CN113918093A - Capacity reduction optimization method and terminal - Google Patents
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Abstract
The invention discloses a capacity reduction optimization method and a terminal, when capacity reduction is applied to the situation that only a first example exists, the request number of the first example is counted regularly; determining whether capacity reduction is needed according to the request number, and determining the capacity after capacity reduction when capacity reduction is needed; creating a second instance consistent with the reduced capacity, and switching the request sent to the application from the first instance to the second instance; the first instance is deleted when all requests sent to the application reach the second instance. The invention adopts an example replacement mode to dynamically perform the capacity reduction operation of the only example of the application, so that the capacity of the only example of the application is further reduced, and more resources can be saved.
Description
Technical Field
The invention relates to the technical field of micro-service deployment, in particular to a capacity reduction optimization method and a terminal.
Background
Today, the mobile internet is developed vigorously, and various system applications are developed, and various data interactions exist among different system applications. With the dramatic increase of the number of users, the server needs to implement horizontal extension in a distributed deployment manner to support access of large data users in a highly concurrent manner. The micro-service architecture mode is a method for realizing distributed deployment by a common server side at present.
Due to the distributed deployment of applications currently employed, there may be multiple instances of an application for the architecture of microservices. The existing micro-service instance operation and maintenance functions have the capacity of dynamic capacity reduction. In general, when a certain instance is found to exceed a resource of a fixed threshold of a machine, the instance is automatically expanded, so that greater throughput can be supported. When a certain fixed condition is not reached in a period of time, it is judged that the capacity reduction operation can be performed, and the capacity reduction operation of the instances is performed subsequently, so that the resource occupation is reduced, and at least 1 instance needs to exist due to the application deployment of the micro-service, so as to provide the corresponding service. However, the current capacity reduction mechanism performs capacity reduction on application instances, and only one instance is reserved as a result of maximum capacity reduction. Meanwhile, when an application instance is created, the memory capacity required by the application instance, such as a 1G memory, a 2G memory, a 4G memory and an 8G memory, is usually required to be set, where the 1G memory is usually the default minimum memory capacity, so that in the existing capacity reduction method for reserving an instance, at least one application instance of the 1G memory needs to be reserved, and in the current practical situation, many applications do not actually have a large access amount, and cannot use up the 1G memory, which causes resource waste.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method and the terminal for optimizing the capacity reduction are provided to dynamically perform the capacity reduction operation of the only application instance, so that more resources can be saved.
In order to solve the technical problems, the invention adopts the technical scheme that:
a method for optimizing a capacity reduction comprises the following steps:
step S1, when the application is scaled to have only the first instance, counting the number of requests of the first instance regularly;
step S2, determining whether capacity reduction is needed according to the request number, and determining the capacity after capacity reduction when capacity reduction is needed;
step S3, creating a second instance consistent with the reduced capacity, and switching the request sent to the application from the first instance to the second instance;
and step S4, deleting the first instance when all the requests sent to the application reach the second instance.
In order to solve the technical problem, the invention adopts another technical scheme as follows:
a capacity-reducing optimization terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
step S1, when the application is scaled to have only the first instance, counting the number of requests of the first instance regularly;
step S2, determining whether capacity reduction is needed according to the request number, and determining the capacity after capacity reduction when capacity reduction is needed;
step S3, creating a second instance consistent with the reduced capacity, and switching the request sent to the application from the first instance to the second instance;
and step S4, deleting the first instance when all the requests sent to the application reach the second instance.
The invention has the beneficial effects that: a capacity reduction optimization method and a terminal are provided, when the application capacity reduction is carried out until only a first example exists as a unique example, the request number of the first example is counted at regular time, whether the capacity reduction is needed is determined according to the request number, when the capacity reduction is needed, the capacity after the capacity reduction is determined, then a second example which is consistent with the capacity after the capacity reduction is created, the request sent to the application is cut to flow from the first example to the second example, and then the first example is deleted.
