CN115391048A - Micro-service instance dynamic horizontal expansion and contraction method and system based on trend prediction - Google Patents
Micro-service instance dynamic horizontal expansion and contraction method and system based on trend prediction Download PDFInfo
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Abstract
The invention provides a dynamic horizontal expansion and contraction method and system of a micro-service instance based on trend prediction, which belong to the technical field of cloud computing, and the scheme comprises the following steps: periodically acquiring load indexes of a host machine and a micro-service instance in the cloud platform based on an Exporter process; the Exporter process is an index collection process existing in a host and a micro service instance, and provides an index data interface to the outside; storing the obtained load indexes in a time sequence database according to a time sequence; based on the load indexes in the time sequence database, acquiring the load trend of the corresponding service by utilizing a pre-constructed load trend prediction model; based on the obtained load trend, expansion or contraction of the micro-service instance is achieved.
Description
Technical Field
The disclosure belongs to the technical field of cloud computing, and particularly relates to a micro-service instance dynamic horizontal expansion and contraction method and system based on trend prediction.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the rapid development of cloud computing, it is becoming a trend to develop and deploy a service system based on a micro-service architecture in a cloud platform. The micro-service architecture splits the traditional single application into independent subsystems, and reduces the coupling degree between the functional modules. The container technology packs the micro-service and the dependent environment thereof into a mirror image, and the mirror image is shared and downloaded for operation through a mirror image warehouse, so that the cost of the micro-service is reduced, and the flexibility of development and deployment is improved. With the increasing number of containers in a micro-service architecture, a container cloud platform represented by kubernets (k 8 s) has come, and the cloud platform can schedule and manage the containers in a cluster, wherein one necessary arrangement capability of a platform-level project is to realize horizontal expansion/contraction of micro-service instances (namely, to adjust the number of the micro-service instances according to the real level of load), and the number of the micro-service instances can be increased or deleted along with the real load of a service system, so that the resource allocation on demand is realized while the service quality is ensured.
The inventor finds that the most basic means for realizing the horizontal expansion/contraction of the existing micro-service instances is that operation and maintenance personnel reasonably plan the future according to a service system and manually set the number of instances, and the results are often unsatisfactory due to the difference of the capacities of the operation and maintenance personnel and the difference of the complexity of the service system by adopting the manual prediction mode; in addition to the manual prediction method, the responsive scaling can also be realized by setting a specific resource threshold, for example, setting a threshold for the CPU utilization of the micro-service instances, and when the threshold is exceeded, automatically increasing the number of instances to increase the overall service load capacity, but the scheduling of this type of method has hysteresis, and dynamic scaling can only occur when the threshold is reached, which leads to problems of increasing service response time, wasting system resources, and the like.
Disclosure of Invention
In order to solve the problems, the invention provides a dynamic horizontal expansion and contraction method and a dynamic horizontal expansion and contraction system for a micro-service instance based on trend prediction, wherein the scheme is that the horizontal expansion/contraction of the micro-service instance is realized in advance before load peaks and valleys by acquiring specific load indexes and predicting the trend characteristics of the load by using the indexes, so that the method has better foresight property and flexibility; meanwhile, the scheme can more efficiently utilize system resources while ensuring the quality of the micro service.
According to a first aspect of the embodiments of the present disclosure, there is provided a trend prediction based micro-service instance dynamic horizontal expansion and contraction method, including:
periodically acquiring load indexes of a host machine and a micro-service instance in the cloud platform based on an Exporter process; the Exporter process is an index collection process existing in a host machine and a micro-service instance and provides an index data interface to the outside;
storing the obtained load indexes in a time sequence database according to a time sequence;
based on the load indexes in the time sequence database, acquiring the load trend of the corresponding service by utilizing a pre-constructed load trend prediction model;
based on the obtained load trend, expansion or contraction of the micro-service instance is achieved.
Further, the load indexes include hardware indexes of a host machine, load utilization rate of a container where the micro-service instance is located, and application layer load indexes reflecting corresponding service capabilities.
Further, the hardware index of the host and the load utilization rate of the container where the micro service instance is located specifically include a CPU utilization rate, a memory utilization rate, and a network IO; the application layer load indexes embodying the corresponding capabilities of the service comprise query number per second and response time.
