CN111694652A - Task dynamic scheduling method and device, computer equipment and storage medium - Google Patents
Task dynamic scheduling method and device, computer equipment and storage medium Download PDFInfo
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- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
- G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
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Abstract
The invention relates to cloud service, and provides a method and a device for dynamically scheduling tasks, computer equipment and a storage medium, wherein the method for dynamically scheduling the tasks comprises the following steps: if the obtained system pressure value is larger than a preset threshold value, obtaining an execution task in the target system and an influence factor corresponding to the execution task; performing abnormal judgment on the executed task, and extracting a normal task; calculating the real-time priority corresponding to each normal task by using the influence factors; performing real-time scheduling processing on each normal task according to the real-time priority and a preset scheduling scheme, and acquiring a target pressure value; if the target pressure value is smaller than the preset threshold value, finishing the scheduling processing of each normal task; otherwise, real-time scheduling processing is carried out according to the adjusting scheme sent by the monitoring end until the target pressure value is smaller than the preset threshold value. The invention also relates to a blockchain technique, the execution tasks being storable in a blockchain. The technical scheme of the invention realizes the improvement of the accuracy of real-time scheduling of the executed tasks.
Description
Technical Field
The invention relates to the technical field of cloud services, in particular to a task dynamic scheduling method and device, computer equipment and a storage medium.
Background
With the increase of the service time, the data information of the database in the system is continuously expanded, the information acquisition requirement for the database is increasingly increased, when the system is normal and the request amount is not large, the data acquisition of multiple dimensions does not cause too much pressure on the system, but under the condition of large request amount, the load of the system is increased, and the availability and the health state of the system are influenced.
The existing method for scheduling tasks of a system under the condition of abnormal load mainly schedules the tasks according to preset priorities of the tasks, the measurement of the priorities usually only depends on the urgency degree and the fixed priorities, and the accuracy of task scheduling is not high due to the fact that the priorities of the tasks change in different degrees along with the change of time, so that the processing speed of the system is reduced, the system is crashed, and the working efficiency of a user is influenced.
Disclosure of Invention
The embodiment of the invention provides a task dynamic scheduling method and device, computer equipment and a storage medium, and aims to solve the problems that the accuracy of a traditional task scheduling mode is low and the working efficiency of a user is influenced.
A task dynamic scheduling method comprises the following steps:
acquiring a system pressure value of a target system;
comparing the system pressure value with a preset threshold value, and if the system pressure value is greater than the preset threshold value, acquiring all executed tasks in the target system and influence factors corresponding to the executed tasks, wherein the executed tasks comprise the maximum execution time;
for each execution task, carrying out abnormal judgment on the execution task according to the maximum execution time, and extracting a normal task;
performing real-time priority calculation on the normal tasks by using the influence factors to obtain a real-time priority corresponding to each normal task;
performing real-time scheduling processing on each normal task according to the real-time priority and a preset scheduling scheme, and re-acquiring a system pressure value of the target system as a target pressure value after a preset time interval;
comparing the target pressure value with a preset threshold value, and finishing real-time scheduling processing of the normal task if the target pressure value is smaller than the preset threshold value;
and if the target pressure value is greater than or equal to a preset threshold value, sending a preset scheduling scheme to a monitoring end, and acquiring an adjusting scheme sent by the monitoring end to perform real-time scheduling processing on the normal task until the target pressure value is less than the preset threshold value.
A dynamic task scheduler comprising:
the first acquisition module is used for acquiring a system pressure value of a target system;
the second obtaining module is used for comparing the system pressure value with a preset threshold value, and if the system pressure value is greater than the preset threshold value, obtaining all executed tasks in the target system and influence factors corresponding to the executed tasks, wherein the executed tasks comprise the maximum execution time;
the abnormal judgment module is used for carrying out abnormal judgment on the execution tasks according to the maximum execution time and extracting normal tasks aiming at each execution task;
the computing module is used for carrying out real-time priority computing on the normal tasks by utilizing the influence factors to obtain the real-time priority corresponding to each normal task;
the scheduling module is used for performing real-time scheduling processing on each normal task according to the real-time priority and a preset scheduling scheme, and acquiring a system pressure value of the target system again as a target pressure value after a preset time interval;
the first scheduling completion module is used for comparing the target pressure value with a preset threshold value, and if the target pressure value is smaller than the preset threshold value, completing the real-time scheduling processing of the normal task;
and the second scheduling completion module is used for sending a preset scheduling scheme to the monitoring end if the target pressure value is greater than or equal to a preset threshold value, and acquiring an adjustment scheme sent by the monitoring end to perform real-time scheduling processing on the normal task until the target pressure value is smaller than the preset threshold value.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the above task dynamic scheduling method when executing the computer program.
A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned method for dynamically scheduling tasks.
