CN114153609A

CN114153609A - Resource control method and device, electronic equipment and computer readable storage medium

Info

Publication number: CN114153609A
Application number: CN202111446629.5A
Authority: CN
Inventors: 陈博豪; 邱诗鹏; 李凌
Original assignee: China Telecom Corp Ltd
Current assignee: China Telecom Corp Ltd
Priority date: 2021-11-30
Filing date: 2021-11-30
Publication date: 2022-03-08

Abstract

The embodiment of the application discloses a resource control method and device, electronic equipment and a computer readable storage medium, wherein the method comprises the following steps: acquiring a plurality of tasks corresponding to a preset service, wherein the preset service represents a service of which the execution period is less than a preset period threshold and the number of all included tasks is greater than a preset task number threshold; then, determining a blocked target task from the plurality of tasks, and counting the number of the target tasks; and then, if the statistical result indicates that the number of the target tasks reaches the preset blocking number threshold, carrying out capacity expansion operation on the resources. The technical scheme of the embodiment of the application greatly optimizes the resource control scheme and avoids the problem of jitter caused by frequent resource expansion and contraction operations.

Description

Resource control method and device, electronic equipment and computer readable storage medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to a resource control method, a resource control apparatus, an electronic device, and a computer-readable storage medium.

Background

With the development of cloud computing and artificial intelligence, container technology is more and more widely used, and in a cloud computing environment, containers need to be deployed in a large scale, and a corresponding cluster management scheme is developed, wherein a container-based cluster management platform (kubernets) provides a plurality of functions such as service registration, load balancing, service deployment and operation, resource expansion and contraction, resource scheduling and the like for container application.

At present, in the related art, a Horizontal automatic scaling mechanism (HPA) in kubernets is used to implement capacity expansion and capacity reduction of resources, specifically, a usage rate of a resource pool is obtained and compared with a usage upper threshold and a usage lower threshold of a corresponding resource parameter, if the usage rate of the resource pool is greater than a preset usage upper threshold of the corresponding resource parameter, a capacity expansion operation of the resources is performed, and if the usage rate of the resource pool is less than a preset usage lower threshold of the corresponding resource parameter, a capacity reduction operation of the resources is performed. However, this approach may introduce jitter due to frequent resource scaling operations.

Therefore, how to optimize the resource control scheme is an urgent problem to be solved.

Disclosure of Invention

In order to solve the above technical problem, embodiments of the present application provide a resource control method and apparatus, an electronic device, and a computer-readable storage medium, so as to optimize a resource control scheme at least to a certain extent.

According to an aspect of the embodiments of the present application, there is provided a resource control method applied to a container-based cluster management platform, the method including: acquiring a plurality of tasks corresponding to preset services; the preset service represents a service of which the execution period is less than a preset period threshold value and the number of all tasks is greater than a preset task number threshold value; determining a target task with blockage from the plurality of tasks, and counting the number of the target tasks; and if the statistical result indicates that the number of the target tasks reaches a preset blocking number threshold, carrying out capacity expansion operation on the resources.

According to an aspect of the embodiments of the present application, there is provided a resource control apparatus configured on a container-based cluster management platform, the apparatus including: the acquisition module is configured to acquire a plurality of tasks corresponding to the preset service; the preset service represents a service of which the execution period is less than a preset period threshold value and the number of all tasks is greater than a preset task number threshold value; the determining and counting module is configured to determine a target task with blockage from the plurality of tasks and count the number of the target tasks; and the capacity expansion module is configured to perform capacity expansion operation on the resources if the statistical result indicates that the number of the target tasks reaches a preset blocking number threshold.

In an embodiment of the present application, based on the foregoing scheme, the resource control apparatus further includes: the determining module is configured to determine whether the utilization rate of the resource pool is smaller than a preset utilization rate threshold value based on a horizontal automatic scaling mechanism if the statistical result indicates that the number of the target tasks does not reach the preset blocking number threshold value; the capacity reduction module is configured to perform capacity reduction operation on the resources if the utilization rate of the resource pool is determined to be smaller than the preset utilization rate threshold value based on a horizontal automatic expansion and reduction mechanism; the resource parameter corresponding to the resource pool comprises at least one of a memory and a central processing unit.

In an embodiment of the present application, based on the foregoing solution, the determining and counting module includes: the first monitoring unit is configured to monitor the execution conditions of a plurality of tasks in the execution time period of the preset service; the first determining unit is configured to determine a task with execution duration greater than a preset duration threshold from a plurality of tasks, and take the task with execution duration greater than the preset duration threshold as a target task with congestion.

In an embodiment of the present application, based on the foregoing solution, the determining and counting module includes: the second monitoring unit is configured to monitor the execution conditions of a plurality of tasks in the execution time period of the preset service; a second determining unit configured to determine, from the plurality of tasks, an uncompleted task to be executed within an execution time period of the preset service to regard the uncompleted task as a target task where a congestion occurs.

In an embodiment of the application, based on the foregoing scheme, the first monitoring unit is specifically configured to monitor each task of the preset service and record an execution start time of each task if the execution time of the preset service arrives; the first determining unit is specifically configured to determine the execution duration of each task according to the execution starting time and the current time of each task; and according to the execution time length of each task, determining the task with the execution time length larger than a preset time length threshold value from the plurality of tasks.

