CN110728306A - Target parameter selection method in reverse proxy evaluation model and related device - Google Patents
Target parameter selection method in reverse proxy evaluation model and related device Download PDFInfo
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Abstract
The application relates to a method and a device for selecting target parameters in a reverse proxy evaluation model, belonging to the technical field of information, wherein the method comprises the following steps: acquiring a candidate parameter set and a positive and negative sample set; calculating the proportion of positive samples; aiming at each candidate parameter of the candidate parameter set, acquiring a candidate parameter value of each sample in a positive sample set and a negative sample set, sequencing each sample in the positive sample set and the negative sample set from high to low according to the candidate parameter value, judging samples in the proportion of the front positive samples in the sequencing as first samples, judging the rest samples as second samples, setting the number of the samples judged as the first samples in the positive sample set as M, and the number of the positive samples in the positive sample set as N, and calculating the correct judgment rate L of the candidate parameter as M/N; based on the correct determination rate of each candidate parameter of the candidate parameter set, the candidate parameter is selected as the target parameter. The method can achieve the aim of effectively monitoring the performance of the reverse proxy.
Description
Technical Field
The application relates to the technical field of information, in particular to a target parameter selection method and device in a reverse proxy evaluation model, a storage medium and electronic equipment.
Background
Nginx (engine x) is a high-performance HTTP and reverse proxy service that is widely used in internet applications. The performance of the reverse proxy is evaluated by adopting a reverse proxy evaluation model at present. The reverse proxy evaluation model needs to set some parameters in advance, and then measures the performance of the reverse proxy according to some parameters. However, in the prior art, the selection of the evaluation parameters from the parameters is random and has no objectivity, and the purpose of effectively monitoring the performance of the reverse proxy is often not achieved.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The application aims to provide a method and a device for selecting target parameters in a reverse proxy evaluation model, a computer-readable storage medium and electronic equipment, so that the problem of unreasonable parameter selection during reverse proxy performance evaluation caused by the limitations and defects of the related technology is solved at least to a certain extent.
According to one aspect of the application, a method for selecting target parameters in a reverse proxy evaluation model is provided, which comprises the following steps:
acquiring a candidate parameter set;
acquiring a positive sample set and a negative sample set, wherein the positive sample is a known reverse proxy with performance reaching the performance standard, and the negative sample is a known reverse proxy with performance not reaching the performance standard;
calculating a positive sample proportion equal to the number of positive samples in the positive sample set divided by the total number of samples in the positive sample set and the negative sample set;
for each candidate parameter of the candidate parameter set, obtaining the candidate parameter value of each sample in the positive sample set and the negative sample set, sorting each sample in the positive sample set and the negative sample set from high to low according to the candidate parameter value, determining samples of the positive sample proportion before the sorting from high to low as first samples, determining the rest as second samples, setting the number of the samples determined as the first samples in the positive sample set as M, and the number of the positive samples in the positive sample set as N, and calculating the correct determination rate L of the candidate parameter as M/N;
based on the correct determination rate of each candidate parameter of the candidate parameter set, the candidate parameter is selected as the target parameter.
In an exemplary embodiment of the present application, the selecting the candidate parameter as the target parameter based on the correct determination rate of each candidate parameter of the candidate parameter set includes:
and if the correct judgment rate of the candidate parameter exceeds a preset correct judgment rate threshold value, selecting the candidate parameter as the target parameter.
In an exemplary embodiment of the present application, the selecting the candidate parameter as the target parameter based on the correct determination rate of each candidate parameter of the candidate parameter set includes:
and sequencing the correct judgment rates of all the candidate parameters from high to low, and determining the candidate parameters with the preset proportion as target parameters.
In an exemplary embodiment of the present application, after selecting a candidate parameter as a target parameter based on a correct determination rate of each candidate parameter of a candidate parameter set, the method further includes:
obtaining the value of the target parameter of the reverse proxy to be tested;
acquiring a target parameter reference value corresponding to the target parameter;
dividing the value of each item target parameter of the reverse proxy to be tested by the corresponding target parameter reference value to obtain the competence ratio of each item target parameter of the reverse proxy to be tested;
weighted average is carried out on the competence ratio of each item of the reverse proxy to be tested to obtain the competence score of the reverse proxy to be tested;
and classifying the reverse proxy to be tested based on the competence score.
