Normal distribution-based node real-time scheduling method and system

Abstract

The invention discloses a node real-time scheduling method and system based on normal distribution. The method comprises the following steps: acquiring environment parameters of nodes to be scheduled and setting scheduling nodes and scheduling parameters; calculating a maximum deadline satisfaction ratio and a first average deadline satisfaction ratio of the first scheduling task satisfying normal distribution based on the first scheduling state; based on the second scheduling state, calculating a second average deadline satisfaction ratio satisfying normal distribution of the second scheduling task after the first scheduling task is executed; according to the first average deadline satisfaction ratio, the second average deadline satisfaction ratio and the deadline satisfaction ratio, obtaining the deadline satisfaction ratio of the conflict between the first scheduling task and the second scheduling task so as to obtain the deadline satisfaction ratio of each scheduling node; and selecting a node meeting a scheduling condition from the deadline satisfaction ratio of each scheduling node as a node to be scheduled. According to the invention, the problem that the traditional scheduling algorithm lacks consideration of actual network environment factors is solved, and the real-time scheduling performance is improved.

Description

Technical Field

[0001] The invention belongs to the field of network node scheduling, and in particular relates to a node real-time scheduling method and system based on normal distribution. Background Art

[0002] With the advent of the 5G network era, real-time scheduling is widely used in avionics, transportation, energy, healthcare and other fields. It is essential to ensure the efficient operation of the network and can help network administrators better utilize network resources and enhance the stability and reliability of the network. With the increasing complexity of real-time applications, the tasks and resources involved are widely distributed. In the distributed environment of real-time scheduling, task allocation and data migration are required between nodes. In order to ensure the real-time performance of the application, it is necessary to consider external factors such as actual network transmission delay and throughput as well as internal factors such as node processing capacity, data dependency and priority. The real-time scheduling algorithm must balance these factors to achieve efficient task allocation and optimal utilization of resources to ensure timely completion of tasks and instant transmission of data.

[0003] In the real-time scheduling algorithm model, a service node needs to be moved to the next service node and provide real-time service, and the time required for the move must also be included in the real-time scheduling. At the same time, actual factors such as bandwidth utilization, node load, and link quality need to be considered to calculate the real-time performance of node real-time scheduling. However, the traditional real-time scheduling algorithm model is too idealistic, resulting in a large deviation from the model results in actual application. In other words, the traditional uniform distribution of parameters involved in the scheduling calculation during the node scheduling process lacks consideration of the actual situation, leading to underestimation of small probability situations and inaccurate analysis results, which leads to deviations in the relevant real-time performance calculation results in the real-time scheduling process. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides a node real-time scheduling method and system based on normal distribution. The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0005] A node real-time scheduling method based on normal distribution, comprising:

[0006] Get the environment parameters of the node to be scheduled and set the scheduling node and scheduling parameters;

[0007] Based on the first scheduling state, calculating a maximum deadline satisfaction ratio and a first average deadline satisfaction ratio of the first scheduling task satisfying a normal distribution;

[0008] Based on the second scheduling state, calculating a second average deadline satisfaction ratio of the second scheduled task after the first scheduled task is executed that satisfies the normal distribution;

[0009] Obtaining a deadline satisfaction ratio of a conflict between a first scheduling task and a second scheduling task according to the first average deadline satisfaction ratio, the second average deadline satisfaction ratio and the deadline satisfaction ratio, so as to obtain a deadline satisfaction ratio of each scheduling node;

[0010] The nodes that meet the scheduling conditions are selected from the deadline satisfaction ratio of each scheduling node as the nodes to be scheduled.

[0011] In a specific embodiment, the environmental parameters of the node to be scheduled include: one or more of actual network transmission delay, data packets, throughput, bandwidth utilization, node load and link quality; the scheduling parameters include relaxation time, execution time and migration time.

[0012] In a specific implementation, the first scheduling state is: a state in which the first scheduling task in the current scheduling node does not conflict with other tasks when executing;

[0013] The second scheduling state is a state in which the current scheduling node arrives at the first scheduling task just before the second scheduling task and the first scheduling task has no conflict during execution.

