CN111475251A - Cluster container scheduling method, system, terminal and storage medium - Google Patents
Cluster container scheduling method, system, terminal and storage medium Download PDFInfo
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Abstract
The invention provides a method, a system, a terminal and a storage medium for dispatching a cluster container, wherein the method comprises the following steps: collecting the average utilization rate of a CPU and the average utilization rate of a memory of each node of a cluster; collecting CPU resource consumption and memory consumption of each node of the cluster; calculating the CPU-memory utilization similarity of the nodes according to the average CPU utilization, the average memory utilization, the CPU resource consumption and the memory consumption; and evaluating the nodes of the cluster according to the average CPU utilization rate, the average memory utilization rate and the CPU-memory utilization rate similarity, and selecting the optimal node as a container deployment node. According to the invention, the use balance condition of the node resources can be obviously improved by scheduling according to the use condition of the real-time resources of the nodes and the use influence of the deployment Pod on the node use rate.
Description
Technical Field
The invention relates to the technical field of container deployment, in particular to a method, a system, a terminal and a storage medium for dispatching a cluster container.
Background
Kubernetes is a Google open-source container cluster management system, provides functions of application deployment, maintenance, expansion and the like, and can conveniently and effectively manage containerized application running across clusters by using the Kubernetes. The Pod is a basic unit of resource scheduling in Kubernetes, and the component responsible for Pod scheduling is Kubernetes scheduler, which is responsible for receiving a new Pod created by a controller manager and selecting a node with the highest priority for deployment according to a scheduling algorithm.
The existing scheduling algorithm mainly uses a balanced resources Allocation strategy, the algorithm respectively calculates the total amount of applications of CPU and memory of Pod already running on a candidate Node and the request amount of CPU and memory of Pod to be scheduled, respectively wants to add and then divide the sum by the total amount of the Node, calculates CpuFra and MemFra, scores by the following formula, preferentially selects the Node with the highest score to deploy the Pod
Score＝10-|CpuFra-MemFra|*10
Although the utilization rate of the CPU and the memory resources of the candidate Node is also considered in the preferred algorithm, the algorithm measures the scheduling priority by calculating according to the request amount of the CPU and the memory, and cannot accurately reflect the real-time resource utilization of the Node.
Disclosure of Invention
In view of the above-mentioned deficiencies of the prior art, the present invention provides a method, a system, a terminal and a storage medium for scheduling a cluster container, so as to solve the above-mentioned technical problems.
In a first aspect, the present invention provides a method for scheduling a cluster container, including:
collecting the average utilization rate of a CPU and the average utilization rate of a memory of each node of a cluster;
collecting CPU resource consumption and memory consumption of each node of the cluster;
calculating the CPU-memory utilization similarity of the nodes according to the average CPU utilization, the average memory utilization, the CPU resource consumption and the memory consumption;
and evaluating the nodes of the cluster according to the average CPU utilization rate, the average memory utilization rate and the CPU-memory utilization rate similarity, and selecting the optimal node as a container deployment node.
Further, the acquiring the average utilization rate of the CPU and the average utilization rate of the memory of each node of the cluster includes:
setting a data acquisition period;
collecting the CPU utilization rate and the memory utilization rate of each node of the cluster in a collection period;
and calculating the average CPU utilization rate and the average memory utilization rate of each node in the acquisition period.
Further, the acquiring CPU resource consumption and memory consumption of each node of the cluster includes:
acquiring CPU application amount of an existing container on a node, and dividing the CPU application amount by the total amount of node CPU resources to obtain node CPU resource consumption;
and acquiring the memory application amount of the existing container on the node, and dividing the memory application amount by the total amount of the node memory to obtain the memory consumption of the node.
Further, the calculating the CPU-memory utilization similarity of the nodes according to the average CPU utilization, the average memory utilization, the CPU resource consumption, and the memory consumption includes:
setting a first coefficient and a second coefficient, wherein the sum of the first coefficient and the second coefficient is 1, and the ratio of the first coefficient to the second coefficient is equal to the ratio of the CPU resource consumption to the memory consumption;
setting a fixed constant;
taking the difference between the first coefficient and the CPU average utilization rate and the difference between the second coefficient and the memory average utilization rate as constant coefficients;
and the difference value obtained by subtracting the product of the fixed constant and the constant coefficient from the fixed constant is used as the CPU-memory utilization approximation.
