CN115237570A - Strategy generation method based on cloud computing and cloud platform - Google Patents
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Abstract
The invention discloses a strategy generation method based on cloud computing, a cloud platform and a cloud platform, wherein the method comprises the following steps: the method comprises the steps of firstly configuring a periodically-operated resource index calculation task in a Pod cluster, constructing a Pod cluster intelligent strategy generation model, training the Pod cluster intelligent strategy generation model by using a preset training method according to a calculation result of the periodically-operated resource index calculation task to obtain the trained Pod cluster intelligent strategy generation model, finally inputting information of a project to be executed into the trained Pod cluster intelligent strategy generation model to obtain a strategy generation mode of the project to be executed, realizing Pod dynamic expansion and capacity expansion through resource indexes, dynamically adjusting distributed resources of newly-created Pod nodes based on utilization rates of multi-class resource indexes on the Pod nodes when capacity expansion is needed, and preferentially releasing Pod with low comprehensive utilization rate of resources when capacity expansion is needed so as to continuously improve the comprehensive utilization rate of the Pod cluster on the multi-class resources.
Description
Technical Field
The invention belongs to the field of computer science and technology, and particularly relates to a strategy generation method based on cloud computing and a cloud platform.
Background
Kubernets, K8s for short, is an open-source application for managing containerization on a plurality of hosts in a cloud platform, aims to make the application for deploying containerization simple and efficient, and provides a mechanism for application deployment, planning, updating and maintenance. Kubernetes is used for managing containerization application on a plurality of hosts in a cloud platform, is an open-source platform, and can realize the functions of automatic deployment, capacity expansion and contraction, maintenance and the like of a container cluster.
Pod is the atomic scheduling unit of the Kubernetes project. The resource allocation available for the Pod is accumulated from the allocation of all containers in the Pod, and is generally fixed, for example, the resource (requests) applied for the Pod is 0.5 CPU, and the resource (limits) used at most is 1 CPU. In order to realize high availability, kubernets realize automatic level expansion and capacity expansion of Pod in Pod clusters through functions of HPA (Horizontal Pod automation) and the like based on single measurement indexes such as CPU (Central processing Unit), memory utilization rate and the like. When the capacity is automatically expanded, newly created Pod nodes are created according to Pod application resources (requests), and resources used by most services are far smaller than the resource quota applied by the service, which often causes low utilization rate of physical resources of the server. For services with high computing power or CPU requirements, the memory utilization rate may be very low when the CPU utilization rate is high, and if a new Pod is created according to the CPU and the memory with fixed specifications, the memory resource utilization rate may be low. This is a problem to be solved.
Disclosure of Invention
The invention aims to provide a strategy generation method based on cloud computing and a cloud platform, aiming at solving the defects in the prior art, the method realizes the dynamic expansion and contraction of Pod through resource indexes on the basis of not changing the original functions of Kubernets, dynamically adjusts the distributed resources of newly created Pod nodes on the basis of the utilization rate of various resource indexes on the Pod nodes when the expansion is needed, and preferentially releases the Pod with low comprehensive utilization rate of resources when the contraction is needed, thereby continuously improving the comprehensive utilization rate of Pod clusters on various resources.
An embodiment of the present application provides a policy generation method based on cloud computing, which is applied to a Pod cluster, and the method includes:
configuring a resource index calculation task which runs periodically in the Pod cluster;
constructing a Pod intelligent strategy generation model;
training the Pod intelligent strategy generation model by using a preset training method according to the calculation result of the periodically operated resource index calculation task to obtain a trained Pod intelligent strategy generation model;
and inputting information of the project to be executed into the trained Pod intelligent strategy generation model to obtain a strategy generation mode of the project to be executed, wherein the strategy generation mode comprises the steps of dynamically adjusting resource allocation of the newly-built Pod and/or releasing the Pod with low resource utilization rate in the Pod.
Optionally, after the periodically running resource indicator calculation task is configured in the Pod cluster, the method further includes:
and acquiring the historical resource utilization rate of the currently running Pod and the resource utilization rate of the available nodes in the Pod cluster through the number of the currently running Pod nodes and the resource index calculation task which runs periodically, wherein the resource utilization rate comprises one of the memory resource utilization rate and the CPU resource utilization rate or the comprehensive utilization rate based on weight.
Optionally, the constructing a Pod cluster intelligent policy generation model includes:
obtaining a plurality of training models for intelligent strategy generation of the Pod cluster by utilizing the historical resource utilization rate of the current operating Pod and the resource utilization rate of available nodes in the Pod cluster;
calculating the maximum mean difference between the training models to form a construction function;
and processing the training model according to the construction function to obtain a corresponding Pod intelligent strategy generation model.
Optionally, the training the Pod intelligent policy generation model according to the calculation result of the periodically running resource index calculation task by using a preset training method to obtain a trained Pod intelligent policy generation model includes:
obtaining a residual function of a calculation result of the resource index calculation task based on the periodic operation, and inputting the residual function to the Pod intelligent strategy generation model;
respectively calculating the historical resource utilization rate of the current operating Pod and the loss of the resource utilization rate of the available nodes in the Pod cluster according to the output result of the training model;
calculating the joint loss according to the historical resource utilization rate of the current operating Pod and the loss of the resource utilization rate of the available nodes in the Pod cluster, and optimizing the parameters of the training model based on the joint loss until the trained Pod cluster intelligent strategy generation model is obtained.
