CN110061930B - Method and device for determining data flow limitation and flow limiting values - Google Patents
Method and device for determining data flow limitation and flow limiting values Download PDFInfo
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Abstract
The application discloses a method and a device for limiting data flow and determining a flow limiting value, wherein the method for limiting the data flow comprises the following steps: collecting data request quantity of a plurality of services; obtaining a model for determining a flow limiting value, wherein the model is obtained based on sample data request quantity and maximum data request quantity allowed by the plurality of services under the sample data request quantity through training; determining the flow limiting values of the plurality of services according to the data request quantity and the model; limiting the flow of the plurality of services based on the flow limit value.
Description
Technical Field
The present application relates to the field of internet technologies, and in particular, to a method and an apparatus for determining a data traffic limit and a traffic limit value.
Background
An existing business system can provide a plurality of services to the outside to meet business requirements. In the process of providing multiple services by a service system, in order to protect the service system, a flow limit value of each service generally needs to be configured, and when the data request volume of a certain service exceeds the flow limit value, the service system will refuse to receive other data requests of the service, so as to avoid system downtime and achieve the purpose of protecting the system.
When configuring the current limit value for each service, the current limit value of each service may be configured based on a mixed application scenario in which a business system simultaneously provides a plurality of services to the outside. However, in practical applications, when the current limit value configured according to the above method is used to limit a plurality of services of a service system, there is a problem that system resources are not fully utilized, which results in waste of system resources.
Disclosure of Invention
The embodiment of the application provides a method and a device for limiting data traffic and determining a current limit value, which are used for solving the problem that system resources are wasted because system resources cannot be fully utilized when a plurality of services provided by a service system are limited by using the current limit value configured by the existing method.
In order to solve the above technical problem, the embodiment of the present application is implemented as follows:
in a first aspect, a method for limiting data traffic is provided, including:
collecting data request quantity of a plurality of services;
obtaining a model for determining a flow limiting value, wherein the model is obtained based on sample data request quantity and maximum data request quantity allowed by the plurality of services under the sample data request quantity through training;
determining the flow limiting values of the services according to the data request quantity and the model;
limiting the flow of the plurality of services based on the flow limit value.
In a second aspect, a device for limiting data traffic is provided, including:
the acquisition unit is used for acquiring data request volumes of a plurality of services;
the acquisition unit is used for acquiring a model for determining a flow limiting value, and the model is obtained based on the sample data request quantity and the maximum data request quantity allowed by the plurality of services under the sample data request quantity through training;
the determining unit is used for determining the flow limiting values of the services according to the data request quantity and the model;
and the current limiting unit limits the current of the plurality of services based on the current limiting value.
In a third aspect, an electronic device is provided, which includes:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
collecting data request quantity of a plurality of services;
obtaining a model for determining a flow limiting value, wherein the model is obtained based on sample data request quantity and maximum data request quantity allowed by the plurality of services under the sample data request quantity through training;
determining the flow limiting values of the plurality of services according to the data request quantity and the model;
throttling the plurality of services based on the throttling value.
In a fourth aspect, a computer-readable storage medium is presented, the computer-readable storage medium storing one or more programs that, when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method of:
collecting data request quantity of a plurality of services;
obtaining a model for determining a flow limiting value, wherein the model is obtained based on sample data request quantity and maximum data request quantity allowed by the plurality of services under the sample data request quantity through training;
determining the flow limiting values of the plurality of services according to the data request quantity and the model;
limiting the flow of the plurality of services based on the flow limit value.
In a fifth aspect, a method for determining a current limiting value is provided, including:
collecting data request quantity of a plurality of services;
obtaining a model for determining a flow limiting value, wherein the model is obtained based on sample data request quantity and maximum data request quantity allowed by the plurality of services under the sample data request quantity through training;
and determining the flow limiting values of the services according to the data request quantity and the model.
In a sixth aspect, an apparatus for determining a restriction value is provided, including:
the acquisition unit is used for acquiring data request volumes of a plurality of services;
the acquisition unit is used for acquiring a model for determining a flow limiting value, and the model is obtained based on the sample data request quantity and the maximum data request quantity allowed by the plurality of services under the sample data request quantity through training;
and the determining unit is used for determining the flow limiting values of the plurality of services according to the data request quantity and the model.