According to the task dynamic scheduling method, the task dynamic scheduling device, the computer equipment and the storage medium, when the system pressure value of the target system is greater than a preset threshold value, all executed tasks in the target system and the influence factors corresponding to the executed tasks are obtained; performing abnormal judgment on the execution task according to the maximum execution time contained in the execution task, and extracting a normal task; then, carrying out real-time priority calculation on the normal tasks by utilizing the influence factors to obtain the real-time priority corresponding to each normal task; performing real-time scheduling processing on each normal task according to the real-time priority and a preset scheduling scheme, and re-acquiring a system pressure value of a target system as a target pressure value after a preset time interval; if the target pressure value is smaller than the preset threshold value, finishing the real-time scheduling processing of the normal task; and if the target pressure value is greater than or equal to the preset threshold value, sending the preset scheduling scheme to the monitoring end, and acquiring an adjusting scheme sent by the monitoring end to perform real-time scheduling processing on the normal task until the target pressure value is less than the preset threshold value. Whether the target system is abnormal or not can be identified by identifying the pressure value of the system, so that real-time scheduling processing can be performed in time later, and the stability of the target system is ensured; the method for judging the abnormity of the executed task in the real-time scheduling process can effectively filter the abnormal task, avoid the abnormal condition of the target system caused by the intervention of the abnormal task in the subsequent scheduling processing process, and thus can improve the processing efficiency and the safety of the target system; by calculating the real-time priority corresponding to each normal task, the importance of the normal tasks can be analyzed in real time, the priority processing of the normal tasks with high importance is facilitated, and the accuracy of the subsequent real-time scheduling processing is improved; and under the condition that the target pressure value is still abnormal after scheduling processing, the current preset scheduling scheme is fed back to the monitoring end for analysis, so that the monitoring end can be helped to know the actual condition of the target system, and the accuracy of the adjustment scheme fed back by the monitoring end is improved. Therefore, under the condition that the load of the target system is abnormal, the accuracy of real-time scheduling of the executed task is improved, the target system is prevented from being broken down, and the working efficiency of a user is further improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a flowchart of a method for dynamically scheduling tasks according to an embodiment of the present invention;
fig. 2 is a flowchart of step S3 in the method for dynamically scheduling tasks according to the embodiment of the present invention;
fig. 3 is a flowchart of step S34 in the method for dynamically scheduling tasks according to the embodiment of the present invention;
fig. 4 is a flowchart of step S5 in the method for dynamically scheduling tasks according to the embodiment of the present invention;
fig. 5 is a flowchart illustrating adjusting the running state of a normal task in a task dynamic scheduling method according to an embodiment of the present invention;
FIG. 6 is a diagram of a dynamic task scheduler according to an embodiment of the present invention;
fig. 7 is a block diagram of a basic mechanism of a computer device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The task dynamic scheduling method is applied to the server side, and the server side can be specifically realized by an independent server or a server cluster consisting of a plurality of servers. In an embodiment, as shown in fig. 1, a method for dynamically scheduling tasks is provided, which includes the following steps:
s1: and acquiring a system pressure value of the target system.
In the embodiment of the present invention, the system pressure value is used for representing a load condition corresponding to the target system, and the system pressure value is proportional to the load condition corresponding to the target system, that is, the larger the system pressure value is, the heavier the load condition corresponding to the target system is.
The current system pressure value of the target system is directly obtained from a preset log table, wherein the preset log table is a data table specially used for recording the system pressure value of the target system in real time.
S2: and comparing the system pressure value with a preset threshold, and if the system pressure value is greater than the preset threshold, acquiring all executed tasks in the target system and influence factors corresponding to the executed tasks, wherein the executed tasks comprise the maximum execution time.
In the embodiment of the present invention, the system pressure value is compared with the preset threshold, and if the system pressure value is greater than the preset threshold, it indicates that the current load condition of the target system reaches the upper limit value set by the user, the execution tasks existing in the target system need to be scheduled, and all current execution tasks of the target system and the influence factors corresponding to each execution task are obtained from the preset database.
The preset database is a database which is specially used for storing the executing tasks currently executed by the target system and the influence factors corresponding to each executing task.
It is emphasized that the executive tasks may also be stored in a node of a blockchain in order to further ensure privacy and security of the executive tasks.
S3: and aiming at each execution task, carrying out abnormal judgment on the execution task according to the maximum execution time, and extracting a normal task.
In the embodiment of the invention, the maximum execution time indicates that the execution task needs to be completed within the time, otherwise, the execution task is considered to be failed. And screening abnormal execution tasks according to the maximum execution time and preset screening requirements aiming at each execution task, and reserving normal execution tasks as normal tasks.
The preset screening requirement refers to a filtering rule for performing exception judgment on the executed task by combining the maximum execution time.
It should be noted that, by extracting the normal tasks, the abnormal tasks can be effectively filtered, the situation that the target system is abnormal due to the intervention of the abnormal tasks in the subsequent scheduling processing process is avoided, the processing efficiency of the target system is affected, and the safety of the target system can be effectively ensured.
S4: and performing real-time priority calculation on the normal tasks by using the influence factors to obtain the real-time priority corresponding to each normal task.
Specifically, the influence factor corresponding to each normal task is imported into a preset calculation port, and after the preset calculation port detects the influence factor, the preset calculation port calculates the real-time priority corresponding to the influence factor by using a preset real-time priority calculation function, that is, the real-time priority corresponding to the normal task.
The preset calculation port refers to a processing port which is preset by a user and used for calculating the real-time priority, and the port comprises a real-time priority calculation function which is preset by the user.
S5: and performing real-time scheduling processing on each normal task according to the real-time priority and a preset scheduling scheme, and re-acquiring a system pressure value of the target system as a target pressure value after a preset time interval.
