CN110995855B - Microservice cluster scheduling method, scheduling device and computer readable storage medium - Google Patents
Microservice cluster scheduling method, scheduling device and computer readable storage medium Download PDFInfo
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
Abstract
The invention provides a micro-service cluster scheduling method, which comprises the following steps: step S1, the control module obtains the available resource of the physical machine in the micro service cluster, and calculates the available capacity and cost of the physical machine; step S2, the control module calculates the address allocated to the service container of each layer in the micro service system architecture diagram by using the minimum cost maximum flow algorithm; in step S3, the control module delivers the task scheduling scheme to the scheduler for scheduling. Compared with the prior art, the invention has the beneficial effects that: the invention provides a micro-service cluster method, which gives consideration to the resource utilization rate of a physical machine cluster and the service quality of the micro-service cluster.
Description
Technical Field
The invention relates to the technical field of information, in particular to a micro-service cluster scheduling method, a scheduling device and a computer readable storage medium.
Background
Cloud computing has gained favor in the industry and academia as a new service providing method. The key technology of cloud computing is virtualization technology, and by virtualizing various resources, a cloud computing service provider can conveniently customize and deliver various resources to users for use, and numerous applications gradually start to migrate into a cloud computing cluster.
Conventional virtualization technologies include KVM and Xen, among others. However, the traditional virtualization technology is too cumbersome and complicated to create, modify and migrate a certain component in an application cluster, so that cloud computing service providers need a more lightweight virtualization technology. Container technology is a lightweight operating system level virtualization technology. Compared with the traditional virtualization technology for the virtualization of the hardware layer, the virtualization of the container is stopped at the operating system layer, so that the container is convenient and fast to create, modify and migrate.
As container technology has matured, container technology-based cloud computing systems (hereinafter referred to as container clouds) have begun to gradually replace traditional virtual machine-based cloud computing systems. Because the container has the characteristics of lightweight, the deployment of container is more convenient. The lightweight container enables one service to run in a single container, thereby reducing coupling between services and improving the efficiency of service update iteration. With this property of containers, more and more developers choose to deploy online service applications with containers. These container technology based online service applications are called microservice systems.
Container technology is quickly used by various types of cloud computing service providers. Due to the characteristics of the container, a user often operates each component in an independent container when deploying the application, so that the application can be conveniently and quickly maintained. This creates a complex internal structure of the vessel cloud. Meanwhile, the poor isolation of the containers also causes the serious mutual interference among the containers. When multiple containers are deployed on a physical machine, the containers compete with each other. This ensures efficient use of physical machine resources, but causes a degradation of service quality, such as reconnection after a connection interruption, packet loss and retransmission. Although the micro service system based on the container technology can achieve convenient deployment and rapid iterative update, dozens or even hundreds of containers are often operated on each physical machine in the cloud cluster due to the light weight characteristic of the containers. The containers compete with each other, so that although the utilization rate of system resources is high, the service quality of each micro service is poor, the whole service quality cannot be guaranteed, and the user experience is seriously influenced.
Therefore, a scheduling system for a container technology based micro service system is needed to improve the service quality of the micro service system. A cloud computing platform based on container technology generally consists of thousands of physical machines, each physical machine usually runs tens of containers, and each container runs a service. The existing cluster task scheduling system realizes higher cluster resource utilization rate aiming at the application of the traditional virtual machine platform. However, when scheduling tasks in the container technology-based micro service system, the existing scheduling system often only can simply improve the resource utilization rate of the cluster, but lacks consideration for the quality of service. That is, when the traditional scheduling algorithm facing to the virtual machine is applied to the micro service system, only the optimized resource utilization rate is usually considered, but the mutual interference between different services is neglected, and finally the overall service quality is reduced.
In summary, it is urgently needed to provide a scheduling method and a scheduling system based on the minimum cost and the maximum flow for a micro-service system, aiming at the disadvantages of the existing cluster task scheduling system.
Disclosure of Invention
In view of this, the problem that the existing cluster task scheduling system cannot simultaneously consider both the resource utilization rate and the service quality is solved. The invention provides a micro-service cluster scheduling method, which comprises the following steps:
step S1, the control module obtains the available resource of the physical machine in the micro service cluster, and calculates the available capacity and cost of the physical machine;
step S2, the control module calculates the address allocated to the service container of each layer in the architecture diagram of the microservice system by using the minimum cost maximum flow algorithm;
in step S3, the control module delivers the task scheduling scheme to the scheduler for scheduling.
