CN113157544A - Equipment performance adjusting method, device, equipment and medium - Google Patents
Equipment performance adjusting method, device, equipment and medium Download PDFInfo
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Abstract
The embodiment of the disclosure relates to a method, a device, equipment and a medium for adjusting equipment performance, wherein the method comprises the following steps: respectively performing fault analysis on a plurality of log files generated by the operation of target equipment to obtain a fault analysis result corresponding to each log file; extracting scene characteristics and fault characteristics from a fault analysis result; and determining a performance adjustment strategy according to the scene characteristics and the fault characteristics, and performing performance adjustment on the target equipment based on the performance adjustment strategy. The embodiment of the disclosure can realize batch automatic analysis, and further improves the optimization efficiency of the equipment performance on the basis of saving labor cost.
Description
Technical Field
The present disclosure relates to the field of information processing technologies, and in particular, to a method, an apparatus, a device, and a medium for adjusting device performance.
Background
Electronic devices such as mobile phones, computers, etc. usually generate various log files during operation, and a log file is a text for recording the operation conditions of device systems, device hardware, and device software. The content recorded by the log file can fully reflect the current performance of the equipment, the existing problems and other information.
In the related art, the logs are manually read and analyzed one by one so as to correspondingly adjust the equipment to optimize the performance of the equipment, but the method has the disadvantages of high labor cost, low efficiency, time waste and labor waste.
Disclosure of Invention
In order to solve the technical problem described above or at least partially solve the technical problem, the present disclosure provides a device performance adjustment method, apparatus, device, and medium.
The embodiment of the disclosure provides a method for adjusting equipment performance, which comprises the following steps: respectively carrying out fault analysis on a plurality of log files generated by the operation of target equipment to obtain a fault analysis result corresponding to each log file; extracting scene characteristics and fault characteristics from the fault analysis result; and determining a performance adjustment strategy according to the scene characteristics and the fault characteristics, and performing performance adjustment on the target equipment based on the performance adjustment strategy.
Optionally, the step of determining a performance adjustment policy according to the scenario characteristic and the fault characteristic includes: analyzing a key performance bottleneck existing in the target equipment according to the scene characteristics and the fault characteristics; determining a performance adjustment policy based on the key performance bottleneck.
Optionally, the step of analyzing a key performance bottleneck existing in the target device according to the scene characteristic and the fault characteristic includes: screening out key log files from the plurality of log files according to the scene characteristics and the fault characteristics; determining target scene characteristics and target fault characteristics corresponding to the key log files; searching information related to the target scene characteristics and the target fault characteristics from the key log files, and analyzing key performance bottlenecks existing in the target equipment based on the information.
Optionally, the step of screening out a key log file from the plurality of log files according to the scene characteristic and the fault characteristic includes: screening out a log file corresponding to the scene feature with the highest priority from the plurality of log files according to preset scene priority information to serve as a key log file; and/or counting the occurrence frequency of each scene characteristic, and screening out a log file corresponding to the scene characteristic with the highest occurrence frequency from the plurality of log files as a key log file; and/or screening out the log file corresponding to the fault feature with the highest severity level from the plurality of log files according to a preset fault severity level as a key log file.
Optionally, the step of analyzing a key performance bottleneck existing in the target device according to the scene characteristic and the fault characteristic includes: determining a performance bottleneck corresponding to each fault analysis result based on the scene characteristics and the fault characteristics extracted from each fault analysis result; classifying the performance bottlenecks corresponding to the fault analysis results according to the pre-divided bottleneck categories; determining a target bottleneck category to which a performance bottleneck related to the target fault belongs; wherein the target fault is determined based on the fault signature; and taking the performance bottleneck belonging to the target bottleneck category as the existing key performance bottleneck of the target equipment.
The embodiment of the present disclosure further provides an apparatus for adjusting device performance, including: the fault analysis module is used for respectively carrying out fault analysis on a plurality of log files generated by the operation of target equipment to obtain a fault analysis result corresponding to each log file; the characteristic extraction module is used for extracting scene characteristics and fault characteristics from the fault analysis result; and the performance adjusting module is used for determining a performance adjusting strategy according to the scene characteristics and the fault characteristics and adjusting the performance of the target equipment based on the performance adjusting strategy.
Optionally, the performance adjusting module includes: the bottleneck analysis unit is used for analyzing a key performance bottleneck existing in the target equipment according to the scene characteristics and the fault characteristics; a performance adjustment unit to determine a performance adjustment policy based on the key performance bottleneck.