In a seventh aspect, an electronic device is provided, where the electronic device includes:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
collecting data request quantity of a plurality of services;
obtaining a model for determining a flow limiting value, wherein the model is obtained based on sample data request quantity and maximum data request quantity allowed by the plurality of services under the sample data request quantity through training;
and determining the flow limiting values of the plurality of services according to the data request quantity and the model.
In an eighth aspect, a computer readable storage medium is presented, the computer readable storage medium storing one or more programs that, when executed by an electronic device that includes a plurality of application programs, cause the electronic device to perform a method of:
collecting data request quantity of a plurality of services;
obtaining a model for determining a flow limiting value, wherein the model is obtained based on sample data request quantity and maximum data request quantity allowed by the plurality of services under the sample data request quantity through training;
and determining the flow limiting values of the services according to the data request quantity and the model.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
according to the technical scheme provided by the embodiment of the application, the model is obtained by training in advance according to the sample data request quantity and the maximum data request quantity which can be received by a plurality of services under the sample data request quantity, so that the data request quantity of the plurality of services can be collected when the plurality of services are subjected to current limiting, the current limiting values of the plurality of services are determined according to the model which is trained in advance, and the current limiting is carried out based on the current limiting values. The acquired data request quantity of a plurality of services is changed in real time, so that the flow limiting value determined based on the data request quantity can be adjusted in a self-adaptive manner, and the flexibility is good; in addition, because the maximum data request amount allowed by a plurality of services under the sample data request amount is taken as sample data during model training, and the maximum data request amount is premised on the maximum utilization of system resources, when the flow limit value determined based on the model is used for flow limit, the system resources can be fully utilized on the basis of flow limit, and the waste of the system resources is avoided.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the description below are only some embodiments described in the present application, and for those skilled in the art, other drawings may be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart diagram illustrating a method for limiting data traffic according to an embodiment of the present application;
FIG. 2 is a flow chart illustrating a method for limiting data traffic according to an embodiment of the present application;
FIG. 3 is a flow chart illustrating a method for determining a restriction value according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a data traffic limiting device according to an embodiment of the present application;
FIG. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a device for determining a restriction value according to an embodiment of the present application.
Detailed Description
When the existing service system configures a current limit value for a plurality of services provided by the existing service system, considering that in an actual application scenario, the service system generally provides a plurality of services to the outside at the same time instead of a single service, and therefore, the configured current limit value is generally a mixed application scenario for the plurality of services. Because the resources of the service system are limited, the flow limit value configured for a plurality of services by the service system in a mixed application scenario is smaller than the maximum data request amount that can be received when the service system provides a single service to the outside, that is, the maximum data request amount that can be allowed by the single service.
For example, the service system provides two services a and B to the outside, when the service a is provided to the outside separately, the maximum data request amount allowed by the service a is a, and when the service B is provided to the outside separately, the maximum data request amount allowed by the service B is B. When the service system configures the restriction value for the service a and the service B, in a mixed application scenario, the restriction value for the service a may be configured to be a1, and the restriction value for the service B may be configured to be B1, where a1 is smaller than a, and B1 is smaller than B.
Generally, when the flow limiting value of each service in a hybrid application scene is configured, the configuration can be performed according to the resources of the service system, so that when the data request volume of each service reaches the flow limiting value, the system resources can be fully utilized, and the service system does not go down.
However, in practical application, the data request volumes of different services usually do not reach the flow limit value at the same time, so that when the data request volume of a certain service has reached the flow limit value and the data request volumes of other services have not reached the flow limit value, the service system has idle resources, and for the service that has reached the flow limit value, the service system can theoretically receive the data request of the service, but due to the effect of flow limit, the service system will actually refuse to receive the data request of the service, so that the phenomenon that the service system refuses to provide the service to the outside when idle resources exist occurs, and system resources are wasted.
Still taking the service a and the service B as an example, assuming that a1 is 300 and a2 is 600, if the current data request amount of the service a is 400 and the current data request amount of the service B is 300, the service system will perform the current limitation on the service a.
However, since the data request amount of the service B has not yet reached the flow limit value, the service system has idle resources, and theoretically, after receiving 300 data requests of the service a, the service system can continue to receive the data requests of the service a, but since the service system has limited the flow of the service a, the service system will not actually receive other data requests of the service a, and thus, the service system refuses to provide the service a when the service system has idle resources, which causes waste of system resources.
In addition, after the traffic system configures a current limit value for a plurality of services, the current limit value is usually fixed, which results in long-time waste of system resources when limiting the plurality of services based on the fixed current limit value.