Drawings
Fig. 1 is a schematic flow chart of a method for optimizing a reduction capacity according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a capacity-reduction optimization terminal according to an embodiment of the present invention.
Description of reference numerals:
1. a capacity-reducing optimization terminal; 2. a processor; 3. a memory.
Detailed Description
In order to explain technical contents, achieved objects, and effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.
Referring to fig. 1, a method for optimizing a reduction capacity includes:
step S1, when the application is scaled to have only the first instance, counting the number of requests of the first instance regularly;
step S2, determining whether capacity reduction is needed according to the request number, and determining the capacity after capacity reduction when capacity reduction is needed;
step S3, creating a second instance consistent with the reduced capacity, and switching the request sent to the application from the first instance to the second instance;
and step S4, deleting the first instance when all the requests sent to the application reach the second instance.
From the above description, the beneficial effects of the present invention are: when the application is subjected to capacity reduction until only the unique instance of the first instance exists, counting the number of requests of the first instance at regular time, determining whether capacity reduction is needed according to the number of requests, when capacity reduction is needed, determining the capacity after capacity reduction, then creating a second instance consistent with the capacity after capacity reduction, switching the flow of the request sent to the application from the first instance to the second instance, and then deleting the first instance.
Further, the step S1 specifically includes the following steps:
when the application is reduced to only have a first example, counting the total number of requests of the first example every other time period, and calculating to obtain the number of requests per second;
the step S2 specifically includes the following steps:
and judging whether the request numbers per second in a plurality of continuous time periods are all larger than a capacity reduction threshold, if so, not needing capacity reduction processing, otherwise, determining the capacity after capacity reduction according to the threshold interval range in which the request numbers per second in the plurality of continuous time periods fall.
From the above description, it can be known that the access condition of the application is accurately judged by whether the number of requests per second in a plurality of continuous time periods is within a certain threshold interval range, so that the abnormal phenomenon caused by sudden change of the requests at a certain time point is avoided.
Further, the step S3 specifically includes the following steps:
creating a second instance consistent with the scaled capacity;
in the configuration content of the application, the IP address and the port address pointing to the first instance are modified to the IP address and the port address pointing to the second instance, so that the request sent to the application is cut from the first instance to the second instance.
From the above description, it can be known that the application performs a further capacity reduction operation on the unique instance of the application by modifying the IP address and the port address of the corresponding instance in the application configuration content, so that the application responds to the request by a new instance with smaller capacity.
Further, the step S4 specifically includes the following steps:
and monitoring whether the first instance receives a new request in real time, if no new request reaches the first instance after a first preset time, considering that all requests sent to the application reach the second instance, and deleting the first instance.
From the above description, it can be seen that when none of the new requests reaches the first instance after the first preset time, it means that all the requests of the application have been switched to the new second instance, and at this time, the first instance with larger capacity is deleted and the second instance with smaller capacity is reserved to realize the capacity reduction.
Further, the step S1 is to count the number of requests of the first instance at regular time, specifically, count the number of requests of the first instance at regular time by using an asynchronous timed task counting procedure.
Referring to fig. 2, a capacity-reduction optimization terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the following steps:
step S1, when the application is scaled to have only the first instance, counting the number of requests of the first instance regularly;
step S2, determining whether capacity reduction is needed according to the request number, and determining the capacity after capacity reduction when capacity reduction is needed;
step S3, creating a second instance consistent with the reduced capacity, and switching the request sent to the application from the first instance to the second instance;
and step S4, deleting the first instance when all the requests sent to the application reach the second instance.
From the above description, the beneficial effects of the present invention are: when the application is subjected to capacity reduction until only the unique instance of the first instance exists, counting the number of requests of the first instance at regular time, determining whether capacity reduction is needed according to the number of requests, when capacity reduction is needed, determining the capacity after capacity reduction, then creating a second instance consistent with the capacity after capacity reduction, switching the flow of the request sent to the application from the first instance to the second instance, and then deleting the first instance.