In an embodiment of the application, based on the foregoing solution, the capacity expansion module includes: the first operation unit is configured to perform quotient calculation on the target task quantity and the task quantity executed by a single instance to obtain a first instance quantity; and the capacity expansion unit is configured to perform capacity expansion operation on the resources according to the first instance quantity.

In an embodiment of the present application, based on the foregoing solution, the capacity reduction module includes: the second operation unit is configured to perform quotient calculation on all task quantities of the preset service and the task quantity executed by a single instance to obtain a second instance quantity; and the capacity reduction unit is configured to perform capacity reduction operation on the resources according to the second instance quantity.

In an embodiment of the application, based on the foregoing scheme, the preset service includes a collection service for collecting network operation data, where the network operation data includes at least one of alarm data, performance data, configuration data, and log data.

According to an aspect of an embodiment of the present application, there is provided an electronic device including: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the electronic device to implement the resource control method as described above.

According to an aspect of embodiments of the present application, there is provided a computer-readable storage medium having stored thereon computer-readable instructions, which, when executed by a processor of a computer, cause the computer to execute the resource control method as described above.

According to an aspect of an embodiment of the present application, there is provided a computer program product comprising computer instructions which, when executed by a processor, implement the resource control method as described above.

In the technical scheme provided by the embodiment of the application, a plurality of tasks corresponding to the preset service are obtained, wherein the preset service represents the service of which the execution period is less than the preset period threshold value and the number of all the included tasks is greater than the preset task number threshold value; then, determining a blocked target task from the plurality of tasks, and counting the number of the target tasks; and then, if the statistical result indicates that the number of the target tasks reaches the preset blocking number threshold, carrying out capacity expansion operation on the resources. That is, in the technical solution provided in this embodiment of the present application, when it is counted that the number of target tasks included in the preset service and having a block reaches the preset block number threshold, capacity expansion of the resource is performed, instead of when the utilization rate of the resource pool is greater than the preset utilization rate upper threshold of the corresponding resource parameter, capacity expansion operation of the resource is performed, so that the resource can operate at full load, thereby avoiding a problem of jitter caused by frequent resource capacity expansion and reduction operations, and capacity expansion operation of the resource is performed only when the number of target tasks having a block reaches the preset block number threshold, so that the resource can sufficiently bear the task amount within the preset service execution time period to support normal operation of the preset service.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:

FIG. 1 is a schematic illustration of an exemplary implementation environment in which aspects of embodiments of the present application may be applied;

FIG. 2 is a flow diagram illustrating a resource control method in accordance with an exemplary embodiment of the present application;

FIG. 3 is a flow chart of step S220 in the embodiment shown in FIG. 2 in an exemplary embodiment;

FIG. 4 is a flow chart of step S220 in the embodiment shown in FIG. 2 in an exemplary embodiment;

FIG. 5 is a flow chart of step S220 in the embodiment shown in FIG. 2 in an exemplary embodiment;

FIG. 6 is a flow chart of step S230 in the embodiment shown in FIG. 2 in an exemplary embodiment;

FIG. 7 is a flow chart illustrating a resource control method according to an exemplary embodiment of the present application;

FIG. 8 is a flowchart of step S720 in the embodiment shown in FIG. 7 in an exemplary embodiment;

FIG. 9 is a flow diagram illustrating a resource control method in accordance with an exemplary embodiment of the present application;

FIG. 10 is a flow chart illustrating a resource control method according to an exemplary embodiment of the present application;

fig. 11 is a schematic structural diagram of a resource control device according to an exemplary embodiment of the present application;

FIG. 12 is a block diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.

The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.

It should also be noted that: reference to "a plurality" in this application means two or more. "and/or" describe the association relationship of the associated objects, meaning that there may be three relationships, e.g., A and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.

Before the technical solutions of the embodiments of the present application are described, terms and expressions referred to in the embodiments of the present application are explained, and the terms and expressions referred to in the embodiments of the present application are applied to the following explanations.

kubernets: kubernets are widely applied to a container cluster management system, are open sources, are used for managing containerized applications on a plurality of hosts in a cloud platform, and are container arranging engines; the Kubernetes cluster (also called container cluster), abbreviated as k8s cluster, is an abbreviation formed by replacing 8 characters "ubernet" with 8 characters. kubernets support automated deployment, large-scale scalable, application containerization management. When an application is deployed in a production environment, multiple instances of the application are typically deployed to load balance application requests. In kubernets, a plurality of containers can be created, each container runs an application instance, and then management, discovery and access of the group of application instances are achieved through a built-in load balancing strategy, and operation and maintenance personnel are not required to perform complex manual configuration and processing in detail. kubernets have a horizontal auto-scaling feature. Generally, monitoring of the kubernets cluster is mainly monitoring and elastic scaling of the service-type pod and other resources, and depends on monitoring data provided by a monitoring component. The monitoring component of the kubernets cluster is one of the core components, and can monitor various resources (such as pod, node, deployment, etc.) in the kubernets cluster, and the monitoring items include a CPU, a memory, traffic, health conditions, a disk usage rate, and the like. The monitoring data can be used as basic data of other components to provide decision support for the other components.

pod: pod refers to the application load in a kubernets cluster, with the pod running on a node. A pod consists of one or more containers, such as Container containers created by the Docker Container engine, that share Container storage, network, and Container run configuration items. Containers in the Pod are scheduled simultaneously, with a common operating environment.

