CN111598390A - Server high availability evaluation method, device, equipment and readable storage medium - Google Patents
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Abstract
The application relates to a method and a device for evaluating high availability of a server, a computer readable storage medium and computer equipment, 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 a server for 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 for using the resources in the server within a preset time period according to the historical use value; acquiring an actual use value of the resource in the server used by the target user in the preset time period; calculating user satisfaction according to the actual use value and the predicted value; 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 a chinese patent application entitled "a method for high availability assessment using SLA" filed by the chinese patent office on 16/10/2019, application number 2019109841043, 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 reach an unprecedented level. In order to solve the serious value loss caused by server shutdown, a high-availability server cluster is generated, and the high-availability server cluster mainly provides continuous service as far as possible for 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 method, an apparatus, a device and a readable storage medium for evaluating high availability of a server to solve the technical problem that the conventional high availability evaluation cannot meet the requirement of a user on high available service quality.
A high availability evaluation method for a server 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 a server for 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 for using the resources in the server within a preset time period according to the historical use value;
acquiring an actual use value of the resource in the server used by the target user in the preset time period;
calculating user satisfaction according to the actual use value and the predicted value;
determining the availability of the server according to the user satisfaction.
In one embodiment, the selecting a target feature vector from the high available service directory as an evaluation index includes:
and selecting a target characteristic vector from the high-availability service directory as an evaluation index through a machine learning algorithm based on a decision tree algorithm.
In one embodiment, the allocating corresponding resources in the server to the target user according to the evaluation index includes:
analyzing the user data by adopting consistency analysis of an alpha algorithm to obtain the matching degree of the user requirements and the actual process and the fitness of the user requirements;
and distributing corresponding resources in the server for the target user according to the evaluation index, the matching degree and the fitness.
In one embodiment, the predicting, according to the historical usage value, a predicted value of the target user using the resource in the server within a preset time period includes:
acquiring a preset time period;
inputting the preset time period into a prediction model; the prediction model is a time series model obtained based on the historical usage values;
and predicting the predicted value of the resource in the server used by the target user within the preset time 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 time period meets the user requirement of the target user;
and if not, re-allocating the corresponding resources in the server for 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 availability evaluation through the evaluation model to obtain an availability evaluation result;
and calculating the user satisfaction according to the usability evaluation result.
In one embodiment, the calculating user satisfaction from the usability assessment 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, YPAnd Y is the availability evaluation result.
A server high availability evaluation apparatus, the apparatus comprising:
the high-availability service directory acquisition module is used for acquiring a high-availability service directory;
the evaluation index determining module is used for selecting a target feature vector from the high-availability service directory 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;
the resource use value acquisition module is used for acquiring the historical use value of the target user for using the resource in the server in the historical time period;
the prediction module is used for predicting the predicted value of the resource in the server used by the target user within a preset time period according to the historical use value;
the resource use value acquisition module is further configured to acquire an actual use value of the resource in the server used by the target user within the preset time period;
the user satisfaction calculation module is used for calculating user satisfaction according to the actual use value and the predicted value;
and the availability determining 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 characteristic vector from the high-availability service directory as an evaluation index through a machine learning algorithm based on a decision tree algorithm.
In one embodiment, the server resource allocation module is further configured to:
analyzing the user data by adopting consistency analysis of an alpha algorithm to obtain the matching degree of the user requirements and the actual process and the fitness of the user requirements;
and distributing corresponding resources in the server for the target user according to the evaluation index, the matching degree and the fitness.
In one embodiment, the prediction module is further configured to:
acquiring a preset time period;
inputting the preset time period into a prediction model; the prediction model is a time series model obtained based on the historical usage values;
and predicting the predicted value of the resource in the server used by the target user within the preset time period through the prediction model.
In one embodiment, the apparatus further comprises:
the user requirement 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 time period meets the user requirement of the target user;
and the server resource allocation module is further used for reallocating the corresponding resources in the server to the target user if the actual use value of the resources in the server used by the target user in the preset time period does not meet the user requirement of 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 availability evaluation through the evaluation model to obtain an availability evaluation result;
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, YPAnd Y is the availability evaluation result.
A computer-readable storage medium, in which a computer program is stored which, when executed by a processor, causes the processor to carry out the steps of the above-mentioned method.
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 above method.
