CN114944993A - Capacity expansion and reduction method and device for microservice - Google Patents
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H04L41/5003—Managing SLA; Interaction between SLA and QoS
- H04L41/5009—Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF]
- H04L41/5012—Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF] determining service availability, e.g. which services are available at a certain point in time
- H04L41/5016—Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF] determining service availability, e.g. which services are available at a certain point in time based on statistics of service availability, e.g. in percentage or over a given time
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Abstract
The invention discloses a method and a device for expanding and shrinking a micro-service, and relates to the field of cloud computing. The method comprises the following steps: acquiring micro-service information on a micro-service calling chain; calculating the service capacity of each micro service on the micro service call chain according to the micro service information; performing resource allocation according to the micro-service information and the service capacity of each micro-service, and determining the number of instances corresponding to each micro-service; and carrying out capacity expansion and reduction operation on each micro service according to the number of the instances corresponding to each micro service. The method and the device can carry out accurate automatic expansion and contraction capacity operation on each micro service, and improve the resource utilization rate while ensuring the application service quality.
Description
Technical Field
The disclosure relates to the field of cloud computing, and in particular, to a method and an apparatus for scaling micro-services.
Background
Compared with single application, the micro-service application provides services to the outside through mutual calling of all micro-services on the micro-service calling chain, so that the service quality of the micro-service application is closely related to each micro-service on the service calling chain, and the request response timeout may be caused by insufficient resources of any micro-service, which affects the user experience.
Each micro Service on the micro Service calling chain has a complex calling relationship, and the resource requirements of different micro Service applications are different, but the existing strategy is to ensure that the whole resource is effectively utilized rather than to ensure the SLA (Service Level agent) of each micro Service on the calling chain. Therefore, the use of single applications for bulk analysis followed by scalability is not suitable for the automated scalability of microservice applications.
Disclosure of Invention
The technical problem to be solved by the present disclosure is to provide a method and an apparatus for expanding and shrinking a micro service, which can perform precise operation of expanding and shrinking a micro service, and improve the resource utilization rate while ensuring the quality of the application service.
According to an aspect of the present disclosure, a method for expanding and shrinking a micro service is provided, including: acquiring micro-service information on a micro-service calling chain; calculating the service capacity of each micro service on the micro service call chain according to the micro service information; performing resource allocation according to the micro-service information and the service capacity of each micro-service, and determining the number of instances corresponding to each micro-service; and carrying out capacity expansion and reduction operation on each micro service according to the number of the instances corresponding to each micro service.
In some embodiments, the service capabilities of the individual microservices include: one or more of average service time, average queuing time, service busy rate, service arrival rate, average response time, and request rate.
In some embodiments, the average value of the service response time of each micro service is calculated to obtain the average service time of each micro service; calculating the average queuing time of each micro service according to the service completion duration, the service arrival time, the service request quantity and the resource allocation condition of each micro service; determining the service busy rate of each micro service according to the service request number which can be processed by a single instance of each micro service in unit time; taking the request rate of each micro-service at the next unit time interval as the service arrival rate; calculating the average response time of each micro service according to the average service time and the average queuing time of each micro service; and taking the number of the service requests responded by the single instance of each micro service when the application service quality reaches the maximum value in unit time as the request rate of each micro service.
In some embodiments, the average service time, average queuing time, service busy rate, service arrival rate, average response time, and request rate of each microservice are weighted to obtain the service capability of each microservice.
In some embodiments, the microservice information includes: the micro-service call chain basic information, the receiving request time of each micro-service, the response time of each micro-service, the receiving request number of each micro-service, the service completion time of each micro-service and the service arrival time of each micro-service.
According to another aspect of the present disclosure, there is also provided a device for scaling a micro service, including: an information acquisition unit configured to acquire micro-service information on a micro-service call chain; a capability calculation unit configured to calculate service capabilities of the respective microservices on the microservice call chain according to the microservice information; the resource allocation unit is configured to perform resource allocation according to the micro-service information and the service capacity of each micro-service and determine the number of instances corresponding to each micro-service; and the scaling operation unit is configured to perform scaling operation on each micro service according to the number of the corresponding instances of each micro service.
In some embodiments, the service capabilities of the individual microservices include: one or more of average service time, average queuing time, service busy rate, service arrival rate, average response time, and request rate.
In some embodiments, the capability calculation unit is configured to perform an average calculation on the service response time of each micro-service to obtain an average service time of each micro-service; calculating the average queuing time of each micro service according to the service completion time, the service arrival time, the service request amount and the resource distribution condition of each micro service; determining the service busy rate of each micro service according to the service request number which can be processed by a single instance of each micro service in unit time; taking the request rate of each micro-service at the next unit time interval as the service arrival rate; calculating the average response time of each micro service according to the average service time and the average queuing time of each micro service; and taking the number of the service requests responded by the single instance of each micro service when the application service quality reaches the maximum value in unit time as the request rate of each micro service.