According to an aspect of the present application, there is provided a target parameter selecting apparatus in a reverse proxy evaluation model, including:
the parameter acquisition module is used for acquiring a candidate parameter set;
the system comprises a sample acquisition module and a data processing module, wherein the sample acquisition module is used for acquiring a positive sample set and a negative sample set, the positive sample is a known reverse proxy with performance reaching the performance standard, and the negative sample is a known reverse proxy with performance not reaching the performance standard;
a ratio calculation module for calculating a positive sample ratio equal to the number of positive samples in the positive sample set divided by the total number of samples in the positive sample set and the negative sample set;
the sequencing judgment module is used for acquiring the candidate parameter value of each sample in the positive sample set and the negative sample set aiming at each candidate parameter of the candidate parameter set, sequencing each sample in the positive sample set and the negative sample set from high to low according to the candidate parameter value, judging the sample of the positive sample proportion before the sequencing from high to low as a first sample, judging the rest samples as second samples, setting the number of positive samples judged as the first sample in the positive sample set as M, setting the number of positive samples in the positive sample set as N, and calculating the correct judgment rate L of the candidate parameter as M/N;
and the parameter selection module is used for selecting the candidate parameters as the target parameters based on the correct judgment rate of each candidate parameter of the candidate parameter set.
According to an aspect of the present application, there is provided a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements a method for selecting a target parameter in a reverse proxy evaluation model according to any one of the above-mentioned items.
According to an aspect of the present application, there is provided an electronic device including:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to execute the method for selecting the target parameter in the reverse proxy evaluation model by executing the executable instruction.
The application relates to a method and a device for selecting target parameters in a reverse proxy evaluation model, wherein a positive sample is a reverse proxy with known good performance and a negative sample is a reverse proxy with known poor performance by obtaining a candidate parameter set and a positive sample set and a negative sample set, and a positive sample proportion is calculated, wherein the positive sample proportion is equal to the number of positive samples in the positive sample set divided by the total number of samples in the positive sample set and the negative sample set. The method comprises the steps of obtaining a candidate parameter value of each sample in a positive sample set and a negative sample set aiming at each candidate parameter of a candidate parameter set, sequencing each sample in the positive sample set and the negative sample set from high to low according to the candidate parameter value, judging samples of the positive sample proportion in the sequence from high to low as first samples, judging the rest samples as second samples, obtaining the correct judgment rate of the candidate parameter, and identifying which usable parameters are available and which unusable parameters by utilizing the misjudgment rate of each parameter on all samples, so that the selection of the parameters is more objective, and the aim of effectively monitoring the performance of a reverse proxy can be achieved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
Fig. 1 schematically illustrates an application scenario example of a target parameter selection method in a reverse proxy evaluation model.
Fig. 2 schematically shows a flowchart of a target parameter selection method in a reverse proxy evaluation model.
Fig. 3 schematically shows a flowchart for evaluating a reverse proxy under test after a target parameter selection method in a reverse proxy evaluation model according to fig. 2.
Fig. 4 schematically shows a block diagram of a target parameter selecting apparatus in a reverse proxy evaluation model.
Fig. 5 schematically illustrates an example block diagram of an electronic device for implementing the target parameter selection method in the reverse proxy evaluation model.
Fig. 6 schematically illustrates a computer-readable storage medium for implementing the target parameter selection method in the reverse proxy evaluation model described above.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
Fig. 1 is a diagram of an implementation environment of a method for selecting target parameters in a reverse proxy evaluation model according to an embodiment, as shown in fig. 1, in the implementation environment, the implementation environment includes a reverse proxy evaluation model training apparatus 110, a reverse proxy evaluation model 120, a reverse proxy evaluation apparatus 130, and a user end 140.