After the high-availability service directory is obtained, the target characteristic vector is selected from the high-availability service directory to serve as an evaluation index, the corresponding resource in the server is distributed to the target user according to the evaluation index, the historical use value of the resource in the server used by the target user in the historical period is obtained, the predicted value of the resource in the server used by the target user in the preset period is predicted according to the historical use value, the actual use value of the resource in the server used by the target user in the preset period is obtained, therefore, the user satisfaction is calculated according to the actual use value and the predicted value, 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 exemplary implementation of a method for evaluating high availability of a server;
FIG. 2 is a model diagram of a method for server high availability assessment in one embodiment;
FIG. 3 is a flow diagram of a method for server high availability evaluation in one embodiment;
FIG. 4 is a flowchart illustrating a method for evaluating high availability of a server according to another embodiment;
FIG. 5 is a schematic diagram of an embodiment in which a simulation system uses VMware workstations;
FIG. 6 is a block diagram showing the structure of a server high availability evaluation apparatus according to an embodiment;
FIG. 7 is a block diagram showing the structure of a server high availability evaluation apparatus according to another embodiment;
FIG. 8 is a block diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The method for evaluating the high availability of the server can be applied to the application environment shown in fig. 1. Wherein the terminal 102 is in network communication with the high availability server 106 via the proxy server 104. Taking the above-mentioned server high availability evaluation method implemented in the proxy server 104 as an example, and described with reference to the server high availability evaluation model diagram shown in fig. 2, the proxy server 104 obtains a high available service directory; selecting a target characteristic vector from the high-availability service directory 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 resource in a server used by a target user in a historical period; predicting a predicted value of the resource in the server used by the target user within a preset time period according to the historical use value; acquiring an actual use value of resources in a server used by a target user in a preset time 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, smart phones, tablet computers, and portable wearable devices, the proxy server 104 may be a service level proxy, and stores a service level agreement, and the high-availability server 106 may be a high-availability server proxy server 104 that provides a service for the terminal, and the high-availability server 106 may be implemented by an independent server or a server cluster formed by a plurality of servers.
As shown in FIG. 3, in one embodiment, a method for server high availability assessment is provided. The embodiment is mainly illustrated by applying the method to the proxy server 104 in fig. 1. Referring to fig. 3, the method for evaluating high availability of the server specifically includes the following steps:
s302, a high available service directory is obtained.
The high-availability service directory contains information such as service types and service contents provided by the high-availability server for different users.
In one embodiment, before acquiring the highly available Service directory, the proxy server receives a Service Level Agreement (SLA) sent by the highly available server, then creates the highly available Service directory according to the SLA, and stores the highly available Service directory in the 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 about the service quality level determined by the negotiation between the service provider and the user, and the agreement or contract is made for the purpose of making the service provider and the user agree on the service quality, level, performance, priority, responsibility, etc., and the service level agreement includes the parameters related to the service level agreement, and the description of the parameters and the correspondence thereof is shown in the following table.
Table 1 service level agreement parameter table
| Parameter(s) | Description of the invention |
| CPU capacity | CPU running speed of high-availability system virtual machine |
| Memory capacity | High availability system virtual machine flash memory space size |
| Lead-in time | Preparation and lead-in time before official use |
| Storing | Size of storage space required for user data |
| Availability | Availability of SLA services to which a user subscribes during a designated time interval |
| Service response time | Service provider response and processing time of user's high available service request for signing SLA |
And S304, selecting a target feature vector from the high available service directory as an evaluation index.
The target feature vector is a feature vector capable of representing individual features of users corresponding to different terminals, the feature vector may specifically be at least one of Memory capacity (Memory), response Time (Time), and Storage (Storage), and the high-availability service directory includes all measurable feature vectors.
In one embodiment, after obtaining the highly available service directory, the proxy server selects the target feature vector from the highly available service directory by using a machine learning algorithm, where the machine learning algorithm used may be a machine learning algorithm based on a decision tree, specifically, an ID3 algorithm, and the decision tree is a tree built 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 diverging 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, if there are multiple outputs, separate decision trees can be built to handle the different outputs, and the ID3 algorithm is a greedy algorithm used to construct the decision tree.