Furthermore, the Exporter process is an API service deployed together with related micro-services, internally collects load indexes, and externally provides a query interface for the index monitoring component.
Furthermore, the load trend prediction model adopts a mixed model formed by a plurality of time series prediction algorithms, and specifically comprises Holt-Winters, ARIMA and STL models.
Further, the training of the load trend prediction model specifically comprises:
constructing a training set based on pre-obtained historical load index data;
respectively training a plurality of time series prediction models based on the training set;
and evaluating the training results of the plurality of time series prediction models based on the SMAPE algorithm, and selecting the optimal model as a final load trend prediction model.
According to a second aspect of the embodiments of the present disclosure, there is provided a trend prediction-based micro-service instance dynamic horizontal expansion and contraction system, including:
the data acquisition unit is used for periodically acquiring load indexes of hosts and micro-service instances in the cloud platform based on the Exporter process; the Exporter process is an index collection process existing in a host machine and a micro-service instance and provides an index data interface to the outside;
the data storage unit is used for storing the obtained load indexes in a time sequence database according to time sequence;
the load trend prediction unit is used for obtaining the load trend of the corresponding service by utilizing a pre-constructed load trend prediction model based on the load indexes in the time sequence database;
and the micro service instance expansion or contraction control unit is used for realizing expansion or contraction of the micro service instance based on the obtained load trend.
According to a third aspect of the embodiments of the present disclosure, there is provided an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the memory, where the processor implements the method for trend prediction based micro-service instance dynamic horizontal expansion and contraction when executing the program.
According to a fourth aspect of the embodiments of the present disclosure, there is provided a non-transitory computer readable storage medium, on which a computer program is stored, the program, when executed by a processor, implements the method for trend prediction based microservice instance dynamic level expansion contraction.
Compared with the prior art, the beneficial effect of this disclosure is:
(1) The scheme is that the specific load indexes are collected, the trend characteristics of the load are predicted by using the indexes, the horizontal expansion/contraction of the micro service instance is realized in advance before the load peak and the load valley come, and the method and the system have better foresight property and flexibility; meanwhile, the scheme can utilize system resources more efficiently while ensuring the quality of the micro service.
(2) According to the scheme, in the process of predicting the trend through index data, an optimal prediction model is obtained by combining three algorithms of Holt-Winters, ARIMA and STL and reasonably optimizing the training process; the three selected algorithms realize mutual complementation in use scenes and advantages and disadvantages, and the prediction data fitted by each algorithm can be further subjected to relevant verification, so that the prediction is ensured to be as accurate as possible.
Advantages of additional aspects of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to be construed as limiting the disclosure.
FIG. 1 is a schematic flow chart of a dynamic horizontal expansion and contraction method of a micro-service example based on trend prediction according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of index collection according to an embodiment of the disclosure;
fig. 3 is a schematic diagram of a predictive model training process according to an embodiment of the disclosure.
Detailed Description
The present disclosure is further illustrated by the following examples in conjunction with the accompanying drawings.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
The first embodiment is as follows:
the embodiment aims to provide a dynamic horizontal expansion and contraction method of the micro-service instance based on trend prediction.
A trend prediction-based micro-service instance dynamic horizontal expansion and contraction method comprises the following steps:
periodically acquiring load indexes of a host machine and a micro-service instance in the cloud platform based on an Exporter process; the Exporter process is an index collection process existing in a host machine and a micro-service instance and provides an index data interface to the outside;
storing the obtained load indexes in a time sequence database according to a time sequence;
based on the load indexes in the time sequence database, acquiring the load trend of the corresponding service by utilizing a pre-constructed load trend prediction model;
based on the obtained load trend, expansion or contraction of the micro-service instance is achieved.
Further, the load indexes include hardware indexes of a host machine, load utilization rate of a container where the micro-service instance is located, and application layer load indexes reflecting corresponding service capabilities.
Further, the hardware index of the host and the load utilization rate of the container where the micro service instance is located specifically include a CPU utilization rate, a memory utilization rate, and a network IO; the application layer load indexes embodying the corresponding capabilities of the service comprise query number per second and response time.