As shown in fig. 1, the reverse proxy evaluation model training device 110 obtains the reverse proxy evaluation model 120 by training a large amount of training data, and the reverse proxy evaluation model training device 110 performs training by acquiring a candidate parameter set and a positive and negative sample set to select target parameters to be used in the reverse proxy evaluation model 120.
The reverse proxy evaluation apparatus 130 implements performance evaluation on the reverse proxy by using the reverse proxy evaluation model 120, and the reverse proxy evaluation model 120 may be embedded in the reverse proxy evaluation apparatus 130. The reverse proxy evaluation model training apparatus 110 and the reverse proxy evaluation apparatus 130 may be deployed independently of each other, or may be integrated in the same device. After the reverse proxy to be tested is input into the reverse proxy evaluation device 130, the reverse proxy evaluation model 120 evaluates the reverse proxy to be tested by using the selected target parameter to obtain an evaluation result, the reverse proxy evaluation device 130 may have a display screen, and the evaluation result is directly displayed to the user through the display screen, of course, the reverse proxy evaluation device 130 may also send the evaluation result to the user end 140, and the user end 140 checks the evaluation result through the user end 140, where the user end 140 refers to a user device capable of displaying information, such as a smart phone, a notebook, a tablet, and the like.
The reverse proxy evaluation device 130 may be an independent server or a cluster server, and when the number of target objects to be evaluated is large, evaluation of the target objects can be quickly and concurrently implemented by using the cluster server.
As shown in fig. 2, in an embodiment, a method for selecting target parameters in a reverse proxy evaluation model is provided, and the method for selecting target parameters in a reverse proxy evaluation model may be applied to the reverse proxy evaluation model training apparatus 110, and specifically may include the following steps:
step S210, acquiring a candidate parameter set;
step S220, a positive sample set and a negative sample set are obtained, wherein the positive sample is a known reverse proxy with performance reaching the performance standard, and the negative sample is a known reverse proxy with performance not reaching the performance standard;
step S230, calculating a positive sample proportion, wherein the positive sample proportion is equal to the number of positive samples in the positive sample set divided by the total number of samples in the positive sample set and the negative sample set;
step S240, for each candidate parameter of the candidate parameter set, obtaining the candidate parameter value of each sample in the positive sample set and the negative sample set, and sorting each sample in the positive sample set and the negative sample set from high to low according to the candidate parameter value, determining the sample of the positive sample proportion before the sorting from high to low as a first sample, determining the rest as a second sample, setting the number of samples determined as the first sample in the positive sample set as M, and the number of positive samples in the positive sample set as N, and then calculating the correct determination rate L of the candidate parameter as M/N;
step S250, selecting a candidate parameter as a target parameter based on the correct determination rate of each candidate parameter of the candidate parameter set.
In step S210, the candidate parameter set refers to a set of good or bad evaluation parameters that can be selected by the reverse-proxy evaluation model to evaluate the performance of the reverse proxy. The reverse proxy evaluation model is used to evaluate the performance of the reverse proxy by presetting some parameters, which may include Active Connections (currently Active user Connections), accounts (total number of received user Connections), Handled (total number of user Connections processed by the reverse proxy), Requests (total number of user Requests), Reading (number of reverse proxy read request headers in the current connection), Writing (number of reverse proxy write returned to the user in the current connection), Waiting (number of Active user Connections that are not currently requested), a reverse proxy access log, whether a backend instance is alive, success rate, total average consumed time, backend average consumed time, network transmission consumed time, and statistics of error keywords, and the like, which is not limited in this embodiment.
In step S220, a positive sample set and a negative sample set are obtained, where the positive sample set includes a plurality of positive samples, the negative sample set includes a plurality of negative samples, the positive samples are known reverse proxies whose performance meets the performance standard, and the negative samples are known reverse proxies whose performance does not meet the performance standard, where the performance standard may be qualitative performance description, such as stability, security, reliability, and scalability of the reverse proxies, or may be a quantitative performance standard value, such as a physical resource utilization rate reaching a preset value, and the physical resource utilization rate includes a CPU utilization rate, a memory utilization rate, and a disk utilization rate. For example, if the reverse proxy has good stability, high security, strong reliability, and high scalability, the performance of the reverse proxy is determined to meet the performance standard, and if the reverse proxy does not meet the requirements for stability, security, reliability, and scalability, the performance of the reverse proxy is determined to not meet the performance standard.