The above-described embodiment is explained as an example. Firstly, a given training set is set as TR, the elements of the TR are represented by feature vectors and classification results thereof, and an attribute table Attrlist of a classification object is [ Al，A2，...An]The Class set consisting of all classification results is { Cl，C2...CmIn general, n.gtoreq.1 and m.gtoreq.2. For each attribute AiValue range ofIs ValueType (A)i) The value range is discrete. Thus, one element of TR can be represented as<X，C>Wherein X ═ a (a)1，...an)，aiCorresponding to the value of the ith attribute of the example X, and C ∈ Class is the classification result of the example X, then randomly selecting the subset of the training examples to form a training window, and repeatedly executing the following steps:
firstly, constructing a decision tree for an instance set in a window;
searching a counter example of the decision tree;
if the counterexample exists, adding the counterexample into a training window, and executing the step I; and if the counterexample does not exist, returning the obtained decision tree. And selecting the attribute with the maximum obtained information amount as the extended attribute. This heuristic rule, also known as the principle of minimum entropy, maximizes the amount of information obtained equivalent to minimizing uncertainty, i.e., minimizing entropy. Given a subset of positive and negative instances as S, a training window is formed. When the decision has k different outputs, then the entropy of S is:
wherein, PiIs the probability of taking the value as the ith value of the 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 its value range is (V)l，V2，...Vn) Then, the information gain of the attribute a in the training instance S can be defined as follows:
wherein S isiThe value of the attribute A in the training example S is represented as ViIs given as a subset of, | Si| 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 the corresponding resources in the server for 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 an actual process and fitness of the user requirements, and then allocates corresponding resources in the server to a target user according to the evaluation index, the matching degree and the fitness.
The function of the alpha algorithm is explained: and matching the event log L to a Petri net, wherein the Petri net becomes the behavior expression 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 SLA limits a provider and ensures the quality in the whole process of serving a user.
Since the participants may access some states that are not contained in the desired model when completing the task or the desired model may contain events that do not appear in the log file, a variable (log coverage C) is defined to evaluate the degree of match of the desired model (user requirements) to the actual process.
Where | E | represents the number of all events and | E ∈ E | represents the number of events that actually occurred.
Fitness refers to the degree of association of the actual operation trajectory with the desired model. And (3) carrying out consistency analysis on the actual instance process through the expectation model, and obtaining the fitness (based on the Petri net) of each instance and the whole task relative to the expectation model. The fitness of the desired model for each example process is defined as follows:
wherein: m is the number of tokens (tokens) lost when the actual process recurs 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 the historical use value of the resource in the server used by the target user in the historical period.
And S310, predicting the predicted value of the resource in the server used by the target user within the preset time period according to the historical use value.
In one embodiment, after allocating the corresponding resources in the high-availability server to the target user according to the evaluation index, the proxy server monitors the practical performance of the application program operated by the target application corresponding terminal and the use condition of the resources in real time, and stores the corresponding resource use value in the database.
In one embodiment, the proxy server obtains a historical usage value of the resource in the server used by the target user in a historical period from the database, and then constructs a prediction model according to the obtained historical usage value, wherein the created prediction model is used for predicting a predicted value of the resource in the server used by the target user in a preset period, and the prediction model can be specifically a time series prediction model.
In one embodiment, the proxy server obtains a preset time period to be predicted, inputs the preset time period into the prediction model, and predicts a predicted value of the resource in the high-availability server used by the target user in the preset time period through the prediction model. For example, when the evaluation index is the memory capacity, the historical use value of the memory capacity of the server used by the target user in the historical time period is acquired, and the predicted value of the memory capacity of the server used by the target user in the preset time period is predicted; 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 in the preset period for responding to the target user request by using the resource in the server is predicted.
S312, acquiring the actual use value of the resources in the server used by the target user in the preset time period.
And S314, calculating the user satisfaction according to the actual use value and the predicted value.
In one embodiment, after predicting the predicted value of the resource in the server used by the target user within the preset time period, the proxy server obtains the predicted actual use value of the resource in the server used by the target user within the preset time period, and calculates the user satisfaction according to the actual use value and the predicted value.
In one embodiment, after predicting a predicted value of a resource in a server used by a target user within a preset time period, the proxy server constructs an evaluation model according to an actual use value and the predicted value; and performing availability evaluation through the evaluation model to obtain an availability evaluation result, and then calculating the satisfaction according to the availability evaluation result, wherein the user satisfaction can be calculated by inputting the predicted value and the availability evaluation result into a user satisfaction formula.
And S316, determining the availability of the server according to the user satisfaction.
In one embodiment, after the user satisfaction degree is calculated, a service level agreement corresponding to the target user is obtained, an availability threshold value is extracted from the service agreement level, when the user satisfaction degree is larger than the availability threshold value, the availability of the corresponding high-availability server is determined to be normal, and when the user satisfaction degree is smaller than the availability threshold value, the availability of the corresponding high-availability server is determined to be abnormal.