Therefore, when the current limit value configured by the existing method is used for limiting a plurality of services provided by a service system, system resources cannot be fully utilized, and the system resources are wasted.
In order to solve the above technical problem, an embodiment of the present application provides a method and an apparatus for determining a restriction value and a restriction value of a data traffic, where the method for restricting a data traffic includes: collecting data request quantity of a plurality of services; obtaining a model for determining a restriction value, wherein the model is obtained based on a sample data request amount and the maximum data request amount allowed by the services under the sample data request amount; determining the flow limiting values of the services according to the data request quantity and the model; limiting the flow of the plurality of services based on the flow limit value.
When the plurality of services are subjected to current limiting, the collected data request quantity of the plurality of services is changed in real time, so that the current limiting value determined and obtained based on the data request quantity can be adjusted in a self-adaptive mode, and the flexibility is good; in addition, because the maximum data request amount allowed by a plurality of services under the sample data request amount is taken as sample data during model training, and the maximum data request amount is premised on the maximum utilization of system resources, when the flow limit value determined based on the model is used for flow limit, the system resources can be fully utilized on the basis of flow limit, and the waste of the system resources is avoided.
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical scheme provided by the embodiment of the application can be used for determining the flow limiting values of a plurality of services externally provided by the service system and limiting the flow of the plurality of services based on the flow limiting values, wherein the flow limiting values of the plurality of services can be adaptively adjusted, the flexibility is good, the plurality of services are limited based on the flow limiting values, system resources can be fully utilized, and waste of the system resources is avoided.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a method for limiting data traffic according to an embodiment of the present application. The execution subject of the method may be a business system that provides a plurality of services to the outside, and the method includes the following steps.
S102: data request volumes for a plurality of services are collected.
In S102, when the service system provides multiple services to the outside, the service system may collect data request volumes of the multiple services in real time. Taking one of the services as an example, the data request amount of the service may represent the amount of the data request of the service received by the service system in the current unit time, and may be specifically represented by QPS (Query Per Second configured).
After collecting the respective data request amounts of the plurality of services, S104 may be performed.
S104: a model for determining a restriction value is obtained.
In this embodiment, the model may be a linear regression model, and may include a plurality of linear regression equations, where the number of the plurality of linear regression equations may be the same as the number of the plurality of services provided by the service system, and a one-to-one correspondence relationship exists between the plurality of linear regression equations and the plurality of services, and the one-to-one correspondence relationship is used to determine the restriction values of the plurality of services. Taking one of the linear regression equations as an example, the linear regression equation may be used to determine a restriction value of a service corresponding to the linear regression method.
The model can be obtained by pre-training, so that after the data request quantity of a plurality of services is acquired, the pre-trained model can be obtained.
In this embodiment, the model may be obtained by training in the following manner, including:
acquiring a sample data request quantity, wherein the sample data request quantity comprises historical data request quantities of the plurality of services;
determining a maximum data request amount allowed by the plurality of services under the sample data request amount;
and training the sample data request quantity and the maximum data request quantity by adopting a linear regression algorithm to obtain the model.
Specifically, first, historical data request volumes of a plurality of services may be acquired from a business system, and after the historical data request volumes of the services are acquired, the historical data request volumes may be used as sample data request volumes for model training.
Secondly, according to the sample data request quantity, the maximum data request quantity which can be allowed by each of the plurality of services provided by the business system under the sample data request quantity can be determined, wherein the maximum data request quantity of the plurality of services is for a mixed application scene of the plurality of services.
When the maximum data request volume allowed by each of the multiple services is determined, the embodiment may perform a stress test on the service system in the mixed application scenario under the condition of the sample data request volume, so as to obtain a test result. When the pressure test is performed, resources of the service system can be fully utilized to perform the test under the condition that the service system is not down, and specific implementation can refer to specific implementation of an existing pressure test method, which is not described in detail herein.
After the test result is obtained, the maximum data request amount allowed by each service provided by the service system in the mixed application scene can be determined according to the test result. Since the maximum data request amount is based on the premise of utilizing the resources of the service system to the maximum, after the model is obtained through subsequent training, when the current limiting value determined by the model is used for limiting the current, the system resources can be fully utilized, and the waste of the system resources is avoided.
Finally, a linear regression algorithm can be adopted to perform learning training on the sample data request quantity and the maximum data request quantity allowed by a plurality of services to obtain the model.