In the embodiment of the present invention, the preset scheduling scheme refers to that a user presets a scheduling rule for scheduling tasks in combination with real-time priorities. The preset time interval refers to a time interval set according to actual needs of a user, and specifically may be 1 hour or half an hour, which is not limited herein.
Specifically, each normal task is scheduled in real time according to the real-time priority and a preset scheduling scheme, and after a preset time interval, the system pressure value of the target system is obtained from the preset log table again to serve as the target pressure value.
S6: and comparing the target pressure value with a preset threshold value, and finishing real-time scheduling processing on the normal task if the target pressure value is smaller than the preset threshold value.
Specifically, the target pressure value is compared with a preset threshold, and if the target pressure value is smaller than the preset threshold, it indicates that the real-time scheduling processing mode for the normal tasks of the target system can effectively reduce the system load, i.e., complete the scheduling processing of each normal task.
S7: and if the target pressure value is greater than or equal to the preset threshold value, sending the preset scheduling scheme to the monitoring end, and acquiring an adjusting scheme sent by the monitoring end to perform real-time scheduling processing on the normal task until the target pressure value is less than the preset threshold value.
In the embodiment of the present invention, the monitoring end refers to a processing port that is dedicated to analyzing the preset scheduling scheme and outputting the adjustment scheme according to the analysis result when the target pressure value is greater than or equal to the preset threshold value.
Specifically, if the target pressure value is greater than or equal to the preset threshold value, it indicates that the scheduling processing scheme executed for the target system cannot effectively reduce the system load, and sends the preset scheduling scheme for the normal task to the monitoring end according to the preset sending mode, after receiving the information, the monitoring end may analyze the actual situation and feed back a new adjustment scheme to the service end again, and when receiving the adjustment scheme fed back by the monitoring end, the normal task is scheduled according to the adjustment scheme until the target pressure value is smaller than the preset threshold value.
The preset sending mode may specifically be in the form of a mail, and is not limited herein.
In the embodiment, when the system pressure value of the target system is greater than the preset threshold, all executed tasks in the target system and the influence factors corresponding to the executed tasks are obtained; performing abnormal judgment on the execution task according to the maximum execution time contained in the execution task, and extracting a normal task; then, carrying out real-time priority calculation on the normal tasks by utilizing the influence factors to obtain the real-time priority corresponding to each normal task; performing real-time scheduling processing on each normal task according to the real-time priority and a preset scheduling scheme, and re-acquiring a system pressure value of a target system as a target pressure value after a preset time interval; if the target pressure value is smaller than the preset threshold value, finishing the real-time scheduling processing of the normal task; and if the target pressure value is greater than or equal to the preset threshold value, sending the preset scheduling scheme to the monitoring end, and acquiring an adjusting scheme sent by the monitoring end to perform real-time scheduling processing on the normal task until the target pressure value is less than the preset threshold value. Whether the target system is abnormal or not can be identified by identifying the pressure value of the system, so that real-time scheduling processing can be performed in time later, and the stability of the target system is ensured; the method for judging the abnormity of the executed task in the real-time scheduling process can effectively filter the abnormal task, avoid the abnormal condition of the target system caused by the intervention of the abnormal task in the subsequent scheduling processing process, and thus can improve the processing efficiency and the safety of the target system; by calculating the real-time priority corresponding to each normal task, the importance of the normal tasks can be analyzed in real time, the priority processing of the normal tasks with high importance is facilitated, and the accuracy of the subsequent real-time scheduling processing is improved; and under the condition that the target pressure value is still abnormal after scheduling processing, the current preset scheduling scheme is fed back to the monitoring end for analysis, so that the monitoring end can be helped to know the actual condition of the target system, and the accuracy of the adjustment scheme fed back by the monitoring end is improved. Therefore, under the condition that the load of the target system is abnormal, the accuracy of real-time scheduling of the executed task is improved, the target system is prevented from being broken down, and the working efficiency of a user is further improved.
In an embodiment, the executed tasks are stored in the block chain, as shown in fig. 2, in step S3, that is, for each executed task, the executed task is determined to be abnormal according to the maximum execution time, and the extracting the normal task includes the following steps:
s31: and acquiring historical execution time corresponding to the executed task from a preset historical library aiming at each executed task.
In the embodiment of the invention, each execution task has the corresponding task id, the task id is compared with the historical id in the preset historical library, and if the task id is the same as the historical id, the execution time corresponding to the historical id is obtained and used as the historical execution time of the execution task corresponding to the task id.
The preset history library is a database which is specially used for storing different history ids and execution time corresponding to the history ids, and the history id which is the same as the task id must exist.
S32: based on historical execution time and maximum execution time, abnormal execution tasks are removed through a Lauda criterion, and normal execution tasks are kept as initial tasks.
In the embodiment of the invention, the Layouda criterion is that a group of detection data is supposed to only contain random errors, the detection data is calculated to obtain a standard deviation, an interval is determined according to a certain probability, and if the error exceeding the interval is considered to be not the random error but a coarse error, the data containing the error is rejected.
Specifically, the abnormal execution task is screened out according to the formula (1) and is used as the abnormal task, the abnormal task is eliminated, and the normal execution task is reserved and is used as the initial task.