The above-described embodiment is explained as an example. Respectively by f1、f2And f3Representing the memory capacity, response time and storage obtained by sampling, sorting historical data into time series and expressing the time series by Sn, and respectively expressing the time series by f1、f2And f3Predicting a time sequence S 'corresponding to a preset time period'nPrediction result YpRespectively with Y1、Y2And Y3And (4) showing. Let SitEvaluating the actual utilization value of the index for the ith availability at time t, YitAnd predicting a predicted value obtained by predicting the ith availability evaluation index by the prediction model at the time t, wherein the prediction error is as follows:
the sum of the squared errors is:
wherein, ω isiAnd the weight corresponding to the ith evaluation index. Determining f with minimum sum of squares of prediction errors as optimization target1、f2And f3Is optimized as a weight coefficient omega1、ω2And ω3Then the evaluation model can be expressed as:
f＝f1ω1+f2ω2+f3ω3，ω1+ω2+ω3＝1
after an evaluation model is built, inputting a target feature vector corresponding to an evaluation index into the evaluation model, calculating to obtain an availability evaluation result Y, and then, predicting a value YpAnd inputting the usability evaluation result Y into a user satisfaction formula so as to calculate the user satisfaction, wherein the user satisfaction formula is as follows:
wherein Q is user satisfaction, YPAnd Y is the availability evaluation result.
After the user satisfaction degree Q is calculated, the availability threshold value Q corresponding to the target user is obtainedtThen according to the user satisfaction Q and the sexual threshold QtCalculating the availability A of the high available server, wherein:
where a-1 may indicate that the availability of the high available server is normal, a-0 indicates that the availability of the high available server is abnormal,
the following table is a pass simulation system(FIG. 3) availability of highly available servers calculated, simulation System selects time point t1To t7And recording T, M two evaluation indexes determined by the decision tree to calculate the user satisfaction, wherein T is response time, corresponding units are mus, M is memory capacity rate, Q is the user satisfaction and A is the availability of the system. Wherein at a point in time t4At the moment, the state of the simulation system is artificially adjusted so that the time point t4The corresponding predicted values have larger deviations, and it can be seen from the following table that T, M and S change correspondingly when the availability of the high-availability server is artificially damaged, and when the availability of the server is low, the change of the availability of the server can be accurately reported by the server high-availability evaluation method of the application.
TABLE 2 availability of high availability servers
| Point in time | t1 | t2 | t3 | t4 | t5 | t6 | t7 |
| 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 of an application program operated by a terminal corresponding to a target user and the use condition of resources, and judges whether the actual use value of the resources in the server used by the target user in a preset time period meets the user requirement of the target user; and if not, re-allocating the corresponding resources in the server for the target user. Specifically, a threshold F of the fitness is set, and when | fitness | > F, the system goes into an interrupt, selecting either an automatic or manual reset, depending on the specific instance of the user.
In the above embodiment, after obtaining the high available service directory, the proxy server selects the target feature vector from the high available service directory as an evaluation index, allocates a corresponding resource in the server to the target user according to the evaluation index, obtains a historical usage value of the resource in the server used by the target user in a historical period, predicts a predicted value of the resource in the server used by the target user in a preset period according to the historical usage value, obtains an actual usage value of the resource in the server used by the target user in the preset period, calculates the user satisfaction according to the actual usage value and the predicted value, and determines 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 method for evaluating high availability of a server is also provided, which is illustrated by applying the method to the proxy server 104 in fig. 1. Referring to fig. 4 and 5, the method for evaluating high availability of the server specifically includes the following steps:
step 1, evaluating the high available service, including establishing a high available 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 evaluation is improved by a machine learning algorithm from the user's corresponding evaluation and simulation of the system's recorded data.
And 3, allocating SLAs.
A consistency analysis based on alpha algorithm is developed to mine user data, and event logs L are matched to a Petri net, so that the Petri net becomes the behavior expression of the event logs.
And 4, monitoring SLA.
The raw data should reflect the performance of the SLA after processing and analysis against SLA goals or SLA thresholds. Specifically, a threshold F of fitness is set, and when | fitness | > F, the system goes into an interrupt, selecting either an automatic or manual reset, depending on the specific instance of the user.
And 5, evaluating SLA.
And evaluating the high-availability server according to the SLA static evaluation system and the SLA implementation evaluation system.
And 6, correcting SLA.