[0014] In one specific implementation, the maximum deadline satisfaction ratio is:

[0015] DM A (λ,M)=e -λM ,

[0016] The first average deadline satisfaction ratio is:

[0017]

[0018] Among them, λ is the parameter of the relaxation time satisfying the exponential distribution, M is the moving time M i The parameters to be set.

[0019] In a specific implementation, the second average deadline satisfaction ratio is:

[0020]

[0021] Among them, λ is the parameter of the relaxation time satisfying the exponential distribution, M is the moving time M i The parameters set, E is the execution time E i The parameters to be set.

[0022] In one specific implementation, the deadline satisfaction ratio is:

[0023]

[0024] The present invention also provides a node real-time scheduling system based on normal distribution, comprising:

[0025] The node initialization module is used to obtain the environment parameters of the node to be scheduled and set the scheduling node and scheduling parameters;

[0026] A first scheduling task calculation unit, configured to calculate, based on the first scheduling state, a maximum deadline satisfaction ratio and a first average deadline satisfaction ratio of the first scheduling task satisfying a normal distribution;

[0027] A second scheduling task calculation unit, configured to calculate, based on a second scheduling state, a second average deadline satisfaction ratio of the second scheduling task after the first scheduling task is executed, which satisfies a normal distribution;

[0028] A deadline satisfaction ratio calculation unit, configured to obtain a deadline satisfaction ratio of a conflict between a first scheduling task and a second scheduling task according to the first average deadline satisfaction ratio, the second average deadline satisfaction ratio and the deadline satisfaction ratio, so as to obtain a deadline satisfaction ratio of each scheduling node;

[0029] The scheduling node selection unit is used to select a node that meets the scheduling condition from the deadline satisfaction ratio of each scheduling node as a node to be scheduled.

[0030] In a specific embodiment, the environmental parameters of the node to be scheduled include: one or more of actual network transmission delay, data packets, throughput, bandwidth utilization, node load and link quality; the scheduling parameters include relaxation time, execution time and migration time.

[0031] In a specific implementation, the first scheduling state is: a state in which the first scheduling task in the current scheduling node does not conflict with other tasks when executing;

[0032] The second scheduling state is a state in which the current scheduling node arrives at the first scheduling task just before the second scheduling task and the first scheduling task has no conflict during execution.

[0033] In one specific implementation, the maximum deadline satisfaction ratio is:

[0034] DM A (λ,M)=e -λM ,

[0035] The first average deadline satisfaction ratio is:

[0036]

[0037] Among them, λ is the parameter of the relaxation time satisfying the exponential distribution, M is the moving time M i The parameters to be set;

[0038] The second average deadline satisfaction ratio is:

[0039]

[0040] Among them, λ is the parameter of the relaxation time satisfying the exponential distribution, M is the moving time M i The parameters set, E is the execution time E i The parameters to be set;

[0041] Correspondingly, the deadline satisfaction ratio is:

[0042]

[0043] Beneficial effects of the present invention:

[0044] The node real-time scheduling method based on normal distribution of the present invention performs real-time scheduling of tasks in a real-time system, calculates the average deadline satisfaction ratio of the task based on normal distribution in different situations according to the relaxation time, execution time and movement time required for task execution scheduling, thereby obtaining the real-time performance of the task during execution. Firstly, it solves the problem of lack of consideration of actual network environment factors in traditional scheduling algorithms, and secondly, it provides a prerequisite for reducing resource consumption, improving real-time scheduling performance, and realizing effective allocation of real-time scheduling resources.

[0045] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic flow chart of a node real-time scheduling method based on normal distribution provided by an embodiment of the present invention;

[0047] Figure 2 It is a real-time performance result diagram of a node real-time scheduling method based on normal distribution provided by an embodiment of the present invention;

[0048] Figure 3 It is a module block diagram of a node real-time scheduling system based on normal distribution provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0050] Embodiment 1

[0051] See also Figure 1 , Figure 1 The present invention provides a schematic diagram of a real-time node scheduling method based on normal distribution, including:

[0052] S1. Obtain the environment parameters of the node to be scheduled and set the scheduling node and scheduling parameters;

[0053] It should be noted that the environmental parameters of the nodes to be scheduled are the relevant parameters of the actual network environment during operation, such as actual factors such as network transmission delay, data packets, throughput, bandwidth utilization, node load, and link quality. When setting the scheduling nodes, in order to improve the scheduling performance, the number of nodes can be selected as many as possible, but considering that the calculation amount of more nodes is large, therefore, it is sufficient to select according to actual needs. If the scheduling performance requirements are high, more nodes are selected for calculation. If the scheduling time efficiency requirements are high, fewer nodes are selected for calculation. In this embodiment, 10,000 data points are selected for calculation.