Equation (1) of | vb | ═ xb-x | >3 σ
Wherein vb is an error, xb is a maximum execution time, x is a historical execution time, and σ is a preset standard deviation.
And if vb is larger than 3 sigma, indicating that the execution task corresponding to the error is an abnormal task.
S33: and acquiring the current execution state and the estimated execution state of the initial task.
In the embodiment of the invention, the current execution state and the current execution time of the initial task are obtained according to the task id contained in the initial task, all the execution states corresponding to the successfully matched basic id and the execution time corresponding to each execution state are obtained by using a mode that the task id is matched with the basic id in a preset pre-estimation library, and the execution state corresponding to the execution time which is the same as the current execution time is selected from all the execution states as the pre-estimation execution state according to the current execution time.
The preset pre-estimation library is a database which is specially used for storing the basic id, all execution states corresponding to the basic id and execution time corresponding to each execution state.
S34: and comparing the current execution state with the estimated execution state, and determining a normal task according to the comparison result.
Specifically, the current execution state and the estimated execution state obtained in step S33 are compared, and a normal task is determined according to the comparison result and the preset rule. The preset rule refers to a rule that a user presets a normal task according to different comparison results.
In the embodiment, by screening the initial task from the executed tasks according to the historical execution time and the maximum execution time, part of data which do not meet the standard set by the user can be effectively removed, the subsequent calculation of redundant data is avoided, and the calculation efficiency of the target system is improved; and normal tasks are screened from the initial tasks according to the current execution state and the estimated execution state, abnormal tasks can be effectively filtered, the condition that the target system is abnormal due to the intervention of the abnormal tasks in the subsequent scheduling processing process is avoided, and therefore the processing efficiency and the safety of the target system can be improved.
In one embodiment, as shown in fig. 3, the step S34 of comparing the current execution state with the predicted execution state and determining the normal task according to the comparison result includes the following steps:
s341: the current execution state is compared with the estimated execution state.
Specifically, the current execution state and the estimated execution state are compared.
S342: and if the current execution state is the same as the estimated execution state, determining the initial task corresponding to the current execution state as a normal task, wherein the normal task comprises task priority.
In the embodiment of the present invention, according to the comparison method in step S341, if the current execution state is the same as the estimated execution state, the initial task corresponding to the current execution state is determined as a normal task, and the normal task includes a task priority.
The task priority is used to indicate the order of execution of the tasks.
S343: and if the current execution state is different from the estimated execution state, identifying whether the current execution state is a delay state, wherein the current execution state comprises the delay state.
In the embodiment of the present invention, according to the comparison method in step S341, if the current execution state is different from the estimated execution state, it is identified whether the current execution state is the delayed state.
S344: and improving the task priority corresponding to the initial task in the delay state, and determining the initial task with the improved task priority as a normal task.
Specifically, the task priority corresponding to the initial task in the delay state is increased according to a preset increase standard, and the initial task with the increased task priority is determined as a normal task.
It should be noted that, if the current execution state is delayed, it indicates that the initial task corresponding to the current execution state is due to insufficient system resources or an abnormality in other situations, and therefore an early warning needs to be issued to increase the priority of the initial task, so as to prevent the starvation phenomenon.
In the embodiment, the normal task can be rapidly and accurately distinguished by comparing the current execution state with the estimated execution state; under the condition that the current execution state is different from the estimated execution state, the current execution state is subjected to secondary identification, so that inaccuracy of first identification caused by errors can be avoided, and the accuracy of normal task acquisition is improved; and finally, the task priority corresponding to the initial task in the delay state is improved and determined as a normal task, and early warning can be effectively sent to improve the priority, so that the starvation phenomenon is prevented, the accuracy of acquiring the normal task is ensured, and the accuracy of subsequent real-time scheduling processing is improved.
In an embodiment, the impact factors include an urgency degree, an execution value, and a balance factor, and the step S4 of performing real-time priority calculation on the normal tasks by using the impact factors to obtain a real-time priority corresponding to each normal task includes the following steps:
s41: calculating the real-time priority corresponding to each normal task according to the formula (2):
p ═ ds ═ (1-a) + wr ═ a formula (2)
Where p is the real-time priority, ds is the urgency, a is the equalization factor, and wr is the execution value.
Specifically, the real-time priority corresponding to each normal task is utilized in formula (2).
Further, the urgency ds in the formula (2) can be calculated by the formula (3), and the execution value wr can be calculated by the formula (4).
Wherein, tsMaximum execution time for normal task, (d)iSystemtime) is a preset absolute deadline for a normal task.
The preset absolute deadline is that the normal task must produce a meaningful result within the deadline, otherwise the normal task is considered to fail to execute.
In addition, ds and tsThe real-time priority is higher, and the real-time priority is higher, so that the real-time priority is higher, and the real-time priority is higher; ds and diThe system mtime is inversely proportional, and the smaller the preset absolute deadline corresponding to the normal task is, the higher the real-time priority corresponding to the normal task is.
Preferably, ds is in the range of (0, 1).
Wherein e is the base number of the natural logarithm, u is a preset attenuation coefficient corresponding to the normal task, t is the difference value between the current execution time and the maximum execution time of the normal task, and d is a preset absolute deadline.