According to another aspect of the present disclosure, a scalable apparatus for micro services is further provided, including: a memory; and a processor coupled to the memory, the processor configured to perform the above-described method of scaling the microservices based on instructions stored in the memory.
According to another aspect of the present disclosure, a non-transitory computer-readable storage medium is also proposed, on which computer program instructions are stored, which instructions, when executed by a processor, implement the method of scaling up or scaling down a microservice as described above.
In the embodiment of the disclosure, resource allocation is performed according to the micro-service information on the micro-service call chain and the service capacity of each micro-service, the number of instances corresponding to each micro-service is determined, and then accurate automatic capacity expansion and reduction operation is performed on each micro-service, so that the resource utilization rate is improved while the application service quality is ensured.
Other features of the present disclosure and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the disclosure.
The present disclosure may be understood more clearly from the following detailed description, taken with reference to the accompanying drawings,
wherein:
fig. 1 is a flowchart illustrating a method for scaling a microservice according to some embodiments of the present disclosure.
Fig. 2 is a flowchart illustrating a method for scaling a microservice according to another embodiment of the disclosure.
Fig. 3 is a schematic structural diagram of some embodiments of the scalable apparatus for microservices according to the present disclosure.
Fig. 4 is a schematic structural diagram of another embodiment of the scale and shrink device for microservices according to the present disclosure.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of parts and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as exemplary only and not as limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be discussed further in subsequent figures.
To make the objects, technical solutions and advantages of the present disclosure more apparent, the present disclosure will be described in further detail below with reference to specific embodiments and the accompanying drawings.
Fig. 1 is a flowchart illustrating a method for scaling a microservice according to some embodiments of the present disclosure.
At step 110, microservice information on a microservice call chain is obtained.
In some embodiments, the microservice information includes: the micro-service call chain basic information, the receiving request time of each micro-service, the response time of each micro-service, the receiving request number of each micro-service, the service completion time of each micro-service and the service arrival time of each micro-service.
Etc. of
At step 120, the service capabilities of each microservice on the microservice call chain are calculated based on the microservice information.
In some embodiments, the service capabilities of the individual microservices include: average service time, average queuing time, service busy rate, service arrival rate, average response time, request rate, and the like.
In step 130, resource allocation is performed according to the micro service information and the service capability of each micro service, and the number of instances corresponding to each micro service is determined.
In some embodiments, after data preprocessing is performed on the microservice information and the service capability of each microservice, the microservice information and the service capability of each microservice are input to a resource allocation model, and the number of instances corresponding to each microservice is obtained. The resource allocation model is a pre-trained model and can be trained by using a related machine learning algorithm.
In step 140, the capacity expansion and reduction operations are performed on each microservice according to the number of instances corresponding to each microservice.
For example, it is calculated that the number of instances corresponding to a certain microservice is 10, but the number of instances corresponding to the existing microservice is 7, the capacity expansion operation needs to be performed on the microservice, and if the number of instances corresponding to the existing microservice is 12, the capacity reduction operation needs to be performed on the microservice.
In the embodiment, resource allocation is performed according to the micro-service information on the micro-service call chain and the service capacity of each micro-service, the number of instances corresponding to each micro-service is determined, and then accurate capacity expansion and reduction operations are performed on each micro-service, so that the application service quality is guaranteed and the resource utilization rate is improved.
Fig. 2 is a flowchart illustrating a method for scaling a microservice according to another embodiment of the disclosure.
In step 210, the identity of the microservice call chain, the identity of each microservice, is obtained from the call chain monitoring system.
In some embodiments, the microservice call chain's identification traceId is a global ID in the docked external microservice call chain monitoring system, being the unique ID of the call chain. The identity of each microservice, spanId, is the ID of each calling node on the calling chain, by which each node can be identified. Through these two identifications, individual microservices on the microservice call chain can be located.
In step 220, various micro-service information on the scheduled micro-service call chain is obtained according to the identifier of the micro-service call chain and the identifiers of the micro-services.
In step 230, the average service time, average queuing time, service busy rate, service arrival rate, average response time and request rate of each micro-service on the micro-service call chain are calculated according to the micro-service information.
In some embodiments, the average of the service response time of each microservice is calculated to obtain the average service time of each microservice.
In some embodiments, the average queuing time of each microservice is calculated according to the service completion time, the service arrival time, the service request amount and the resource allocation condition of each microservice.