In the related art, the capacity expansion and capacity reduction of resources are realized by using HPAs in kubernets, and specifically, resource parameters of a resource pool, such as Central Processing Units (CPUs) and usage rates of memories, are obtained and compared with upper and lower thresholds of usage of corresponding resource parameters (where, the usage rate of a CPU is compared with an upper threshold of a preset usage rate of a corresponding CPU, and the usage rate of a memory is compared with an upper threshold of a preset usage rate of a corresponding memory), if the usage rate of the resource pool is greater than the upper threshold of the preset usage rate of a corresponding resource parameter, a capacity expansion operation of the resource is performed, and if the usage rate of the resource pool is less than the lower threshold of the preset usage rate of a corresponding resource parameter, a capacity reduction operation of the resource is performed. However, this approach may introduce jitter due to frequent resource scaling operations.

Based on this, embodiments of the present application provide a resource control method and apparatus, an electronic device, and a computer-readable storage medium, which can solve the problem of jitter caused by frequent resource scaling operations, and optimize a resource control scheme.

Referring to FIG. 1, FIG. 1 is a schematic diagram of an exemplary implementation environment of the present application. The implementation environment includes a terminal device 110 and a server 120, and the terminal device 110 and the server 120 communicate with each other through a wired or wireless network.

It should be understood that the number of terminal devices 110 and servers 120 in fig. 1 is merely illustrative. There may be any number of end devices 110 and servers 120, as desired.

The terminal device 110 may be any electronic device having a user input interface, including but not limited to a smart phone, a tablet, a notebook, a computer, etc., wherein the user input interface includes but not limited to a touch screen, a keyboard, physical keys, an audio pickup device, etc.

The server 120 may be a server providing various services, may be an independent physical server, may be a server cluster or a distributed system formed by a plurality of physical servers, and may also be a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a CDN (Content Delivery Network), a big data and artificial intelligence platform, which is not limited herein.

In some embodiments of the present application, the resource control method may be executed by the server 120, and accordingly, the resource control device is configured in the server 120 corresponding to the kubernets. The server 120 may obtain a plurality of tasks corresponding to a preset service, where the preset service represents a service whose execution period is less than a preset period threshold and the number of all included tasks is greater than a preset task number threshold; then, determining a blocked target task from the plurality of tasks, and counting the number of the target tasks; and then, if the statistical result indicates that the number of the target tasks reaches the preset blocking number threshold, carrying out capacity expansion operation on the resources.

In some embodiments of the present application, the terminal device 110 may also have a function similar to that of the server 120, so as to execute the resource control method provided by the embodiments of the present application.

Various implementation details of the technical solution of the embodiments of the present application are set forth in detail below:

referring to fig. 2, fig. 2 is a flowchart illustrating a resource control method according to an embodiment of the present application. The method may be applied to the implementation environment shown in FIG. 1 and performed by the server 120 in the implementation environment shown in FIG. 1. As shown in fig. 2, the resource control method at least includes steps S210 to S230, which are described in detail as follows:

step S210, acquiring a plurality of tasks corresponding to the preset service; and the preset service represents the service of which the execution period is less than the preset period threshold value and the number of all the tasks is greater than the preset task number threshold value.

The preset service in the embodiment of the application refers to a service of which the execution cycle is less than a preset cycle threshold and the number of all included tasks is greater than a preset task number threshold.

Wherein, the execution cycle refers to the execution cycle of the preset service; for example, the preset service is performed every 60 minutes. The following refers to the execution time period of the preset service, where the two need to be distinguished, the execution time period of the preset service refers to the time taken for the execution of the preset service; for example, it takes 5 minutes for a preset service to be performed once.

It should be clear that the execution cycle of the preset service is less than the preset cycle threshold value to represent that the execution of the preset service is more frequent or intensive, and for the preset service, resource expansion and contraction are often faced; therefore, in the embodiment of the present application, resource control is required for this type of preset service. In practical application, the preset period threshold can be flexibly set according to a specific application scene.

When the preset service is executed, the preset service is specifically divided into a plurality of tasks, and then the plurality of tasks are allocated with instances.

It should be clear that the number of all tasks included in the preset service is greater than the preset task number threshold to represent that the task load of the preset service is large, and the preset service is often subjected to resource expansion and contraction; therefore, in the embodiment of the present application, resource control is required for this type of preset service. In practical application, the preset task quantity threshold value can be flexibly set according to a specific application scene.

It should be noted that, in the embodiment of the present application, the control of the resource is performed by counting the number of target tasks that are blocked, and therefore, a phenomenon of task blocking may be involved, so the preset service in the embodiment of the present application is a service that can tolerate a certain number of task blocking to a certain extent.

In one embodiment of the present application, the preset service includes, but is not limited to, a collection service for collecting network operation data, wherein the network operation data includes at least one of alarm data, performance data, configuration data, and log data.

That is, in an optional embodiment, the preset service may be an acquisition service, and specifically may be an acquisition service corresponding to an acquisition service of a 5G core network, because the execution cycle of the acquisition service is relatively frequent or dense, and the number of all tasks included in the acquisition service is also large; therefore, the collection service may be an example of the preset service. In practical applications, the preset service may be determined according to a specific application scenario, as long as it belongs to a category where the execution period is relatively frequent or dense and the number of all tasks included is relatively large. It is understood that, in general, a service with a relatively frequent or dense execution cycle includes a relatively large number of all tasks, but in special cases, only one of the conditions may be satisfied, for example, only the service with a relatively frequent or dense execution cycle or only the service with a relatively large number of all tasks.