Optionally, the inputting information of the to-be-executed item into the trained Pod intelligent policy generation model to obtain a policy generation manner of the to-be-executed item includes:
when the strategy generation mode is to dynamically adjust the resource allocation of the newly-built Pod, the trained Pod intelligent strategy generation model operates in the following mode:
calculating the number of newly-built Pod according to the HPA function and through a resource index s;
calculating the average utilization rate H of the resource index j of the currently running Pod in the Pod cluster j Wherein, the
N is the number of currently operating Pod nodes, M ij Is the actual usage of the resource index j on the node i, said R ij A first allocation of a resource indicator j on node i;
judging the average utilization rate H of the resource index j j And if the resource index k is smaller than the lower limit of the utilization rate of the resource index j in the Pod cluster, if so, correspondingly establishing the resource index k according to the number of the newly established pods when the pods are newly established, and distributing the resource index j according to a preset principle.
Optionally, the allocating the resource indicator j according to a preset principle includes:
judgment C j /(1-Redis j ) If the number of the Pod nodes is less than 1, if so, distributing the resource index j according to a second distribution amount when the Pod nodes i are newly built, wherein the second distribution amount meets the requirement
The described
A second allocation of resource indicator j on node i, the Redis j Presetting redundancy for a resource index j in the Pod node;
if not, the resource index j is according to the first allocation amount R ij And (6) distributing.
Optionally, the inputting information of the to-be-executed item into the trained Pod intelligent policy generation model to obtain a policy generation manner of the to-be-executed item, further includes:
when the strategy generation mode is to release the Pod with low resource utilization rate in the Pod cluster, the trained Pod cluster intelligent strategy generation model operates in the following mode:
obtaining the average utilization rate H of other resource indexes on the node i except the resource index s i Wherein, the
The m is the type number of the Pod node resource indexes;
according to the average utilization rate H i And determining and releasing the Pod nodes with low resource utilization rate in the Pod cluster.
Optionally, the preset training method includes a neural network algorithm.
Another embodiment of the present application provides a policy generation cloud platform based on cloud computing, which is applied to a Pod cluster, and includes:
the configuration module is used for configuring resource index calculation tasks which run periodically in the Pod cluster;
the building module is used for building a Pod intelligent strategy generation model;
the training module is used for training the Pod intelligent strategy generating model by using a preset training method according to the calculation result of the periodically operated resource index calculation task to obtain a trained Pod intelligent strategy generating model;
and the generating module is used for inputting the information of the project to be executed into the trained Pod intelligent strategy generating model to obtain a strategy generating mode of the project to be executed, wherein the strategy generating mode comprises the steps of dynamically adjusting resource allocation of the newly-built Pod and/or releasing the Pod with low resource utilization rate in the Pod.
Optionally, after the configuring module, the cloud platform further includes:
and the obtaining module is used for obtaining the historical resource utilization rate of the currently running Pod and the resource utilization rate of the available nodes in the Pod cluster according to the number of the currently running Pod nodes and the periodically running resource index calculation task, wherein the resource utilization rate comprises one of the memory resource utilization rate and the CPU resource utilization rate or the comprehensive utilization rate based on weight.
Optionally, the building module includes:
a first obtaining unit, configured to obtain multiple training models for Pod cluster intelligent policy generation by using a historical resource utilization rate of the currently-operating Pod and a resource utilization rate of an available node in the Pod cluster;
the first calculation unit is used for calculating the maximum mean difference between the training models to form a construction function;
and the second obtaining unit is used for processing the training model according to the building function to obtain a corresponding Pod intelligent strategy generation model.
Optionally, the training module includes:
the obtaining unit is used for obtaining a residual error function of a calculation result of the resource index calculation task based on the periodic operation and inputting the residual error function to the Pod intelligent strategy generation model;
the second calculation unit is used for calculating the historical resource utilization rate of the currently running Pod and the loss of the resource utilization rate of the available nodes in the Pod cluster according to the output result of the training model;
and the optimization unit is used for calculating the joint loss according to the historical resource utilization rate of the currently-operating Pod and the loss of the resource utilization rate of the available nodes in the Pod cluster, and optimizing the parameters of the training model based on the joint loss until the trained Pod cluster intelligent strategy generation model is obtained.
Optionally, the generating module includes:
when the strategy generation mode is to dynamically adjust the resource allocation of the newly-built Pod, the trained Pod intelligent strategy generation model operates in the following mode:
the third calculating unit is used for calculating the number of the newly-built Pod according to the HPA function and through the resource index k;
a fourth calculating unit, configured to calculate an average utilization rate C of a resource indicator j of a currently-operating Pod in the Pod cluster j Wherein, the
N is the number of currently operating Pod nodes, U ij Is the actual usage of the resource index j on the node i, said R ij A first allocation of a resource indicator j on node i;
a judging unit for judging the average utilization rate C of the resource index j j And if the number of the newly-built Pod is smaller than the lower limit of the utilization rate of the resource index j in the Pod cluster, correspondingly creating the resource index k according to the number of the newly-built Pod when the newly-built Pod is built, and distributing the resource index j according to a preset principle.