In some embodiments, the feature engineering is constructed by using the service completion time length, the service arrival time, the service request amount and the resource allocation condition, and a plurality of matrixes, such as ETC, CO and the like, are set. ETCi, k represents the execution time of the task of the application i on the micro-service k; COi, k represents service completion time of the application i on the microservice k, etc. And processing the data by adopting a recurrent neural network model, and outputting the data as the average queuing time of the application i on the micro-service k.
In some embodiments, the service busy rate for each microservice is determined based on the number of service requests that a single instance of each microservice can handle per unit time.
In some embodiments, the request rate for the next unit time interval per microservice is taken as the service arrival rate.
In some embodiments, the service arrival rate may be calculated from the ingress service next time interval request rate (request arrival rate of the ingress service last time interval/request arrival rate of the micro service last time interval).
In some embodiments, the average response time of each microservice is calculated based on the average service time and average queuing time of each microservice. The index may be used to comprehensively determine microservice application characteristics.
In some embodiments, the number of service requests per microservice that a single instance of each microservice responds to per unit time when the application quality of service reaches a maximum is taken as the request rate per microservice.
In some embodiments, the average service time, service busy rate, service arrival rate, average response time of the micro-service may reflect the micro-service application static characteristics, and the average queuing time of the micro-service may reflect the micro-service application dynamic characteristics. Therefore, the average service time, the service busy rate, the service arrival rate and the average response time of the micro-service can be calculated off-line, and the average queuing time of the micro-service can be calculated on-line.
In step 240, the average service time, average queuing time, service busy rate, service arrival rate, average response time and request rate of each microservice are weighted to obtain the service capability of each microservice.
In some embodiments, the integrated service capability of each microservice is calculated based on the weight of the service capability indicators of the microservice.
In step 250, resource allocation is performed according to the micro service information and the service capability of each micro service, and the number of instances corresponding to each micro service is determined.
In step 260, the capacity expansion and reduction operation is performed on each microservice according to the number of instances corresponding to each microservice.
In some embodiments, after resource allocation calculation is performed, the calculation result is fed back, and after the system obtains feedback information, a corresponding decision is taken, for example, capacity expansion or capacity reduction operation is performed on a certain micro service.
In the embodiment, the connection between the service quality of the micro-service and the resource can be established, each micro-service on the call chain is accurately expanded and contracted, and the resource utilization rate is improved while the application service quality is ensured.
Fig. 3 is a schematic structural diagram of some embodiments of the scalable apparatus for microservices according to the present disclosure. The expansion and contraction volume device comprises: an information acquisition unit 310, a capability calculation unit 320, a resource allocation unit 330, and a scaling operation unit 340.
The information obtaining unit 310 is configured to obtain micro-service information on the micro-service call chain.
In some embodiments, the microservice information includes: the micro-service call chain basic information, the receiving request time of each micro-service, the response time of each micro-service, the receiving request number of each micro-service, the service completion time of each micro-service, the service arrival time of each micro-service, and the like.
The capability calculation unit 320 is configured to calculate service capabilities of the individual microservices on the microservice call chain according to the microservice information.
In some embodiments, the service capabilities of the individual microservices include: average service time, average queuing time, service busy rate, service arrival rate, average response time, request rate, and the like.
In some embodiments, the average value of the service response time of each micro service is calculated to obtain the average service time of each micro service; calculating the average queuing time of each micro service according to the service completion duration, the service arrival time, the service request quantity and the resource allocation condition of each micro service; determining the service busy rate of each micro service according to the service request number which can be processed by a single instance of each micro service in unit time; taking the request rate of each micro-service at the next unit time interval as the service arrival rate; calculating the average response time of each micro service according to the average service time and the average queuing time of each micro service; and taking the number of the service requests responded by the single instance of each micro service when the application service quality reaches the maximum value in unit time as the request rate of each micro service.
In some embodiments, the average service time, the average queuing time, the service busy rate, the service arrival rate, the average response time, and the request rate of each microservice are weighted to obtain the service capability of each microservice.
The resource allocation unit 330 is configured to perform resource allocation according to the micro service information and the service capabilities of the respective micro services, and determine the number of instances corresponding to each micro service.
In some embodiments, after data preprocessing is performed on the microservice information and the service capability of each microservice, the microservice information and the service capability of each microservice are input to a resource allocation model, and the number of instances corresponding to each microservice is obtained.
The scaling operation unit 340 is configured to perform scaling operation on each micro-service according to the number of instances corresponding to each micro-service.
In the embodiment, resource allocation is performed according to the micro-service information on the micro-service call chain and the service capability of each micro-service, the number of instances corresponding to each micro-service is determined, and then precise capacity expansion and contraction operations are performed on each micro-service, so that the application service quality is ensured and the resource utilization rate is improved.