In this embodiment, the process of performing learning training by using a linear regression algorithm may be a process of performing linear fitting on the sample data request amount and the maximum data request amount of multiple services. The following describes the whole process of model training by taking an example that a business system provides m services to the outside.
Assuming that the acquired sample data request amount is n groups, the sample data request amount may be expressed as:
after performing a stress test based on the sample data request amount, the maximum data request amount of m services can be obtained, which can be specifically expressed as:
wherein, y 1n ，y 2n ，……，y mn M service sample data request quantity x n1 ，x n2 ，……，x nm Maximum data request volume for the lower run.
When model training is performed, a linear regression equation can be obtained by training for each service respectively based on the sample data request quantity and the maximum data request quantity, and then the model is obtained. The specific implementation mode is as follows:
first, the maximum data request amount may be divided according to different services to obtain m groups of maximum data request amounts corresponding to m services one to one, which may be specifically expressed as:
secondly, combining the m groups of maximum data request quantities with the sample data request quantities respectively to obtain m groups of sample data, which can be specifically expressed as:
and finally, training each group of sample data respectively to obtain m linear regression equations in one-to-one correspondence with the m groups of sample data.
Using the first set of sample data
For example, the set of sample data includes n rows of data, and based on the n rows of data, a linear regression algorithm may be used to fit a multiple linear regression equation y 1 ＝ω 10 +ω 11 x 1 +ω 12 x 2 +……+ω 1m x m And obtain the weight ω in the equation i (i =1,2, … …, m), and a multiple linear regression equation can be obtained.
According to the same method, m linear regression equations corresponding to m services one to one can be obtained, and the m linear regression equations are the models. Wherein the m linear regression equations can be expressed as:
in S104, after the pre-trained model is acquired, S106 may be performed.
S106: and determining the flow limiting values of the plurality of services according to the data request quantity and the model.
In S106, the respective throttling values of the multiple services may be determined and obtained according to the data request volumes of the multiple services acquired in S102 and the model acquired in S104.
Taking one of the services (for the sake of convenience of distinction, the following may be represented by the target service) as an example, when determining the restriction value of the target service, the following steps may be included:
first, a linear regression equation (which may be represented by a target linear regression equation herein) corresponding to the target service may be determined from the model. The target service corresponds to the target linear regression equation, and the target linear regression equation can be characterized to determine the current limiting value of the target service.
Secondly, the data request quantity of the target service can be used as the input of the target linear regression equation to obtain the output of the target linear regression equation, and the output is the current limiting value of the target service.
In this way, the respective restriction values of the plurality of services can be obtained based on the same method.
It should be noted that, because the data request volume of the multiple services may be collected in real time in S102, the flow limit values of the multiple services determined in S106 may be adaptively adjusted according to the data request volume in S102, and have very good flexibility.
After obtaining the respective restriction values for the plurality of services, S108 may be performed.
S108: limiting the flow of the plurality of services based on the flow limit value.
In S108, the plurality of services may be throttled based on their respective throttling values.
In this embodiment, when limiting the flow of the plurality of services, at least two cases may be used, where one case limits the current data request amount of the plurality of services according to the flow limit value, and the other case limits the data request amount of the plurality of services in the next unit time according to the flow limit value.
For the first case, before the service is limited, the service system receives the data request of the service without current limitation, so that to avoid the data request amount received being too large to cause the service system to be down, the service system may further set a buffer to avoid the situation that the data request amount of the service system is suddenly increased.
In this embodiment, when performing current limiting on multiple services, taking the first case as an example, for a target service in multiple services, it may be determined whether a current data request amount of the target service is greater than a current limit value of the target service, and if so, it may be stated that the data request amount of the target service exceeds the current limit value, at this time, the service system may trigger a current limiting operation on the target service, and currently refuse to receive the data request of the target service.
If the current data request volume of the target service is not greater than the current limit value of the target service, it may be indicated that the data request volume of the target service does not exceed the current limit value, and at this time, the service system may normally receive the data request of the target service and provide the target service to the outside.
In this embodiment, since the flow limit values of the plurality of services determined in S106 may be adaptively adjusted following the data request amounts of the plurality of services in S102, in S108, the plurality of services may be flexibly limited according to the adaptively adjusted flow limit values. Meanwhile, the flow limiting value is determined and obtained based on the model, and the maximum data request amount which can be allowed by a plurality of services under the sample data request amount is taken as sample data during model training, and the maximum data request amount is on the premise of utilizing system resources to the maximum extent, so that when the flow limiting value determined and obtained based on the model is used for flow limiting, the system resources can be fully utilized under the condition that the service is not influenced and the breakdown of a service system is avoided, and the waste of the system resources is avoided.