In step S230, the positive sample ratio is a ratio of the number of positive samples in the positive sample set to the total number of samples in the positive and negative sample sets.
In step S240, each sample in the positive sample set and the negative sample set is input into a reverse proxy evaluation model, the reverse proxy evaluation model selects each candidate parameter in the candidate parameter set for evaluation, outputs a candidate parameter value corresponding to each candidate parameter in the candidate parameter set for each sample, determines the sample of the positive sample ratio before the ranking from high to low as a first sample and determines the rest as a second sample according to the ranking of the candidate parameter values from high to low, and calculates the ratio of the number of samples determined as the first sample in the positive sample set to the number of positive samples in the positive sample set.
For example, if there are 3 positive samples in the positive sample set, they are: positive sample 1, positive sample 2, positive sample 3, there are 2 negative samples in the negative sample set, are respectively: negative sample 1, negative sample 2, there are 5 candidate parameters in the candidate parameter set, respectively: and calculating a positive sample proportion of 60% according to the candidate parameters 1, 2, 3, 4 and 5. For each candidate parameter of the candidate parameter set, obtaining the candidate parameter value of each sample in the positive sample set and the negative sample set, and referring to the example in table 1, the candidate parameter values of 6 samples obtained by the reverse proxy evaluation model using the candidate parameter 1 evaluation are ordered as follows: 4.78>3.46>2.68>2.35>0.35, if samples corresponding to the first 60% of the 3 candidate parameter values 4.78, 3.46, and 2.68 in the high-to-low order are determined as first samples, the number M of samples determined as the first samples in the positive sample set is 2, the number N of positive samples in the positive sample set is 3, and the correct determination rate of the candidate parameter 1 is calculated to be L which is 66.67%; the reverse proxy evaluation model ranks the candidate parameter values of 6 samples obtained by evaluating the candidate parameter 2 into 6.78>5.55>4.32>2.36>2.28, so that the number M of samples determined as the first sample in the positive sample set is 3, the number N of samples in the positive sample set is 3, and the correct determination rate L of the candidate parameter 2 is calculated to be 100%; the reverse proxy evaluation model evaluates the candidate parameter values of 6 samples by using the candidate parameter 3, and the rank of the candidate parameter values is 14.36>10.23>9.88>8.23>6.39, so that the number M of samples determined as the first sample in the positive sample set is 3, the number N of positive samples in the positive sample set is 3, and the correct determination rate of the candidate parameter 3 is calculated to be L-100%; the reverse proxy evaluation model evaluates the candidate parameter values of 6 samples by using the candidate parameter 4, and the rank of the candidate parameter values is 7.17>4.26>3.44>2>1.48, so that the number M of the samples determined as the first sample in the positive sample set is 2, the number N of the samples in the positive sample set is 3, and the correct determination rate of the candidate parameter 4 is calculated to be L which is 66.67%; the reverse proxy evaluation model evaluates the candidate parameter 5 to obtain a candidate parameter value sequence of 6 samples, namely 8.88>7.26>5.21>4.21>3.26, so that the number M of samples determined as the first sample in the positive sample set is 1, the number N of samples determined as the positive sample set is 3, and the candidate parameter is calculated, wherein the correct determination rate of 5 is L-33.33%.
| Candidate parameter 1 | Candidate parameter 2 | Candidate parameter 3 | Candidate parameter 4 | Candidate parameter 5 | |
| Positive sample 1 | 2.35 | 6.78 | 10.23 | 3.44 | 3.26 |
| Positive sample 2 | 3.46 | 5.55 | 9.88 | 2 | 4.21 |
| Positive sample 3 | 4.78 | 4.32 | 14.36 | 7.17 | 8.88 |
| Negative sample 1 | 0.35 | 2.36 | 6.39 | 4.26 | 5.21 |
| Negative sample 2 | 2.68 | 2.28 | 8.23 | 1.48 | 7.26 |
TABLE 1 examples of candidate parameter values for positive and negative examples
Step S250, selecting a candidate parameter as a target parameter based on the correct determination rate of each candidate parameter of the candidate parameter set.