In an embodiment of the application, a preset service corresponds to a task pool, wherein the task pool contains all tasks corresponding to the preset service; therefore, in the optional embodiment, the plurality of tasks corresponding to the preset service can be acquired from the task pool corresponding to the preset service, so that the plurality of tasks corresponding to the preset service can be simply and conveniently acquired.

Step S220, determining a target task in which a block occurs from the plurality of tasks, and counting the number of the target tasks.

After the plurality of tasks corresponding to the preset service are obtained, the blocked tasks can be determined from the plurality of tasks, the blocked tasks are used as target tasks, and the number of the target tasks is counted.

In an embodiment of the present application, referring to fig. 3, the process of determining a target task with a block from a plurality of tasks in step S220 may include steps S310 to S320, which are described in detail as follows:

step S310, monitoring the execution condition of a plurality of tasks in the execution time period of the preset service;

step S320, determining a task with an execution duration greater than a preset duration threshold from the plurality of tasks, and taking the task with the execution duration greater than the preset duration threshold as a target task with a block.

That is, in an optional embodiment, by monitoring the execution conditions of the plurality of tasks in the execution time period of the preset service, where the monitored execution conditions of the plurality of tasks include the execution time length of the tasks, the task whose execution time length is greater than the preset time length threshold is determined from the plurality of tasks according to the execution time length of the tasks, and the task whose execution time length is greater than the preset time length threshold is determined to be the target task with the congestion.

In an alternative embodiment, the process of monitoring the execution of the plurality of tasks within the execution time period of the preset service in step S310 may include the following steps:

if the execution time of the preset service arrives, monitoring each task of the preset service, and recording the execution starting time of each task;

accordingly, in an alternative embodiment, the process of determining, from the plurality of tasks, the task whose execution time length is greater than the preset time length threshold in step S320 may include the following steps:

respectively determining the execution duration of each task according to the execution starting time and the current time of each task;

and according to the execution time length of each task, determining the task with the execution time length larger than a preset time length threshold value from the plurality of tasks.

That is, in an optional embodiment, when an execution cycle of the preset service comes, each task of the preset service may be monitored, and when it is monitored that each task starts to be executed, the start time at which each task starts to be executed is respectively recorded, then, after a period of time elapses, the execution time of each task is determined according to the start time at which each task starts to be executed and the current time, and then, according to the execution time of each task, a task whose execution time is greater than the preset time threshold value is determined from the plurality of tasks.

For example, it is assumed that the preset service 1 includes 5 tasks, where the monitored execution conditions of the multiple tasks in the execution time period of the preset service are shown in table 1 below, and the execution time lengths of the tasks 11 and 12 are greater than the preset time length threshold; therefore, at this time, it can be determined from the 5 tasks 11, 12, 13, 14, 15 included in the preset service 1 that the tasks 11, 12 are the tasks where the congestion occurs, that is, the tasks 11, 12 are the target tasks. In practical application, the preset duration threshold can be flexibly set according to a specific application scene.

Preset service 1	Execution start time	Current time of day	Duration of execution
				Task 11	T11	T	T-T11
Task 12	T12	T	T-T12
				Task 13	T13	T	T-T13
Task 14	T14	T	T-T14
				Task 15	T15	T	T-T15

TABLE 1

In an embodiment of the present application, referring to fig. 4, the process of determining a target task with a block from a plurality of tasks in step S220 may include steps S410 to S420, which are described in detail as follows:

step S410, monitoring the execution condition of a plurality of tasks in the execution time period of the preset service;

in step S420, an uncompleted task is determined to be executed within the execution time period of the preset service from the plurality of tasks, so that the uncompleted task is executed as a target task where the congestion occurs.

That is, in an optional embodiment, by monitoring the execution conditions of the plurality of tasks in the execution time period of the preset service, where the monitored execution conditions of the plurality of tasks include whether the execution of the task is completed/successful, the unfinished/failed task is determined from the plurality of tasks in the execution time period of the preset service according to the unfinished/failed task, and the determined unfinished/failed task is the target task with the congestion.

For example, it is assumed that the preset service 2 includes 5 tasks, where the monitored execution conditions of the multiple tasks in the execution time period of the preset service are shown in table 2 below, and it can be seen that the tasks 23 and 24 are not executed completely; therefore, at this time, it can be determined from the 5 tasks 21, 22, 23, 24, 25 included in the preset service 2 that the tasks 23, 24 are the tasks where the congestion occurs, that is, the tasks 23, 24 are the target tasks.

Preset service 2	Execution situation
		Task 21	Execution completion
Task 22	Execution completion
		Task 23	Execution is not complete
Task 24	Execution is not complete
		Task 25	Execution completion

TABLE 2

In an embodiment of the present application, referring to fig. 5, the process of determining a target task with a block from a plurality of tasks in step S220 may include steps S510 to S520, which are described in detail as follows:

step S510, monitoring the execution condition of a plurality of tasks in the execution time period of the preset service;

step S520, determining a task with an execution duration greater than a preset duration threshold from the plurality of tasks, and determining an uncompleted task from the plurality of tasks to be executed within an execution time period of a preset service, so as to take the task with the execution duration greater than the preset duration threshold and the uncompleted task to be executed within the execution time period of the preset service as a target task with congestion.

