Scheduling method based on probability model prediction

Abstract

The invention relates to a scheduling method based on probability model prediction. The scheduling method based on probability model prediction comprises the following steps: acquiring multiple times of operation time of a target task; establishing a probability statistical model based on the multiple times of operation time; calculating target operation time corresponding to a target confidence probability based on the probability statistical model; and setting a running time parameter of a task scheduling strategy based on the target running time, and performing multi-task scheduling through the task scheduling strategy. According to the embodiment of the invention, the operation time parameter can be reasonably set, the operation time parameter is prevented from being too large or too small, and the adjustment frequency is reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology applications, and in particular to a scheduling method based on probability model prediction. Background Art

[0002] Earliest Deadline First (EDF) is a task scheduling mechanism in operating systems and is often used for multi-task scheduling in real-time operating systems. The EDF algorithm usually requires setting three parameters for each task: period, deadline, and run time. The period and deadline are usually set according to demand, but how to accurately determine the run time is a problem to be solved.

[0003] In the related art, the maximum running time is directly used as the running parameter value in the EDF algorithm. In the actual scheduling process, if the actual running time is larger, it may cause the program to run beyond the deadline. If the running time parameter is set too large, it will cause multi-task scheduling to fail or the central processing unit (CPU) computing resources to be wasted. Summary of the invention

[0004] In order to solve the above technical problems, the present disclosure provides a scheduling method based on probability model prediction.

[0005] In a first aspect, the present disclosure provides a scheduling method based on probability model prediction, comprising: Get multiple running times of the target task; Establishing a probability statistical model based on the multiple running times; Based on the probability statistical model, calculating the target running time corresponding to the target confidence probability; The runtime parameters of the task scheduling strategy are set based on the target runtime, and multi-task scheduling is performed using the task scheduling strategy.

[0006] In a second aspect, the present disclosure provides a scheduling device based on probability model prediction, comprising: The time acquisition module is used to obtain multiple running times of the target task; A model building module, used for building a probability statistical model based on the multiple running times; A time calculation module, used to calculate the target running time corresponding to the target confidence probability based on the probability statistical model; The parameter setting module is used to set the runtime parameters of the task scheduling strategy based on the target runtime, and perform multi-task scheduling through the task scheduling strategy.

[0007] In a third aspect, the present disclosure provides a scheduling device based on probability model prediction, including: processor; A memory for storing executable instructions; The processor is used to read executable instructions from the memory and execute the executable instructions to implement the scheduling method based on probability model prediction of the first aspect.

[0008] In a fourth aspect, the present disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor implements the scheduling method based on probability model prediction of the first aspect.

[0009] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages: The scheduling method based on probability model prediction of the disclosed embodiment can obtain multiple running times of the target task, then establish a probability statistical model based on the multiple running times, then calculate the target running time corresponding to the target confidence probability based on the probability statistical model, and finally set the running time parameters of the task scheduling strategy based on the target running time, and perform multi-task scheduling through the task scheduling strategy. Thus, a probability statistical model is established through multiple running times of the target task, and the target running time corresponding to the target confidence probability is calculated based on the probability statistical model, so that the running time parameters of the task scheduling strategy are set according to the target running time, and the running time parameters are reasonably set to avoid being too large or too small, and the number of adjustments is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0011] Figure 1 A flowchart of a scheduling method based on probability model prediction provided by an embodiment of the present disclosure; Figure 2 A flowchart of another scheduling method based on probability model prediction provided by an embodiment of the present disclosure; Figure 3 A flowchart of another scheduling method based on probability model prediction provided by an embodiment of the present disclosure; Figure 4 A schematic diagram of the structure of a scheduling device based on probability model prediction provided by an embodiment of the present disclosure; Figure 5 A schematic diagram of the structure of a scheduling device based on probability model prediction provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0012] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0013] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0014] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0015] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0016] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0017] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0018] In order to solve the above problems, the present disclosure provides a scheduling method based on probability model prediction. Figures 1 to 3 The scheduling method based on probability model prediction provided by the embodiment of the present disclosure is described in detail.