Further, the step S1 specifically includes the following steps:
when the application is reduced to only have a first example, counting the total number of requests of the first example every other time period, and calculating to obtain the number of requests per second;
the step S2 specifically includes the following steps:
and judging whether the request numbers per second in a plurality of continuous time periods are all larger than a capacity reduction threshold, if so, not needing capacity reduction processing, otherwise, determining the capacity after capacity reduction according to the threshold interval range in which the request numbers per second in the plurality of continuous time periods fall.
From the above description, it can be known that the access condition of the application is accurately judged by whether the number of requests per second in a plurality of continuous time periods is within a certain threshold interval range, so that the abnormal phenomenon caused by sudden change of the requests at a certain time point is avoided.
Further, the step S3 specifically includes the following steps:
creating a second instance consistent with the scaled capacity;
in the configuration content of the application, the IP address and the port address pointing to the first instance are modified to the IP address and the port address pointing to the second instance, so that the request sent to the application is cut from the first instance to the second instance.
From the above description, it can be known that the application performs a further capacity reduction operation on the unique instance of the application by modifying the IP address and the port address of the corresponding instance in the application configuration content, so that the application responds to the request by a new instance with smaller capacity.
Further, the step S4 specifically includes the following steps:
and monitoring whether the first instance receives a new request in real time, if no new request reaches the first instance after a first preset time, considering that all requests sent to the application reach the second instance, and deleting the first instance.
From the above description, it can be seen that when none of the new requests reaches the first instance after the first preset time, it indicates that all the requests of the application have been switched to the new second instance, and at this time, the first instance with larger capacity is deleted, and the second instance with smaller capacity is reserved, so as to implement capacity reduction.
Further, the step S1 is to count the number of requests of the first instance at regular time, specifically, count the number of requests of the first instance at regular time by using an asynchronous timed task counting procedure.
Referring to fig. 1, a first embodiment of the present invention is:
a method for optimizing a capacity reduction comprises the following steps:
step S1, when the application is reduced to the first example, counting the request number of the first example at regular time;
wherein, step S1 specifically includes the following steps:
when the application is reduced to only have the first example, counting the total number of the requests of the first example every other time period through an asynchronous timing task counting program, and calculating to obtain the number of the requests per second;
in this embodiment, since all external requests pass through the nginx server (a high-performance HTTP and reverse proxy web server), the nginx server finally forwards the requests to the application corresponding to the backend. Therefore, when the application is reduced to the state that only the instance A exists, the request total number of the instance A is counted once every 10 seconds, and the request number per second in the time period can be calculated according to the request total number/10, namely the time period is 10 seconds in the embodiment, and other equivalent embodiments can be set according to specific service conditions.
Step S2, determining whether capacity reduction is needed according to the number of requests, and determining the capacity after capacity reduction when capacity reduction is needed;
wherein, step S2 specifically includes the following steps:
and judging whether the number of requests per second in a plurality of continuous time periods is larger than a capacity reduction threshold value, if so, not needing capacity reduction processing, otherwise, determining the capacity after capacity reduction according to the threshold value interval range in which the number of requests per second in the plurality of continuous time periods falls.
The capacity reduction threshold, the threshold interval range, and the capacity after capacity reduction may be determined according to the minimum content capacity of the specific service and the example, in this embodiment, the first example is a minimum memory capacity 1G, and at this time:
when the number of requests per second in 10 continuous time periods is greater than 10, the application still has a certain access amount, and the capacity reduction processing is not needed.
When the number of requests per second in the continuous 10 time periods is in the threshold interval range of (5,10], it is indicated that the application still has an access amount, but not large, the capacity reduction processing can be performed once, at this time, the capacity after the application is reduced is 512M.
When the number of requests per second in 10 consecutive time periods is within the threshold interval range of (0, 5), it is said that the application access amount is small, and the capacity reduction processing can be performed once, at this time, half of the memory capacity is reduced again, and the capacity after the application capacity reduction is changed to 256M.