Preferably, the specific step of step S1 includes:
step S11, the control module determines a source node, a destination node and an intermediate node;
step S12, the control module calculates the available capacity of each physical machine of the overhead flow network and the flow of each service node;
step S13, the control module calculates the cost of each service node of the cost flow network distributed to different physical machines;
step S14, the control module calls the algorithm of the minimum cost and the maximum flow to calculate the maximum flow and the corresponding distribution method;
in step S15, the control module updates the remaining amount of available resources of the physical machine.
Preferably, in the step S11, the source node includes a service node of the micro service cluster and a virtual occupied node, and the virtual occupied node is used for simulating the occupied resource as a request submitted by the virtual service node.
Preferably, the intermediate node is a physical machine node, and the physical machine node includes a location mark of the physical machine, that is, information of a rack, a cabinet and a machine room to which the physical machine belongs.
Preferably, the control module determines whether the resource required by a certain service component is greater than the remaining available resource of a certain physical machine, and if so, the service component is forced not to be allowed to be allocated to the physical machine, and all the other nodes can be allocated arbitrarily.
Preferably, the specific step of step S2 includes:
step S21, the control module receives the micro-service architecture diagram and calculates the initial system capacity;
step S22, the control module divides the architecture diagram of the micro service cluster to form a micro service level diagram;
step S23, the control module takes the node of the ith layer of the micro service level diagram, and recalculates the cost and the capacity according to the construction method of the cost network and the capacity network;
step S24, the control module calls the minimal overhead maximum flow method of the micro service cluster to calculate the distribution strategy of the node of the i-th layer;
step S25, the control module determines whether all the nodes are completely allocated, if so, continues to execute step S26, and if not, selects the next layer of the micro service level map, and repeats step S23;
in step S26, the result of the assignment for each node is output.
The invention also provides a micro-service cluster scheduling device which comprises a control module, wherein the control module can realize the micro-service cluster scheduling method when executing the program.
The present invention is also directed to a computer-readable storage medium storing a computer program, wherein the computer program is capable of implementing the above-mentioned micro-service cluster scheduling method when executed.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a micro-service cluster method, which gives consideration to the resource utilization rate of a physical machine cluster and the service quality of the micro-service cluster.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic flow chart illustrating a micro-service cluster scheduling method according to the present invention;
FIG. 2 is a flowchart illustrating a detailed step S1 of the micro service cluster scheduling method according to the present invention;
fig. 3 is a flowchart illustrating a specific step of the micro service cluster scheduling method step S2 in the present invention.
Detailed Description
The above and further features and advantages of the present invention are described in more detail below with reference to the accompanying drawings.
In the description of the present invention, it is to be understood that the terms "first", "second" and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the feature. In the description of the present invention, "a plurality" means at least two unless specifically defined otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; may be mechanically coupled, may be electrically coupled or may be in communication with each other; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention.
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
The invention provides a micro-service cluster scheduling method, which comprises the following steps:
step S1, the control module obtains the available resource of the physical machine in the micro service cluster, and calculates the available capacity and cost of the physical machine;
step S2, the control module calculates the address allocated to the service container of each layer in the architecture diagram of the microservice system by using the minimum cost maximum flow algorithm;
in step S3, the control module delivers the task scheduling scheme to the scheduler for scheduling.
The specific step of executing step S1 includes:
in step S11, the control module determines a source node, a destination node, and an intermediate node.
The source node comprises a service node and a virtual occupation node of the micro service cluster. The virtual occupied node is used for abstracting occupied resources on the physical machine node, and the occupied resources are regarded as a request submitted by a virtual service node. The beneficial effect of abstracting the occupied physical resources into one virtual node is that the complexity of recalculating the available capacity in the service node scheduling process is reduced.
The intermediate node is a physical machine node, and the physical machine node contains position marks of the physical machine, namely information of a rack, a cabinet, a machine room and the like of the physical machine. The end point is a virtual node and does not correspond to an actual node, and the end point is used for calculating the minimum overhead maximum flow.
In step S12, the control module calculates the available capacity of each physical machine of the overhead traffic network and the traffic size of each service node.
In the method, the process of scheduling the micro service cluster service nodes is abstracted again, the micro service cluster node scheduling is abstracted into an overhead flow network, and the time complexity of the service node scheduling is reduced.
