CN111158883B - Method, device and computer for classifying tasks of operating system - Google Patents
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Abstract
The application relates to the technical field of computers and discloses a method for classifying tasks of an operating system. The method comprises the following steps: acquiring the execution time of a task in the running process of an operating system and the creation time of the task; and classifying the tasks according to the utilization rate according to the execution time of the tasks and the creation time of the tasks. The method can classify the tasks according to the utilization rate based on the execution time of the tasks and the creation time of the tasks in the running process of the operating system, so that the task classification is more accurate, and the classified tasks are conveniently oriented and optimized. The application also discloses a device and a computer for classifying the tasks of the operating system.
Description
Technical Field
The present application relates to the field of computer technologies, and for example, to a method, an apparatus, and a computer for classifying tasks of an operating system.
Background
The time-sharing operating system simultaneously serves a plurality of tasks, which use a mechanism of time slice polling, and each task alternately monopolizes the CPU according to the priority and the weight of the task. The task scheduling and execution is realized by monopolizing the time of the CPU, namely the time of the obtained time slice.
In the process of implementing the embodiments of the present disclosure, it is found that at least the following problems exist in the related art: in the prior art, when a task is created, an operating system classifies the task based on the input fixed parameters so as to optimize the priority of the task and the size of an occupied time slice.
Disclosure of Invention
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview, and is intended to neither identify key/critical elements nor delineate the scope of such embodiments, but is intended as a prelude to the more detailed description that follows.
The embodiment of the disclosure provides a method, a device and a computer for classifying tasks of an operating system, which are used for solving the technical problem of how to enable the classification of the tasks of the operating system to be more accurate.
In some embodiments, the method comprises:
acquiring the execution time of a task in the running process of an operating system and the creation time of the task;
and classifying the tasks according to the utilization rate according to the execution time of the tasks and the creation time of the tasks.
In some embodiments, the apparatus comprises: comprising a processor and a memory storing program instructions, the processor being configured to perform the above-described method for operating system task classification when executing the program instructions.
In some embodiments, the computer comprises: device for classifying tasks of operating system
The method, the device and the computer for classifying the tasks of the operating system provided by the embodiment of the disclosure can realize the following technical effects: the tasks are automatically classified according to the utilization rate based on the execution time of the tasks and the creation time of the tasks in the running process of the operating system, and compared with the task classification realized through preset fixed parameters, the task classification is more accurate, and meanwhile the task classification optimization based on the classified tasks is facilitated.
The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.
Drawings
One or more embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements, and in which like reference numerals refer to similar elements, and in which:
FIG. 1 is a schematic diagram of a method for operating system task classification provided by an embodiment of the present disclosure;
FIG. 2 is a flowchart of a clustering method provided by an embodiment of the present disclosure;
FIG. 3 is a flow chart of one practical application provided by an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of an apparatus for operating system task classification provided by an embodiment of the present disclosure.
Detailed Description
So that the manner in which the features and techniques of the disclosed embodiments can be understood in more detail, a more particular description of the embodiments of the disclosure, briefly summarized below, may be had by reference to the appended drawings, which are not intended to be limiting of the embodiments of the disclosure. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may still be practiced without these details. In other instances, well-known structures and devices may be shown simplified in order to simplify the drawing.
The terms first, second and the like in the description and in the claims of the embodiments of the disclosure and in the above-described figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe embodiments of the present disclosure. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion.
The term "plurality" means two or more, unless otherwise indicated.
In the embodiment of the present disclosure, the character "/" indicates that the front and rear objects are an or relationship. For example, A/B represents: a or B.
The term "and/or" is an associative relationship that describes an object, meaning that there may be three relationships. For example, a and/or B, represent: a or B, or, A and B.
Referring to fig. 1, an embodiment of the present disclosure provides a method for classifying tasks of an operating system, including:
s101, acquiring execution time of a task and creation time of the task in the running process of an operating system;
s102, classifying the tasks according to the utilization rate according to the execution time of the tasks and the creation time of the tasks.
By adopting the method for classifying the tasks of the operating system, which is provided by the embodiment of the application, the tasks can be classified according to the utilization rate based on the execution time of the tasks and the creation time of the tasks in the running process of the operating system, so that the tasks are more accurately classified, the higher the utilization rate is, the higher the priority is, and the more convenient the classified tasks are directionally optimized.