That is, in an optional embodiment, it is determined that the execution time length is greater than the preset time length threshold from the multiple tasks, and the uncompleted tasks are executed within the execution time period of the preset service, that is, the tasks screened from the two directions are both used as target tasks with congestion, which better meets the requirements of the scene.

For example, it is assumed that the preset service 3 includes 5 tasks, where the monitored execution conditions of the multiple tasks in the execution time period of the preset service are shown in table 3 below, and meanwhile, the execution time lengths of the tasks 31 and 32 are greater than the preset time length threshold, and as can be seen from table 3, the execution of the tasks 34 and 35 is not completed; therefore, at this time, it can be determined that the tasks 31, 32, 34, 35 are the tasks where the congestion occurs, that is, the tasks 31, 32, 34, 35 are the target tasks, from the 5 tasks 31, 32, 33, 34, 35 included in the preset service 3.

Preset service 3	Whether execution is complete	Duration of execution
			Task 31	Execution completion	T-T31
Task32	Execution completion	T-T32
			Task 33	Execution completion	T-T33
Task 34	Execution is not complete	T-T34
			Task 35	Execution is not complete	T-T35

TABLE 3

It should be noted that the foregoing illustrates three ways of determining the target task with the block from the multiple tasks, and in practical applications, the ways may be adjusted according to specific application scenarios as long as the target task with the block can be determined from the multiple tasks.

In step S230, if the statistical result indicates that the number of the target tasks reaches the preset blocking number threshold, performing capacity expansion operation on the resource.

In the embodiment of the application, the target tasks with the blockage are determined from the plurality of tasks, and after the number of the target tasks is counted, the resource control operation can be carried out according to the counting result; if the statistical result indicates that the number of the target tasks reaches the preset blocking number threshold, the capacity expansion operation of the resources can be performed at this time. In other words, in the embodiment of the present application, when it is counted that the number of target tasks included in the preset service and having a congestion reaches the preset congestion number threshold, capacity expansion of the resource is performed, and no longer when the utilization rate of the resource pool is greater than the preset utilization rate upper threshold of the corresponding resource parameter, capacity expansion operation of the resource is performed, so that the resource can run at full load, thereby avoiding a problem of jitter caused by frequent resource capacity expansion and contraction operations.

In an embodiment of the present application, referring to fig. 6, the process of performing the capacity expansion operation on the resource in step S230 may include steps S610 to S620, which are described in detail as follows:

step S610, carrying out quotient calculation on the target task quantity and the task quantity executed by a single instance to obtain a first instance quantity;

step S620, perform capacity expansion operation on the resource according to the first instance quantity.

That is, in the optional embodiment, quotient calculation may be performed on the target task quantity and the task quantity executed by a single instance to obtain the first instance quantity, and then, capacity expansion operation of the resource is performed according to the first instance quantity. The obtained first example number is the example number corresponding to the capacity expansion operation; therefore, the capacity expansion operation of the resources is performed according to the first instance quantity, so that the target task can be executed to sufficiently support the normal operation of the preset service, and the phenomenon of excessive or insufficient capacity expansion caused by the random capacity expansion operation of the resources is avoided.

Specifically, the first instance number may be obtained by the following formula:

optionally, if the target task exists, the total number of instances required by the operation of the preset service can be calculated; specifically, the total number of instances required for the preset service to run can be obtained by the following formula:

in an embodiment of the present application, referring to fig. 7, the resource control method may further include steps S710 to S720, which are described in detail as follows:

step S710, if the statistical result indicates that the number of the target tasks does not reach the preset blocking number threshold, determining whether the utilization rate of the resource pool is smaller than the preset utilization rate threshold based on a horizontal automatic scaling mechanism;

step S720, if the utilization rate of the resource pool is determined to be smaller than a preset utilization rate threshold value based on the horizontal automatic scaling mechanism, performing capacity reduction operation on the resource; the resource parameters corresponding to the resource pool comprise at least one of a memory and a central processing unit.

That is, in the optional embodiment, if the statistical result indicates that the number of the target tasks does not reach the preset blocking number threshold, determining whether the usage rate of the resource pool is smaller than the preset usage rate threshold based on the horizontal automatic scaling mechanism; if the utilization rate of the resource pool is determined to be smaller than the preset utilization rate threshold value based on the horizontal automatic scaling mechanism, the capacity scaling operation of the resource can be carried out at the moment. That is, in the embodiment of the present application, when the utilization rate of the resource pool is smaller than the preset lower utilization rate threshold, the capacity reduction operation of the resource is performed.

The resource parameters corresponding to the resource pool in the optional embodiment include, but are not limited to, a memory and a central processing unit; optionally, the resource parameter corresponding to the resource pool includes one or more of a memory and a central processing unit.

In an embodiment of the present application, referring to fig. 8, the process of performing the capacity reduction operation on the resource in step S720 may include steps S810 to S820, which are described in detail as follows:

step S810, carrying out quotient calculation on all task quantities of the preset service and the task quantity executed by a single instance to obtain a second instance quantity;

and step S820, performing capacity reduction operation on the resources according to the second example number.