To facilitate understanding of the whole technical solution, see fig. 2. Fig. 2 is a flowchart illustrating a method for limiting data traffic according to an embodiment of the present application. The embodiment shown in fig. 2 belongs to the same inventive concept as the embodiment shown in fig. 1, and may specifically include the following steps.
S201: data request volumes for a plurality of services are collected.
In S201, the business system may obtain a data request amount of a plurality of services provided by the business system in a current unit time, specifically, obtain a current QPS of the plurality of services.
S202: a pre-trained model is obtained.
The model is used to determine the restriction values of the services, and may specifically include a plurality of linear regression equations, where the plurality of linear regression equations are in one-to-one correspondence with the services, and one linear regression equation is used to determine the restriction value of the corresponding service.
The specific training process of the model can refer to the related contents described in the embodiment shown in fig. 1, and the description is not repeated here.
S203: and determining the flow limiting values of the plurality of services according to the data request quantity and the model.
In S203, a restriction value of each service may be determined according to a plurality of linear regression methods included in the model, respectively. The specific implementation manner can refer to the related contents described in the embodiment shown in fig. 1, and the description is not repeated here.
S204: and judging whether the data request quantity of the target service is larger than the flow limit value of the target service or not.
The target service is one of the multiple services, and if the data request volume of the target service is greater than the flow limit value of the target service, S205 may be executed; otherwise, S206 may be performed.
S205: denying receipt of the data request for the target service.
In S205, the service system may trigger a current limiting operation and refuse to receive the data request of the target service.
S206: and providing service for the data request of the target service.
In S206, the business system may normally receive the data request of the target service when the data request amount of the target service does not exceed the flow limit value.
It should be noted that, in an actual application, the foregoing S201 to S206 may be executed in a loop, that is, after the S205 or S206 is executed, the S201 may be continuously executed, so as to obtain a data request amount in a next unit time, and further determine a current limit value of a plurality of services in the next unit time, and further limit the plurality of services.
According to the technical scheme provided by the embodiment of the application, the model is obtained by training in advance according to the sample data request quantity and the maximum data request quantity which can be received by a plurality of services under the sample data request quantity, so that the data request quantity of the plurality of services can be collected when the plurality of services are subjected to current limiting, the current limiting values of the plurality of services are determined according to the model which is trained in advance, and the current limiting is carried out based on the current limiting values. The acquired data request quantity of a plurality of services is changed in real time, so that the flow limiting value determined and obtained based on the data request quantity can be adjusted in a self-adaptive manner, and the flexibility is good; in addition, when the model is trained, the maximum data request amount which can be allowed by a plurality of services under the sample data request amount is taken as sample data, and the maximum data request amount is on the premise of utilizing system resources to the maximum extent, so that when the flow limit value determined based on the model is used for flow limit, the system resources can be fully utilized on the basis of flow limit, and waste of the system resources is avoided.
Fig. 3 is a flowchart illustrating a method for determining a restriction value according to an embodiment of the present application. The execution subject of this embodiment may be a business system that provides a plurality of services to the outside, and the method for determining the restriction value may include the following steps.
S302: data request volumes for a plurality of services are collected.
S304: a model for determining a restriction value is obtained.
The model is trained based on the sample data request amount and the maximum data request amount allowed by the plurality of services under the sample data request amount.
The model includes a plurality of linear regression equations in one-to-one correspondence with the plurality of services, one linear regression equation for determining a restriction value for the corresponding service.
S306: and determining the flow limiting values of the plurality of services according to the data request quantity and the model.
In this embodiment, because the data request volumes of the multiple services may be collected in real time in S302, the flow limiting values of the multiple services determined in S306 may be adaptively adjusted according to the data request volumes in S302, and the flexibility is better.
The specific implementation of S302 to S306 can refer to the specific implementation of corresponding steps in the embodiment shown in fig. 1, and a description of one or more embodiments in this specification is not repeated here.