[0019] Figure 1 A flow chart of a scheduling method based on probability model prediction provided by an embodiment of the present disclosure is shown.

[0020] In the disclosed embodiment, the scheduling method based on probability model prediction may be executed by an electronic device, wherein the electronic device may include but is not limited to devices such as computer devices, cloud servers or cloud server clusters.

[0021] like Figure 1 As shown, the scheduling method based on probability model prediction may include the following steps.

[0022] S110: Obtain multiple running times of the target task.

[0023] In an embodiment of the present disclosure, the electronic device may obtain multiple running times of the target task.

[0024] Optionally, the target task may be a task that needs to be scheduled.

[0025] Specifically, the electronic device may run the target task multiple times and obtain corresponding multiple running times.

[0026] S120: Establish a probability statistical model based on the multiple running times.

[0027] In the embodiment of the present disclosure, the electronic device may establish a probability statistical model based on the multiple running times.

[0028] Alternatively, the probability statistical model may refer to a model established based on probability theory and using mathematical statistical methods.

[0029] Specifically, after obtaining multiple operating times, the electronic device can establish a probability statistical model according to the multiple operating times.

[0030] S130. Calculate the target running time corresponding to the target confidence probability based on the probability statistical model.

[0031] In an embodiment of the present disclosure, the electronic device may calculate the target running time corresponding to the target confidence probability based on the probability statistical model.

[0032] Optionally, the target confidence probability may be a confidence probability set according to requirements.

[0033] Optionally, the target running time may be the time required to run the target task.

[0034] Specifically, the electronic device may calculate the target running time corresponding to the target confidence probability after determining the required target confidence probability according to the probability statistics model.

[0035] S140: Setting a runtime parameter of a task scheduling strategy based on the target runtime, and performing multi-task scheduling using the task scheduling strategy.

[0036] In an embodiment of the present disclosure, the electronic device may set a runtime parameter of a task scheduling strategy based on the target runtime, and perform multi-task scheduling using the task scheduling strategy.

[0037] Optionally, the task scheduling strategy ‌EDF can be used for multi-task scheduling in a real-time operating system, wherein the task scheduling strategy may include a cycle parameter, a deadline parameter and the runtime parameter.

[0038] Optionally, the runtime parameter refers to a parameter used to control the runtime of a program or task during its execution.

[0039] Specifically, after determining the target running time, the electronic device can set the running time parameters of the task scheduling policy according to the target running time, and perform multi-task scheduling through the set task scheduling policy.

[0040] Thus, in the disclosed embodiment, it is possible to obtain multiple running times of the target task, then establish a probability statistical model based on the multiple running times, then calculate the target running time corresponding to the target confidence probability based on the probability statistical model, and finally set the running time parameters of the task scheduling strategy based on the target running time, and perform multi-task scheduling through the task scheduling strategy. Thus, a probability statistical model is established through multiple running times of the target task, and the target running time corresponding to the target confidence probability is calculated based on the probability statistical model, so that the running time parameters of the task scheduling strategy are set according to the target running time, and the running time parameters are reasonably set to avoid being too large or too small, and to reduce the number of adjustments.

[0041] Optionally, S110 may specifically include: performing multiple execution processes on the target task, and recording corresponding multiple execution times.

[0042] In the disclosed embodiment, the electronic device may perform multiple execution processes on the target task and record the corresponding multiple execution times.

[0043] Specifically, the electronic device may perform multiple execution processing on the target task, for example, executing the target task n times, and recording the corresponding multiple execution times. Among them, n is as large as possible to ensure the accuracy of the subsequent model building.

[0044] Figure 2 A flow chart of another scheduling method based on probability model prediction provided by an embodiment of the present disclosure is shown.