Optionally, classifying the tasks according to the usage rate according to the execution time of the tasks and the creation time of the tasks includes: obtaining sample points corresponding to the tasks according to the execution time of the tasks and the creation time of the tasks; clustering the sample points to obtain a clustering set; and classifying the cluster sets according to the utilization rate. In this way, based on the execution time and the creation time of the task in the running process of the operating system, namely the multidimensional characteristic value of the task, the task is classified according to the utilization rate by a k-means (k-means clustering algorithm ) clustering algorithm, and the higher the utilization rate is, the higher the priority is.
In some embodiments, the execution time of a task is the total time period for executing the task after the task is created, the creation time of the task is the time period from the time point when the task is created to the current time point, for example, the task created in 8:00 a.m. is executed for 1 hour on the days 8:00 to 9:00, and for 1 hour on the days 12:00 to 13:00, the execution time of the task is 2 hours by 13:00 pm on the day, and the creation time of the task is 5 hours.
Optionally, the abscissa of the sample point is the execution time of the task, and the ordinate of the sample point is the creation time of the task; or, the abscissa of the sample point is the creation time of the task, and the ordinate of the sample point is the execution time of the task.
Optionally, classifying the cluster set according to the usage rate includes: obtaining the utilization rate according to the ratio of the execution time to the creation time of the task; classifying the utilization rate; and classifying the cluster set according to the classified utilization rate. Optionally, selecting one sample point from each cluster set at will, classifying the selected sample point according to the use ratio, and obtaining the type of the sample point; and classifying the cluster set according to the type of the sample point. Optionally, the type of any sample point is determined, and the cluster set is marked with the type, or all sample points in the set and corresponding application programs are marked. For example, a sample point is arbitrarily selected from any cluster set, and the ratio of the execution time to the creation time of the corresponding task, that is, the execution time of the task/the creation time of the task, is greater than or equal to a set threshold, and the cluster set corresponding to the sample point is marked as high-usage or the tasks corresponding to all sample points in the cluster set corresponding to the sample point are marked as high-usage; when the ratio of the execution time to the creation time of the corresponding task, that is, the execution time of the task/the creation time of the task, is smaller than a set threshold, the cluster set corresponding to the sample point is marked as low-usage or the tasks corresponding to all sample points in the cluster set corresponding to the sample point are marked as low-usage. By the method, the type of each cluster set is judged only once, namely the judgment times are the same as the number of the cluster sets, and compared with the mode of judging each sample point in the prior art, the method has the advantages that the calculated amount is greatly saved, and the classification efficiency is improved.
Optionally, clustering the sample points includes: taking k sample points as a first centroid; and calculating the distance between each sample point and each first centroid, dividing the sample points according to the distance between each sample point and each first centroid to obtain a clustering set, wherein k is a positive integer. Optionally, the euclidean distance is used to calculate the distance between each sample point and each first centroid.
Optionally, calculating the distance between each sample point and each first centroid includes:
calculating label i ＝arg min||x i -μ j Obtaining the distance between the sample point and the first centroid; label i X is the distance from the ith sample point to the first centroid i Mu for the i-th sample point j The j first centroid is positive integer, j is integer and 1.ltoreq.j.ltoreq.k. When the distance from the sample point i to the jth first centroid is nearest, the sample point i belongs to the cluster set of the jth first centroid. For example, the 5 th sample point is closest to the 1 st first centroid, then the 5 th sample point belongs to the cluster set of the 1 st first centroid.
In some embodiments, taking k as 2, taking 2 first centroids, dividing according to the distance between the calculated sample point and each first centroid, and then, the sample point close to the 1 st first centroid belongs to the 1 st first centroid set, the sample point close to the 2 nd first centroid belongs to the 2 nd first centroid set, so that two clustering sets are obtained, and the clustering sets represent a high-use-rate task and a low-use-rate task respectively. Optionally, the number of first centroids is taken to be equal to the number of clusters obtained.
Optionally, after the first clustering set partitioning, the method for classifying the tasks of the operating system further includes: re-computing a second centroid of each cluster set; and stopping iteration when the distance between the second centroid and the first centroid of the cluster set is smaller than a set threshold value.