Optionally, the bottleneck analysis unit is specifically configured to: screening out key log files from the plurality of log files according to the scene characteristics and the fault characteristics; determining target scene characteristics and target fault characteristics corresponding to the key log files; searching information related to the target scene characteristics and the target fault characteristics from the key log files, and analyzing key performance bottlenecks existing in the target equipment based on the information.
Optionally, the bottleneck analysis unit is specifically configured to: screening out a log file corresponding to the scene feature with the highest priority from the plurality of log files according to preset scene priority information to serve as a key log file; and/or counting the occurrence frequency of each scene characteristic, and screening out a log file corresponding to the scene characteristic with the highest occurrence frequency from the plurality of log files as a key log file; and/or screening out the log file corresponding to the fault feature with the highest severity level from the plurality of log files according to a preset fault severity level as a key log file.
Optionally, the bottleneck analysis unit is specifically configured to: determining a performance bottleneck corresponding to each fault analysis result based on the scene characteristics and the fault characteristics extracted from each fault analysis result; classifying the performance bottlenecks corresponding to the fault analysis results according to the pre-divided bottleneck categories; determining a target bottleneck category to which a performance bottleneck related to the target fault belongs; wherein the target fault is determined based on the fault signature; and taking the performance bottleneck belonging to the target bottleneck category as the existing key performance bottleneck of the target equipment.
An embodiment of the present disclosure further provides an electronic device, which includes: a processor; a memory for storing the processor-executable instructions; the processor is used for reading the executable instructions from the memory and executing the instructions to realize the device performance adjusting method provided by the embodiment of the disclosure.
The embodiment of the disclosure also provides a computer-readable storage medium, which stores a computer program for executing the method for adjusting the performance of the device provided by the embodiment of the disclosure.
According to the technical scheme provided by the embodiment of the disclosure, firstly, a plurality of log files generated by the operation of target equipment are subjected to fault analysis respectively, and a fault analysis result corresponding to each log file is obtained; and then extracting scene characteristics and fault characteristics from the fault analysis result, determining a performance adjustment strategy according to the scene characteristics and the fault characteristics, and finally performing performance adjustment on the target equipment based on the performance adjustment strategy. Compared with the prior art that logs need to be manually analyzed one by one, the method can directly perform fault analysis on the log files to extract scene characteristics and fault characteristics, so that performance adjustment is performed according to the scene characteristics and the fault characteristics, the method is more efficient and reliable, batch automatic analysis can be realized, and the optimization efficiency of equipment performance is further improved on the basis of saving labor cost.
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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.
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present disclosure, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flow chart of an apparatus performance adjustment method according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a method for determining a key performance bottleneck according to an embodiment of the present disclosure;
fig. 3 is a flowchart of another method for determining a critical performance bottleneck according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an apparatus performance adjusting device according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of another apparatus performance adjusting device according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, aspects of the present disclosure will be further described below. It should be noted that the embodiments and features of the embodiments of the present disclosure may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced in other ways than those described herein; it is to be understood that the embodiments disclosed in the specification are only a few embodiments of the present disclosure, and not all embodiments.
In order to optimize the performance of the equipment, laboratory tests and trial tests of users can be carried out on the equipment, a large number of logs are usually generated in the test process, and the logs are mainly analyzed one by one manually in the related technology, so that the problems possibly existing in the equipment at present are obtained, and the corresponding performance optimization is carried out. For massive logs, the labor cost required by the method is extremely high, the efficiency is extremely low, and a plurality of application developers cannot know the bottom mechanism of the equipment, so that certain difficulty exists in analyzing problems. To at least partially improve the above problem, embodiments of the present disclosure provide a device performance adjustment method, apparatus, device and medium, where the method is applicable to a target device, such as an electronic device that may be a mobile phone, a tablet computer, a computer, and the like, and is not limited herein. By means of the technical scheme provided by the embodiment of the disclosure, batch automatic analysis can be realized, and the optimization efficiency of the equipment performance is further improved on the basis of saving labor cost. For ease of understanding, the following are specifically set forth:
fig. 1 is a schematic flowchart of a device performance adjustment method provided in an embodiment of the present disclosure, where the method may be executed by a device performance adjustment apparatus, where the apparatus may be implemented by software and/or hardware, and may be generally integrated in an electronic device. As shown in fig. 1, the method mainly includes the following steps S102 to S106:
step S102, respectively carrying out fault analysis on a plurality of log files generated by the operation of the target equipment to obtain a fault analysis result corresponding to each log file.