[0054] In addition, the real-time system tasks are scheduled in real time. In the real-time scheduling process, a node moves to the location of another node to provide services and complete the scheduling. Therefore, the relevant parameters in the real-time scheduling process are set: the relaxation time is the maximum time that a scheduling process can be postponed without affecting the completion of the entire task. Let the relaxation time be L i And meet The exponential distribution of the execution time is the time from the start of a task to its completion, which is set to E i And it satisfies the normal distribution of μ = 0, σ = 1. The migration time is the time required for a task or node to move to another task or node, which is set to M i And satisfy the normal distribution of μ=0, σ=1, set the relevant scheduling parameters for subsequent calculation steps.

[0055] S2. Based on the first scheduling state, calculate the maximum deadline satisfaction ratio and the first average deadline satisfaction ratio of the first scheduling task that satisfy the normal distribution;

[0056] In this step, the first scheduling state is a state in which the first scheduling task in the current scheduling node does not conflict with other tasks when executing.

[0057] The tasks are classified by the scheduling situation in the real-time scheduling process. If the first scheduled task does not conflict with any other tasks during execution, the deadline of the first scheduled task based on the normal distribution meets the DM A (λ,M) is the slack time L of the first scheduled task A Greater than the moving time M A The probability of the first scheduled task being satisfied by the deadline is calculated as:

[0058] DM A (λ,M)=e -λM ,

[0059] λ is the parameter of the relaxation time satisfying the exponential distribution, M is the moving time M i Set the parameters. By moving time MA The average deadline satisfaction ratio of task A that satisfies the normal distribution is Mean(DM A The calculation formula of (λ,M) is:

[0060]

[0061] λ is the parameter of the relaxation time satisfying the exponential distribution, M is the moving time M i The parameters to be set.

[0062] S3. Based on the second scheduling state, calculate a second average deadline satisfaction ratio of the second scheduled task after the first scheduled task is executed that satisfies the normal distribution;

[0063] In this step, the second scheduling state is a state in which the current scheduling node arrives at the first scheduling task just before the second scheduling task and the first scheduling task has no conflict during execution.

[0064] When performing real-time scheduling of tasks or nodes, if the first scheduled task arrives just before the second scheduled task without conflict and the first scheduled task does not conflict during execution, since the second scheduled task is scheduled after the first scheduled task, the slack time L of the second scheduled task is B Greater than the moving time M of the first scheduled task A and the execution time E of the first scheduled task A The moving time M of the second scheduled task B The sum of the two, so the average deadline of the second scheduled task satisfies Mean(DM B (λ,M A ,E A ,M B )) is calculated as:

[0065]

[0066] λ is the parameter of the relaxation time satisfying the exponential distribution, M is the moving time M i The parameters set, E is the execution time E i The parameters to be set.

[0067] S4. Obtain a deadline satisfaction ratio when the first scheduling task and the second scheduling task conflict according to the first average deadline satisfaction ratio, the second average deadline satisfaction ratio and the deadline satisfaction ratio, so as to obtain a deadline satisfaction ratio of each scheduling node; that is, after obtaining the average deadline satisfaction ratio of the first scheduling task and the second scheduling task, calculate the deadline satisfaction ratio when the first scheduling task and the second scheduling task conflict, i.e., the real-time performance DM of the task in the real-time scheduling process, and the calculation formula is:

[0068]

[0069] DM is the deadline satisfaction ratio of the conflicting first and second scheduled tasks, which is the real-time performance of the real-time scheduling algorithm process.

[0070] S5. Select a node that meets the scheduling conditions from the deadline satisfaction ratio of each scheduling node as a node to be scheduled.

