for a preset period of time based on the historical use value, to specifically perform the steps of: acquiring a preset period; inputting a preset period into a prediction model; the prediction model is a time series model obtained based on historical use values; and predicting a predicted value of the resource in the server used by the target user in a preset period by the prediction model.
In one embodiment, the computer program, when executed by the processor, causes the processor to further perform the steps of: judging whether the actual use value of the resources in the server used by the target user in a preset period meets the user requirement of the target user or not; if not, the corresponding resources in the server are allocated again to the target user.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the step of calculating user satisfaction from the actual usage value and the predicted value, to specifically perform the steps of: constructing an evaluation model according to the actual use value and the predicted value; carrying out usability evaluation through an evaluation model to obtain a usability evaluation result; and calculating the user satisfaction according to the usability evaluation result.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the step of calculating user satisfaction from the usability assessment results, to specifically perform the steps of: inputting the predicted value and the usability evaluation result into a user satisfaction formula to obtain user satisfaction; the user satisfaction formula is as follows:
wherein Q is user satisfaction, Y P As a predicted value, Y is an availability evaluation result.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A server high availability assessment method, comprising:
acquiring a high-availability service directory; the high-availability service catalog comprises service types and service contents provided by the high-availability server aiming at different users;
selecting a target feature vector from the high-availability service directory as an evaluation index; the evaluation index comprises a memory capacity index, a response time index and a storage index;
Distributing corresponding resources in the server to the target user according to the evaluation index;
acquiring a historical use value of the target user using the resources in the server in a historical period;
predicting a predicted value of the target user using the resources in the server in a preset period according to the historical use value;
acquiring an actual use value of the resources in the server used by the target user within the preset period;
determining a prediction error value corresponding to the evaluation index according to the actual use value and the prediction value corresponding to each evaluation index, and determining a prediction error square sum expression corresponding to the evaluation index; based on the prediction error square sum expression, determining weights corresponding to the evaluation indexes respectively by taking the minimum prediction error square sum as an optimization target; constructing an evaluation model based on weights corresponding to the evaluation indexes respectively, performing availability evaluation through the evaluation model to obtain an availability evaluation result, and calculating user satisfaction according to the availability evaluation result; the usability assessment model is as follows: f=f 1 ω 1 +f 2 ω 2 +f 3 ω 3 ，ω 1 +ω 2 +ω 3 =1, where f 1 Indicating the memory capacity index, f 2 Indicating the response time index, f 3 Representing a storage index, f representing an availability evaluation result; omega 1 、ω 2 And omega 3 Respectively f 1 、f 2 And f 3 Is determined by the optimal weighting coefficient of the (a);
and determining the availability of the server according to the user satisfaction.
2. The method of claim 1, wherein selecting the target feature vector from the high availability service directory as an evaluation index comprises:
and selecting a target feature vector from the high-availability service catalogue by a machine learning algorithm based on a decision tree algorithm as an evaluation index.
3. The method according to claim 1, wherein the allocating the corresponding resources in the server to the target user according to the evaluation index comprises:
analyzing the user data by adopting the consistency analysis of the alpha algorithm to obtain the matching degree of the user requirement and the actual process and the fitness of the user requirement;
and distributing corresponding resources in a server for the target user according to the evaluation index, the matching degree and the adaptability.
4. The method according to claim 1, wherein predicting a predicted value of the target user's use of the resource in the server for a preset period of time based on the historical use value comprises:
Acquiring a preset period;
inputting the preset time period into a prediction model; the predictive model is a time series model based on the historical use value;
and predicting a predicted value of the target user using the resources in the server in the preset period through the prediction model.
5. The method according to claim 1, wherein the method further comprises:
judging whether the actual use value of the resources in the server used by the target user in a preset period meets the user requirement of the target user or not;
if not, the corresponding resources in the server are allocated again to the target user.
6. The method according to claim 1, wherein the method further comprises:
a service level agreement sent by the high availability server is received and then a high availability service directory is created according to the service level agreement.
7. The method of claim 1, wherein said calculating user satisfaction from said usability assessment results comprises:
inputting the predicted value and the usability evaluation result into a user satisfaction formula to obtain user satisfaction; the user satisfaction formula is as follows:
Wherein, Q for user satisfaction, Y P And Y is the availability evaluation result for the predicted value.
8. A server high availability assessment apparatus, the apparatus comprising:
the high-availability service catalog acquisition module is used for acquiring the high-availability service catalog;
the evaluation index determining module is used for selecting a target feature vector from the high-availability service catalogue as an evaluation index; the evaluation index comprises a memory capacity index, a response time index and a storage index;
the server resource allocation module is used for allocating corresponding resources in the server to the target user according to the evaluation index;
a resource use value acquisition module, configured to acquire a historical use value of the target user using the resource in the server in a historical period;
the prediction module is used for predicting a predicted value of the target user using the resources in the server in a preset period according to the historical use value;
the resource use value acquisition module is further used for acquiring an actual use value of the resources in the server used by the target user in the preset period;
a user satisfaction calculating module for calculating the user satisfaction according to each evaluation fingerDetermining a prediction error value corresponding to the evaluation index by marking the actual use value and the prediction value, and determining a prediction error square sum expression corresponding to the evaluation index; based on the prediction error square sum expression, determining weights corresponding to the evaluation indexes respectively by taking the minimum prediction error square sum as an optimization target; constructing an evaluation model based on weights corresponding to the evaluation indexes respectively, performing availability evaluation through the evaluation model to obtain an availability evaluation result, and calculating user satisfaction according to the availability evaluation result; the usability assessment model is as follows: f=f 1 ω 1 +f 2 ω 2 +f 3 ω 3 ，ω 1 +ω 2 +ω 3 =1, where f 1 Indicating the memory capacity index, f 2 Indicating the response time index, f 3 Representing a storage index, f representing an availability evaluation result; omega 1 、ω 2 And omega 3 Respectively f 1 、f 2 And f 3 Is determined by the optimal weighting coefficient of the (a);
and the availability determination module is used for determining the availability of the server according to the user satisfaction.
9. A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the method of any one of claims 1 to 7.
10. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method of any of claims 1 to 7.
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