It is understood that the fault analysis result can indicate whether a fault exists in the corresponding log file, and in some embodiments, the fault analysis result indicating that a fault exists includes fault information parsed from the log file, such as a scene characteristic, a fault characteristic, and the like when the fault occurs. For example, the failure analysis results may indicate: the application software has the problems of light and tiny jamming of animation in the starting process, jamming caused when a user slides and browses a page on a touch screen of the equipment and the like.
And step S104, extracting scene characteristics and fault characteristics from the fault analysis result.
The scene characteristics are scenes when a fault occurs, taking the target device as a mobile phone as an example, in some embodiments, the scene characteristics include but are not limited to one or more of a start scene, a gesture operation scene, and a window element initialization scene, and the types of the scene characteristics may be specifically determined according to the device type and the application environment. The starting scene comprises a cold starting scene, a warm starting scene and a hot starting scene, and can be identified by analyzing a function calling sequence when a workflow (activity) is started; the gesture operation scene comprises a multi-point gesture scene, a long-touch gesture scene, a sliding gesture scene and the like, and the gesture operation scene can be determined by identifying the gesture type by analyzing the number of input points and the like; the window element initialization scene includes, for example, a view initialization scene, a Fragment switching scene, and the like. The foregoing is by way of example only and should not be construed as limiting.
The fault signature may include information such as a delayed response (or stuck), such as that the fault analysis result indicates that the user has stuck while swiftly browsing the page for 2 seconds, the scene signature is a swipe gesture scene, and the fault signature is a delayed response for 2 seconds. For another example, the fault analysis result indicates that the application software has a stuck phenomenon in a cold start process for 3 seconds; the scenario is characterized as a cold start scenario and the fault is characterized as a delayed response of 3 seconds.
And S106, determining a performance adjustment strategy according to the scene characteristics and the fault characteristics, and performing performance adjustment on the target equipment based on the performance adjustment strategy.
In some embodiments, a key performance bottleneck existing in the target device can be analyzed according to the scene characteristics and the fault characteristics; a performance tuning strategy is then determined based on the key performance bottlenecks. The performance bottleneck is a key factor limiting the performance of the device, and can be understood as the current performance defect of the device. It can be understood that the problems existing in the current performance of the target device can be fully reflected through the scene characteristics and the fault characteristics, so that the key performance bottleneck of the target device is positioned, a performance adjustment strategy is determined based on the key performance bottleneck, the performance of the target device which needs to be optimized at present is adjusted efficiently, and the target device is further improved.
The performance adjustment strategy may include, but is not limited to, system parameter adjustment, response strategy adjustment, and the like, and in practical applications, the performance adjustment strategy may be directly formulated according to a key performance bottleneck, and the performance adjustment strategy may also be directly searched for by setting a corresponding relationship between the performance bottleneck and the performance adjustment strategy in advance based on prior knowledge.
The method provided by the embodiment of the disclosure can directly perform fault analysis on the log file to extract the scene characteristics and the fault characteristics, so that performance adjustment is performed according to the scene characteristics and the fault characteristics, the method is more efficient and reliable, batch automatic analysis can be realized, and the optimization efficiency of equipment performance is further improved on the basis of saving labor cost.
In order to realize the performance optimization of the equipment, the key point is to locate the performance bottleneck, the method and the device fully consider that the root cause of the fault performance of the equipment is the current performance bottleneck of the equipment by analyzing the fault of the log file, analyzing the scene characteristics and the fault characteristics from the fault analysis result and determining the key performance bottleneck based on the scene characteristics and the fault characteristics, so that the scene characteristics and the fault characteristics are analyzed from the log, and the performance bottleneck can be efficiently and reliably located based on the scene characteristics and the fault characteristics. The embodiment of the present disclosure provides two implementation manners for determining a key performance bottleneck, such as fig. 2 and fig. 3, which are specifically set forth as follows:
referring to fig. 2, a flowchart of a method for determining a key performance bottleneck is shown, which illustrates an implementation manner of resolving a key performance bottleneck existing in a target device according to a scene feature and a fault feature, and mainly includes the following steps S202 to S206:
step S202, a key log file is screened out from a plurality of log files according to the scene characteristics and the fault characteristics. In practical application, the method can be realized in one or more of the following ways:
(1) and screening out the log file corresponding to the scene feature with the highest priority from the plurality of log files according to preset scene priority information to serve as a key log file. The scene priority information includes corresponding relations between various scene features and priorities, and the priorities of the scene features may be preset in practical application, such as setting the priority of a hot start scene as a high priority, setting the priority of a warm start scene as a medium priority, and setting the priority of a cold start scene as a low priority. For the same time length of delay, the delay in the hot start scenario is the most serious problem and needs to be solved and processed most. For another example, it is assumed that the same stuck problem is also the stuck scene is different, one is animation stuck in the application starting process, the other is stuck generated when Fragment switching and view initialization are performed, and the other is stuck generated when a page is browsed in a sliding manner.