In this embodiment, after the flow limiting values of the plurality of services are determined, the plurality of services may be further flow limited based on the flow limiting values of the plurality of services. Taking one of the services as an example, when the data request volume of the service is greater than the current limit value, the service system may trigger a current limit operation on the service; when the data request amount of the service is not larger than the flow limit value, the service system can normally provide the service to the outside and receive the data request of the service. The specific implementation manner may refer to the content recorded in S108 in the embodiment shown in fig. 1, and a description thereof is not repeated here.
According to the technical scheme provided by the embodiment of the application, the model is obtained by training in advance according to the sample data request quantity and the maximum data request quantity which can be received by a plurality of services under the sample data request quantity, so that when the flow limit values of the services are determined, the data request quantities of the services can be collected, and the flow limit values of the services are determined according to the pre-trained model. The acquired data request quantity of a plurality of services is changed in real time, so that the flow limiting value determined and obtained based on the data request quantity can be adjusted in a self-adaptive manner, and the flexibility is good; in addition, during model training, the maximum data request amount which can be allowed by a plurality of services under the sample data request amount is taken as sample data, and the maximum data request amount is on the premise of utilizing system resources to the maximum extent, so that the flow limiting value determined based on the model is more consistent with the requirement of fully utilizing the system resources, and the waste of the system resources can be avoided when the flow limiting value is used for limiting the flow.
The foregoing description has been directed to specific embodiments of this application. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 4, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form a data flow limiting device on a logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
collecting data request quantity of a plurality of services;
obtaining a model for determining a flow limiting value, wherein the model is obtained based on sample data request quantity and maximum data request quantity allowed by the plurality of services under the sample data request quantity through training;
determining the flow limiting values of the plurality of services according to the data request quantity and the model;
throttling the plurality of services based on the throttling value.
The method executed by the data traffic limiting apparatus according to the embodiment shown in fig. 4 of the present application may be applied to a processor, or may be implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and combines hardware thereof to complete the steps of the method.
The electronic device may further execute the method shown in fig. 1 and fig. 2, and implement the function of the limiting apparatus for data traffic in the embodiment shown in fig. 1 and fig. 2, which is not described herein again in this embodiment of the present application.
Of course, besides the software implementation, the electronic device of the present application does not exclude other implementations, such as a logic device or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or a logic device.
Embodiments of the present application also propose a computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a portable electronic device comprising a plurality of application programs, enable the portable electronic device to perform the method of the embodiment shown in fig. 1 and 2, and in particular to perform the following operations:
collecting data request quantity of a plurality of services;
obtaining a model for determining a restriction value, wherein the model is obtained based on a sample data request amount and the maximum data request amount allowed by the services under the sample data request amount;
determining the flow limiting values of the plurality of services according to the data request quantity and the model;
limiting the flow of the plurality of services based on the flow limit value.
Fig. 5 is a schematic structural diagram of a data flow limiting device 50 according to an embodiment of the present application. Referring to fig. 5, in a software implementation, the data traffic limiting device 50 may include: acquisition unit 51, acquisition unit 52, determining unit 53 and current limiting unit 54, wherein:
an acquisition unit 51 that acquires data request volumes of a plurality of services;
the obtaining unit 52 obtains a model for determining the throttling value, where the model is obtained by training based on the sample data request quantity and the maximum data request quantity allowed by the multiple services under the sample data request quantity;
a determining unit 53, configured to determine a flow limit value of the plurality of services according to the data request amount and the model;
and a current limiting unit 54 for limiting the plurality of services based on the current limiting value.
Optionally, the obtaining unit 52 trains the model by:
acquiring a sample data request quantity, wherein the sample data request quantity comprises historical data request quantities of the plurality of services;
determining a maximum data request amount allowed by the plurality of services under the sample data request amount;
and training the sample data request quantity and the maximum data request quantity by adopting a linear regression algorithm to obtain the model.
Optionally, the determining, by the obtaining unit 52, a maximum data request amount allowed by the plurality of services under the sample data request amount includes:
obtaining a test result of performing a pressure test on a service system under the sample data request quantity, wherein the service system is a system for providing the plurality of services;
and determining the maximum data request quantity allowed by the plurality of services according to the test result.
Optionally, the model includes a plurality of linear regression equations, the plurality of linear regression equations are in one-to-one correspondence with the plurality of services, and one linear regression equation is used to determine the current limit value of the corresponding service.
Optionally, the determining unit 53 determines the flow limiting values of the plurality of services according to the data request amount and the model, and includes:
for one of the target services, the following operations are performed:
determining a target linear regression equation corresponding to the target service from the model;
and determining the flow limiting value of the target service according to the data request quantity of the target service and the target linear regression equation.