Further, the evaluating nodes of the cluster according to the average utilization rate of the CPU, the average utilization rate of the memory, and the CPU-memory utilization similarity and selecting an optimal node as a container deployment node includes:
setting a CPU weight and a memory weight;
calculating the weighted sum of the average CPU utilization rate and the average memory utilization rate of each node;
screening out the node with the minimum weighted sum in the cluster as a node to be selected;
and if a plurality of nodes to be selected exist, selecting the nodes to be selected with high CPU-memory utilization rate similarity as container deployment nodes.
In a second aspect, the present invention provides a cluster container scheduling system, including:
the average acquisition unit is configured for acquiring the average utilization rate of the CPU and the average utilization rate of the memory of each node of the cluster;
the consumption acquisition unit is configured for acquiring the CPU resource consumption and the memory consumption of each node of the cluster;
the close calculation unit is configured for calculating the CPU-memory utilization close degree of the node according to the average CPU utilization rate, the average memory utilization rate, the CPU resource consumption degree and the memory consumption degree;
and the node selection unit is configured to evaluate the nodes of the cluster according to the average CPU utilization rate, the average memory utilization rate and the CPU-memory utilization rate similarity and select the optimal node as a container deployment node.
Further, the consumption collection unit includes:
the CPU acquisition module is used for acquiring the CPU application amount of the existing container on the node and dividing the CPU application amount by the total amount of the node CPU resources to obtain the consumption degree of the node CPU resources;
and the memory acquisition module is configured for acquiring the memory application amount of the existing container on the node and dividing the memory application amount by the total amount of the node memory to obtain the memory consumption of the node.
Further, the node selecting unit includes:
the weight setting module is configured for setting a CPU weight and a memory weight;
the weighted summation module is configured to calculate a weighted sum of the CPU average utilization rate and the memory average utilization rate of each node;
the primary screening module is configured to screen out the node with the minimum weighted sum in the cluster as a node to be selected;
and the secondary screening module is configured to select a node to be selected with high CPU-memory utilization rate similarity as a container deployment node if a plurality of nodes to be selected exist.
In a third aspect, a terminal is provided, including:
a processor, a memory, wherein,
the memory is used for storing a computer program which,
the processor is used for calling and running the computer program from the memory so as to make the terminal execute the method of the terminal.
In a fourth aspect, a computer storage medium is provided having stored therein instructions that, when executed on a computer, cause the computer to perform the method of the above aspects.
The beneficial effect of the invention is that,
according to the cluster container scheduling method, the cluster container scheduling system, the terminal and the storage medium, the average resource utilization rate and the resource consumption degree are adopted to evaluate the nodes of the cluster, and the resource utilization rate and the resource balance degree are considered. According to the invention, the use balance condition of the node resources can be obviously improved by scheduling according to the use condition of the real-time resources of the nodes and the use influence of the deployment Pod on the node use rate.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention.
FIG. 2 is a schematic block diagram of a system of one embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The following explains key terms appearing in the present invention.
Container, pod, task processing node like virtual machine.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention. The execution subject in fig. 1 may be a cluster container scheduling system.
As shown in fig. 1, the method 100 includes:
and 140, evaluating the nodes of the cluster according to the average CPU utilization rate, the average memory utilization rate and the CPU-memory utilization rate similarity, and selecting the optimal node as a container deployment node.
In order to facilitate understanding of the present invention, the following further describes the cluster container scheduling method provided in the present invention with reference to the principle of the cluster container scheduling method of the present invention and the process of scheduling the cluster container in the embodiment.
Specifically, the cluster container scheduling method includes:
and S1, acquiring the average utilization rate of the CPU and the average utilization rate of the memory of each node of the cluster.
Assuming that the acquisition period is set to be 1h, the kubernets acquires the CPU utilization rate and the memory utilization rate of each node in the cluster within 1 h. Taking the average utilization rate of the CPU as an example, assuming that 60 acquisition points exist in an acquisition period of 1h, the average CPU utilization rate of the node can be obtained by dividing the sum of all the CPU utilization rates by 60 for the CPU utilization rates of the nodes of 60 acquisition points. The average memory utilization rate calculation methods are the same.
And S2, collecting the CPU resource consumption and the memory consumption of each node of the cluster.
The Pod divides the total amount of CPU resources on the node by the CPU demand amount for Pod for the consumption level (Rc) of CPU resources on the node.
And the Pod divides the total resource amount of the memory on the node by the memory application amount of the Pod aiming at the consumption degree (Rm) of the memory on the node.
And S3, calculating the CPU-memory utilization rate similarity of the nodes according to the average CPU utilization rate, the average memory utilization rate, the CPU resource consumption rate and the memory consumption rate.