Fig. 4 is a schematic structural diagram of another embodiment of the micro-service scaling device of the present disclosure. The apparatus includes a memory 410 and a processor 420. Wherein: the memory 410 may be a magnetic disk, flash memory, or any other non-volatile storage medium. The memory 410 is used for storing instructions in the embodiments corresponding to fig. 1 and 2. Processor 420 is coupled to memory 410 and may be implemented as one or more integrated circuits, such as a microprocessor or microcontroller. The processor 420 is configured to execute instructions stored in memory.
In some embodiments, processor 420 is coupled to memory 410 by a BUS BUS 430. The apparatus 400 may also be connected to an external storage system 450 through a storage interface 440 for calling external data, and may also be connected to a network or another computer system (not shown) through a network interface 460. And will not be described in detail herein.
In the embodiment, the data instruction is stored by the memory and processed by the processor, so that the resource utilization rate is improved while the application service quality is ensured.
In further embodiments, a computer-readable storage medium has stored thereon computer program instructions which, when executed by a processor, implement the steps of the method in the embodiments corresponding to fig. 1 and 2. As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, apparatus, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Thus far, the present disclosure has been described in detail. Some details that are well known in the art have not been described in order to avoid obscuring the concepts of the present disclosure. Those skilled in the art can now fully appreciate how to implement the teachings disclosed herein, in view of the foregoing description.
Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the foregoing examples are for purposes of illustration only and are not intended to limit the scope of the present disclosure. It will be appreciated by those skilled in the art that modifications may be made to the above embodiments without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.
Claims (10)
1. A method for expanding and shrinking a micro service comprises the following steps:
acquiring micro-service information on a micro-service calling chain;
calculating the service capacity of each micro service on the micro service call chain according to the micro service information;
performing resource allocation according to the micro-service information and the service capacity of each micro-service, and determining the number of instances corresponding to each micro-service; and
and carrying out capacity expansion and reduction operation on each micro service according to the number of the corresponding examples of each micro service.
2. The method for scaling a volume according to claim 1,
the service capabilities of each microservice include: one or more of average service time, average queuing time, service busy rate, service arrival rate, average response time, and request rate.
3. The capacity enlarging and reducing method according to claim 2,
carrying out average calculation on the service response time of each micro service to obtain the average service time of each micro service;
calculating the average queuing time of each micro service according to the service completion time, the service arrival time, the service request amount and the resource distribution condition of each micro service;
determining the service busy rate of each micro service according to the service request number which can be processed by a single instance of each micro service in unit time;
taking the request rate of each micro-service at the next unit time interval as the service arrival rate;
calculating the average response time of each micro service according to the average service time and the average queuing time of each micro service; and
and taking the number of the service requests responded when the application service quality reaches the maximum value in unit time of a single instance of each micro service as the request rate of each micro service.
4. The method for scaling a volume according to claim 2,
and carrying out weighted calculation on the average service time, the average queuing time, the service busy rate, the service arrival rate, the average response time and the request rate of each micro-service to obtain the service capacity of each micro-service.
5. The method for capacity expansion and contraction according to any one of claims 1 to 4,
the micro service information comprises: the micro-service call chain basic information, the receiving request time of each micro-service, the response time of each micro-service, the receiving request number of each micro-service, the service completion time of each micro-service and the service arrival time of each micro-service.
6. A micro-service scale-up and scale-down apparatus, comprising:
an information acquisition unit configured to acquire micro-service information on a micro-service call chain;
a capability calculation unit configured to calculate service capabilities of the respective micro services on the micro service call chain according to the micro service information;
the resource allocation unit is configured to perform resource allocation according to the micro service information and the service capacity of each micro service and determine the number of instances corresponding to each micro service; and
and the scaling operation unit is configured to perform scaling operation on each micro service according to the number of the instances corresponding to each micro service.
7. The capacitance expansion and reduction device of claim 6,
the service capabilities of the individual microservices include: one or more of average service time, average queuing time, service busy rate, service arrival rate, average response time, and request rate.
8. The device of claim 7,
the capacity calculation unit is configured to perform average calculation on the service response time of each micro service to obtain the average service time of each micro service; calculating the average queuing time of each micro service according to the service completion duration, the service arrival time, the service request quantity and the resource allocation condition of each micro service; determining the service busy rate of each micro service according to the service request number which can be processed by a single instance of each micro service in unit time; taking the request rate of each micro-service at the next unit time interval as the service arrival rate; calculating the average response time of each micro service according to the average service time and the average queuing time of each micro service; and taking the number of the service requests responded by the single instance of each micro service when the application service quality reaches the maximum value in unit time as the request rate of each micro service.
9. A micro-service scale-and-scale device, comprising:
a memory; and
a processor coupled to the memory, the processor configured to perform the method of scaling a microservice of any of claims 1-5 based on instructions stored in the memory.
10. A non-transitory computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method of scaling the micro-services of any of claims 1 to 5.
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