Optionally, the current limiting unit 53, based on the current limiting value, performs current limiting on the plurality of services, including:
for one of the target services, the following operations are performed:
judging whether the data request quantity of the target service is larger than the flow limit value of the target service or not;
and if so, rejecting to receive the data request of the target service.
The data traffic limiting device 50 provided in this embodiment of the present application may also perform the method shown in fig. 1 and fig. 2, and implement the functions of the data traffic limiting device 50 in the embodiment shown in fig. 1 and fig. 2, which are not described herein again.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 6, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other by an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 6, but that does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the non-volatile memory into the memory and runs the computer program to form the device for determining the current limiting value on the logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
collecting data request quantity of a plurality of services;
obtaining a model for determining a flow limiting value, wherein the model is obtained based on sample data request quantity and maximum data request quantity allowed by the plurality of services under the sample data request quantity through training;
and determining the flow limiting values of the plurality of services according to the data request quantity and the model.
The method performed by the apparatus for determining a current-limiting value as disclosed in the embodiment of fig. 6 of the present application may be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The electronic device may further execute the method in fig. 3, and implement the function of the device for determining a current limiting value in the embodiment shown in fig. 3, which is not described herein again in this embodiment of the present application.
Of course, besides the software implementation, the electronic device of the present application does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or logic devices.
Embodiments of the present application also propose a computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a portable electronic device comprising a plurality of application programs, enable the portable electronic device to perform the method of the embodiment shown in fig. 1 and 2, and in particular to perform the following operations:
collecting data request quantity of a plurality of services;
obtaining a model for determining a flow limiting value, wherein the model is obtained based on sample data request quantity and maximum data request quantity allowed by the plurality of services under the sample data request quantity through training;
and determining the flow limiting values of the plurality of services according to the data request quantity and the model.
Fig. 7 is a schematic structural diagram of a device 70 for determining a restriction value according to an embodiment of the present application. Referring to fig. 7, in a software implementation, the device 70 for determining the restriction value may include: an acquisition unit 71, an acquisition unit 72 and a determination unit 73, wherein:
an acquisition unit 71 that acquires data request volumes of a plurality of services;
the obtaining unit 72 obtains a model for determining a throttling value, where the model is obtained by training based on a sample data request quantity and a maximum data request quantity allowed by the plurality of services under the sample data request quantity;
the determining unit 73 determines the flow limit values of the plurality of services according to the data request amount and the model.
Optionally, the device 70 for determining the restriction value further comprises: a current limiting unit 74, wherein:
the current limiting unit 74 limits the current of the plurality of services based on the current limiting values of the plurality of services after the determining unit 73 determines the current limiting values of the plurality of services.
The device 70 for determining a restriction value provided in this embodiment of the present application may also execute the method in fig. 3, and implement the function of the device 70 for determining a restriction value in the embodiment shown in fig. 3, which is not described herein again.
In short, the above description is only a preferred embodiment of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
Claims (12)
1. A method of restricting data traffic, comprising:
collecting data request quantity of a plurality of services;
obtaining a model for determining a current limiting value, wherein the model is obtained by training based on a sample data request quantity and the maximum data request quantity allowed by the plurality of services under the sample data request quantity, the model comprises a plurality of linear regression equations, the linear regression equations are in one-to-one correspondence with the plurality of services, and one linear regression equation is used for determining the current limiting value of the corresponding service;
determining the flow limiting values of the plurality of services according to the data request quantity and the model;
limiting the flow of the plurality of services based on the flow limit value.
2. The method of claim 1, the model being trained by:
acquiring a sample data request quantity, wherein the sample data request quantity comprises historical data request quantities of the plurality of services;
determining a maximum data request amount allowed by the plurality of services under the sample data request amount;
and training the sample data request quantity and the maximum data request quantity by adopting a linear regression algorithm to obtain the model.
3. The method of claim 2, determining a maximum amount of data requests allowed for the plurality of services at the sample data request amount, comprising:
obtaining a test result of performing a pressure test on a service system under the sample data request quantity, wherein the service system is a system for providing the plurality of services;
and determining the maximum data request quantity allowed by the plurality of services according to the test result.