Optionally, recalculating the second centroid of each cluster set includes:
calculation ofObtaining a second centroid mu' j ；
c j For the j-th cluster set, x a C is j Inner a sample point, |c j J-th cluster set c j The number of sample points in the inner part; j is an integer, j is more than or equal to 1 and less than or equal to k, and a is a positive integer.
When the distance between the newly calculated mass center of each cluster set and the original mass center of the cluster set is smaller than a set threshold value, the position of the newly calculated mass center is not changed greatly, the stability is achieved, namely convergence is achieved, the clusters are considered to reach the expected result, and the algorithm is terminated.
And when the change of the distance between the newly calculated centroid and the original centroid is greater than or equal to a set threshold, the distance between each sample point and each first centroid is recalculated, then the clustering set is divided, and then the second centroid is calculated again until the distance between the newly calculated centroid and the original centroid of the clustering set is smaller than the set threshold, namely the clustering reaches the expected result. The tasks of the operating system are classified by autonomous learning through a k-means algorithm in a long-term dynamic mode, so that the task classification of the operating system is more accurate.
Alternatively, samples that are stable in a certain cluster for a long period are regarded as stable samples, and samples that have frequently replaced cluster records are regarded as active samples. The number of clusters replaced is considered to be frequent when the number of clusters reaches a set threshold, and the sample is an active sample. The stable sample is not involved in the calculation of the centroid in the future, so that the sample size for calculating the centroid is reduced, the calculation complexity of the centroid is reduced, and the optimization effect is achieved. After the set time, judging whether the stable centroid is still in the original cluster again, and if so, adding the stable sample into the sample library again. The scheme optimizes the k-means clustering algorithm, and the effects of reducing CPU load and power consumption are obtained by sacrificing accuracy, and the scheme is more suitable for the application of the embedded equipment because the system running on the embedded equipment is required to have the characteristics of low power consumption and stable system.
In some embodiments, the system is a time-sharing operating system.
In some embodiments, further comprising: optimizing according to the classification. The method for classifying the tasks of the operating system, which is provided by the embodiment of the application, has the functions of long-term autonomous learning and task feature statistics, automatically classifies the tasks according to different feature dimensions, and is more convenient for the operating system to directionally optimize the tasks by utilizing the classification.
As shown in fig. 2, a flowchart of a k-means clustering algorithm provided by an embodiment of the present disclosure includes the following specific steps:
s201, setting a k value as 2, taking characteristic values of 2 dimensions of execution time of a task and creation time of the task, setting an abscissa as the execution time of the task, and setting an ordinate as the creation time of the task, wherein the k value as 2 is divided into 2 clustering sets which respectively represent high-utilization-rate tasks and low-utilization-rate tasks;
s202, taking 2 sample points in the coordinates as a first centroid, wherein the number of the taken sample points is equal to the k value.
S203, calculating the distance between each sample point and each first centroid;
s204, judging whether each sample point is closer to the 1 st first centroid, if so, executing a step S205, and if not, executing a step S206;
s205, the sample point belongs to the 1 st first centroid set, and the step S207 is continuously executed;
s206, the sample point belongs to the 2 nd first centroid set, and the step S207 is continuously executed;
s207, judging whether residual points exist, if yes, returning to the step S203, and if no, executing the step S208;
s208, recalculating the second barycenters of the two clustering sets respectively;
s209, judging whether the distance between the second centroid and the first centroid is smaller than a set threshold, if so, executing the step S210, and if not, executing the step S211;
s210, the algorithm is terminated, and clustering reaches an expected value;
s211, reserving the second centroid, and then returning to execute step S203.
Thus, through 1 set of k-means algorithm, feature values with different dimensions are transmitted, and task classification is realized.
In practical application, as shown in fig. 3, the method provided by the embodiment is used for classifying tasks according to the actual application scenario flowchart, so as to optimize task priority and ensure time slices of high-priority tasks. The method comprises the following specific steps:
s301, a user runs an operating system, for example, a time-sharing operating system;
s302, the operating system counts task feature values, and the background of the operating system records feature values of all tasks, wherein the feature values include but are not limited to: the method comprises the steps of executing the task, occupying the stack space, applying for the stack memory times, executing the task, and creating the task;
s303, classifying tasks according to task utilization rates through a K-means algorithm, transmitting execution time of the tasks and creation time of the tasks into the K-means algorithm, setting a K value to be 2, and automatically counting 2 sets by the algorithm, wherein the sets are a task set with high utilization rate and a task set with low utilization rate respectively;
s304, judging whether the current task is high in utilization rate, if so, executing a step S305, and if not, executing a step S307;
s305, setting high priority, and then executing step S306;
s306, long time slices are allocated;
s307, setting low priority, and then executing step S308;
s308, a short time slice is allocated.