To improve the quality of service of a microservice cluster, the traffic is defined as the number of packets per second that the physical machine can handle. The maximum traffic per edge is therefore the upper bound on the packets that the physical machine can handle, fdmaxiIs defined as follows:
fdmaxi＝min(k1Ci,k2Mi,k3Di,k4Ni)
wherein k is1K represents the ratio of the number of data packets that the ith physical machine can process to the number of CPUs2K represents the ratio of the number of data packets that the ith physical machine can process to the size of the memory3Represents the proportion of the number of data packets which can be processed by the ith physical machine to the IO rate of the disk, k4The ratio between the number of data packets which can be processed by the ith physical machine and the network rate is shown, and the ratio can be obtained by performing pressure test on the server and is a relatively fixed value.
The upper limit of the data packet which can be processed by the physical machine is used as the flow, so that the condition of insufficient resources can be avoided when the physical machine processes the flow data, and the service quality is improved.
After the maximum capacity is determined, the current capacity in the present invention can be defined according to this definition as:
fdpi＝min(k1Cpi,k2Mpi,k3Dpi,k4Npi)
wherein C ispiIndicating the remaining available resources, M, of the ith host CPUpiIndicating the remaining available resources of the i-th host memory, DpiIndicating the remaining available resources, N, of the ith host diskpiRespectively, represent the remaining available resources of the ith host network.
The traffic of the ith node is defined as max (k)1Cri,k2Mri,k3Dri,k4Nri) Wherein, CriIndicating the requirements of the i-th node CPU, MriIndicating the memory requirement of the ith node, DriIndicating the requirements of the ith node disk,Nriindicating the requirements of the ith network of nodes.
In addition to traffic, the sum of the resources of all container components operated by each physical machine node in the present invention is less than the maximum remaining available resource of the physical machine, thus defining rrpi＝(Cpi,Mpi,Dpi,Npi) If the resource needed by a certain service component is larger than the remaining available resource of a certain physical machine, the service component is forced not to be allocated to the physical machine, and all the other nodes can be arbitrarily allocated to any machine.
In step S13, the control module calculates the cost of each service node of the cost traffic network to be allocated to different physical machines.
The overhead of each edge is the connection overhead of the nodes between the current level and the previous level of the structure graph. The cost of each service node of the current hierarchy is divided into three categories according to the position of the node of the previous hierarchy. Let the current node be vpAnd the upper node with connection is vpp. If v isppOn the physical machine to be allocated, then vpThe overhead allocated to the machine is a1If v isppOn the same rack of the physical machine to be allocated, vpThe overhead allocated to the machine is a2，vppOn the same cabinet of the physical machine to be allocated, vpThe overhead allocated to the machine is a3If v isppIn the same machine room of the physical machine to be allocated, vpThe overhead allocated to the machine is a4If v isppIn different rooms of the physical machine to be allocated, vpThe overhead allocated to the machine is a5。a1、a2、a3、a4And a5According to different physical machine cluster tests.
Step S14, the control module calls the algorithm of the minimum cost and the maximum flow to calculate the maximum flow and the corresponding distribution method;
in step S15, the control module updates the remaining amount of available resources of the physical machine.
The specific step of executing step S2 includes:
step S21, the control module receives the micro-service architecture diagram and calculates the initial system capacity;
step S22, the control module divides the architecture diagram of the micro service cluster to form a micro service level diagram; the control module divides the application level according to the architecture diagram of the micro-service cluster, thereby forming a micro-service level diagram.
The micro service in charge of receiving the user request in the micro service cluster is made to be a root node vrEach microservice viThe hierarchy of which is max (v)r,vi) Where dist (v)r,vi) Denotes viAnd vrAnd recording the number of the node connected with the node according to the distance.
Step S23, the control module takes the node of the ith layer of the micro service level diagram, and recalculates the cost and the capacity according to the construction method of the cost network and the capacity network; in the method, the connection between nodes in different levels is used as the basis for constructing the overhead network, so that the communication quality between the service nodes is ensured.
Step S24, the control module calls the minimal overhead maximum flow method of the micro service cluster to calculate the distribution strategy of the node of the i-th layer; in the method, the expenses among the physical machines at different positions are set as different weights, so that the communication quality between adjacent nodes in the micro-service structure chart is ensured as much as possible.
Step S25, the control module determines whether all the nodes are completely allocated, if so, continues to execute step S26, and if not, selects the next layer of the micro service level map, and repeats step S23;
in step S26, the result of the assignment for each node is output.