Parameters first α and second β -memory usage proximity (Score) calculation formula are introduced as follows:
Score＝10-|αCpuSr-βMemSr|*10
wherein the content of the first and second substances,
α+β＝1；
α/β＝Rc/Rm；
CpuSr is the CPU average utilization, and MemSR is the memory average utilization.
And S4, evaluating the nodes of the cluster according to the average CPU utilization rate, the average memory utilization rate and the CPU-memory utilization rate similarity, and selecting the optimal node as a container deployment node.
Setting a CPU weight and a memory weight, calculating the weighted sum of the average CPU utilization rate and the average memory utilization rate of each node, and screening out the node with the minimum weighted sum in the cluster as a node to be selected. And if a plurality of nodes to be selected are screened out, selecting the nodes to be selected with high CPU-memory utilization rate similarity as container deployment nodes.
As shown in fig. 2, the system 200 includes:
the average acquisition unit 210 is configured to acquire the average utilization rate of the CPU and the average utilization rate of the memory of each node of the cluster;
a consumption acquisition unit 220 configured to acquire CPU resource consumption and memory consumption of each node of the cluster;
a proximity calculation unit 230 configured to calculate a CPU-memory usage proximity of the node according to the CPU average utilization rate, the memory average utilization rate, the CPU resource consumption, and the memory consumption;
and the node selection unit 240 is configured to evaluate the nodes of the cluster according to the average CPU utilization, the average memory utilization and the CPU-memory utilization similarity, and select an optimal node as a container deployment node.
Optionally, as an embodiment of the present invention, the consumption collecting unit includes:
the CPU acquisition module is used for acquiring the CPU application amount of the existing container on the node and dividing the CPU application amount by the total amount of the node CPU resources to obtain the consumption degree of the node CPU resources;
and the memory acquisition module is configured for acquiring the memory application amount of the existing container on the node and dividing the memory application amount by the total amount of the node memory to obtain the memory consumption of the node.
Optionally, as an embodiment of the present invention, the node selecting unit includes:
the weight setting module is configured for setting a CPU weight and a memory weight;
the weighted summation module is configured to calculate a weighted sum of the CPU average utilization rate and the memory average utilization rate of each node;
the primary screening module is configured to screen out the node with the minimum weighted sum in the cluster as a node to be selected;
and the secondary screening module is configured to select a node to be selected with high CPU-memory utilization rate similarity as a container deployment node if a plurality of nodes to be selected exist.
Fig. 3 is a schematic structural diagram of a terminal system 300 according to an embodiment of the present invention, where the terminal system 300 may be used to execute the cluster container scheduling method according to the embodiment of the present invention.
The terminal system 300 may include: a processor 310, a memory 320, and a communication unit 330. The components communicate via one or more buses, and those skilled in the art will appreciate that the architecture of the servers shown in the figures is not intended to be limiting, and may be a bus architecture, a star architecture, a combination of more or less components than those shown, or a different arrangement of components.
The memory 320 may be used for storing instructions executed by the processor 310, and the memory 320 may be implemented by any type of volatile or non-volatile storage terminal or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The executable instructions in memory 320, when executed by processor 310, enable terminal 300 to perform some or all of the steps in the method embodiments described below.
The processor 310 is a control center of the storage terminal, connects various parts of the entire electronic terminal using various interfaces and lines, and performs various functions of the electronic terminal and/or processes data by operating or executing software programs and/or modules stored in the memory 320 and calling data stored in the memory. The processor may be composed of an Integrated Circuit (IC), for example, a single packaged IC, or a plurality of packaged ICs connected with the same or different functions. For example, the processor 310 may include only a Central Processing Unit (CPU). In the embodiment of the present invention, the CPU may be a single operation core, or may include multiple operation cores.
A communication unit 330, configured to establish a communication channel so that the storage terminal can communicate with other terminals. And receiving user data sent by other terminals or sending the user data to other terminals.
The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and the program may include some or all of the steps in the embodiments provided by the present invention when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a Random Access Memory (RAM).
Therefore, the invention evaluates the nodes of the cluster by adopting the average resource utilization rate and the resource consumption degree, and gives consideration to the resource utilization rate and the resource balance degree. According to the node real-time resource use condition, scheduling is performed in combination with the use influence of deployment Pod on the node use rate, so that the node resource use balance condition can be obviously improved.
Those skilled in the art will readily appreciate that the techniques of the embodiments of the present invention may be implemented as software plus a required general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied in the form of a software product, where the computer software product is stored in a storage medium, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like, and the storage medium can store program codes, and includes instructions for enabling a computer terminal (which may be a personal computer, a server, or a second terminal, a network terminal, and the like) to perform all or part of the steps of the method in the embodiments of the present invention.