Optionally, the determining unit includes:
a first judgment subunit for judging C j /(1-Red j ) If the number of the Pod nodes is less than 1, if so, distributing the resource index j according to a second distribution amount when the Pod nodes i are newly built, wherein the second distribution amount meets the requirement
The above-mentioned
Is on node iA second allocation of a resource indicator j, the Red j Presetting redundancy for a resource index j in the Pod node;
a second determining subunit, configured to, if not, determine the resource indicator j according to the first allocation amount R ij And (6) distributing.
Optionally, the generating module further includes:
when the strategy generation mode is to release the Pod with low resource utilization rate in the Pod cluster, the trained Pod cluster intelligent strategy generation model operates in the following mode:
an obtaining unit, configured to obtain an average utilization rate C of the remaining resource indicators on the node i except the resource indicator k i Wherein, the
The m is the type number of the Pod node resource indexes;
a release unit for releasing the average utilization rate C i And determining and releasing the Pod nodes with low resource utilization rate in the Pod cluster.
Yet another embodiment of the present application provides a cloud platform for generating policies based on cloud computing, comprising a processor and a memory; the processor is in communication with the memory, and the processor is configured to read the computer program from the memory and execute the computer program to implement the method of any one of the above.
A further embodiment of the application provides a storage medium having a computer program stored thereon, wherein the computer program is arranged to perform the method of any of the above when executed.
Yet another embodiment of the present application provides an electronic device comprising a memory having a computer program stored therein and a processor configured to execute the computer program to perform the method described in any of the above.
Compared with the prior art, the method comprises the steps of firstly configuring a periodically-operated resource index calculation task in a Pod cluster, constructing a Pod cluster intelligent strategy generation model, training the Pod cluster intelligent strategy generation model by using a preset training method according to the calculation result of the periodically-operated resource index calculation task to obtain the trained Pod cluster intelligent strategy generation model, and finally inputting information of a project to be executed into the trained Pod cluster intelligent strategy generation model to obtain a strategy generation mode of the project to be executed, wherein the strategy generation mode comprises the steps of dynamically adjusting resource allocation of newly-built pods and/or releasing pods with low resource utilization rate in the Pod cluster, dynamic Pod expansion and contraction are realized through resource indexes on the basis of not changing the original functions of Kubernetes, when expansion is needed, the allocated resources of newly-created Pod nodes are dynamically adjusted on the basis of the utilization rate of various resource indexes on the Pod nodes, and when contraction is needed, the pods with low comprehensive utilization rate of the resources are preferentially released, so that the comprehensive utilization rate of the multi-type resources of the Pod cluster on the various resources is continuously improved.
Drawings
Fig. 1 is a block diagram of a hardware structure of a computer terminal of a policy generation method based on cloud computing according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a policy generation method based on cloud computing according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a policy generation cloud platform based on cloud computing according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a policy generation cloud platform based on cloud computing according to an embodiment of the present invention.
Detailed Description
The embodiments described below with reference to the drawings are illustrative only and should not be construed as limiting the invention.
The embodiment of the invention firstly provides a strategy generation method based on cloud computing, and the method can be applied to electronic equipment, such as computer terminals, specifically common computers, quantum computers and the like.
This will be described in detail below by way of example as it would run on a computer terminal. Fig. 1 is a block diagram of a hardware structure of a computer terminal of a policy generation method based on cloud computing according to an embodiment of the present invention. As shown in fig. 1, the computer terminal may include one or more (only one shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, and optionally, a transmission device 106 for communication functions and an input-output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the computer terminal. For example, the computer terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be configured to store software programs and modules of application software, such as program instructions/modules corresponding to the cloud computing-based policy generation method in the embodiment of the present application, and the processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, so as to implement the method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to a computer terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 can be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
Referring to fig. 2, fig. 2 is a schematic flowchart of a policy generation method based on cloud computing according to an embodiment of the present invention, and the method may include the following steps:
s201: and configuring a resource index calculation task which runs periodically in the Pod cluster.
Specifically, after the resource index calculation task that runs periodically is configured in the Pod cluster, the method further includes:
and acquiring the historical resource utilization rate of the currently running Pod and the resource utilization rate of the available nodes in the Pod cluster through the number of the currently running Pod nodes and the resource index calculation task which runs periodically, wherein the resource utilization rate comprises one of the memory resource utilization rate and the CPU resource utilization rate or the comprehensive utilization rate based on weight.
Kubernets is a common container arrangement tool, which is concerned by the industry with advanced design concepts and is widely applied in practical production environments, an important task of kubernets is to select a proper node (node) to run Pod (the minimum unit for creation and deployment in kubernets, which is a running instance), the load of the whole cluster is determined by the resource utilization rate of each node in the cluster, and the utilization rate of each node is related to the Pod information running on the node. Therefore, the policy generation manner for the cluster may be determined by the load status and resource utilization rate of the cluster.