It should be noted that the preset attenuation coefficient may be used to control the real-time priority corresponding to the normal task, and under the condition that the preset attenuation coefficients are different, different attenuation speeds may exist, and if the preset absolute deadline is 500, the different preset attenuation coefficients will classify the normal task into 3 categories, which are respectively a critical type, a sensitive type, and an insensitive type.
Wherein, the value range of the critical u is as follows: u is more than or equal to 4 and less than or equal to 6, which indicates that the normal tasks have mandatory requirements on the preset absolute deadline, the gains are good as long as the tasks are completed before the preset absolute deadline, and the tasks are quickly attenuated once the preset absolute deadline is exceeded.
The value range of the sensitive u is as follows: u is more than or equal to 0.01 and less than or equal to 1, which indicates that the normal tasks are sensitive to the completion time and the corresponding benefits are different along with the different completion time.
The value range of the insensitive u is as follows: and u is 0, which means that the requirement of the normal task for the preset absolute deadline is relatively loose, and the profit value is basically fixed.
In this embodiment, the real-time priority corresponding to each normal task can be quickly and accurately calculated through the formula (2), and the accuracy and the processing efficiency of performing real-time scheduling processing on the normal tasks according to the real-time priority in the following process are improved.
In an embodiment, the step S5, namely, performing real-time scheduling processing on each normal task according to the real-time priority and the preset scheduling scheme, and re-acquiring the system pressure value of the target system as the target pressure value after the preset time interval includes the following steps:
s51: and comparing the resource residual value contained in each execution platform with a preset normal value.
In the embodiment of the present invention, since the target system includes k execution platforms for executing the normal task, and each execution platform includes the resource remaining value, the resource remaining value included in each execution platform is compared with the preset normal value.
S52: and if the resource residual value is larger than or equal to the preset normal value, selecting the execution platform with the maximum resource residual value as the target platform.
In the embodiment of the present invention, according to the comparison method in step S51, if the resource remaining value is greater than or equal to the preset normal value, the resource remaining values of each execution platform are compared, and the execution platform with the largest resource remaining value is selected as the target platform.
The resource residual value is used for representing the current load condition of the execution platform, and the resource residual value is inversely proportional to the current load condition of the execution platform, namely the larger the resource residual value is, the lighter the current load condition of the execution platform is.
The target platform is a dedicated platform for executing normal tasks, and when the target platform is selected, an execution platform with a large resource remaining value is preferably selected in order to balance the use of existing resources.
In addition, if the execution platform resource is saved, an execution platform with a heavy load condition, that is, an execution platform with a small resource residual value, may be selected as the target platform.
S53: and if the resource residual value is smaller than the preset normal value, sending all the execution platforms and the resource residual value of each execution platform to an audit user.
In the embodiment of the present invention, according to the comparison method in step S51, if the resource remaining value is smaller than the preset normal value, all the execution platforms and the resource remaining value of each execution platform are sent to the auditing user.
S54: and acquiring an execution platform for auditing the feedback of the user as a target platform.
In the embodiment of the invention, the auditing user refers to a user who specially analyzes the execution platforms and the resource residual values of each execution platform, when the auditing user receives the resource residual values of the execution platforms and each execution platform, the auditing user analyzes according to actual conditions, selects one execution platform from the analysis platforms for feedback, and when the execution platform which is fed back by the auditing user is obtained, the execution platform is used as a target platform.
S55: and scheduling all normal tasks on the target platform according to the real-time priority sequence, and re-acquiring the system pressure value of the target system as the target pressure value after the preset time interval.
Specifically, all the normal tasks are sequenced according to the real-time priority sequence, and are scheduled one by one on the target platform according to the sequencing, that is, after one normal task is executed, a second normal task is executed, and after a preset time interval, the system pressure value of the target system is obtained from the preset log table again to serve as the target pressure value.
Further, when the real-time priorities are the same, the normal task with the earlier execution time is scheduled preferentially.
It should be noted that, if the continuous scheduling of the normal task on the target platform fails, the preset execution platform is reselected as the target platform for scheduling, so as to improve the robustness of the system.
In this embodiment, a suitable target platform can be selected according to analysis under different conditions by comparing the resource residual value contained in each execution platform with a preset normal value, so that a normal task can be stably operated on the target platform, and the condition that real-time scheduling processing fails due to resource congestion is avoided, thereby effectively improving the accuracy of real-time scheduling processing, avoiding the situation that a target system collapses, and further improving the working efficiency of a user.
In an embodiment, the normal task includes a running state, and the running state includes an original state and a scheduled state, as shown in fig. 5, after step S6, the method for dynamically scheduling a task further includes:
s8: and if the target pressure value is smaller than the preset restart value, acquiring the running states of all the current normal tasks of the target system from a preset state library.
In the embodiment of the invention, if the target pressure value is smaller than the preset restart value, the running states of all the current normal tasks of the target system are obtained from the preset state library. The preset restart value is a numerical value for restoring the original state of the normal task according to the actual requirement of a user.
The preset state library is a database specially used for storing the current normal task of the target system and the running state corresponding to the normal task, wherein the running state comprises an original state and a scheduling state.
S9: and if the running state is detected to be the scheduling state, adjusting the running state of the normal task corresponding to the scheduling state to be the original state.