[0045] like Figure 2 As shown, the scheduling method based on probability model prediction may include the following steps.

[0046] S210: Determine a minimum running time and the target running time based on the multiple running times.

[0047] S220: Establish a Pareto distribution model based on the multiple running times, the minimum running time, the target running time and the confidence probability, wherein the target running time is greater than the minimum running time.

[0048] In the embodiment of the present disclosure, the electronic device may determine the minimum running time and the target running time based on the multiple running times.

[0049] Optionally, the minimum running time may be the minimum running time among multiple running times.

[0050] Optionally, the target runtime is greater than the minimum runtime.

[0051] Specifically, after obtaining multiple operating times, the electronic device may determine a minimum operating time among the multiple operating times, and determine a target operating time, and the target operating time may be any time greater than the minimum operating time.

[0052] Further, the electronic device may establish a Pareto distribution model based on the multiple running times, the minimum running time, the target running time and the confidence probability.

[0053] Optionally, the Pareto distribution model may be a probability statistics model.

[0054] Specifically, after determining the multiple running times, the minimum running time, the target running time, and the confidence probability, the electronic device can establish a Pareto distribution model according to the minimum running time, the target running time, and the confidence probability. The Pareto distribution model can be: .

[0055] in, is the minimum running time, For any value greater than The time, that is, the target running time, For a certain running time Exceed The probability of , that is, the confidence probability, To adjust the parameters.

[0056] Figure 3 A flow chart of another scheduling method based on probability model prediction provided by an embodiment of the present disclosure is shown.

[0057] like Figure 3 As shown, the scheduling method based on probability model prediction may include the following steps.

[0058] S310: Determine the target confidence probability.

[0059] S320: Substitute the target confidence probability into the Pareto distribution model, and calculate the corresponding target running time through the Pareto distribution model.

[0060] In an embodiment of the present disclosure, the electronic device may determine the target confidence probability.

[0061] For example, according to operational requirements, the electronic device may determine the target confidence probability to be 0.01%, 0.02%, etc., which is not limited here.

[0062] Furthermore, the electronic device may substitute the target confidence probability into the Pareto distribution model, and calculate the corresponding target running time through the Pareto distribution model.

[0063] Specifically, the electronic device may substitute the target confidence probability (such as 0.01%) into the Pareto distribution model, and calculate the corresponding target running time ( ).

[0064] Optionally, determining the target confidence probability includes: determining the target confidence probability when a probability that the multiple running times exceed the target running time is less than a preset probability.

[0065] In an embodiment of the present disclosure, when the probability that the multiple running times exceed the target running time is less than a preset probability, the electronic device may determine the target confidence probability.

[0066] For example, when the probability that the multiple running times exceed the target running time is less than the preset probability, that is, the multiple running times ( ) exceeds the target runtime ( ) is less than the preset probability (0.01%), that is, the electronic device can determine that the target confidence probability is 0.01%.

[0067] Optionally, S140 may specifically include: setting the running time parameter of the earliest deadline first scheduling strategy to the target running time; and performing multi-task scheduling through the earliest deadline first scheduling strategy based on the cycle parameter, the deadline parameter and the running time parameter.

[0068] In the embodiment of the present disclosure, the electronic device may set the runtime parameter of the task scheduling strategy as the target runtime.

[0069] Specifically, the electronic device obtains the target operating time ( ), the runtime parameter of the earliest deadline first scheduling strategy (EDF) may be set to the target runtime ( ), Furthermore, the electronic device may perform multi-task scheduling based on the period parameter, the deadline parameter and the run time parameter through the earliest deadline first scheduling strategy.

[0070] Specifically, in a real-time operating system, the electronic device may determine the cycle parameter, the deadline parameter, and the run time parameter in an earliest deadline first scheduling strategy (EDF), and perform multi-task scheduling by using the earliest deadline first scheduling strategy.