Illustratively, the historical resource utilization rate of the currently scheduled Pod and the resource utilization rate of the available nodes in the cluster may be obtained by deploying a preset cluster resource monitoring policy and a resource index calculation task that runs periodically in the Pod cluster. The resource may specifically include three indexes: CPU utilization, memory utilization, and weight-based overall utilization.
S202: and constructing a Pod intelligent strategy generation model.
Specifically, constructing a Pod intelligent policy generation model may include:
1. obtaining a plurality of training models for intelligent strategy generation of the Pod cluster by utilizing the historical resource utilization rate of the current operating Pod and the resource utilization rate of available nodes in the Pod cluster;
2. calculating the maximum mean difference between the training models to form a construction function;
3. and processing the training model according to the construction function to obtain a corresponding Pod intelligent strategy generation model.
In an optional implementation manner, historical resource utilization rate of a currently operating Pod and resource utilization rate of an available node in the Pod are obtained, and a first generative model and a second generative model for the Pod cluster intelligent policy generative model can be obtained by training the historical resource utilization rate of the currently operating Pod and the resource utilization rate of the available node in the Pod. And carrying out normalization index processing on the characteristic information output by the characteristic layer of the second training model to obtain a second probability distribution Y. Here, the normalization index processing means converting the multi-class output into probability by using an index function and a normalization method, and may be specifically expressed as: and mapping the multi-classification result to zero to positive infinity by using an exponential function, then carrying out normalization processing to obtain approximate probability, and calculating the maximum mean difference according to the first probability distribution X and the second probability distribution Y to form a construction function. And finally, processing the first training model according to a construction function, comparing the processed first generation model with the processed second generation model, training the second generation model by using the probability distribution information obtained after comparison, the historical resource utilization rate of the currently-operated Pod and the resource utilization rate information of the available nodes in the Pod cluster, and determining the learning degree of the second generation model to the first generation model after the dimension reduction processing by using the construction function. And obtaining a corresponding Pod intelligent strategy generation model by judging whether the construction function is converged.
S203: and training the Pod intelligent strategy generation model by using a preset training method according to the calculation result of the periodically operated resource index calculation task to obtain the trained Pod intelligent strategy generation model.
Specifically, training the Pod intelligent policy generation model by using a preset training method according to the calculation result of the periodically-running resource index calculation task to obtain a trained Pod intelligent policy generation model, which may include:
step 1: and obtaining a residual function of a calculation result of the resource index calculation task based on the periodic operation, and inputting the residual function to the Pod intelligent strategy generation model.
Step 2: and respectively calculating the historical resource utilization rate of the current operating Pod and the loss of the resource utilization rate of the available nodes in the Pod cluster according to the output result of the training model.
And 3, step 3: calculating the joint loss according to the historical resource utilization rate of the current operating Pod and the loss of the resource utilization rate of the available nodes in the Pod cluster, and optimizing the parameters of the training model based on the joint loss until the trained Pod cluster intelligent strategy generation model is obtained.
Specifically, the Pod cluster intelligent strategy generation model can use a preset connection mode to make a reference function for the input of each layer, so as to learn and form a residual error function, the residual error function is easier to optimize, and the number of layers of the network can be greatly increased. When the accuracy of the network model reaches saturation, an identity mapping layer can be added on the basis of the accuracy, namely the output is equal to the input, and the output of the previous layer is directly transmitted to the subsequent network. Therefore, the network depth is increased, and the error is ensured not to be increased.
Illustratively, the calculation results of the resource indicator calculation tasks that run on a periodic basis are written in a row as a single two-dimensional sample to generate two-dimensional data, which is normalized based on a maximum-minimum normalization method. For example, generating 180 x 2 by 3 training or reasoning data, normalized data can improve the convergence rate of model training. The training data can be automatically retrained regularly by only setting fixed time to empty sample data, reasoning samples only keep 180 latest samples, if the Pod stops running due to some abnormity and the data stops collecting, resource utilization rate data of the Pod running, which is obtained within a certain time by using the CPU and the memory finally generated during the Pod running, is reserved in the file, and an initial Pod intelligent strategy generating model is input for training to obtain a final Pod intelligent strategy generating model for Pod resource utilization rate prediction.
The preset training method comprises a neural network algorithm.
S204: and inputting information of the project to be executed into the trained Pod intelligent strategy generation model to obtain a strategy generation mode of the project to be executed, wherein the strategy generation mode comprises the steps of dynamically adjusting resource allocation of the newly-built Pod and/or releasing the Pod with low resource utilization rate in the Pod.
Specifically, the original HPA function of Kubernetes realizes Pod dynamic scaling through a single resource indicator, which may cause low resource utilization of other servers besides the single resource. The method comprises the steps that a Pod capacity expansion and reduction strategy is calculated based on the use condition of various resources of a Pod which is operated in a Pod cluster, a generation scheme of a Pod level automatic capacity expansion and reduction strategy is provided, and the allocated resources of newly created Pod nodes are dynamically adjusted based on the utilization rate of various resource indexes on the Pod nodes when capacity expansion is needed on the basis that Pod dynamic capacity expansion and reduction are achieved through a single resource index without changing the original HPA function of Kubernets; when capacity reduction is needed, the pod with low comprehensive utilization rate of resources is released preferentially, and therefore the comprehensive utilization rate of the pod cluster to various resources is continuously improved.