In this way, by counting the execution time and the creation time of the tasks in the running process of the system as characteristic values, the tasks are classified according to the utilization rate, and the higher the utilization rate of the tasks is, the higher the priority is, so that the task classification is more accurate.
As shown in connection with FIG. 4, an embodiment of the present disclosure provides an apparatus for operating system task classification, including a processor (processor) 100 and a memory (memory) 101. Optionally, the apparatus may further comprise a communication interface (Communication Interface) 102 and a bus 103. The processor 100, the communication interface 102, and the memory 101 may communicate with each other via the bus 103. The communication interface 102 may be used for information transfer. Processor 100 may invoke logic instructions in memory 101 to perform the methods for operating system task classification of the above-described embodiments.
Further, the logic instructions in the memory 101 described above may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand alone product.
The memory 101 is a computer readable storage medium that can be used to store a software program, a computer executable program, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 100 executes functional applications and data processing by running program instructions/modules stored in the memory 101, i.e. implements the method for operating system task classification in the embodiments described above.
The memory 101 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage data area may store data created according to the use of the terminal device, etc. Further, the memory 101 may include a high-speed random access memory, and may also include a nonvolatile memory.
By adopting the device for classifying the tasks of the operating system, which is provided by the embodiment of the application, the tasks can be automatically classified according to the utilization rate based on the execution time of the tasks and the creation time of the tasks in the running process of the operating system, and compared with the task classification realized through the preset fixed parameters, the device for classifying the tasks is more accurate, and meanwhile, the device is convenient for carrying out directional optimization according to the classified tasks.
The embodiment of the disclosure provides a computer comprising the device for classifying the tasks of an operating system. The computer can automatically classify the tasks according to the utilization rate based on the execution time of the tasks and the creation time of the tasks in the operation process of the operating system, and compared with the method for classifying the tasks through preset fixed parameters, the method is more accurate in classification, and meanwhile the method is convenient for carrying out directional optimization according to the classified tasks.
Embodiments of the present disclosure provide a computer readable storage medium storing computer executable instructions configured to perform the above-described method for operating system task classification.
The disclosed embodiments provide a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the above-described method for operating system task classification.
The computer readable storage medium may be a transitory computer readable storage medium or a non-transitory computer readable storage medium.
Embodiments of the present disclosure may be embodied in a software product stored on a storage medium, including one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of a method according to embodiments of the present disclosure. And the aforementioned storage medium may be a non-transitory storage medium including: a plurality of media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or a transitory storage medium.
The above description and the drawings illustrate embodiments of the disclosure sufficiently to enable those skilled in the art to practice them. Other embodiments may involve structural, logical, electrical, process, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in, or substituted for, those of others. Moreover, the terminology used in the present application is for the purpose of describing embodiments only and is not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a," "an," and "the" (the) are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this disclosure is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, when used in the present disclosure, the terms "comprises," "comprising," and/or variations thereof, mean that the recited features, integers, steps, operations, elements, and/or components are present, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Without further limitation, an element defined by the phrase "comprising one …" does not exclude the presence of other like elements in a process, method or apparatus comprising such elements. In this context, each embodiment may be described with emphasis on the differences from the other embodiments, and the same similar parts between the various embodiments may be referred to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method sections disclosed in the embodiments, the description of the method sections may be referred to for relevance.