[0071] This embodiment presents the final result through a three-dimensional image, for example. Figure 2 As shown, in Figure 2 In the 100*100 grid, 10,000 nodes in the image are scheduled and the final results are presented. The top white part of the figure is the data point that the real-time scheduling algorithm finally selects for scheduling. The scheduling algorithm conditions and schedules the nodes, leaving only the nodes that need to be scheduled, which are the top white part of the figure.

[0072] The node real-time scheduling method based on normal distribution of the present invention performs real-time scheduling of tasks in a real-time system, calculates the average deadline satisfaction ratio of the task based on normal distribution in different situations according to the relaxation time, execution time and movement time required for task execution scheduling, thereby obtaining the real-time performance of the task during execution. Firstly, it solves the problem of lack of consideration of actual network environment factors in traditional scheduling algorithms, and secondly, it provides a prerequisite for reducing resource consumption, improving real-time scheduling performance, and realizing effective allocation of real-time scheduling resources.

[0073] Please continue to see Figure 3 , Figure 3 The present invention also provides a module block diagram of a node real-time scheduling system based on normal distribution, including:

[0074] The node initialization module is used to obtain the environment parameters of the node to be scheduled and set the scheduling node and scheduling parameters;

[0075] A first scheduling task calculation unit, configured to calculate, based on the first scheduling state, a maximum deadline satisfaction ratio and a first average deadline satisfaction ratio of the first scheduling task satisfying a normal distribution;

[0076] A second scheduling task calculation unit, configured to calculate, based on a second scheduling state, a second average deadline satisfaction ratio of the second scheduling task after the first scheduling task is executed, which satisfies a normal distribution;

[0077] A deadline satisfaction ratio calculation unit, configured to obtain a deadline satisfaction ratio of a conflict between a first scheduling task and a second scheduling task according to the first average deadline satisfaction ratio, the second average deadline satisfaction ratio and the deadline satisfaction ratio, so as to obtain a deadline satisfaction ratio of each scheduling node;

[0078] The scheduling node selection unit is used to select a node that meets the scheduling condition from the deadline satisfaction ratio of each scheduling node as a node to be scheduled.

[0079] In a specific embodiment, the environmental parameters of the node to be scheduled include: one or more of actual network transmission delay, data packets, throughput, bandwidth utilization, node load and link quality; the scheduling parameters include relaxation time, execution time and migration time.

[0080] In a specific implementation, the first scheduling state is: a state in which the first scheduling task in the current scheduling node does not conflict with other tasks when executing;

[0081] The second scheduling state is a state in which the current scheduling node arrives at the first scheduling task just before the second scheduling task and the first scheduling task has no conflict during execution.

[0082] In one specific implementation, the maximum deadline satisfaction ratio is:

[0083] DM A (λ,M)=e -λM ,

[0084] The first average deadline satisfaction ratio is:

[0085]

[0086] Among them, λ is the parameter of the relaxation time satisfying the exponential distribution, M is the moving time M i The parameters to be set;

[0087] The second average deadline satisfaction ratio is:

[0088]

[0089] Among them, λ is the parameter of the relaxation time satisfying the exponential distribution, M is the moving time M i The parameters set, E is the execution time E i The parameters to be set;

[0090] Correspondingly, the deadline satisfaction ratio is:

[0091]

[0092] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0093] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0094] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification.

[0095] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the claimed application, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "one" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in a claim. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0096] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, devices (equipment), or computer program products. Therefore, the present application may adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware, which are collectively referred to as "modules" or "systems" herein. Moreover, the present application may adopt the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program codes. The computer program is stored / distributed in a suitable medium, provided together with other hardware or as a part of hardware, or may adopt other distribution forms, such as by Internet or other wired or wireless telecommunication systems.