The same and similar parts in the various embodiments in this specification may be referred to each other. Especially, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant points can be referred to the description in the method embodiment.
In the embodiments provided in the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, 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 through some interfaces, systems or units, and may be in an electrical, mechanical 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.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for scheduling a cluster container, comprising:
collecting the average utilization rate of a CPU and the average utilization rate of a memory of each node of a cluster;
collecting CPU resource consumption and memory consumption of each node of the cluster;
calculating the CPU-memory utilization similarity of the nodes according to the average CPU utilization, the average memory utilization, the CPU resource consumption and the memory consumption;
and evaluating the nodes of the cluster according to the average CPU utilization rate, the average memory utilization rate and the CPU-memory utilization rate similarity, and selecting the optimal node as a container deployment node.
2. The method of claim 1, wherein the collecting the average utilization rate of the CPU and the average utilization rate of the memory of each node of the cluster comprises:
setting a data acquisition period;
collecting the CPU utilization rate and the memory utilization rate of each node of the cluster in a collection period;
and calculating the average CPU utilization rate and the average memory utilization rate of each node in the acquisition period.
3. The method of claim 1, wherein the collecting CPU resource consumption and memory consumption of each node of the cluster comprises:
acquiring CPU application amount of an existing container on a node, and dividing the CPU application amount by the total amount of node CPU resources to obtain node CPU resource consumption;
and acquiring the memory application amount of the existing container on the node, and dividing the memory application amount by the total amount of the node memory to obtain the memory consumption of the node.
4. The method of claim 1, wherein calculating the CPU-memory utilization proximity of the node based on the average CPU utilization, the average memory utilization, the CPU resource consumption, and the memory consumption comprises:
setting a first coefficient and a second coefficient, wherein the sum of the first coefficient and the second coefficient is 1, and the ratio of the first coefficient to the second coefficient is equal to the ratio of the CPU resource consumption to the memory consumption;
setting a fixed constant;
taking the difference between the first coefficient and the CPU average utilization rate and the difference between the second coefficient and the memory average utilization rate as constant coefficients;
and the difference value obtained by subtracting the product of the fixed constant and the constant coefficient from the fixed constant is used as the CPU-memory utilization approximation.
5. The method of claim 1, wherein the evaluating nodes of the cluster according to the average utilization rate of the CPU, the average utilization rate of the memory, and the CPU-memory utilization similarity and selecting an optimal node as a container deployment node comprises:
setting a CPU weight and a memory weight;
calculating the weighted sum of the average CPU utilization rate and the average memory utilization rate of each node;
screening out the node with the minimum weighted sum in the cluster as a node to be selected;
and if a plurality of nodes to be selected exist, selecting the nodes to be selected with high CPU-memory utilization rate similarity as container deployment nodes.
6. A cluster container scheduling system, comprising:
the average acquisition unit is configured for acquiring the average utilization rate of the CPU and the average utilization rate of the memory of each node of the cluster;
the consumption acquisition unit is configured for acquiring the CPU resource consumption and the memory consumption of each node of the cluster;
the close calculation unit is configured for calculating the CPU-memory utilization close degree of the node according to the average CPU utilization rate, the average memory utilization rate, the CPU resource consumption degree and the memory consumption degree;
and the node selection unit is configured to evaluate the nodes of the cluster according to the average CPU utilization rate, the average memory utilization rate and the CPU-memory utilization rate similarity and select the optimal node as a container deployment node.
7. The system of claim 6, wherein the consumption collection unit comprises:
the CPU acquisition module is used for acquiring the CPU application amount of the existing container on the node and dividing the CPU application amount by the total amount of the node CPU resources to obtain the consumption degree of the node CPU resources;
and the memory acquisition module is configured for acquiring the memory application amount of the existing container on the node and dividing the memory application amount by the total amount of the node memory to obtain the memory consumption of the node.
8. The system of claim 6, wherein the node selection unit comprises:
the weight setting module is configured for setting a CPU weight and a memory weight;
the weighted summation module is configured to calculate a weighted sum of the CPU average utilization rate and the memory average utilization rate of each node;
the primary screening module is configured to screen out the node with the minimum weighted sum in the cluster as a node to be selected;
and the secondary screening module is configured to select a node to be selected with high CPU-memory utilization rate similarity as a container deployment node if a plurality of nodes to be selected exist.
9. A terminal, comprising:
a processor;
a memory for storing instructions for execution by the processor;
wherein the processor is configured to perform the method of any one of claims 1-5.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-5.
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