Step S3, creating a second instance consistent with the reduced capacity, and switching the request sent to the application from the first instance to the second instance;
wherein, step S3 specifically includes the following steps:
creating a second instance consistent with the scaled capacity;
in the configuration content of the application, the IP address and the port address pointing to the first instance are modified to the IP address and the port address pointing to the second instance, so that the request sent to the application is cut from the first instance to the second instance.
At this time, assuming that the capacity after the capacity reduction is 512M in step S2, an instance B with a memory capacity of 512M is created through k8S container management (kubernets, a container cluster management system), and after the creation of the instance B is completed, the capacity reduction module notifies the nginx modification module to modify the configuration content of the application in the nginx server, modifies the IP address and the port address pointing to the instance a configured in the nginx service to the IP address and the port address pointing to the example B after the capacity reduction, and restarts the nginx system to validate the configuration.
And step S4, deleting the first instance when all the requests sent to the application reach the second instance.
Wherein, step S4 specifically includes the following steps:
and monitoring whether the first instance receives a new request in real time, if no new request reaches the first instance after the first preset time, considering that all requests sent to the application reach the second instance, and deleting the first instance.
That is, after step S3 is completed, the capacity reduction module monitors instance a in real time, whether there is still a request, and if it is determined that no new request reaches instance a after the first preset time interval, instance a may be deleted, and a capacity reduction operation is completed. Wherein, the first preset time may be 5 minutes.
Referring to fig. 2, the second embodiment of the present invention is:
a capacity-reduction optimization terminal 1 comprises a memory 3, a processor 2 and a computer program stored on the memory 3 and capable of running on the processor 2, wherein the processor 2 realizes the steps of the first embodiment when executing the computer program.
In summary, according to the method and terminal for optimizing a capacity reduction provided by the present invention, when a capacity reduction is applied to a unique instance only including a first instance, the number of requests of the first instance is counted regularly, whether the capacity reduction is required is determined by determining whether the number of requests per second in a plurality of consecutive time periods is within a certain threshold interval, when the capacity reduction is required, the capacity after the capacity reduction is determined, then a second instance consistent with the capacity after the capacity reduction is created, the IP address and the port address of the corresponding instance are modified in the application configuration content, so as to cut the flow of the request sent to the application from the first instance to the second instance, when none of the new requests reaches the first instance after the first preset time, it is described that all the requests for changing the application have been cut to flow to the new second instance, at this time, the first instance with a larger capacity is deleted, the second instance with a smaller capacity is retained, therefore, the capacity reduction operation of the unique instance of the application is dynamically carried out in an instance replacement mode, so that the capacity of the unique instance of the application is further reduced, and more resources can be saved.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to the related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method for optimizing a capacity reduction is characterized by comprising the following steps:
step S1, when the application is scaled to have only the first instance, counting the number of requests of the first instance regularly;
step S2, determining whether capacity reduction is needed according to the request number, and determining the capacity after capacity reduction when capacity reduction is needed;
step S3, creating a second instance consistent with the reduced capacity, and switching the request sent to the application from the first instance to the second instance;
and step S4, deleting the first instance when all the requests sent to the application reach the second instance.
2. The method for optimizing a shrinkage capacity according to claim 1, wherein the step S1 specifically includes the steps of:
when the application is reduced to only have a first example, counting the total number of requests of the first example every other time period, and calculating to obtain the number of requests per second;
the step S2 specifically includes the following steps:
and judging whether the request numbers per second in a plurality of continuous time periods are all larger than a capacity reduction threshold, if so, not needing capacity reduction processing, otherwise, determining the capacity after capacity reduction according to the threshold interval range in which the request numbers per second in the plurality of continuous time periods fall.