Specifically, if the running state is detected to be the scheduling state, the running state of the normal task corresponding to the scheduling state is adjusted to be the original state.
In this embodiment, under the condition that the target pressure value is smaller than the preset restart value, the running states of all the current normal tasks of the target system are obtained, and the running states of the normal tasks corresponding to the scheduling state are adjusted to the original state. By adjusting the running state, the target system can restore the original running state of the normal task after the load condition is improved, and the processing efficiency of the normal task is ensured, so that the working efficiency of a user is ensured.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
In an embodiment, a dynamic task scheduling device is provided, where the dynamic task scheduling device corresponds to the dynamic task scheduling method in the foregoing embodiment one to one. As shown in fig. 6, the dynamic task scheduler includes a first obtaining module 61, a second obtaining module 62, an exception determining module 63, a calculating module 64, a scheduling module 65, a first scheduling completion module 66 and a second scheduling completion module 67. The functional modules are explained in detail as follows:
a first obtaining module 61, configured to obtain a system pressure value of a target system;
the second obtaining module 62 is configured to compare the system pressure value with a preset threshold, and if the system pressure value is greater than the preset threshold, obtain all executed tasks in the target system and influence factors corresponding to the executed tasks, where the executed tasks include a maximum execution time; it should be emphasized that, in order to further ensure the privacy and security of the executive task, the executive task may also be stored in a node of a block chain;
an exception judgment module 63, configured to perform exception judgment on the execution task according to the maximum execution time for each execution task, and extract a normal task;
the calculating module 64 is configured to perform real-time priority calculation on the normal tasks by using the impact factors to obtain a real-time priority corresponding to each normal task;
the scheduling module 65 is configured to perform real-time scheduling processing on each normal task according to the real-time priority and a preset scheduling scheme, and based on a preset time interval, reacquire a system pressure value of the target system as a target pressure value;
the first scheduling completion module 66 is configured to compare the target pressure value with a preset threshold, and if the target pressure value is smaller than the preset threshold, complete real-time scheduling processing on the normal task;
and the second scheduling completion module 67 is configured to send the preset scheduling scheme to the monitoring terminal if the target pressure value is greater than or equal to the preset threshold, and obtain an adjustment scheme sent by the monitoring terminal to perform real-time scheduling processing on the normal task until the target pressure value is less than the preset threshold.
Further, the abnormality determining module 63 includes:
the third obtaining sub-module is used for obtaining historical execution time corresponding to the executed task from a preset historical library aiming at each executed task;
the initial task determination submodule is used for eliminating abnormal execution tasks through a Lauda criterion based on historical execution time and maximum execution time, and keeping normal execution tasks as initial tasks;
the fourth obtaining submodule is used for obtaining the current execution state and the estimated execution state of the initial task;
and the comparison submodule is used for comparing the current execution state with the estimated execution state and determining a normal task according to the comparison result.
Further, the comparison submodule includes:
the state comparison unit is used for comparing the current execution state with the estimated execution state;
the state comparison identical unit is used for determining the initial task corresponding to the current execution state as a normal task if the current execution state is identical to the estimated execution state, and the normal task comprises task priority;
the state comparison difference unit is used for identifying whether the current execution state is a delay state or not if the current execution state is different from the estimated execution state, wherein the current execution state comprises the delay state;
and the normal task determining unit is used for improving the task priority corresponding to the initial task in the delay state and determining the initial task with the improved task priority as the normal task.
Further, the calculation module 64 includes:
the real-time priority calculating submodule is used for calculating the real-time priority corresponding to each normal task according to a formula (2):
p ═ ds ═ (1-a) + wr ═ a formula (2)
Where p is the real-time priority, ds is the urgency, a is the equalization factor, and wr is the execution value.
Further, the scheduling module 65 includes:
the numerical value comparison submodule is used for comparing the resource residual value contained in each execution platform with a preset normal value;
the first numerical value comparison submodule is used for selecting the execution platform with the largest resource residual value as a target platform if the resource residual value is greater than or equal to a preset normal value;
the second numerical value comparison submodule is used for sending the resource residual values of all the execution platforms and each execution platform to an auditing user if the resource residual values are smaller than a preset normal value;
a fifth obtaining sub-module, configured to obtain an execution platform fed back by the audit user as a target platform;
and the target platform scheduling submodule is used for scheduling all the normal tasks on the target platform according to the real-time priority sequence, and acquiring the system pressure value of the target system again as the target pressure value after the preset time interval.
Further, the task dynamic scheduling device further includes:
the running state acquisition module is used for acquiring the running states of all current normal tasks of the target system from the preset state library if the target pressure value is smaller than the preset restart value;
and the running state adjusting module is used for adjusting the running state of the normal task corresponding to the scheduling state to be the original state if the running state is detected to be the scheduling state.
Some embodiments of the present application disclose a computer device. Referring specifically to fig. 7, a basic structure block diagram of a computer device 90 according to an embodiment of the present application is shown.