[0097] The present application is described with reference to the flowcharts and / or block diagrams of the methods, apparatuses (devices) and computer program products of the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0098] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0100] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A node real-time scheduling method based on normal distribution, characterized in that: include: Get the environment parameters of the node to be scheduled and set the scheduling node and scheduling parameters; Based on the first scheduling state, calculating a maximum deadline satisfaction ratio and a first average deadline satisfaction ratio of the first scheduling task satisfying a normal distribution; Based on the second scheduling state, calculating a second average deadline satisfaction ratio of the second scheduled task after the first scheduled task is executed that satisfies the normal distribution; Obtaining a deadline satisfaction ratio of a conflict between a first scheduling task and a second scheduling task according to the first average deadline satisfaction ratio, the second average deadline satisfaction ratio and the deadline satisfaction ratio, so as to obtain a deadline satisfaction ratio of each scheduling node; The nodes that meet the scheduling conditions are selected from the deadline satisfaction ratio of each scheduling node as the nodes to be scheduled.

2. The node real-time scheduling method based on normal distribution according to claim 1, characterized in that: The environmental parameters of the node to be scheduled include: one or more of actual network transmission delay, data packets, throughput, bandwidth utilization, node load and link quality; the scheduling parameters include relaxation time, execution time and migration time.

3. The node real-time scheduling method based on normal distribution according to claim 1, characterized in that: The first scheduling state is a state in which the first scheduling task in the current scheduling node does not conflict with other tasks when executing; The second scheduling state is a state in which the current scheduling node arrives at the first scheduling task just before the second scheduling task and the first scheduling task has no conflict during execution.

4. The node real-time scheduling method based on normal distribution according to claim 1, characterized in that: The maximum deadline satisfaction ratio is: DM A (λ,M)=e -λM , The first average deadline satisfaction ratio is: Among them, λ is the parameter of the relaxation time satisfying the exponential distribution, M is the moving time M i The parameters to be set.

5. The node real-time scheduling method based on normal distribution according to claim 4 is characterized in that: The second average deadline satisfaction ratio is: Among them, λ is the parameter of the relaxation time satisfying the exponential distribution, M is the moving time M i The parameters set, E is the execution time E i The parameters to be set.

6. The node real-time scheduling method based on normal distribution according to claim 5 is characterized in that: The deadline meeting ratio is:

7. A node real-time scheduling system based on normal distribution, characterized in that: include: The node initialization module is used to obtain the environment parameters of the node to be scheduled and set the scheduling node and scheduling parameters; A first scheduling task calculation unit, configured to calculate, based on the first scheduling state, a maximum deadline satisfaction ratio and a first average deadline satisfaction ratio of the first scheduling task satisfying a normal distribution; A second scheduling task calculation unit, configured to calculate, based on a second scheduling state, a second average deadline satisfaction ratio of the second scheduling task after the first scheduling task is executed, which satisfies a normal distribution; A deadline satisfaction ratio calculation unit, configured to obtain a deadline satisfaction ratio of a conflict between a first scheduling task and a second scheduling task according to the first average deadline satisfaction ratio, the second average deadline satisfaction ratio and the deadline satisfaction ratio, so as to obtain a deadline satisfaction ratio of each scheduling node; The scheduling node selection unit is used to select a node that meets the scheduling condition from the deadline satisfaction ratio of each scheduling node as a node to be scheduled.

8. The node real-time scheduling system based on normal distribution according to claim 7, characterized in that: The environmental parameters of the node to be scheduled include: one or more of actual network transmission delay, data packets, throughput, bandwidth utilization, node load and link quality; the scheduling parameters include relaxation time, execution time and migration time.

9. The node real-time scheduling system based on normal distribution according to claim 7, characterized in that: The first scheduling state is a state in which the first scheduling task in the current scheduling node does not conflict with other tasks when executing; The second scheduling state is a state in which the current scheduling node arrives at the first scheduling task just before the second scheduling task and the first scheduling task has no conflict during execution.

10. The node real-time scheduling system based on normal distribution according to claim 7, characterized in that: The maximum deadline satisfaction ratio is: DM A (λ,M)=e -λM , The first average deadline satisfaction ratio is: Among them, λ is the parameter of the relaxation time satisfying the exponential distribution, M is the moving time M i The parameters to be set; The second average deadline satisfaction ratio is: Among them, λ is the parameter of the relaxation time satisfying the exponential distribution, M is the moving time M i The parameters set, E is the execution time E i The parameters to be set; Correspondingly, the deadline satisfaction ratio is:

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