That is, in an optional embodiment, quotient calculation may be performed on all task numbers of the preset service and the task number executed by a single instance to obtain a second instance number, and then, capacity reduction operation of the resource may be performed according to the second instance number. The obtained second example number is the example number corresponding to the capacity reduction operation; therefore, the capacity reduction operation of the resources is carried out according to the second number of the instances, so that a reasonable number of the instances can be recycled, and idle resources are recycled for other services while the normal operation of the preset service is ensured.

In the optional embodiment, the capacity reduction operation of the resources according to the second instance number is performed after the execution time period of the preset service, so that the normal operation of the preset service can be ensured, and the situation that the preset service cannot normally operate due to the capacity reduction operation of the resources performed in advance is avoided.

Specifically, the second number of instances may be obtained by the following formula:

optionally, if the target task does not exist, calculating to obtain the total number of instances required by the operation of the preset service; specifically, the total number of instances required for the preset service to run can be obtained by the following formula:

in the embodiment of the application, when the number of the blocked target tasks included in the preset service is counted to reach the preset blocking number threshold, the capacity expansion of the resource is performed, and no longer when the utilization rate of the resource pool is greater than the preset utilization rate upper threshold of the corresponding resource parameter, the capacity expansion operation of the resource is performed, so that the resource can run at full load, thereby avoiding the problem of jitter caused by frequent resource expansion and contraction operations, and only when the number of the blocked target tasks reaches the preset blocking number threshold, the capacity expansion of the resource is performed, so that the resource can sufficiently bear the task amount in the preset service execution time period, and the normal operation of the preset service is supported.

The following describes a specific application scenario of the embodiment of the present application in detail:

referring to fig. 9, fig. 9 is a flowchart illustrating a resource control method according to an embodiment of the present application. In the embodiment of the present application, a preset service is taken as an example for explanation, as shown in fig. 9, the resource control method at least includes steps S910 to S970, which are described in detail as follows:

step S910, obtaining a plurality of collection tasks corresponding to the collection service from the collection task pool corresponding to the collection service.

Optionally, the specific implementation process of step S910 may refer to the technical solutions of the foregoing embodiments.

Step S920, monitoring the execution condition of a plurality of collection tasks within the execution time period of the collection service.

Optionally, in this embodiment of the application, the acquisition task monitoring module may monitor execution conditions of a plurality of acquisition tasks of the acquisition service within an execution time period; in the embodiment of the application, the acquisition task monitoring module monitors the execution conditions of the acquisition service in the execution time period of the plurality of acquisition tasks, including the execution duration of the acquisition tasks and whether the execution of the acquisition tasks is completed/successful.

For example, it is assumed that 8 collection tasks 11, 12, 13, 14, 15, 16, 17, and 18 included in the collection service 1 are acquired from a collection task pool corresponding to the collection service; therefore, it is necessary to monitor the execution of the acquisition tasks 11, 12, 13, 14, 15, 16, 17, 18 during the execution period of the acquisition service, in particular, the execution duration of the acquisition tasks and whether the acquisition tasks are performed completely/successfully.

Step S930, determining, from the plurality of collecting tasks, a collecting task whose execution time is greater than a preset time threshold, and determining, from the plurality of collecting tasks, an uncompleted collecting task to be executed within an execution time period of the collecting service, so as to take the collecting task whose execution time is greater than the preset time threshold and the uncompleted collecting task to be executed within the execution time period of the collecting service as a target collecting task in which the congestion occurs.

In the embodiment of the application, the simultaneous screening is performed from the execution time of the acquisition task and whether the acquisition task is executed and completed in the execution time period of the acquisition service, so that the blocked target acquisition task is determined from the plurality of acquisition tasks.

In connection with the above example, further, it is assumed that, although the execution of the acquisition tasks 11 and 12 in the 8 acquisition tasks included in the acquisition service 1 is completed in the execution time period, the execution time lengths of the acquisition tasks and the acquisition tasks are greater than the preset time length threshold, and the execution of the acquisition tasks 13, 14 and 15 is not completed in the execution time period; therefore, it is determined from the 8 acquisition tasks 11, 12, 13, 14, 15, 16, 17, 18 comprised by the acquisition service 1 that the acquisition tasks 11, 12, 13, 14, 15 are acquisition tasks in which a blockage occurs, i.e. the acquisition tasks 11, 12, 13, 14, 15 are target acquisition tasks.

And step S940, counting the number of target collection tasks.

In connection with the foregoing example, further, the number of the target collection tasks is counted to be 5.

In step S950, if the statistical result indicates that the number of the target collection tasks reaches the preset blocking number threshold, performing capacity expansion operation on the resource.

In connection with the foregoing example, further, if the preset blocking number threshold is set to 5, the statistical result indicates that the number of the target collection tasks reaches the preset blocking number threshold, and at this time, a capacity expansion operation of the resource needs to be performed. Optionally, the performing the capacity expansion operation of the resource may specifically include performing a quotient operation on the target collection task number and the collection task number executed by a single instance to obtain a first instance number, and then performing the capacity expansion operation of the resource according to the first instance number.

Step S960, if the statistical result indicates that the number of the target collection tasks does not reach the preset blocking number threshold, determining whether the utilization rate of the resource pool is less than the preset utilization rate threshold based on the horizontal automatic scaling mechanism.

Optionally, the specific implementation process of step S960 may refer to the technical solutions of the foregoing embodiments.