As illustrated in fig. 7, the computer device 90 includes a memory 91, a processor 92, and a network interface 93 communicatively connected to each other through a system bus. It is noted that only a computer device 90 having components 91-93 is shown in FIG. 7, but it is understood that not all of the illustrated components are required to be implemented, and that more or fewer components may alternatively be implemented. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 91 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the storage 91 may be an internal storage unit of the computer device 90, such as a hard disk or a memory of the computer device 90. In other embodiments, the memory 91 may also be an external storage device of the computer device 90, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device 90. Of course, the memory 91 may also include both internal and external memory units of the computer device 90. In this embodiment, the memory 91 is generally used for storing an operating system installed in the computer device 90 and various types of application software, such as program codes of the task dynamic scheduling method. Further, the memory 91 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 92 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 92 is typically used to control the overall operation of the computer device 90. In this embodiment, the processor 92 is configured to execute the program code stored in the memory 91 or process data, for example, execute the program code of the task dynamic scheduling method.
The network interface 93 may include a wireless network interface or a wired network interface, and the network interface 93 is generally used to establish a communication connection between the computer device 90 and other electronic devices.
The present application further provides another embodiment, which is to provide a computer-readable storage medium storing an executive task information entry program, where the executive task information entry program is executable by at least one processor to cause the at least one processor to perform the steps of any one of the above-mentioned methods for dynamically scheduling tasks.
It is emphasized that the executive tasks may also be stored in a node of a blockchain in order to further ensure privacy and security of the executive tasks
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a computer device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Finally, it should be noted that the above-mentioned embodiments illustrate only some of the embodiments of the present application, and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.
Claims (10)
1. A task dynamic scheduling method is characterized by comprising the following steps:
acquiring a system pressure value of a target system;
comparing the system pressure value with a preset threshold value, and if the system pressure value is greater than the preset threshold value, acquiring all executed tasks in the target system and influence factors corresponding to the executed tasks, wherein the executed tasks comprise the maximum execution time;
for each execution task, carrying out abnormal judgment on the execution task according to the maximum execution time, and extracting a normal task;
performing real-time priority calculation on the normal tasks by using the influence factors to obtain a real-time priority corresponding to each normal task;
performing real-time scheduling processing on each normal task according to the real-time priority and a preset scheduling scheme, and re-acquiring a system pressure value of the target system as a target pressure value after a preset time interval;
comparing the target pressure value with a preset threshold value, and finishing real-time scheduling processing of the normal task if the target pressure value is smaller than the preset threshold value;
and if the target pressure value is greater than or equal to a preset threshold value, sending a preset scheduling scheme to a monitoring end, and acquiring an adjusting scheme sent by the monitoring end to perform real-time scheduling processing on the normal task until the target pressure value is less than the preset threshold value.
2. The method according to claim 1, wherein the execution tasks are storable in a block chain, and the step of extracting normal tasks, for each execution task, by performing an abnormal judgment on the execution task according to the maximum execution time includes:
acquiring historical execution time corresponding to the execution task from a preset historical library aiming at each execution task;
based on the historical execution time and the maximum execution time, rejecting abnormal execution tasks through a Lauda criterion, and keeping normal execution tasks as initial tasks;
acquiring the current execution state and the estimated execution state of the initial task;
and comparing the current execution state with the estimated execution state, and determining the normal task according to the comparison result.
3. The method for dynamically scheduling tasks according to claim 2, wherein the step of comparing the current execution state with the predicted execution state and determining the normal task according to the comparison result comprises:
comparing the current execution state with the estimated execution state;
if the current execution state is the same as the estimated execution state, determining an initial task corresponding to the current execution state as the normal task, wherein the normal task comprises a task priority;
if the current execution state is different from the estimated execution state, identifying whether the current execution state is a delay state, wherein the current execution state comprises the delay state;
and improving the task priority corresponding to the initial task in the delay state, and determining the initial task with the improved task priority as the normal task.
4. The method according to claim 1, wherein the impact factors include an urgency degree, an execution value, and a balance factor, and the step of calculating the real-time priority of the normal tasks by using the impact factors to obtain the real-time priority corresponding to each of the normal tasks comprises:
calculating the real-time priority corresponding to each normal task according to the following formula:
p＝ds*(1-a)+wr*a
wherein p is the real-time priority, ds is the urgency, a is the equalization factor, and wr is the execution value.
5. The method according to claim 1, wherein the target system includes k execution platforms for executing the normal tasks, and each of the execution platforms includes a resource residual value, where k is a positive integer greater than 1, and the step of performing real-time scheduling processing on each of the normal tasks according to the real-time priority and a preset scheduling scheme, and reacquiring a system pressure value of the target system as a target pressure value after a preset time interval includes:
comparing the resource residual value contained in each execution platform with a preset normal value;
if the resource residual value is larger than or equal to a preset normal value, selecting an execution platform with the largest resource residual value as a target platform;
if the resource residual value is smaller than a preset normal value, sending all the execution platforms and the resource residual value of each execution platform to an auditing user;
acquiring an execution platform fed back by the auditing user as the target platform;
and scheduling all the normal tasks on the target platform according to the real-time priority sequence, and re-acquiring the system pressure value of the target system as a target pressure value after a preset time interval.
6. The method according to claim 1, wherein the normal task includes a running state, the running state includes an original state and a scheduling state, the target pressure value is compared with a preset threshold, and if the target pressure value is smaller than the preset threshold, after the step of performing real-time scheduling processing on the normal task is completed, the method further includes:
if the target pressure value is smaller than a preset restart value, acquiring the running states of all the current normal tasks of the target system from a preset state library;
and if the running state is detected to be the dispatching state, adjusting the running state of the normal task corresponding to the dispatching state to be the original state.