Inputting information of the to-be-executed project into the trained Pod intelligent strategy generation model to obtain a strategy generation mode of the to-be-executed project, where the strategy generation mode may include:
when the strategy generation mode is to dynamically adjust the resource allocation of the newly-built Pod, the trained Pod intelligent strategy generation model operates in the following mode:
according to the HPA function, calculating through a resource index k to obtain the number of newly-built Pod;
calculating the average utilization rate H of the resource index j of the currently running Pod in the Pod cluster j Wherein, the
N isNumber of currently operating Pod nodes, M ij Is the actual usage of the resource index j on the node i, said R ij A first allocation of a resource indicator j on node i;
judging the average utilization rate H of the resource index j j And if the number of the newly-built Pod is smaller than the lower limit of the utilization rate of the resource index j in the Pod cluster, correspondingly creating the resource index k according to the number of the newly-built Pod when the newly-built Pod is built, and distributing the resource index j according to a preset principle.
In an alternative embodiment, the resource index j is allocated according to a preset principle, which may include:
judgment of C j /(1-Redis j ) If the number of the Pod nodes is less than 1, if so, distributing the resource index j according to a second distribution amount when the Pod nodes i are newly built, wherein the second distribution amount meets the requirement
The above-mentioned
A second allocation of resource index j on node i, the Redis j Presetting redundancy for a resource index j in the Pod node;
if not, the resource index j is according to the first allocation amount R ij And (6) distributing.
Specifically, inputting information of the to-be-executed item into the trained Pod intelligent policy generation model to obtain a policy generation manner of the to-be-executed item, and may further include:
when the strategy generation mode is to release the Pod with low resource utilization rate in the Pod cluster, the trained Pod cluster intelligent strategy generation model operates in the following mode:
obtaining the average utilization rate H of other resource indexes on the node i except the resource index s i Wherein, the
The m is the type number of the Pod node resource indexes;
according to the average utilization rate H i And determining and releasing the Pod nodes with low resource utilization rate in the Pod cluster.
In another optional embodiment, the method and the device are optimized based on the original HPA function of kubernets, and the comprehensive utilization rate of the Pod cluster on the resources of the servers of multiple types can be improved by cooperation of modules such as an automatic scaling device, a data collector, an actuator and a Pod. The automatic expansion and contraction device, the data acquisition device and the actuator module can be combined or separated with KuberneteseHPA, metrics API, depolymement and other modules.
The application provides a cloud resource dynamic capacity expansion scheme based on a business time delay priority strategy based on monitoring and analyzing the most important customer experience index 'business time delay' of mobile edge calculation, and in the specific scheme: according to different characteristics of the service on various resource requirements, a Pod capacity expansion and reduction strategy is calculated based on quantitative data of the use conditions of various resources of the Pod already running in the Pod, so that the comprehensive utilization rate of various resources of a server corresponding to the Pod can be improved; dynamically adjusting the allocated resources of newly created Pod nodes based on the utilization rate of multi-class resource indexes on the Pod nodes when capacity expansion is needed on the basis of realizing Pod dynamic expansion capacity through a single resource index without changing the original HPA function of Kubernetes; when the capacity needs to be reduced, the Pod with low comprehensive utilization rate of the resources is released preferentially, so that the comprehensive utilization rate of the Pod cluster to various resources is continuously improved.
It can be seen that, the invention firstly configures a periodically running resource index calculation task in a Pod cluster, constructs a Pod cluster intelligent strategy generation model, trains the Pod cluster intelligent strategy generation model by using a preset training method according to the calculation result of the periodically running resource index calculation task, obtains the trained Pod cluster intelligent strategy generation model, and finally inputs the information of the project to be executed into the trained Pod cluster intelligent strategy generation model, so as to obtain a strategy generation mode of the project to be executed, wherein the strategy generation mode comprises dynamically adjusting the resource allocation of newly-built pods and/or releasing pods with low resource utilization rate in the Pod cluster, and dynamically expanding and shrinking the Pod by using the resource index on the basis of not changing the original functions of Kubernetes, when needing expanding, dynamically adjusting the allocated resources of newly-built Pod nodes based on the utilization rate of multi-class resource indexes on the Pod nodes, when needing shrinking, preferentially releasing the pods with low comprehensive utilization rate of resources, so as to continuously improve the comprehensive utilization rate of the multi-class resources by the Pod cluster.
Yet another embodiment of the present application provides a policy generation cloud platform based on cloud computing, as shown in fig. 3, which includes:
a configuration module 301, configured to configure a resource indicator calculation task that runs periodically in a Pod cluster;
a building module 302, configured to build a Pod intelligent policy generation model;
the training module 303 is configured to train the Pod intelligent policy generation model by using a preset training method according to a calculation result of the periodically-running resource index calculation task, so as to obtain a trained Pod intelligent policy generation model;
and the generating module 304 is configured to input the information of the to-be-executed project into the trained Pod intelligent policy generating model, and obtain a policy generating manner of the to-be-executed project, where the policy generating manner includes dynamically adjusting resource allocation of a newly-built Pod and/or releasing a Pod with a low resource utilization rate in the Pod.