Based on the correct determination rate calculated in step S240, the reverse-proxy evaluation model may select candidate parameters from the candidate parameter set as target parameters to evaluate the performance of the reverse proxy.
The embodiment has the advantages that the candidate parameter value of each sample is obtained aiming at each candidate parameter, the positive and negative samples are judged according to the positive sample proportion, the candidate parameter value of each sample is firstly sorted from high to low, and the reference value larger than the candidate parameter value is determined as the identification positive sample. The judged positive sample and the actual positive sample have access, and the smaller the access degree is, the better the candidate parameter is; the greater the degree of access, the less useful the candidate parameter is for evaluating the reverse-proxy. Based on the correct decision rate for each candidate parameter of the candidate parameter set, the candidate parameter may be selected as the target parameter.
Optionally, fig. 3 is a detailed description of step S250 in the method for selecting a target parameter in a reverse proxy evaluation model proposed in fig. 2, where the step S250 may include the following steps, based on a correct determination rate of each candidate parameter of the candidate parameter set, and selecting the candidate parameter as the target parameter:
and if the correct judgment rate of the candidate parameter exceeds a preset correct judgment rate threshold value, selecting the candidate parameter as the target parameter.
In this embodiment, a correct determination rate threshold value may be set according to actual conditions, for example, the correct determination rate threshold value is 50%, the correct determination rate of each candidate parameter calculated in step S250 is compared with the correct determination rate threshold value, and the candidate parameter exceeding the threshold value is taken as the target parameter.
Optionally, step S250 may include the following steps:
and sequencing the correct judgment rates of all the candidate parameters from high to low, and determining the candidate parameters with the preset proportion as target parameters.
In this embodiment, a predetermined ratio is set, the correct determination rates of all candidate parameters are ranked from high to low, and the candidate parameters of the previous predetermined ratio are determined as target parameters.
Optionally, fig. 3 is a further supplement to the method for selecting target parameters in the reverse proxy evaluation model proposed in fig. 2, and after step S250, the method further includes:
step S260, obtaining the value of the target parameter of the reverse proxy to be tested;
step S270, acquiring a target parameter reference value corresponding to the target parameter;
step S280, dividing the value of each item target parameter of the reverse proxy to be tested by the corresponding target parameter reference value to obtain the competence ratio of each item target parameter of the reverse proxy to be tested;
step S290, weighted average is carried out on the competence ratio of each item of the reverse proxy to be tested to obtain the competence score of the reverse proxy to be tested;
step S2100, classifying the reverse proxy to be tested based on the competence score.
In step S260, since the reverse proxy evaluation model has selected the target parameter, the model is used to perform actual evaluation on the reverse proxy to be tested, and after the reverse proxy to be tested is input, the reverse proxy evaluation model may output the value of the target parameter of the reverse proxy to be tested.
In step S270, during evaluation, the values of the target parameters of the reverse proxy to be evaluated are obtained, where the values are absolute values and there is no way to compare, for example, the value of one target parameter is 2, and the value of another target parameter is 3, and the latter is not necessarily better than the former, because the latter may have a larger value, in general, therefore, in step S280, the value of each target parameter of the reverse proxy to be evaluated is divided by the corresponding target parameter reference value, and this relative value, i.e., competence ratio, can reflect whether the value of the target parameter is superior or not when evaluating the performance of the reverse proxy. In step S290, weighted average is performed on the competence ratios of each item of target parameters of the reverse proxy to be tested to obtain the competence score of the reverse proxy to be tested, for example, the competence ratio of each target parameter of the reverse proxy to be tested is A, B, C, D, and the respective weight values are 0.4, 0.3, 0.5, and 0.2, so that the competence score of the reverse proxy to be tested is 0.4 a + B + 0.3+ 0.5C + 0.2D.