[0071] Figure 4 A schematic diagram of the structure of a scheduling device based on probability model prediction provided by an embodiment of the present disclosure is shown.

[0072] like Figure 4 As shown, the scheduling device 400 based on probability model prediction may include a time acquisition module 410 , a model building module 420 , a time calculation module 430 and a parameter setting module 440 .

[0073] The time acquisition module 410 can be used to acquire multiple running times of the target task.

[0074] The model building module 420 may be used to build a probability statistical model based on the multiple running times.

[0075] The time calculation module 430 may be used to calculate the target running time corresponding to the target confidence probability based on the probability statistical model.

[0076] The parameter setting module 440 can be used to set the runtime parameters of the task scheduling strategy based on the target runtime, and perform multi-task scheduling through the task scheduling strategy.

[0077] Thus, in the disclosed embodiment, it is possible to obtain multiple running times of the target task, then establish a probability statistical model based on the multiple running times, then calculate the target running time corresponding to the target confidence probability based on the probability statistical model, and finally set the running time parameters of the task scheduling strategy based on the target running time, and perform multi-task scheduling through the task scheduling strategy. Thus, a probability statistical model is established through multiple running times of the target task, and the target running time corresponding to the target confidence probability is calculated based on the probability statistical model, so that the running time parameters of the task scheduling strategy are set according to the target running time, and the running time parameters are reasonably set to avoid being too large or too small, and to reduce the number of adjustments.

[0078] In some embodiments of the present disclosure, the time acquisition module 410 may specifically include a first processing unit.

[0079] The first processing unit may be used to perform multiple execution processes on the target task and record the corresponding multiple execution times.

[0080] In some embodiments of the present disclosure, the model building module 420 may specifically include a first determining unit and a model building unit.

[0081] The first determining unit may be configured to determine a minimum running time and the target running time based on the multiple running times.

[0082] The model building unit may be used to build a Pareto distribution model based on the multiple running times, the minimum running time, the target running time and the confidence probability, wherein the target running time is greater than the minimum running time.

[0083] In some embodiments of the present disclosure, the time calculation module 430 may specifically include a second determination unit and a second processing unit.

[0084] The second determination unit may be configured to determine the target confidence probability.

[0085] The second processing unit may be used to substitute the target confidence probability into the Pareto distribution model, and calculate the corresponding target running time through the Pareto distribution model.

[0086] In some embodiments of the present disclosure, the second determination unit may also be configured to determine the target confidence probability when the probability that the multiple running times exceed the target running time is less than a preset probability.

[0087] In some embodiments of the present disclosure, the task scheduling strategy may include a period parameter, a deadline parameter and the runtime parameter.

[0088] In some embodiments of the present disclosure, the parameter setting module 440 may specifically include a parameter setting unit and a task scheduling unit.

[0089] The parameter setting unit may be configured to set the runtime parameter of the earliest deadline first scheduling strategy as the target runtime.

[0090] The task scheduling unit may be used to perform multi-task scheduling based on the period parameter, the deadline parameter and the run time parameter through the earliest deadline first scheduling strategy.

[0091] It should be noted that Figure 4 The scheduling device 400 shown in FIG. 4 can execute Figures 1 to 3The various steps in the method embodiment shown in the figure are implemented Figures 1 to 3 The various processes and effects in the method embodiment shown are not described in detail here.

[0092] Figure 5 A schematic diagram of the structure of a scheduling device based on probability model prediction provided by an embodiment of the present disclosure is shown.

[0093] In some embodiments of the present disclosure, Figure 5 The scheduling device shown based on probability model prediction may be an electronic device. Specifically, the electronic device may include but is not limited to devices such as computer devices, cloud servers or cloud server clusters.

[0094] like Figure 5 As shown, the scheduling device based on probability model prediction may include a processor 501 and a memory 502 storing computer program instructions.