Information from the SLA static evaluation system and the SLA enforcement evaluation system is integrated to obtain a final value of the trustworthiness of the high availability service provider.
It should be understood that although the various steps in the flowcharts of fig. 3 and 4 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 3 and 4 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 6, there is provided a server high availability evaluation apparatus, including: a highly available service directory obtaining module 602, a server resource allocation module 606, a resource usage value obtaining module 608, a prediction module 610, a user satisfaction calculation module 612, and an availability determination module 614, wherein:
a highly available service directory obtaining module 602, configured to obtain a highly available 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, according to the evaluation index, a corresponding resource in the server to the target user;
a resource usage value obtaining module 608, configured to obtain a historical usage value of the resource in the server used by the target user in a historical period;
the prediction module 610 is configured to predict, according to the historical usage value, a predicted value of the resource in the server used by the target user within a preset time period;
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 within the preset time 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 for determining the availability of the server according to the user satisfaction.
In one embodiment, the evaluation index determining module 604 is further configured to:
and selecting a target characteristic vector from the high-availability service directory as an evaluation index through a machine learning algorithm based on a decision tree algorithm.
In one embodiment, the server resource allocation module 606 is further configured to:
analyzing the user data by adopting consistency analysis of an alpha algorithm to obtain the matching degree of the user requirements and the actual process and the fitness of the user requirements;
and distributing corresponding resources in the server for the target user according to the evaluation index, the matching degree and the fitness.
In one embodiment, the prediction module 610 is further configured to:
acquiring a preset time period;
inputting the preset time period into a prediction model; the prediction model is a time series model obtained based on the historical usage values;
and predicting the predicted value of the resource in the server used by the target user within the preset time period through the prediction model.
In one embodiment, as shown in fig. 7, the apparatus further comprises: a user requirement determining module 616, wherein:
a user requirement determining module 616, configured to determine whether an actual usage value of the resource in the server used by the target user within a preset time period meets a user requirement of the target user;
the server resource allocation module 606 is further configured to, if the actual usage value of the resource in the server used by the target user in the preset time period does not meet the user requirement of the target user, reallocate the corresponding resource 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 availability evaluation through the evaluation model to obtain an availability evaluation result;
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, YPAnd Y is the availability evaluation result.
In the above embodiment, after obtaining the high available service directory, the proxy server selects the target feature vector from the high available service directory as an evaluation index, allocates a corresponding resource in the server to the target user according to the evaluation index, obtains a historical usage value of the resource in the server used by the target user in a historical period, predicts a predicted value of the resource in the server used by the target user in a preset period according to the historical usage value, obtains an actual usage value of the resource in the server used by the target user in the preset period, calculates the user satisfaction according to the actual usage value and the predicted value, and determines 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.
For specific limitations of the server high availability evaluation device, reference may be made to the above limitations of the server high availability evaluation method, which are not described herein again. The modules in the server high availability evaluation device can be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram 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 comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. 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 evaluation method.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, there is provided 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: acquiring a high-availability service directory; selecting a target characteristic vector from the high-availability service directory 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 resource in a server used by a target user in a historical period; predicting a predicted value of the resource in the server used by the target user within a preset time period according to the historical use value; acquiring an actual use value of resources in a server used by a target user in a preset time period; and 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.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the steps of selecting a target feature vector from a high available service directory as an evaluation index, in particular: based on a decision tree algorithm, selecting a target feature vector from a high-availability service directory as an evaluation index through a machine learning algorithm.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the following steps in particular, when the step of allocating the corresponding resource in the server to the target user is performed according to the evaluation index: analyzing the user data by adopting consistency analysis of an alpha algorithm to obtain the matching degree of the user requirements and the actual process and the fitness of the user requirements; and distributing corresponding resources in the server for the target user according to the evaluation index, the matching degree and the fitness.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the step of predicting, based on the historical usage value, a predicted value of usage of a resource in the server by the target user within a preset time period, the step of: acquiring a preset time period; inputting a preset time period into a prediction model; the prediction model is a time series model obtained based on historical use values; and predicting the predicted value of the resource in the server used by the target user in the preset time period through 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 time period meets the user requirements of the target user; and if not, re-allocating the corresponding resources in the server for the target user.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the steps of calculating user satisfaction from the actual usage value and the predicted value, in particular: constructing an evaluation model according to the actual use value and the predicted value; carrying out availability evaluation through the evaluation model to obtain an availability 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 steps of calculating user satisfaction from the availability assessment result, in particular: inputting the predicted value and the availability evaluation result into a user satisfaction formula to obtain user satisfaction; the user satisfaction formula is as follows:
wherein Q is user satisfaction, YPAs a predicted value, Y is the availability evaluation result.