In step S970, if it is determined that the usage rate of the resource pool is smaller than the preset usage rate threshold based on the horizontal automatic scaling mechanism, the capacity reduction operation of the resource is performed.

Optionally, the capacity reduction operation of the resource may specifically include performing quotient calculation on the number of all the collection tasks of the collection service and the number of the collection tasks executed by a single instance to obtain a second instance number, and then performing the capacity reduction operation of the resource according to the second instance number.

Referring to fig. 10, fig. 10 is a flowchart illustrating a resource control method according to an embodiment of the present application. In the embodiment of the present application, the preset service is still taken as an example for explanation, as shown in fig. 10, the resource control method at least includes steps S1010 to S1040, which are described in detail as follows:

step S1010, the acquisition service corresponding to the acquisition task monitoring module acquires a plurality of acquisition tasks corresponding to the acquisition service from the acquisition task pool, and monitors the execution conditions of the plurality of acquisition tasks.

The acquisition service can determine whether the plurality of acquisition tasks are blocked according to the monitored execution conditions of the plurality of acquisition tasks.

Step S1020, judging the blocking condition of the acquisition task; if the number of the blocked collection tasks reaches the preset blocking number threshold, step S1030 is performed, and if the number of the blocked collection tasks does not reach the preset blocking number threshold, step S1040 is performed.

Optionally, the specific implementation process of step S1020 may refer to the technical solutions of the foregoing embodiments.

Step S1030, performing a capacity expansion operation on the resource.

Optionally, the specific implementation process of step S1030 may refer to the technical solutions of the foregoing embodiments.

In step S1040, if the CPU and the memory usage rate are lower than the preset lower usage threshold, the capacity reduction operation of the resource is performed.

Optionally, the specific implementation process of step S1040 may refer to the technical solutions of the foregoing embodiments.

In the embodiment of the application, when the number of the target acquisition tasks with the blockage included in the acquisition service reaches a preset blockage number threshold value, the capacity expansion operation of the resources is carried out; when the number of the target acquisition tasks with the blockage included in the acquisition service does not reach a preset blockage number threshold value and the utilization rate of the resource pool is determined to be smaller than a lower threshold value of the preset utilization rate based on a horizontal automatic scaling mechanism, carrying out capacity reduction operation on the resources; therefore, the problem of jitter caused by frequent resource expansion and contraction operations is avoided, and the resource expansion and contraction scheme is optimized to the greatest extent.

Fig. 11 is a block diagram of a resource control device according to an embodiment of the present application. As shown in fig. 11, the apparatus includes:

an obtaining module 1110 configured to obtain a plurality of tasks corresponding to a preset service; the preset service represents a service of which the execution period is less than a preset period threshold value and the number of all tasks is greater than a preset task number threshold value;

a determining and counting module 1120 configured to determine a target task in which a jam occurs from among the plurality of tasks, and count the number of the target tasks;

the capacity expansion module 1130 is configured to perform a capacity expansion operation on the resource if the statistical result indicates that the number of the target tasks reaches the preset blocking number threshold.

In one embodiment of the present application, the apparatus may further include:

the determining module is configured to determine whether the utilization rate of the resource pool is smaller than a preset utilization rate threshold value based on a horizontal automatic scaling mechanism if the statistical result indicates that the number of the target tasks does not reach the preset blocking number threshold value;

the capacity reduction module is configured to perform capacity reduction operation on the resources if the utilization rate of the resource pool is determined to be smaller than a preset utilization rate threshold value based on the horizontal automatic expansion and reduction mechanism; the resource parameters corresponding to the resource pool comprise at least one of a memory and a central processing unit.

In one embodiment of the present application, the determining and statistics module 1120 comprises:

the first monitoring unit is configured to monitor the execution conditions of a plurality of tasks in the execution time period of the preset service;

the first determining unit is configured to determine the task with the execution time length larger than the preset time length threshold from the plurality of tasks, and take the task with the execution time length larger than the preset time length threshold as the target task with the blockage.

the second monitoring unit is configured to monitor the execution conditions of the plurality of tasks in the execution time period of the preset service;

and a second determining unit configured to determine, from the plurality of tasks, to execute the uncompleted task within the execution period of the preset service, so as to take the executed uncompleted task as a target task where the congestion occurs.

In one embodiment of the present application,

the first monitoring unit is specifically configured to monitor each task of the preset service and record the execution starting time of each task if the execution time of the preset service arrives;

the first determining unit is specifically configured to determine the execution duration of each task according to the execution starting time and the current time of each task; and according to the execution time length of each task, determining the task with the execution time length larger than a preset time length threshold value from the plurality of tasks.

In one embodiment of the present application, the capacity expansion module 1130 includes:

the first operation unit is configured to carry out quotient calculation on the target task quantity and the task quantity executed by a single instance to obtain a first instance quantity;

and the capacity expansion unit is configured to perform capacity expansion operation on the resources according to the first instance quantity.

In one embodiment of the present application, a capacity reduction module comprises:

the second operation unit is configured to perform quotient calculation on all task quantities of the preset service and the task quantity executed by a single instance to obtain a second instance quantity;

and the capacity reduction unit is configured to perform capacity reduction operation on the resources according to the second example number.

In one embodiment of the present application, the preset service includes a collection service for collecting network operation data, wherein the network operation data includes at least one of alarm data, performance data, configuration data, and log data.