Of course, the above is only an example, and in practical applications, the priority level of the scene may also be represented by a "first priority level", a "second priority level", a "third priority level", and the like. According to the preset corresponding relation between the scene features and the priorities, the log file with the scene features with the highest priority can be used as the key log file.
(2) And counting the occurrence frequency of each scene characteristic, and screening out the log file corresponding to the scene characteristic with the highest occurrence frequency from the plurality of log files as a key log file. That is, the scene characteristics analyzed from each fault analysis result are counted, the occurrence frequency of each scene characteristic is calculated, and the log file of the scene characteristic with the highest occurrence frequency is used as the key log file. For example, given that most log files indicate a problem with the cold start scenario, attention needs to be drawn to focus on solving such high frequency problems.
(3) And screening out a log file corresponding to the fault feature with the highest severity level from the plurality of log files according to a preset fault severity level as a key log file. In practical application, the severity levels corresponding to various fault characteristics can be preset. After analyzing the fault characteristics according to the fault analysis result of each log file, severity levels of each fault characteristic may be compared, such as taking the fault characteristic as a stuck time delay as an example, the longer the time delay, the higher the severity level, and therefore the log file with the most severe fault characteristic may be used as a key log file, such as the log file with the longest time delay in the fault analysis result.
Step S204, determining target scene characteristics and target fault characteristics corresponding to the key log files. After the key log files are found from the massive logs, scene characteristics (target scene characteristics) of the key log files and fault characteristics (target fault characteristics) of the key log files can be analyzed in a targeted manner, so that the key performance bottleneck which needs to be solved at present of the equipment can be conveniently, efficiently and accurately positioned.
Step S206, searching information related to the target scene characteristics and the target fault characteristics from the key log file, and analyzing key performance bottlenecks existing in the target equipment based on the information. In practical application, the detailed information related to the target scene characteristics and the target fault characteristics recorded in the key log file can be obtained, so that the key performance bottleneck can be positioned by performing sufficient analysis.
By the method, the log files can be screened according to three dimensions of the scene priority, the scene occurrence frequency and the fault severity grade, the screened log files are representative key log files, the current problems and performance conditions of the equipment can be reflected to the greatest extent, the scene characteristics and the fault characteristic related content of the key log files are analyzed, and the current key performance bottleneck of the equipment can be positioned efficiently.
Referring to a flowchart of a method for determining a key performance bottleneck shown in fig. 3, another embodiment for analyzing a key performance bottleneck existing in a target device according to a scene feature and a fault feature is described, which mainly includes the following steps S302 to S306:
step S302, determining performance bottlenecks corresponding to the fault analysis results based on the scene characteristics and the fault characteristics extracted from the fault analysis results. It will be appreciated that there is a performance bottleneck for each fault analysis result indicating the presence of a fault. For example, if the failure analysis result shows that the delay of waiting for the GPU completion is too long, the bottleneck is drawn for the GPU correspondingly; if the fault analysis result shows that the delay of the synthesis stage of the SurfaceFlinger (a system service) is too long, the synthesis stage is correspondingly a bottleneck; if the fault analysis result shows that the time delay of the key thread waiting for the CPU time slice is too long, the key thread is correspondingly a CPU competition bottleneck; if the failure analysis result shows that the IO blocking time delay of the key thread file or other hardware is too long, the IO contention bottleneck is correspondingly formed; if the fault analysis result shows that the problem of high time delay caused by improper setting of the CPU frequency point is solved, the problem is correspondingly a bottleneck of the CPU frequency point; the above are merely exemplary illustrations for easy understanding, and should not be considered as limitations, and in practical applications, many different kinds of bottlenecks may exist according to architectures of systems and application programs, and are not described herein.