Specifically, after the configuring module, the cloud platform further includes:
and the obtaining module is used for obtaining the historical resource utilization rate of the currently running Pod and the resource utilization rate of the available nodes in the Pod cluster according to the number of the currently running Pod nodes and the periodically running resource index calculation task, wherein the resource utilization rate comprises one of the memory resource utilization rate and the CPU resource utilization rate or the comprehensive utilization rate based on weight.
Specifically, the building module includes:
a first obtaining unit, configured to obtain multiple training models for Pod cluster intelligent policy generation by using a historical resource utilization rate of the currently-operating Pod and a resource utilization rate of an available node in the Pod cluster;
the first calculation unit is used for calculating the maximum mean difference between the training models to form a construction function;
and the second obtaining unit is used for processing the training model according to the building function to obtain a corresponding Pod intelligent strategy generation model.
Specifically, the training module includes:
the obtaining unit is used for obtaining a residual error function of a calculation result of the resource index calculation task based on the periodic operation and inputting the residual error function to the Pod intelligent strategy generation model;
the second calculation unit is used for calculating the historical resource utilization rate of the currently running Pod and the loss of the resource utilization rate of the available nodes in the Pod cluster according to the output result of the training model;
and the optimization unit is used for calculating the joint loss according to the historical resource utilization rate of the current operating Pod and the loss of the resource utilization rate of the available nodes in the Pod cluster, and optimizing the parameters of the training model based on the joint loss until the trained Pod cluster intelligent strategy generation model is obtained.
Specifically, the generating module includes:
when the strategy generation mode is to dynamically adjust the resource allocation of the newly-built Pod, the trained Pod intelligent strategy generation model operates in the following mode:
the third calculating unit is used for calculating the number of the newly-built Pod according to the HPA function and through the resource index k;
a fourth calculating unit, configured to calculate an average utilization rate H of a resource indicator j of a currently-operating Pod in the Pod cluster j Wherein, the
N is the number of currently operating Pod nodes, M ij Is the actual usage of the resource index j on the node i, said R ij A first allocation amount of a resource indicator j on a node i;
a judging unit for judging the average utilization rate H of the resource index j j And if the resource index k is smaller than the lower limit of the utilization rate of the resource index j in the Pod cluster, if so, correspondingly establishing the resource index k according to the number of the newly established pods when the pods are newly established, and distributing the resource index j according to a preset principle.
Specifically, the judging unit includes:
a first judgment subunit for judging C j /(1-Redis j ) If the number of the Pod nodes is less than 1, if so, distributing the resource index j according to a second distribution amount when the Pod nodes i are newly built, wherein the second distribution amount meets the requirement
The described
A second allocation of resource indicator j on node i, the Redis j Presetting redundancy for a resource index j in the Pod node;
a second determining subunit, configured to, if not, determine the resource indicator j according to the first allocation amount R ij And (6) distributing.
Specifically, the generating module further includes:
when the strategy generation mode is the mode of releasing the Pod with low resource utilization rate in the Pod cluster, the trained Pod cluster intelligent strategy generation model operates in the following mode:
an obtaining unit, configured to obtain an average utilization rate H of the remaining resource indicators on the node i except the resource indicator s i Wherein, the
The m is the type number of the Pod node resource indexes;
a release unit for releasing the average utilization rate H i Determining and releasing Pod clustersAnd the Pod node with low medium resource utilization rate.
Compared with the prior art, the method comprises the steps of firstly configuring a periodically-operated resource index calculation task in a Pod cluster, constructing a Pod cluster intelligent strategy generation model, training the Pod cluster intelligent strategy generation model by using a preset training method according to a calculation result of the periodically-operated resource index calculation task to obtain the trained Pod cluster intelligent strategy generation model, and finally inputting information of a project to be executed into the trained Pod cluster intelligent strategy generation model to obtain a strategy generation mode of the project to be executed, wherein the strategy generation mode comprises the steps of dynamically adjusting resource allocation of newly-built pods and/or releasing pods with low resource utilization rate in the Pod cluster, dynamically expanding and shrinking the pods through resource indexes on the basis of not changing original functions of Kubernetes, dynamically adjusting allocated resources of newly-built Pod nodes on the basis of utilization rates of various resource indexes on the Pod nodes when expansion is needed, and preferentially releasing the pods with low comprehensive resource utilization rate when contraction is needed, so as to continuously improve the comprehensive utilization rate of the various resources of the Pod cluster on the Pod cluster.
Referring to fig. 4, fig. 4 is a cloud platform 401 for generating a policy based on cloud computing according to another embodiment of the present application, including a processor 402 and a memory 403; the processor 402 is communicatively connected to the memory 403, and the processor 402 is configured to read a computer program from the memory 403 and execute the computer program to implement the method described in any one of the above.