It should be noted that the resource control apparatus provided in the foregoing embodiment and the resource control method provided in the foregoing embodiment belong to the same concept, and specific ways for the modules and units to perform operations have been described in detail in the method embodiment, and are not described again here.

An embodiment of the present application further provides an electronic device, including: one or more processors; a storage device for storing one or more programs, which when executed by one or more processors, cause an electronic device to implement the resource control method provided in the foregoing embodiments.

FIG. 12 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.

It should be noted that the computer system 1200 of the electronic device shown in fig. 12 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

As shown in fig. 12, the computer system 1200 includes a Central Processing Unit (CPU)1201, which can perform various appropriate actions and processes, such as executing the methods in the above-described embodiments, according to a program stored in a Read-Only Memory (ROM) 1202 or a program loaded from a storage section 1208 into a Random Access Memory (RAM) 1203. In the RAM 1203, various programs and data necessary for system operation are also stored. The CPU 1201, ROM 1202, and RAM 1203 are connected to each other by a bus 1204. An Input/Output (I/O) interface 1205 is also connected to bus 1204.

The following components are connected to the I/O interface 1205: an input section 1206 including a keyboard, a mouse, and the like; an output section 1207 including a Display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 1208 including a hard disk and the like; and a communication section 1209 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 1209 performs communication processing via a network such as the internet. A driver 1210 is also connected to the I/O interface 1205 as needed. A removable medium 1211, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is mounted on the drive 1210 as necessary, so that a computer program read out therefrom is mounted into the storage section 1208 as necessary.

In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 1209, and/or installed from the removable medium 1211. The computer program executes various functions defined in the system of the present application when executed by a Central Processing Unit (CPU) 1201.

It should be noted that the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM), a flash Memory, an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with a computer program embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.

Yet another aspect of the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the resource control method as before. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist separately without being incorporated in the electronic device.

Another aspect of the application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the resource control method provided in the above embodiments.

The above description is only a preferred exemplary embodiment of the present application, and is not intended to limit the embodiments of the present application, and those skilled in the art can easily make various changes and modifications according to the main concept and spirit of the present application, so that the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A resource control method is applied to a container-based cluster management platform, and comprises the following steps:

acquiring a plurality of tasks corresponding to preset services; the preset service represents a service of which the execution period is less than a preset period threshold value and the number of all tasks is greater than a preset task number threshold value;

determining a target task with blockage from the plurality of tasks, and counting the number of the target tasks;

and if the statistical result indicates that the number of the target tasks reaches a preset blocking number threshold, carrying out capacity expansion operation on the resources.

2. The method of claim 1, wherein the method further comprises:

if the statistical result indicates that the number of the target tasks does not reach the preset blocking number threshold, determining whether the utilization rate of the resource pool is smaller than a preset utilization rate threshold or not based on a horizontal automatic scaling mechanism;

if the utilization rate of the resource pool is determined to be smaller than the preset utilization rate threshold value based on the horizontal automatic scaling mechanism, performing capacity scaling operation on the resources; the resource parameter corresponding to the resource pool comprises at least one of a memory and a central processing unit.

3. The method of claim 1, wherein said determining a target task of the plurality of tasks for which a blockage occurs comprises:

monitoring the execution conditions of a plurality of tasks within the execution time period of the preset service, and determining the task with the execution time length larger than a preset time length threshold value from the plurality of tasks so as to take the task with the execution time length larger than the preset time length threshold value as a blocked target task;

and/or the presence of a gas in the gas,

and monitoring the execution conditions of a plurality of tasks in the execution time period of the preset service, and determining an uncompleted task to be executed in the execution time period of the preset service from the plurality of tasks so as to take the uncompleted task as a target task with congestion.

4. The method of claim 3, wherein the monitoring of the performance of the plurality of tasks over the execution period of the preset service comprises:

the task of which the execution time length is greater than the preset time length threshold value is determined from the multiple tasks, and the method comprises the following steps:

5. The method of any one of claims 1 to 4, wherein the performing a capacity expansion operation of a resource comprises:

carrying out quotient calculation on the target task quantity and the task quantity executed by a single instance to obtain a first instance quantity;

and carrying out capacity expansion operation on the resources according to the first instance quantity.

6. The method of any of claims 2 to 4, wherein the performing a capacity reduction operation of a resource comprises:

carrying out quotient calculation on all task quantities of the preset service and the task quantity executed by a single instance to obtain a second instance quantity;

and carrying out capacity reduction operation on the resources according to the second example number.

7. The method of any of claims 1-4, wherein the pre-set service comprises a collection service for collecting network operational data, wherein the network operational data comprises at least one of alarm data, performance data, configuration data, and log data.

8. A resource control apparatus configured in a container-based cluster management platform, the apparatus comprising:

the acquisition module is configured to acquire a plurality of tasks corresponding to the preset service; the preset service represents a service of which the execution period is less than a preset period threshold value and the number of all tasks is greater than a preset task number threshold value;

the determining and counting module is configured to determine a target task with blockage from the plurality of tasks and count the number of the target tasks;

and the capacity expansion module is configured to perform capacity expansion operation on the resources if the statistical result indicates that the number of the target tasks reaches a preset blocking number threshold.

9. An electronic device, comprising:

one or more processors;

storage means for storing one or more programs that, when executed by the one or more processors, cause the electronic device to implement the resource control method of any of claims 1-7.

10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the resource control method according to any one of claims 1 to 7.