And step S304, classifying the performance bottlenecks corresponding to the fault analysis results according to the bottleneck classes divided in advance.
In order to facilitate analysis of performance bottlenecks, the performance bottlenecks are divided into a plurality of categories according to requirements according to the embodiments of the present disclosure, and in an implementation manner, for devices such as a mobile phone, the performance bottlenecks may be mainly divided into two categories, i.e., a system bottleneck and an application layer bottleneck, where the system bottleneck may be further subdivided and include, for example, an IO bottleneck, a scheduler bottleneck, a CPU bottleneck, a Graphic bottleneck, and the like, and the application layer bottleneck may be further subdivided and include, for example, a layout layer bottleneck, a bottleneck caused by complex layout, and the like. According to the pre-divided bottleneck categories, the performance bottlenecks currently existing in the target equipment, which are obtained through analysis according to the fault analysis result of the log file, can be classified, and the performance bottlenecks currently contained in the target equipment in each bottleneck category are determined.
Step S306, determining a target bottleneck category to which a performance bottleneck related to the target fault belongs; wherein the target fault is determined based on the fault signature. Such as where the fault signature is a delayed response, the target fault may be stuck. In practical application, there may be a plurality of fault signatures, and different fault signatures may represent different faults, so that a target fault to be solved can be determined from the analyzed fault signatures according to user requirements (such as different performances that may need to be solved by maintenance personnel of different working groups), and then a bottleneck category to which the target fault belongs can be further determined.
Step S308, the performance bottleneck belonging to the target bottleneck category is taken as a key performance bottleneck existing in the target device.
By classifying the bottleneck category, the performance bottlenecks under the category can be used as key performance bottlenecks, so that the overall performance of the bottleneck category can be adjusted conveniently.
In practical application, a listing method can be adopted to display the scene characteristics, fault characteristics and performance bottlenecks corresponding to each log file, the table is sorted and screened according to requirements, logs with high-priority scenes, logs with the most severe stuck scenes, key performance bottlenecks with the most stuck faults, corresponding logs and the like are selected, then the key performance bottlenecks and the extracted key log files are fully analyzed, and corresponding performance adjustment strategies are adopted to optimize equipment performance.
In summary, compared with the method for adjusting the performance of the device that manually analyzes logs one by one in the related art, the method provided by the embodiment of the disclosure can directly perform fault analysis on the log file to extract the scene characteristics and the fault characteristics, so that the performance adjustment is performed according to the scene characteristics and the fault characteristics, the method is more efficient and reliable, batch automatic analysis can be realized, and the optimization efficiency of the performance of the device is further improved on the basis of saving labor cost.
Corresponding to the foregoing device performance adjusting method, an embodiment of the present disclosure further provides a device performance adjusting apparatus, and fig. 4 is a schematic structural diagram of the device performance adjusting apparatus provided in the embodiment of the present disclosure, where the apparatus may be implemented by software and/or hardware, and may be generally integrated in an electronic device, and the device performance adjustment may be implemented by executing the device performance adjusting method. As shown in fig. 4, the apparatus mainly includes:
a fault analysis module 402, configured to perform fault analysis on a plurality of log files generated by operation of a target device, respectively, to obtain a fault analysis result corresponding to each log file;
a feature extraction module 404, configured to extract scene features and fault features from the fault analysis result;
and the performance adjusting module 406 is configured to determine a performance adjusting policy according to the scene characteristics and the fault characteristics, and perform performance adjustment on the target device based on the performance adjusting policy.
The device provided by the embodiment of the disclosure can directly perform fault analysis on the log file to extract scene characteristics and fault characteristics, so that performance adjustment is performed according to the scene characteristics and the fault characteristics, the device is more efficient and reliable, batch automatic analysis can be realized, and the optimization efficiency of equipment performance is further improved on the basis of saving labor cost.
In some embodiments, referring to a schematic structural diagram of another device performance adjustment apparatus shown in fig. 5, on the basis of fig. 4, the performance adjustment module 406 includes:
the bottleneck analysis unit 4062 is configured to analyze a key performance bottleneck existing in the target device according to the scene characteristics and the fault characteristics;
a performance adjustment unit 4064, configured to determine a performance adjustment policy based on the key performance bottleneck.