3. The method for optimizing a shrinkage capacity according to claim 1, wherein the step S3 specifically includes the steps of:
creating a second instance consistent with the scaled capacity;
in the configuration content of the application, the IP address and the port address pointing to the first instance are modified to the IP address and the port address pointing to the second instance, so that the request sent to the application is cut from the first instance to the second instance.
4. The method for optimizing a shrinkage capacity according to claim 1, wherein the step S4 specifically includes the steps of:
and monitoring whether the first instance receives a new request in real time, if no new request reaches the first instance after a first preset time, considering that all requests sent to the application reach the second instance, and deleting the first instance.
5. The method for optimizing a short-cut capacity according to claim 1, wherein the step S1 is to count the number of requests of the first instance periodically by using an asynchronous timed task counting procedure.
6. A capacity reduction optimization terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to perform the steps of:
step S1, when the application is scaled to have only the first instance, counting the number of requests of the first instance regularly;
step S2, determining whether capacity reduction is needed according to the request number, and determining the capacity after capacity reduction when capacity reduction is needed;
step S3, creating a second instance consistent with the reduced capacity, and switching the request sent to the application from the first instance to the second instance;
and step S4, deleting the first instance when all the requests sent to the application reach the second instance.
7. The terminal according to claim 6, wherein the step S1 specifically includes the following steps:
when the application is reduced to only have a first example, counting the total number of requests of the first example every other time period, and calculating to obtain the number of requests per second;
the step S2 specifically includes the following steps:
and judging whether the request numbers per second in a plurality of continuous time periods are all larger than a capacity reduction threshold, if so, not needing capacity reduction processing, otherwise, determining the capacity after capacity reduction according to the threshold interval range in which the request numbers per second in the plurality of continuous time periods fall.
8. The terminal according to claim 6, wherein the step S3 specifically includes the following steps:
creating a second instance consistent with the scaled capacity;
in the configuration content of the application, the IP address and the port address pointing to the first instance are modified to the IP address and the port address pointing to the second instance, so that the request sent to the application is cut from the first instance to the second instance.
9. The terminal according to claim 6, wherein the step S4 specifically includes the following steps:
and monitoring whether the first instance receives a new request in real time, if no new request reaches the first instance after a first preset time, considering that all requests sent to the application reach the second instance, and deleting the first instance.
10. The terminal of claim 6, wherein the step S1 is to count the number of requests of the first instance periodically, specifically, count the number of requests of the first instance periodically through an asynchronous timed task counting procedure.
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| CN110019503A (en) * | 2017-09-01 | 2019-07-16 | 北京京东尚科信息技术有限公司 | The dilatation of Redis cluster and/or the method and device of capacity reducing |
| CN109446032A (en) * | 2018-12-19 | 2019-03-08 | 福建新大陆软件工程有限公司 | The method and system of the scalable appearance of Kubernetes copy |
| CN109918194A (en) * | 2019-01-16 | 2019-06-21 | 深圳壹账通智能科技有限公司 | Intelligent dilatation capacity reduction method, device, computer equipment and storage medium |
| CN110275776A (en) * | 2019-05-17 | 2019-09-24 | 平安科技（深圳）有限公司 | Capacity reduction method, device, computer equipment and the storage medium of container application |
| CN113055199A (en) * | 2019-12-26 | 2021-06-29 | 中国移动通信集团重庆有限公司 | Gateway access method and device and gateway equipment |
| CN113190343A (en) * | 2020-01-14 | 2021-07-30 | 阿里巴巴集团控股有限公司 | Application instance control method, device, equipment and system |
| CN112068934A (en) * | 2020-09-08 | 2020-12-11 | 广州汇智通信技术有限公司 | Control system and method for container cloud service instance contraction |
| CN112925607A (en) * | 2021-02-22 | 2021-06-08 | 深圳前海微众银行股份有限公司 | System capacity expansion and contraction method and device and electronic equipment |
| CN113190329A (en) * | 2021-05-24 | 2021-07-30 | 青岛聚看云科技有限公司 | Server and method for automatically stretching container cloud cluster resources |
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