And step S2100, classifying the reverse proxy to be tested based on the competence score.
And classifying the reverse proxy to be tested based on the competence score, so that whether the reverse proxy is good or not can be evaluated.
The embodiment has the advantages that the competence rate of each item of target parameters of the reverse proxy to be tested is calculated and obtained by obtaining the target parameter reference value corresponding to the target parameter, the competence score of the reverse proxy to be tested is obtained by weighted averaging, and the performance of the reverse proxy to be tested can be judged according to the competence score condition, so that the reverse proxy can be evaluated more objectively, and the evaluation result is more accurate.
As shown in fig. 4, in an embodiment, a device 400 for selecting target parameters in a reverse proxy evaluation model is provided, and the device for selecting target parameters in a reverse proxy evaluation model may specifically include a parameter obtaining module 410, a sample obtaining module 420, a proportion calculating module 430, a ranking determining module 440, and a parameter selecting module 450.
A parameter obtaining module 410, configured to obtain a candidate parameter set;
a sample obtaining module 420, configured to obtain a positive sample set and a negative sample set, where the positive sample is a known reverse proxy whose performance meets the performance standard, and the negative sample is a known reverse proxy whose performance does not meet the performance standard;
a proportion calculation module 430 for calculating a positive sample proportion equal to the number of positive samples in the positive sample set divided by the total number of samples in the positive sample set and the negative sample set;
the ranking determining module 440 is configured to obtain, for each candidate parameter of the candidate parameter set, a candidate parameter value of each sample in the positive sample set and the negative sample set, rank each sample in the positive sample set and the negative sample set from high to low according to the candidate parameter value, determine, as a first sample, samples in a proportion of the positive samples before the positive samples in the high-to-low ranking, determine, as second samples, determine, as M, the number of samples determined as the first sample in the positive sample set, and as N, calculate, as M/N, a correct determination rate L of the candidate parameter;
a parameter selection module 450, configured to select a candidate parameter as the target parameter based on the correct determination rate of each candidate parameter of the candidate parameter set.
The specific details of each module in the target parameter selection device in the reverse proxy evaluation model have been described in detail in the target parameter selection method in the corresponding reverse proxy evaluation model, and therefore are not described herein again.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 500 according to this embodiment of the invention is described below with reference to fig. 5. The electronic device 500 shown in fig. 5 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the electronic device 500 is embodied in the form of a general purpose computing device. The components of the electronic device 500 may include, but are not limited to: the at least one processing unit 510, the at least one memory unit 520, and a bus 530 that couples various system components including the memory unit 520 and the processing unit 510.
Wherein the storage unit stores program code that is executable by the processing unit 510 to cause the processing unit 510 to perform steps according to various exemplary embodiments of the present invention as described in the above section "exemplary methods" of the present specification. For example, the processing unit 510 may execute step S210 as shown in fig. 2, acquiring a candidate parameter set; step S220, a positive sample set and a negative sample set are obtained, wherein the positive sample is a known reverse proxy with performance reaching the performance standard, and the negative sample is a known reverse proxy with performance not reaching the performance standard; step S230, calculating a positive sample proportion, wherein the positive sample proportion is equal to the number of positive samples in the positive sample set divided by the total number of samples in the positive sample set and the negative sample set; step S240, for each candidate parameter of the candidate parameter set, obtaining the candidate parameter value of each sample in the positive sample set and the negative sample set, and sorting each sample in the positive sample set and the negative sample set from high to low according to the candidate parameter value, determining the sample of the positive sample proportion before the sorting from high to low as a first sample, determining the rest as a second sample, setting the number of samples determined as the first sample in the positive sample set as M, and the number of positive samples in the positive sample set as N, and then calculating the correct determination rate L of the candidate parameter as M/N; step S250, selecting a candidate parameter as a target parameter based on the correct determination rate of each candidate parameter of the candidate parameter set.
The memory unit 520 may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM)5201 and/or a cache memory unit 5202, and may further include a read only memory unit (ROM) 5203.