Furthermore, the Exporter process is an API service deployed together with related micro-services, internally collects load indexes, and externally provides a query interface for the index monitoring component.
Furthermore, the load trend prediction model adopts a mixed model formed by a plurality of time series prediction algorithms, and specifically comprises Holt-Winters, ARIMA and STL models.
Further, the training of the load trend prediction model specifically includes:
constructing a training set based on pre-obtained historical load index data;
respectively training a plurality of time series prediction models based on the training set;
and evaluating the training results of the plurality of time series prediction models based on a SMAPE algorithm, and selecting the optimal model as a final load trend prediction model.
Specifically, for the convenience of understanding, the scheme of the present embodiment is described in detail below with reference to the accompanying drawings:
based on the problems in the prior art, the present embodiment provides a dynamic horizontal expansion and contraction method for a micro-service instance based on trend prediction, as shown in fig. 1, which is an overall overview of a micro-service architecture based on automatic horizontal expansion/contraction of trend prediction, and compared with a common micro-service deployment, the method is distinguished by adding a load trend prediction module based on data from index data collected by an index collection module and existing in a time sequence database. And the index collection module obtains the access path of the specific micro-service instance through the service discovery module. After the load trend prediction module predicts a specific trend, the horizontal expansion/contraction of the micro-service instance can be dynamically realized through the instance controller. Based on this, it can be found that the scheme described in this embodiment has two key points, that is, how to collect the load indexes that can affect the expansion/contraction of the instance needs to be defined, and what algorithm is adopted to train the optimal prediction model.
First, as shown in fig. 2, an index collection flow chart is shown, where Exporter is an index collection process existing in a host and a micro-service instance, the Exporter processes collect load indexes of the host and the micro-service instance inward, provide API interface exposure index data outward for an index collection module to collect, and the index collection module finally stores the data in a time sequence database. The Exporter process is an API service deployed together with related micro-services, internally collects indexes and externally provides a query interface for index monitoring software such as Prometheus. The design concept is widely applied to index monitoring software represented by Prometheus, and a corresponding Exporter process can be acquired by a micro service provider or an open source community.
The load indexes are mainly defined into two categories, one is the hardware index of the host and the load utilization rate of the container where the instance is located. The collected load indexes generally include CPU usage, memory usage, network IO, and the like, and these basic load indexes of different microservice instances should be defined to be the same. The other is an application layer load indicator that embodies service Response capability, such as Query Per Second (QPS) and Response Time (RT), and the load indicators of these service layers are different according to service types.
With specific indicators for trend prediction, the next step is to determine a specific algorithm for determining whether the microservice instances need horizontal expansion/contraction based on the load indicators. As shown in fig. 3, a trend prediction model training process diagram is shown, wherein historical data is load index data collected by the previous step, most of the load indexes are stable time series data or can be converted into stable time series data, and an optimal model can be trained by using certain algorithms based on the time series data, so that effective prediction can be performed.
As shown in a Trend prediction model training process diagram, three time series prediction algorithms, namely Holt-winter, ARIMA (automatic regression Integrated Moving Average) and STL (continuous-Trend decomposition using LOESS), are mainly combined; specifically, the method comprises the following steps:
Holt-Winters is a time series analysis and forecasting method. The method can be used for predicting the non-stationary sequence containing linear trend and periodic fluctuation, model parameters are continuously adapted to the change of the non-stationary sequence by utilizing an exponential smoothing method (EMA), a prediction function aiming at the trend and the periodicity is fitted, and the future trend is predicted.
The prediction principle of the ARIMA model is to analyze the self of an input time sequence, extract all relevant information in the sequence to fit a function, and predict by using the function. The ARIMA (p, d, q) model is an extension of the ARMA (p, q) model, and the ARMA is a combination of the AR model and the MA model, wherein the AR (p) model is called a p-order regression model; an MA (q) model which is totally called a q-order moving average model; the ARMA model is called an autoregressive moving average model. And (3) further differentiating the predicted stable sequence on the basis of ARMA (autoregressive moving average), and finally obtaining ARIMA.