Compared with the prior art, the method comprises the steps of firstly configuring a periodically-operated resource index calculation task in a Pod cluster, constructing a Pod cluster intelligent strategy generation model, training the Pod cluster intelligent strategy generation model by using a preset training method according to a calculation result of the periodically-operated resource index calculation task to obtain the trained Pod cluster intelligent strategy generation model, and finally inputting information of a project to be executed into the trained Pod cluster intelligent strategy generation model to obtain a strategy generation mode of the project to be executed, wherein the strategy generation mode comprises the steps of dynamically adjusting resource allocation of newly-built pods and/or releasing pods with low resource utilization rate in the Pod cluster, dynamically expanding and shrinking the pods through resource indexes on the basis of not changing original functions of Kubernetes, dynamically adjusting allocated resources of newly-built Pod nodes on the basis of utilization rates of various resource indexes on the Pod nodes when expansion is needed, and preferentially releasing the pods with low comprehensive resource utilization rate when contraction is needed, so as to continuously improve the comprehensive utilization rate of the various resources of the Pod cluster on the Pod cluster.
An embodiment of the present invention further provides a storage medium, in which a computer program is stored, where the computer program is configured to execute the steps in any of the above method embodiments when running.
Specifically, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:
s201: configuring a resource index calculation task which runs periodically in the Pod cluster;
s202: constructing a Pod intelligent strategy generation model;
s203: training the Pod intelligent strategy generation model by using a preset training method according to the calculation result of the periodically operated resource index calculation task to obtain a trained Pod intelligent strategy generation model;
s204: and inputting information of the project to be executed into the trained Pod intelligent strategy generation model to obtain a strategy generation mode of the project to be executed, wherein the strategy generation mode comprises the steps of dynamically adjusting resource allocation of the newly-built Pod and/or releasing the Pod with low resource utilization rate in the Pod.
Specifically, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Compared with the prior art, the method comprises the steps of firstly configuring a periodically-operated resource index calculation task in a Pod cluster, constructing a Pod cluster intelligent strategy generation model, training the Pod cluster intelligent strategy generation model by using a preset training method according to a calculation result of the periodically-operated resource index calculation task to obtain the trained Pod cluster intelligent strategy generation model, and finally inputting information of a project to be executed into the trained Pod cluster intelligent strategy generation model to obtain a strategy generation mode of the project to be executed, wherein the strategy generation mode comprises the steps of dynamically adjusting resource allocation of newly-built pods and/or releasing pods with low resource utilization rate in the Pod cluster, dynamically expanding and shrinking the pods through resource indexes on the basis of not changing original functions of Kubernetes, dynamically adjusting allocated resources of newly-built Pod nodes on the basis of utilization rates of various resource indexes on the Pod nodes when expansion is needed, and preferentially releasing the pods with low comprehensive resource utilization rate when contraction is needed, so as to continuously improve the comprehensive utilization rate of the various resources of the Pod cluster on the Pod cluster.
An embodiment of the present invention further provides an electronic apparatus, which includes a memory and a processor, where the memory stores a computer program, and the processor is configured to execute the computer program to perform the steps in any one of the method embodiments described above.
Specifically, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Specifically, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s201: configuring a resource index calculation task which runs periodically in the Pod cluster;
s202: constructing a Pod intelligent strategy generation model;
s203: training the Pod intelligent strategy generation model by using a preset training method according to the calculation result of the periodically operated resource index calculation task to obtain a trained Pod intelligent strategy generation model;
s204: and inputting information of the project to be executed into the trained Pod intelligent strategy generation model to obtain a strategy generation mode of the project to be executed, wherein the strategy generation mode comprises the steps of dynamically adjusting resource allocation of the newly-built Pod and/or releasing the Pod with low resource utilization rate in the Pod.
Compared with the prior art, the method comprises the steps of firstly configuring a periodically-operated resource index calculation task in a Pod cluster, constructing a Pod cluster intelligent strategy generation model, training the Pod cluster intelligent strategy generation model by using a preset training method according to the calculation result of the periodically-operated resource index calculation task to obtain the trained Pod cluster intelligent strategy generation model, and finally inputting information of a project to be executed into the trained Pod cluster intelligent strategy generation model to obtain a strategy generation mode of the project to be executed, wherein the strategy generation mode comprises the steps of dynamically adjusting resource allocation of newly-built pods and/or releasing pods with low resource utilization rate in the Pod cluster, dynamic Pod expansion and contraction are realized through resource indexes on the basis of not changing the original functions of Kubernetes, when expansion is needed, the allocated resources of newly-created Pod nodes are dynamically adjusted on the basis of the utilization rate of various resource indexes on the Pod nodes, and when contraction is needed, the pods with low comprehensive utilization rate of the resources are preferentially released, so that the comprehensive utilization rate of the multi-type resources of the Pod cluster on the various resources is continuously improved.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art will appreciate that the embodiments described in this specification are presently preferred and that no acts or modules are required by the invention.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the above-described units is only one logical functional division, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another cloud platform, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the above methods according to the embodiments of the present invention. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The above embodiments of the present invention are described in detail, and the principle and the implementation of the present invention are explained by applying specific embodiments, and the above description of the embodiments is only used to help understanding the method of the present invention and the core idea thereof; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A strategy generation method based on cloud computing is applied to a Pod cluster, and is characterized by comprising the following steps:
configuring a resource index calculation task which runs periodically in the Pod cluster;
constructing a Pod intelligent strategy generation model;
training the Pod intelligent strategy generation model by using a preset training method according to the calculation result of the periodically operated resource index calculation task to obtain a trained Pod intelligent strategy generation model;
and inputting information of the project to be executed into the trained Pod intelligent strategy generation model to obtain a strategy generation mode of the project to be executed, wherein the strategy generation mode comprises the steps of dynamically adjusting resource allocation of the newly-built Pod and/or releasing the Pod with low resource utilization rate in the Pod.