The electronic device 500 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 500, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 500 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 550. Also, the electronic device 500 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 560. As shown, the network adapter 560 communicates with the other modules of the electronic device 500 over the bus 530. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above section "exemplary methods" of the present description, when said program product is run on the terminal device.
Referring to fig. 6, a program product 600 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (7)
1. A method for selecting target parameters in a reverse proxy evaluation model is characterized by comprising the following steps:
acquiring a candidate parameter set;
acquiring a positive sample set and a negative sample set, wherein the positive sample is a known reverse proxy with performance reaching the performance standard, and the negative sample is a known reverse proxy with performance not reaching the performance standard;
calculating a positive sample proportion equal to the number of positive samples in the positive sample set divided by the total number of samples in the positive sample set and the negative sample set;
for each candidate parameter of the candidate parameter set, obtaining the candidate parameter value of each sample in the positive sample set and the negative sample set, sorting each sample in the positive sample set and the negative sample set from high to low according to the candidate parameter value, determining samples of the positive sample proportion before the sorting from high to low as first samples, determining the rest as second samples, setting the number of the samples determined as the first samples in the positive sample set as M, and the number of the positive samples in the positive sample set as N, and calculating the correct determination rate L of the candidate parameter as M/N;
based on the correct determination rate of each candidate parameter of the candidate parameter set, the candidate parameter is selected as the target parameter.
2. The method of claim 1, wherein selecting the candidate parameter as the target parameter based on the correct decision rate of each candidate parameter of the candidate parameter set comprises:
and if the correct judgment rate of the candidate parameter exceeds a preset correct judgment rate threshold value, selecting the candidate parameter as the target parameter.
3. The method of claim 1, wherein selecting the candidate parameter as the target parameter based on the correct decision rate of each candidate parameter of the candidate parameter set comprises:
and sequencing the correct judgment rates of all the candidate parameters from high to low, and determining the candidate parameters with the preset proportion as target parameters.
4. The method of claim 1, wherein after selecting the candidate parameter as the target parameter based on a correct decision rate of each candidate parameter of the candidate parameter set, the method further comprises:
obtaining the value of the target parameter of the reverse proxy to be tested;
acquiring a target parameter reference value corresponding to the target parameter;
dividing the value of each item target parameter of the reverse proxy to be tested by the corresponding target parameter reference value to obtain the competence ratio of each item target parameter of the reverse proxy to be tested;
weighted average is carried out on the competence ratio of each item of the reverse proxy to be tested to obtain the competence score of the reverse proxy to be tested;
and classifying the reverse proxy to be tested based on the competence score.
5. A target parameter selection device in a reverse proxy evaluation model is characterized by comprising:
the parameter acquisition module is used for acquiring a candidate parameter set;
the system comprises a sample acquisition module and a data processing module, wherein the sample acquisition module is used for acquiring a positive sample set and a negative sample set, the positive sample is a known reverse proxy with performance reaching the performance standard, and the negative sample is a known reverse proxy with performance not reaching the performance standard;
a ratio calculation module for calculating a positive sample ratio equal to the number of positive samples in the positive sample set divided by the total number of samples in the positive sample set and the negative sample set;
the sequencing judgment module is used for acquiring the candidate parameter value of each sample in the positive sample set and the negative sample set aiming at each candidate parameter of the candidate parameter set, sequencing each sample in the positive sample set and the negative sample set from high to low according to the candidate parameter value, judging the sample of the positive sample proportion before the sequencing from high to low as a first sample, judging the rest samples as second samples, setting the number of the samples judged as the first sample in the positive sample set as M, setting the number of the positive samples in the positive sample set as N, and calculating the correct judgment rate L of the candidate parameter as M/N;
and the parameter selection module is used for selecting the candidate parameters as the target parameters based on the correct judgment rate of each candidate parameter of the candidate parameter set.
6. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the method for selecting target parameters in the reverse proxy evaluation model according to any one of claims 1 to 4.
7. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to execute the method of target parameter selection in the reverse proxy evaluation model of any of claims 1-4 via execution of the executable instructions.
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