STL (Seasonal and Trend decomposition using Loess) is a very general and robust method for decomposing time series, and can perform local polynomial regression on both Seasonal and trending factors. Among them, LOESS (LOWESS or LOESS) is a local polynomial regression fitting, is a common method for smoothing a two-dimensional scatter diagram, and combines the simplicity of the conventional linear regression and the flexibility of the nonlinear regression. When a response variable value is estimated, a data subset is taken from the vicinity of a predictive variable of the response variable value, then linear regression or quadratic regression is carried out on the data subset, a weighted least square method is adopted during regression, namely the weight of a value which is closer to an estimation point is larger, and finally the value of the response variable is estimated by using an obtained local regression model. The whole fitting curve is obtained by performing point-by-point operation by the method.
The trend prediction in the scheme of the embodiment has no silver bomb in algorithm selection, the three selected algorithms are mutually supplemented in use scenes and advantages and disadvantages, and the prediction data respectively fitted can be further subjected to relevant verification, so that the prediction is ensured to be accurate as much as possible.
The whole training process is to set some timing tasks, train three algorithms of Holt-Winters, ARIMA and STL at the same time, use 2/3 of historical data as training data, and use 1/3 of data as test data. Specific parameters are introduced into each algorithm cycle, each parameter of each algorithm is trained to obtain a model, and then SMAPE (Symmetric Mean Absolute Percentage Error) is used as the evaluation of the model, so that an optimal model is obtained finally, and then trend prediction can be carried out based on the model.
SMAPE, among others, is an accuracy metric based on percentage (or relative) error, which we use as a predictive evaluation index. Generally speaking, the accuracy measurement is to judge the quality of the model based on the deviation between the predicted value and the accurate value. The data which is most reliable to evaluate the prediction model is the real time sequence data which is obtained naturally, and the historical time sequence data can be divided into two parts, wherein one part is used for training the model, and the other part is used for checking the model. If we have data of 4 weeks (28 days), we use the data of the previous 3 weeks (21 days) to adjust the algorithm training model, then use the model to predict the data of the last 7 days step by step, preferably adopt the rolling prediction method, each prediction advances one day, can carry on 7 simulation tests totally, through this test mode, finally get an optimal prediction model.
Further, if the amount of the load index data collected by the user is large enough, a neural network model like RNN and LSTM can be selected and trained for prediction.
Therefore, the load trend of the corresponding service can be predicted based on the historical load index data, the expansion or contraction decision can be intuitively made according to the specific trend, and the decisions are finally realized through the embodiment controller module.
The whole process of automatically realizing horizontal expansion/contraction of the microservice instances based on trend prediction is as follows, wherein two key technical points are as follows: firstly, index data which can really reflect the real load level of the micro-service is required to be defined, the index data comprises a host machine, a micro-service instance level and a physical resource and application layer index level, and then the time sequence data is collected to a database through an index collection module to be used for next prediction. And secondly, selecting a proper algorithm to train a prediction model with high accuracy, then using the index data of the previous step to predict the load trend, and realizing the expansion/contraction of the example in advance according to the future prediction.
The second embodiment:
the embodiment aims to provide a dynamic horizontal expansion and contraction system of a micro-service instance based on trend prediction.
According to a second aspect of the embodiments of the present disclosure, there is provided a trend prediction based micro-service instance dynamic horizontal expansion contraction system, including:
the data acquisition unit is used for periodically acquiring load indexes of hosts and micro-service instances in the cloud platform based on an Exporter process; the Exporter process is an index collection process existing in a host machine and a micro-service instance and provides an index data interface to the outside;
the data storage unit is used for storing the obtained load indexes in a time sequence database according to a time sequence;
the load trend prediction unit is used for obtaining the load trend of the corresponding service by utilizing a pre-constructed load trend prediction model based on the load indexes in the time sequence database;
and the micro service instance expansion or contraction control unit is used for realizing expansion or contraction of the micro service instance based on the obtained load trend.
Further, the system of the present embodiment corresponds to the method of the first embodiment, and the technical details thereof have already been described in detail in the first embodiment, and thus are not described herein again.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of embodiment one. For brevity, further description is omitted herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of embodiment one.