7. A dynamic task scheduler, comprising:
the first acquisition module is used for acquiring a system pressure value of a target system;
the second obtaining module is used for comparing the system pressure value with a preset threshold value, and if the system pressure value is greater than the preset threshold value, obtaining all executed tasks in the target system and influence factors corresponding to the executed tasks, wherein the executed tasks comprise the maximum execution time;
the abnormal judgment module is used for carrying out abnormal judgment on the execution tasks according to the maximum execution time and extracting normal tasks aiming at each execution task;
the computing module is used for carrying out real-time priority computing on the normal tasks by utilizing the influence factors to obtain the real-time priority corresponding to each normal task;
the scheduling module is used for performing real-time scheduling processing on each normal task according to the real-time priority and a preset scheduling scheme, and acquiring a system pressure value of the target system again as a target pressure value after a preset time interval;
the first scheduling completion module is used for comparing the target pressure value with a preset threshold value, and if the target pressure value is smaller than the preset threshold value, completing the real-time scheduling processing of the normal task;
and the second scheduling completion module is used for sending a preset scheduling scheme to the monitoring end if the target pressure value is greater than or equal to a preset threshold value, and acquiring an adjustment scheme sent by the monitoring end to perform real-time scheduling processing on the normal task until the target pressure value is smaller than the preset threshold value.
8. The dynamic task scheduler of claim 7, wherein the exception determining module comprises:
a third obtaining sub-module, configured to obtain, for each executed task, a historical execution time corresponding to the executed task from a preset historical library;
an initial task determination submodule, configured to remove an abnormal execution task according to a lazy-uda criterion based on the historical execution time and the maximum execution time, and keep a normal execution task as an initial task;
the fourth obtaining submodule is used for obtaining the current execution state and the estimated execution state of the initial task;
and the comparison submodule is used for comparing the current execution state with the estimated execution state and determining the normal task according to the comparison result.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method for dynamically scheduling tasks according to any one of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for dynamically scheduling tasks according to any one of claims 1 to 6.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112565104A (en) * | 2020-12-01 | 2021-03-26 | 腾讯科技（深圳）有限公司 | Flow control method, device, medium and electronic equipment of block chain system |
| CN114047875A (en) * | 2021-10-25 | 2022-02-15 | 深圳市硅格半导体有限公司 | Command scheduling method, device, equipment and computer program product |
| CN116841717A (en) * | 2023-09-04 | 2023-10-03 | 成都太阳高科技有限责任公司 | Method and system for generating sequencing in real time according to task emergency degree |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140165070A1 (en) * | 2012-12-06 | 2014-06-12 | Hewlett-Packard Development Company, L.P. | Ranking and scheduling of monitoring tasks |
| CN106790726A (en) * | 2017-03-30 | 2017-05-31 | 电子科技大学 | A kind of priority query's dynamic feedback of load equilibrium resource regulating method based on Docker cloud platforms |
| CN106844027A (en) * | 2017-01-13 | 2017-06-13 | 广西电网有限责任公司电力科学研究院 | A kind of method for scheduling task based on node load |
| CN108881495A (en) * | 2018-08-13 | 2018-11-23 | 中国平安财产保险股份有限公司 | Resource allocation methods, device, computer equipment and storage medium |
-
2020
- 2020-06-24 CN CN202010591152.9A patent/CN111694652B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140165070A1 (en) * | 2012-12-06 | 2014-06-12 | Hewlett-Packard Development Company, L.P. | Ranking and scheduling of monitoring tasks |
| CN106844027A (en) * | 2017-01-13 | 2017-06-13 | 广西电网有限责任公司电力科学研究院 | A kind of method for scheduling task based on node load |
| CN106790726A (en) * | 2017-03-30 | 2017-05-31 | 电子科技大学 | A kind of priority query's dynamic feedback of load equilibrium resource regulating method based on Docker cloud platforms |
| CN108881495A (en) * | 2018-08-13 | 2018-11-23 | 中国平安财产保险股份有限公司 | Resource allocation methods, device, computer equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 程邺华: ""云平台的任务优先级调度研究"", 《中国优秀硕士学位论文全文数据库 (信息科技辑)》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112565104A (en) * | 2020-12-01 | 2021-03-26 | 腾讯科技（深圳）有限公司 | Flow control method, device, medium and electronic equipment of block chain system |
| CN114047875A (en) * | 2021-10-25 | 2022-02-15 | 深圳市硅格半导体有限公司 | Command scheduling method, device, equipment and computer program product |
| CN114047875B (en) * | 2021-10-25 | 2024-04-19 | 深圳市硅格半导体有限公司 | Command scheduling method, device, equipment and computer program product |
| CN116841717A (en) * | 2023-09-04 | 2023-10-03 | 成都太阳高科技有限责任公司 | Method and system for generating sequencing in real time according to task emergency degree |
| CN116841717B (en) * | 2023-09-04 | 2023-11-07 | 成都太阳高科技有限责任公司 | Method and system for generating sequencing in real time according to task emergency degree |
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