2. The method of claim 1, wherein after the periodically running resource indicator calculation task is configured in the Pod cluster, the method further comprises:
and acquiring the historical resource utilization rate of the currently running Pod and the resource utilization rate of the available nodes in the Pod cluster according to the number of the currently running Pod nodes and the periodically running resource index calculation task, wherein the resource utilization rate comprises one of the memory resource utilization rate and the CPU resource utilization rate or the comprehensive utilization rate based on weight.
3. The method of claim 2, wherein the constructing the Pod cluster intelligence policy generation model comprises:
obtaining a plurality of training models for intelligent strategy generation of the Pod cluster by utilizing the historical resource utilization rate of the current operating Pod and the resource utilization rate of available nodes in the Pod cluster;
calculating the maximum mean difference between the training models to form a construction function;
and processing the training model according to the construction function to obtain a corresponding Pod intelligent strategy generation model.
4. The method according to claim 3, wherein the training the Pod intelligent policy generation model according to the calculation result of the periodically running resource index calculation task by using a preset training method to obtain a trained Pod intelligent policy generation model comprises:
obtaining a residual function of a calculation result of the resource index calculation task based on the periodic operation, and inputting the residual function to the Pod intelligent strategy generation model;
respectively calculating the historical resource utilization rate of the current operating Pod and the loss of the resource utilization rate of the available nodes in the Pod cluster according to the output result of the training model;
calculating the joint loss according to the historical resource utilization rate of the current operating Pod and the loss of the resource utilization rate of the available nodes in the Pod cluster, and optimizing the parameters of the training model based on the joint loss until the trained Pod cluster intelligent strategy generation model is obtained.
5. The method according to claim 4, wherein the inputting information of the items to be executed into the trained Pod intelligent policy generation model to obtain a policy generation manner of the items to be executed comprises:
when the strategy generation mode is to dynamically adjust the resource allocation of the newly-built Pod, the trained Pod intelligent strategy generation model operates in the following mode:
according to the HPA function, calculating through a resource index s to obtain the number of newly-built Pod;
calculating outThe average utilization rate H of the resource index j of the currently running Pod in the Pod cluster j Wherein, the
N is the number of currently operating Pod nodes, M ij Is the actual usage of the resource index j on the node i, said R ij A first allocation amount of a resource indicator j on a node i;
judging the average utilization rate H of the resource index j j And if the resource index k is smaller than the lower limit of the utilization rate of the resource index j in the Pod cluster, if so, correspondingly establishing the resource index k according to the number of the newly established pods when the pods are newly established, and distributing the resource index j according to a preset principle.
6. The method according to claim 5, wherein the resource index j is allocated according to a preset rule, comprising:
judgment C j /(1-Redis j ) If the number of the Pod nodes is less than 1, if so, distributing the resource index j according to a second distribution amount when the Pod nodes i are newly built, wherein the second distribution amount meets the requirement
The above-mentioned
A second allocation of resource index j on node i, the Redis j Presetting redundancy for a resource index j in the Pod node;
if not, the resource index j is according to the first allocation amount R ij And (6) distributing.
7. The method of claim 4, wherein the inputting the information of the items to be executed into the trained Pod intelligent policy generation model to obtain the policy generation mode of the items to be executed, further comprises:
when the strategy generation mode is to release the Pod with low resource utilization rate in the Pod cluster, the trained Pod cluster intelligent strategy generation model operates in the following mode:
obtaining the average utilization rate H of other resource indexes on the node i except the resource index s i Wherein, the
The m is the type number of the Pod node resource indexes;
according to the average utilization rate H i And determining and releasing the Pod nodes with low resource utilization rate in the Pod cluster.
8. The method of any one of claims 1 to 7, wherein the predetermined training method comprises a neural network algorithm.
9. A strategy generation cloud platform based on cloud computing is applied to a Pod cluster, and is characterized in that the cloud platform comprises:
the configuration module is used for configuring a resource index calculation task which runs periodically in the Pod cluster;
the building module is used for building a Pod intelligent strategy generation model;
the training module is used for training the Pod intelligent strategy generating model by using a preset training method according to the calculation result of the periodically operated resource index calculation task to obtain a trained Pod intelligent strategy generating model;
and the generating module is used for inputting the information of the project to be executed into the trained Pod intelligent strategy generating model to obtain a strategy generating mode of the project to be executed, wherein the strategy generating mode comprises the steps of dynamically adjusting resource allocation of the newly-built Pod and/or releasing the Pod with low resource utilization rate in the Pod.
10. A cloud platform for generating policies based on cloud computing, comprising a processor and a memory; the processor is connected in communication with the memory, and the processor is configured to read the computer program from the memory and execute the computer program to implement the method of any one of claims 1 to 8.
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