The method in the first embodiment may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, etc. as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and combines hardware thereof to complete the steps of the method. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The micro-service instance dynamic horizontal expansion and contraction method and system based on trend prediction can be realized, and have wide application prospects.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. A dynamic horizontal expansion and contraction method of a micro-service instance based on trend prediction is characterized by comprising the following steps:
periodically acquiring load indexes of a host machine and a micro-service instance in the cloud platform based on an Exporter process; the Exporter process is an index collection process existing in a host and a micro service instance, and provides an index data interface to the outside;
storing the obtained load indexes in a time sequence database according to a time sequence;
based on the load indexes in the time sequence database, acquiring the load trend of the corresponding service by utilizing a pre-constructed load trend prediction model;
based on the obtained load trend, expansion or contraction of the micro-service instance is achieved.
2. The method for micro-service instance dynamic horizontal expansion and contraction based on trend prediction as claimed in claim 1, wherein the load indexes include hardware indexes of a host, load usage rate of a container where the micro-service instance is located, and application layer load indexes embodying corresponding capability of a service.
3. The method according to claim 2, wherein the hardware index of the host and the load utilization of the container in which the micro service instance is located specifically include CPU utilization, memory utilization, and network IO; the application layer load indexes embodying the corresponding capabilities of the service comprise the number of queries per second and the response time.
4. The method as claimed in claim 1, wherein the Exporter process is an API service deployed with related micro-services, and internally collects load metrics and externally provides a query interface for the metrics monitoring component.
5. The method as claimed in claim 1, wherein the load trend prediction model is a hybrid model composed of a plurality of time series prediction algorithms, and is specifically composed of Holt-Winters, ARIMA and STL models.
6. The method according to claim 1, wherein the training of the load trend prediction model specifically comprises:
constructing a training set based on pre-obtained historical load index data;
respectively training a plurality of time sequence prediction models based on the training set;
and evaluating the training results of the plurality of time series prediction models based on a SMAPE algorithm, and selecting the optimal model as a final load trend prediction model.
7. A trend prediction based micro-service instance dynamic horizontal expansion contraction system, comprising:
the data acquisition unit is used for periodically acquiring load indexes of hosts and micro-service instances in the cloud platform based on an Exporter process; the Exporter process is an index collection process existing in a host and a micro service instance, and provides an index data interface to the outside;
the data storage unit is used for storing the obtained load indexes in a time sequence database according to time sequence;
the load trend prediction unit is used for obtaining the load trend of the corresponding service by utilizing a pre-constructed load trend prediction model based on the load indexes in the time sequence database;
and the micro service instance expansion or contraction control unit is used for realizing expansion or contraction of the micro service instance based on the obtained load trend.
8. The trend prediction-based micro-service instance dynamic horizontal expansion contraction system as claimed in claim 7, wherein the load trend prediction model adopts a hybrid model composed of a plurality of time series prediction algorithms, and specifically consists of Holt-Winters, ARIMA and STL models.
9. An electronic device comprising a memory, a processor and a computer program stored and executed on the memory, wherein the processor implements a trend prediction based micro-service instance dynamic level expansion contraction method according to any one of claims 1 to 6 when executing the program.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, wherein the program, when executed by a processor, implements a trend prediction based microservice instance dynamic level expansion contraction method as claimed in any one of claims 1 to 6.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116627660A (en) * | 2023-07-24 | 2023-08-22 | 湖北省楚天云有限公司 | Micro-service resource allocation method based on cloud data center |
| CN116893865A (en) * | 2023-09-11 | 2023-10-17 | 中移(苏州)软件技术有限公司 | Micro-service example adjusting method and device, electronic equipment and readable storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116627660A (en) * | 2023-07-24 | 2023-08-22 | 湖北省楚天云有限公司 | Micro-service resource allocation method based on cloud data center |
| CN116893865A (en) * | 2023-09-11 | 2023-10-17 | 中移(苏州)软件技术有限公司 | Micro-service example adjusting method and device, electronic equipment and readable storage medium |
| CN116893865B (en) * | 2023-09-11 | 2023-12-12 | 中移(苏州)软件技术有限公司 | Micro-service example adjusting method and device, electronic equipment and readable storage medium |
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