The invention provides a micro service cluster scheduling method, which is a scheduling method based on micro service architecture minimum cost maximum flow, and the method ensures the resource utilization rate of a micro service cluster by utilizing a reconstructed flow network, and is mainly embodied in the following aspects:
1. in the method, the process of scheduling the micro service cluster service nodes is abstracted again, the micro service cluster node scheduling is abstracted into an overhead flow network, and the time complexity of the service node scheduling is reduced.
2. In the method, the occupied physical resources are abstracted into a virtual node, so that the complexity of recalculating the available capacity in the scheduling process of the service node is reduced.
3. In the method, the upper limit of the data packet which can be processed by the physical machine is used as the flow, so that the situation of insufficient resources can be avoided when the physical machine processes the flow data, and the service quality is improved.
Further, the method also improves the service quality of the micro-service cluster by using the reconstructed overhead network.
The method is mainly embodied in the following aspects:
1. in the method, the connection between nodes in different levels is used as the basis for constructing the overhead network, so that the communication quality between the service nodes is ensured.
2. In the method, the cost between physical machines at different positions is set as different weights, so that the communication quality between adjacent nodes in the micro-service structure chart is ensured as much as possible.
In the prior art, when scheduling a micro-service cluster task, a scheduling scheme based on available resources is adopted, consideration for the quality of service of key indexes of the micro-service is lacked, and the situation that the quality of service of the micro-service cluster is not high although the utilization rate of resources of the whole physical machine cluster is high is easily caused.
The invention comprehensively considers the resource utilization rate of the physical machine cluster and the service quality of the micro service cluster, and provides a scheduling method based on minimum overhead maximum flow, which not only ensures the high resource utilization rate of the physical machine cluster, but also improves the service quality of the micro service cluster.
The invention also provides a micro service cluster scheduling device, which comprises a control module, wherein the control module can realize the micro service cluster scheduling method from the step S1 to the step S3 when executing the program.
The present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the micro-service cluster scheduling method of steps S1 to S3 proposed in the present invention.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (6)
1. A method for scheduling micro service clusters is characterized by comprising the following steps:
step S1, the control module obtains the available resource of the physical machine in the micro service cluster, and calculates the available capacity and cost of the physical machine;
step S2, the control module calculates the address allocated to the service container of each layer in the architecture diagram of the microservice system by using the minimum cost maximum flow algorithm;
step S3, the control module sends the task scheduling scheme to the scheduler to execute scheduling;
the specific step of step S1 includes:
step S11, the control module determines a source node, a destination node and an intermediate node;
step S12, the control module calculates the available capacity of each physical machine of the overhead flow network and the flow of each service node;
step S13, the control module calculates the cost of each service node of the cost flow network distributed to different physical machines;
step S14, the control module calls the algorithm of the minimum cost and the maximum flow to calculate the maximum flow and the corresponding distribution method;
step S15, the control module updates the remaining available resource amount of the physical machine;
the specific steps of step S2 include:
step S21, the control module receives the micro-service architecture diagram and calculates the initial system capacity;
step S22, the control module divides the architecture diagram of the micro service cluster to form a micro service level diagram;
step S23, the control module takes the node of the ith layer of the micro service level diagram, and recalculates the cost and the capacity according to the construction method of the cost network and the capacity network;
step S24, the control module calls the minimal overhead maximum flow method of the micro service cluster to calculate the distribution strategy of the node of the i-th layer;
step S25, the control module determines whether all the nodes are completely allocated, if so, continues to execute step S26, and if not, selects the next layer of the micro service level map, and repeats step S23;
in step S26, the assignment result for each node is output.
2. The micro-service cluster scheduling method of claim 1, wherein in the step S11, the source node comprises a service node of the micro-service cluster and a virtual appropriation node for approving the appropriated resource as a request submitted by the virtual service node.
3. The micro-service cluster scheduling method of claim 1, wherein the intermediate node is a physical machine node, and the physical machine node contains a location label of a physical machine, i.e., rack, cabinet, and room information to which the physical machine belongs.
4. The method of claim 1, wherein the control module determines whether the resource required by a service component is greater than the remaining available resource of a physical machine, and if so, the service component is forced not to be allocated to the physical machine, and all the remaining nodes can be arbitrarily allocated.
5. A micro-service cluster scheduling device, characterized in that the device comprises a control module, which when executing a program is capable of implementing the micro-service cluster scheduling method according to any one of claims 1 to 4.
6. A computer-readable storage medium, in which a computer program is stored, which, when being executed, is capable of implementing the micro-service cluster scheduling method according to any one of claims 1 to 4.
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