In some embodiments, the bottleneck resolution unit 4062 is specifically configured to:
screening out key log files from the plurality of log files according to the scene characteristics and the fault characteristics;
determining target scene characteristics and target fault characteristics corresponding to the key log files;
and searching information related to the target scene characteristics and the target fault characteristics from the key log file, and analyzing key performance bottlenecks existing in the target equipment based on the information.
In some embodiments, the bottleneck resolution unit 4062 is specifically configured to:
screening out a log file corresponding to the scene feature with the highest priority from the plurality of log files according to preset scene priority information to serve as a key log file; and/or the presence of a gas in the gas,
counting the occurrence frequency of each scene feature, and screening out a log file corresponding to the scene feature with the highest occurrence frequency from a plurality of log files as a key log file; and/or the presence of a gas in the gas,
and screening out a log file corresponding to the fault feature with the highest severity level from the plurality of log files according to a preset fault severity level as a key log file.
In some embodiments, the bottleneck resolution unit 4062 is specifically configured to:
determining a performance bottleneck corresponding to each fault analysis result based on the scene characteristics and the fault characteristics extracted from each fault analysis result;
classifying the performance bottlenecks corresponding to the fault analysis results according to the bottleneck classes divided in advance;
determining a target bottleneck category to which a performance bottleneck related to the target fault belongs;
and taking the performance bottleneck belonging to the target bottleneck category as the existing key performance bottleneck of the target equipment.
The device performance adjusting device provided by the embodiment of the disclosure can execute the device performance adjusting method provided by any embodiment of the disclosure, and has corresponding functional modules and beneficial effects of the executing method.
An embodiment of the present disclosure provides an electronic device, including: a processor; a memory for storing processor-executable instructions; the processor is used for reading the executable instructions from the memory and executing the instructions to realize any one of the above device performance adjusting methods.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 6, the electronic device 600 includes one or more processors 601 and memory 602.
The processor 601 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 600 to perform desired functions.
In one example, the electronic device 600 may further include: an input device 603 and an output device 604, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
The input device 603 may also include, for example, a keyboard, a mouse, and the like.
The output device 604 may output various information including the determined distance information, direction information, and the like to the outside. The output devices 604 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, among others.
Of course, for simplicity, only some of the components of the electronic device 600 relevant to the present disclosure are shown in fig. 6, omitting components such as buses, input/output interfaces, and the like. In addition, electronic device 600 may include any other suitable components depending on the particular application.
In addition to the above-described methods and apparatus, embodiments of the present disclosure may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the apparatus performance adjustment methods provided by embodiments of the present disclosure.
The computer program product may write program code for carrying out operations for embodiments of the present disclosure 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.
Furthermore, embodiments of the present disclosure may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform the device performance adjustment method provided by the embodiments of the present disclosure.
The computer-readable storage medium may take 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 include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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.
Embodiments of the present disclosure also provide a computer program product, which includes a computer program/instruction, and when the computer program/instruction is executed by a processor, the computer program/instruction implements the device performance adjustment method in the embodiments of the present disclosure.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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 an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present disclosure, which enable those skilled in the art to understand or practice the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (12)
1. A method for adjusting device performance, comprising:
respectively carrying out fault analysis on a plurality of log files generated by the operation of target equipment to obtain a fault analysis result corresponding to each log file;
extracting scene characteristics and fault characteristics from the fault analysis result;
and determining a performance adjustment strategy according to the scene characteristics and the fault characteristics, and performing performance adjustment on the target equipment based on the performance adjustment strategy.
2. The method of claim 1, wherein the step of determining a performance tuning strategy based on the scenario characteristic and the fault characteristic comprises:
analyzing a key performance bottleneck existing in the target equipment according to the scene characteristics and the fault characteristics;
determining a performance adjustment policy based on the key performance bottleneck.
3. The method according to claim 2, wherein the step of resolving a critical performance bottleneck existing in the target device according to the scenario characteristic and the fault characteristic comprises:
screening out key log files from the plurality of log files according to the scene characteristics and the fault characteristics;
determining target scene characteristics and target fault characteristics corresponding to the key log files;
searching information related to the target scene characteristics and the target fault characteristics from the key log files, and analyzing key performance bottlenecks existing in the target equipment based on the information.
4. The method of claim 3, wherein the step of screening the plurality of log files for critical log files based on the scenario characteristic and the fault characteristic comprises:
screening out a log file corresponding to the scene feature with the highest priority from the plurality of log files according to preset scene priority information to serve as a key log file; and/or the presence of a gas in the gas,
counting the occurrence frequency of each scene feature, and screening out a log file corresponding to the scene feature with the highest occurrence frequency from the plurality of log files as a key log file; and/or the presence of a gas in the gas,
and screening out the log file corresponding to the fault feature with the highest severity level from the plurality of log files according to a preset fault severity level as a key log file.