In one embodiment, a computer readable storage medium is provided, storing a computer program that, when executed by a processor, causes the processor to perform the steps of: acquiring a high-availability service directory; selecting a target characteristic vector from the high-availability service directory 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 resource in a server used by a target user in a historical period; predicting a predicted value of the resource in the server used by the target user within a preset time period according to the historical use value; acquiring an actual use value of resources in a server used by a target user in a preset time period; and 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.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the steps of selecting a target feature vector from a high available service directory as an evaluation index, in particular: based on a decision tree algorithm, selecting a target feature vector from a high-availability service directory as an evaluation index through a machine learning algorithm.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the following steps in particular, when the step of allocating the corresponding resource in the server to the target user is performed according to the evaluation index: analyzing the user data by adopting consistency analysis of an alpha algorithm to obtain the matching degree of the user requirements and the actual process and the fitness of the user requirements; and distributing corresponding resources in the server for the target user according to the evaluation index, the matching degree and the fitness.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the step of predicting, based on the historical usage value, a predicted value of usage of a resource in the server by the target user within a preset time period, the step of: acquiring a preset time period; inputting a preset time period into a prediction model; the prediction model is a time series model obtained based on historical use values; and predicting the predicted value of the resource in the server used by the target user in the preset time period through 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 time period meets the user requirements of the target user; and if not, re-allocating the corresponding resources in the server for the target user.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the steps of calculating user satisfaction from the actual usage value and the predicted value, in particular: constructing an evaluation model according to the actual use value and the predicted value; carrying out availability evaluation through the evaluation model to obtain an availability 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 steps of calculating user satisfaction from the availability assessment result, in particular: inputting the predicted value and the availability evaluation result into a user satisfaction formula to obtain user satisfaction; the user satisfaction formula is as follows:
wherein Q is user satisfaction, YPAs a predicted value, Y is the availability evaluation result.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for evaluating high availability of a server is characterized by comprising 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 a server for 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 for using the resources in the server within a preset time period according to the historical use value;
acquiring an actual use value of the resource in the server used by the target user in the preset time period;
calculating user satisfaction according to the actual use value and the predicted value;
determining the availability of the server according to the user satisfaction.
2. The method according to claim 1, wherein the selecting a target feature vector from the high available service directory as an evaluation index comprises:
and selecting a target characteristic vector from the high-availability service directory as an evaluation index through a machine learning algorithm based on a decision tree algorithm.
3. The method of 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 consistency analysis of an alpha algorithm to obtain the matching degree of the user requirements and the actual process and the fitness of the user requirements;
and distributing corresponding resources in the server for the target user according to the evaluation index, the matching degree and the fitness.
4. The method according to claim 1, wherein the predicting, according to the historical usage value, a predicted value of the target user using the resource in the server within a preset time period comprises:
acquiring a preset time period;
inputting the preset time period into a prediction model; the prediction model is a time series model obtained based on the historical usage values;
and predicting the predicted value of the resource in the server used by the target user within the preset time period through the prediction model.
5. The method of claim 1, further comprising:
judging whether the actual use value of the resources in the server used by the target user in a preset time period meets the user requirement of the target user;
and if not, re-allocating the corresponding resources in the server for the target user.
6. The method of claim 1, wherein 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 availability evaluation through the evaluation model to obtain an availability evaluation result;
and calculating the user satisfaction according to the usability evaluation result.
7. The method of claim 6, wherein said calculating user satisfaction from said usability assessment 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 is user satisfaction, YPAnd Y is the availability evaluation result.
8. A server high availability evaluation apparatus, the apparatus comprising:
the high-availability service directory acquisition module is used for acquiring a high-availability service directory;
the evaluation index determining module is used for selecting a target feature vector from the high-availability service directory 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;
the resource use value acquisition module is used for acquiring the historical use value of the target user for using the resource in the server in the historical time period;
the prediction module is used for predicting the predicted value of the resource in the server used by the target user within a preset time period according to the historical use value;
the resource use value acquisition module is further configured to acquire an actual use value of the resource in the server used by the target user within the preset time period;
the user satisfaction calculation module is used for calculating user satisfaction according to the actual use value and the predicted value;
and the availability determining 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 carry out the steps of the method according to 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 according to any one of claims 1 to 7.
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