4. The method of claim 1, determining a restriction value for the plurality of services based on the data request volume and the model, comprising:
for one of the target services, the following operations are performed:
determining a target linear regression equation corresponding to the target service from the model;
and determining the flow limiting value of the target service according to the data request quantity of the target service and the target linear regression equation.
5. The method of claim 1, throttling the plurality of services based on the throttling value, comprising:
for one of the target services, the following operations are performed:
judging whether the data request quantity of the target service is larger than the flow limit value of the target service or not;
and if so, rejecting to receive the data request of the target service.
6. A method of determining a restriction value, comprising:
collecting data request quantity of a plurality of services;
obtaining a model for determining a current limiting value, wherein the model is obtained by training based on a sample data request quantity and the maximum data request quantity allowed by the plurality of services under the sample data request quantity, the model comprises a plurality of linear regression equations, the linear regression equations are in one-to-one correspondence with the plurality of services, and one linear regression equation is used for determining the current limiting value of the corresponding service;
and determining the flow limiting values of the plurality of services according to the data request quantity and the model.
7. A device for restricting data traffic, comprising:
the acquisition unit is used for acquiring data request volumes of a plurality of services;
the acquisition unit is used for acquiring a model for determining a current limiting value, the model is obtained based on sample data request quantity and maximum data request quantity allowed by the services under the sample data request quantity in a training mode, the model comprises a plurality of linear regression equations, the linear regression equations are in one-to-one correspondence with the services, and one linear regression equation is used for determining the current limiting value of the corresponding service;
the determining unit is used for determining the flow limiting values of the plurality of services according to the data request quantity and the model;
and the current limiting unit is used for limiting the current of the plurality of services based on the current limiting value.
8. An apparatus for determining a restriction value, comprising:
the acquisition unit is used for acquiring data request volumes of a plurality of services;
the acquisition unit is used for acquiring a model for determining a current limiting value, the model is obtained based on sample data request quantity and maximum data request quantity allowed by the services under the sample data request quantity in a training mode, the model comprises a plurality of linear regression equations, the linear regression equations are in one-to-one correspondence with the services, and one linear regression equation is used for determining the current limiting value of the corresponding service;
and the determining unit is used for determining the flow limiting values of the services according to the data request quantity and the model.
9. An electronic device, comprising:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
collecting data request quantity of a plurality of services;
obtaining a model for determining a current limiting value, wherein the model is obtained by training based on a sample data request quantity and the maximum data request quantity allowed by the plurality of services under the sample data request quantity, the model comprises a plurality of linear regression equations, the linear regression equations are in one-to-one correspondence with the plurality of services, and one linear regression equation is used for determining the current limiting value of the corresponding service;
determining the flow limiting values of the plurality of services according to the data request quantity and the model;
limiting the flow of the plurality of services based on the flow limit value.
10. A computer readable storage medium storing one or more programs which, when executed by an electronic device including a plurality of application programs, cause the electronic device to perform a method of:
collecting data request quantity of a plurality of services;
obtaining a model for determining a current limiting value, wherein the model is obtained by training based on a sample data request quantity and the maximum data request quantity allowed by the services under the sample data request quantity, the model comprises a plurality of linear regression equations, the linear regression equations are in one-to-one correspondence with the services, and one linear regression equation is used for determining the current limiting value of the corresponding service;
determining the flow limiting values of the plurality of services according to the data request quantity and the model;
throttling the plurality of services based on the throttling value.
11. An electronic device, comprising:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
collecting data request quantity of a plurality of services;
obtaining a model for determining a current limiting value, wherein the model is obtained by training based on a sample data request quantity and the maximum data request quantity allowed by the plurality of services under the sample data request quantity, the model comprises a plurality of linear regression equations, the linear regression equations are in one-to-one correspondence with the plurality of services, and one linear regression equation is used for determining the current limiting value of the corresponding service;
and determining the flow limiting values of the plurality of services according to the data request quantity and the model.
12. A computer readable storage medium storing one or more programs which, when executed by an electronic device including a plurality of application programs, cause the electronic device to perform a method of:
collecting data request quantity of a plurality of services;
obtaining a model for determining a current limiting value, wherein the model is obtained by training based on a sample data request quantity and the maximum data request quantity allowed by the plurality of services under the sample data request quantity, the model comprises a plurality of linear regression equations, the linear regression equations are in one-to-one correspondence with the plurality of services, and one linear regression equation is used for determining the current limiting value of the corresponding service;
and determining the flow limiting values of the plurality of services according to the data request quantity and the model.
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