5. The method according to claim 2, wherein the step of resolving a critical performance bottleneck existing in the target device according to the scenario characteristic and the fault characteristic comprises:
determining a performance bottleneck corresponding to each fault analysis result based on the scene characteristics and the fault characteristics extracted from each fault analysis result;
classifying the performance bottlenecks corresponding to the fault analysis results according to the pre-divided bottleneck categories;
determining a target bottleneck category to which a performance bottleneck related to the target fault belongs; wherein the target fault is determined based on the fault signature;
and taking the performance bottleneck belonging to the target bottleneck category as the existing key performance bottleneck of the target equipment.
6. An apparatus performance adjustment device, comprising:
the fault analysis module is used for respectively carrying out fault analysis on a plurality of log files generated by the operation of target equipment to obtain a fault analysis result corresponding to each log file;
the characteristic extraction module is used for extracting scene characteristics and fault characteristics from the fault analysis result;
and the performance adjusting module is used for determining a performance adjusting strategy according to the scene characteristics and the fault characteristics and adjusting the performance of the target equipment based on the performance adjusting strategy.
7. The apparatus of claim 6, wherein the performance adjustment module comprises:
the bottleneck analysis unit is used for analyzing a key performance bottleneck existing in the target equipment according to the scene characteristics and the fault characteristics;
a performance adjustment unit to determine a performance adjustment policy based on the key performance bottleneck.
8. The apparatus according to claim 7, wherein the bottleneck resolution unit is specifically configured to:
screening out key log files from the plurality of log files according to the scene characteristics and the fault characteristics;
determining target scene characteristics and target fault characteristics corresponding to the key log files;
searching information related to the target scene characteristics and the target fault characteristics from the key log files, and analyzing key performance bottlenecks existing in the target equipment based on the information.
9. The apparatus according to claim 8, wherein the bottleneck resolution unit is specifically configured to:
screening out a log file corresponding to the scene feature with the highest priority from the plurality of log files according to preset scene priority information to serve as a key log file; and/or the presence of a gas in the gas,
counting the occurrence frequency of each scene feature, and screening out a log file corresponding to the scene feature with the highest occurrence frequency from the plurality of log files as a key log file; and/or the presence of a gas in the gas,
and screening out the log file corresponding to the fault feature with the highest severity level from the plurality of log files according to a preset fault severity level as a key log file.
10. The apparatus according to claim 7, wherein the bottleneck resolution unit is specifically configured to:
determining a performance bottleneck corresponding to each fault analysis result based on the scene characteristics and the fault characteristics extracted from each fault analysis result;
classifying the performance bottlenecks corresponding to the fault analysis results according to the pre-divided bottleneck categories;
determining a target bottleneck category to which a performance bottleneck related to the target fault belongs; wherein the target fault is determined based on the fault signature;
and taking the performance bottleneck belonging to the target bottleneck category as the existing key performance bottleneck of the target equipment.
11. An electronic device, characterized in that the electronic device comprises:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the device performance adjustment method of any one of claims 1 to 5.
12. A computer-readable storage medium, characterized in that the storage medium stores a computer program for executing the device performance adjustment method of any one of claims 1 to 5.
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| CN202110535002.0A CN113157544A (en) | 2021-05-17 | 2021-05-17 | Equipment performance adjusting method, device, equipment and medium |
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| WO2019202711A1 (en) * | 2018-04-19 | 2019-10-24 | 日本電気株式会社 | Log analysis system, log analysis method and recording medium |
| CN111145460A (en) * | 2019-12-25 | 2020-05-12 | 航天信息股份有限公司 | Method for analyzing tax control equipment, electronic equipment and storage medium |
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| CN107426432A (en) * | 2017-07-31 | 2017-12-01 | 广东欧珀移动通信有限公司 | Resource allocation method and Related product |
| WO2019202711A1 (en) * | 2018-04-19 | 2019-10-24 | 日本電気株式会社 | Log analysis system, log analysis method and recording medium |
| CN111145460A (en) * | 2019-12-25 | 2020-05-12 | 航天信息股份有限公司 | Method for analyzing tax control equipment, electronic equipment and storage medium |
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