[0095] Specifically, the processor 501 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0096] The memory 502 may include a large capacity memory for information or instructions. By way of example and not limitation, the memory 502 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 502 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 502 may be inside or outside the computer. In a particular embodiment, the memory 502 is a non-volatile solid-state memory. In a particular embodiment, the memory 502 includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0097] The processor 501 reads and executes the computer program instructions stored in the memory 502 to perform the steps of the scheduling method based on probability model prediction provided in the embodiment of the present disclosure.

[0098] In one example, the scheduling device based on probability model prediction may further include a transceiver 503 and a bus 504. Figure 5 As shown, the processor 501, the memory 502 and the transceiver 503 are connected via a bus 504 and communicate with each other.

[0099] The bus 504 includes hardware, software, or both. For example, but not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a Memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, the bus 504 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0100] The embodiments of the present disclosure also provide a computer-readable storage medium, which may store a computer program. When the computer program is executed by a processor, the processor implements the scheduling method based on probability model prediction provided by the embodiments of the present disclosure.

[0101] The above-mentioned storage medium may, for example, include a memory 502 of computer program instructions, and the above-mentioned instructions may be executed by the processor 501 of the scheduling device based on probability model prediction to complete the scheduling method based on probability model prediction provided by the embodiment of the present disclosure. Optionally, the storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a ROM, a random access memory (Random Access Memory, RAM), a compact disc read-only memory (Compact Disc ROM, CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0102] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising" is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0103] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A scheduling method based on probability model prediction, characterized in that: include: Get multiple running times of the target task; Establishing a probability statistical model based on the multiple running times; Based on the probability statistical model, calculating the target running time corresponding to the target confidence probability; The runtime parameters of the task scheduling strategy are set based on the target runtime, and multi-task scheduling is performed using the task scheduling strategy.

2. The method according to claim 1, characterized in that: The obtaining of multiple running times of the target task includes: The target task is run multiple times, and corresponding running times are recorded.

3. The method according to claim 1, characterized in that The establishing of a probability statistical model based on the multiple running times includes: Based on the multiple running times, determining a minimum running time and a target running time; A Pareto distribution model is established based on the multiple running times, the minimum running time, the target running time and the confidence probability, and the target running time is greater than the minimum running time.

4. The method according to claim 3, characterized in that: The step of calculating the target running time corresponding to the target confidence probability based on the probability statistical model includes: determining the target confidence probability; The target confidence probability is substituted into the Pareto distribution model, and the corresponding target running time is calculated through the Pareto distribution model.

5. The method according to claim 4, characterized in that Determining the target confidence probability includes: When the probability that the multiple running times exceed the target running time is less than a preset probability, the target confidence probability is determined.

6. The method according to claim 1, characterized in that The task scheduling strategy includes a period parameter, a deadline parameter and the run time parameter.

7. The method according to claim 6, characterized in that The step of setting the runtime parameters of the task scheduling strategy based on the target runtime and performing multi-task scheduling by using the task scheduling strategy includes: Setting the runtime parameter of the earliest deadline first scheduling strategy to the target runtime; Based on the period parameter, the deadline parameter and the run time parameter, multi-task scheduling is performed using the earliest deadline first scheduling strategy.

8. A scheduling device based on probability model prediction, characterized in that: include: The time acquisition module is used to obtain multiple running times of the target task; A model building module, used for building a probability statistical model based on the multiple running times; A time calculation module, used to calculate the target running time corresponding to the target confidence probability based on the probability statistical model; The parameter setting module is used to set the runtime parameters of the task scheduling strategy based on the target runtime, and perform multi-task scheduling through the task scheduling strategy.

9. A scheduling device based on probability model prediction, characterized in that: include: processor; A memory for storing executable instructions; The processor is used to read the executable instructions from the memory and execute the executable instructions to implement the scheduling method based on probability model prediction described in any one of claims 1 to 7.

10. A non-volatile computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the processor implements the scheduling method based on probability model prediction described in any one of claims 1 to 7.

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