Those of skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. The skilled artisan may use different methods for each particular application to achieve the described functionality, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the embodiments disclosed herein, the disclosed methods, articles of manufacture (including but not limited to devices, apparatuses, etc.) may be practiced in other ways. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the units may be merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form. The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to implement the present embodiment. In addition, each functional unit in the embodiments of the present disclosure may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than that disclosed in the description, and sometimes no specific order exists between different operations or steps. For example, two consecutive operations or steps may actually be performed substantially in parallel, they may sometimes be performed in reverse order, which may be dependent on the functions involved. Each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Claims (10)
1. A method for operating system task classification, comprising:
acquiring the execution time of a task in the running process of an operating system and the creation time of the task;
classifying the tasks according to the utilization rate according to the execution time of the tasks and the creation time of the tasks; the execution time of the task is the total duration of executing the task after the task is created; the creation time of the task is the time length from the moment of creating the task to the moment of the task; the usage rate is a ratio of execution time of the task to creation time of the task.
2. The method of claim 1, wherein classifying the tasks according to usage rates based on execution times of the tasks and creation times of the tasks comprises:
obtaining a sample point corresponding to the task according to the execution time of the task and the creation time of the task;
clustering the sample points to obtain a clustering set;
and classifying the cluster set according to the utilization rate.
3. The method of claim 2, wherein the abscissa of the sample point is the execution time of the task and the ordinate of the sample point is the creation time of the task; or alternatively, the first and second heat exchangers may be,
the abscissa of the sample point is the creation time of the task, and the ordinate of the sample point is the execution time of the task.
4. The method of claim 2, wherein classifying the set of clusters according to usage comprises:
obtaining the utilization rate according to the ratio of the execution time to the creation time of the task;
classifying the utilization rate;
and classifying the cluster set according to the classified utilization rate.
5. The method of claim 2, wherein clustering the sample points comprises:
taking k sample points as a first centroid;
and calculating the distance between each sample point and each first centroid, dividing the sample points according to the distance between each sample point and each first centroid to obtain a clustering set, wherein k is a positive integer.
6. The method as recited in claim 5, further comprising:
re-computing a second centroid of each cluster set;
and stopping iteration when the distance between the second centroid and the first centroid of the cluster set is smaller than a set threshold value.
7. The method of claim 6, wherein the recalculating the second centroid of each cluster set comprises:
calculation ofObtaining the second centroid mu' j ；
The c j For the j-th cluster set, the x a C is j Inner a sample point, c j The j-th cluster set c j The number of sample points in the inner part; j is an integer and is more than or equal to 1 and less than or equal to k, and a is a positive integer.
8. The method of claim 5, wherein calculating the distance of each sample point from each first centroid comprises:
calculating label i ＝argminx i -μ j Obtaining a distance between the sample point and the first centroid; label i For the distance from the ith sample point to the first centroid, the x i For the i-th sample point, the mu j The j first centroid is a positive integer, j is an integer and is more than or equal to 1 and less than or equal to k.
9. An apparatus for operating system task classification comprising a processor and a memory storing program instructions, wherein the processor is configured, when executing the program instructions, to perform the method for operating system task classification of any of claims 1 to 8.
10. A computer comprising the apparatus for operating system task classification of claim 9.
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| CN103123575A (en) * | 2011-11-18 | 2013-05-29 | 北京兴宇中科科技开发股份有限公司 | Data write-in method |
| CN107067045A (en) * | 2017-05-31 | 2017-08-18 | 北京京东尚科信息技术有限公司 | Data clustering method, device, computer-readable medium and electronic equipment |
| CN109445936A (en) * | 2018-10-12 | 2019-03-08 | 深圳先进技术研究院 | A kind of cloud computing load clustering method, system and electronic equipment |
| CN110427256A (en) * | 2019-07-23 | 2019-11-08 | 中南民族大学 | Job scheduling optimization method, equipment, storage medium and device priority-based |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102195813A (en) * | 2011-05-04 | 2011-09-21 | 成都勤智数码科技有限公司 | Method and device for intelligently creating operation and maintenance worksheet |
| CN103123575A (en) * | 2011-11-18 | 2013-05-29 | 北京兴宇中科科技开发股份有限公司 | Data write-in method |
| CN107067045A (en) * | 2017-05-31 | 2017-08-18 | 北京京东尚科信息技术有限公司 | Data clustering method, device, computer-readable medium and electronic equipment |
| CN109445936A (en) * | 2018-10-12 | 2019-03-08 | 深圳先进技术研究院 | A kind of cloud computing load clustering method, system and electronic equipment |
| CN110427256A (en) * | 2019-07-23 | 2019-11-08 | 中南民族大学 | Job scheduling optimization method, equipment, storage medium and device priority-based |
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