CN111694652B - Task dynamic scheduling method, device, computer equipment and storage medium - Google Patents
Task dynamic scheduling method, 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
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- 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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Abstract
The invention relates to cloud service and provides a task dynamic scheduling method, a device, computer equipment and a storage medium, wherein the task dynamic scheduling method comprises the following steps: if the obtained system pressure value is larger than a preset threshold value, obtaining an execution task and an influence factor corresponding to the execution task in the target system; performing exception judgment on the execution task, and extracting a normal task; calculating the real-time priority corresponding to each normal task by using the influence factors; carrying out real-time scheduling processing on each normal task according to the real-time priority and a preset scheduling scheme, and obtaining a target pressure value; if the target pressure value is smaller than the preset threshold value, completing the scheduling processing of each normal task; otherwise, carrying out real-time scheduling according to the adjustment scheme sent by the monitoring end until the target pressure value is smaller than the preset threshold value. The present invention also relates to blockchain techniques in which the execution tasks may be stored. The technical scheme of the invention realizes the improvement of the accuracy of real-time scheduling of the execution task.
Description
Technical Field
The present invention relates to the field of cloud services, and in particular, to a method and apparatus for dynamically scheduling tasks, a computer device, 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 quantity is not large, the data acquisition of multiple dimensions cannot cause too much pressure on the system, but under the condition of large request quantity, the load of the system is increased, and the availability and the health state of the system are affected.
The existing scheduling mode for the tasks under the condition of abnormal load of the system mainly schedules according to the preset priority of the tasks, the measurement of the priority is usually only dependent on the urgent degree and the fixed priority, and the task scheduling accuracy is not high due to the fact that the priority of the tasks changes to different degrees along with the change of time, so that the system processing speed is reduced, the system breakdown condition exists, and the working efficiency of a user is affected.
Disclosure of Invention
The embodiment of the invention provides a task dynamic scheduling method, a device, computer equipment and a storage medium, which are used for solving the problems that the accuracy of a traditional task scheduling mode is not high 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 larger than the preset threshold value, acquiring all execution tasks in the target system and influence factors corresponding to the execution tasks, wherein the execution tasks comprise maximum execution time;
for each execution task, performing exception judgment on the execution task according to the maximum execution time, and extracting a normal task;
carrying out real-time priority calculation on the normal tasks by using the influence factors to obtain real-time priorities corresponding to the normal tasks;
carrying out 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 based on a preset time interval;
comparing the target pressure value with a preset threshold value, and if the target pressure value is smaller than the preset threshold value, completing real-time scheduling processing of the normal task;
and if the target pressure value is greater than or equal to a preset threshold value, transmitting a preset scheduling scheme to a monitoring end, and acquiring an adjustment scheme transmitted 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 task dynamic scheduling device, comprising:
the first acquisition module is used for acquiring a system pressure value of the target system;
the second acquisition module is used for comparing the system pressure value with a preset threshold value, and acquiring all execution tasks in the target system and influence factors corresponding to the execution tasks if the system pressure value is larger than the preset threshold value, wherein the execution tasks comprise the maximum execution time;
the abnormality judgment module is used for carrying out abnormality judgment on the execution tasks according to the maximum execution time aiming at each execution task and extracting normal tasks;
the calculation module is used for calculating the real-time priority of 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 carrying out 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 based on 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 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 task dynamic scheduling method described above when the computer program is executed.
A computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the task dynamic scheduling method described above.
According to the task dynamic scheduling method, the device, the computer equipment and the storage medium, when the system pressure value of the target system is larger than the preset threshold value, all execution tasks in the target system and influence factors corresponding to the execution tasks are obtained; performing exception 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 using the influence factors to obtain the real-time priority corresponding to each normal task; carrying out 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 based on a preset time interval; if the target pressure value is smaller than the preset threshold value, completing real-time scheduling processing of the normal task; if the target pressure value is greater than or equal to the preset threshold value, the preset scheduling scheme is sent to the monitoring end, and the adjustment scheme sent by the monitoring end is obtained to perform real-time scheduling processing on the normal task until the target pressure value is smaller than the preset threshold value. Whether the target system is abnormal or not can be identified by means of identifying the system pressure value, so that real-time scheduling processing can be performed later in time, and the stability of the target system is ensured; the abnormal task can be effectively filtered in the mode of performing abnormal judgment on the execution task in the real-time scheduling process, the abnormal condition of the target system caused by the intervention of the abnormal task in the subsequent scheduling processing process is avoided, and therefore the processing efficiency and the safety of the target system can be improved; the importance of the normal tasks can be analyzed in real time by calculating the real-time priority corresponding to each normal task, so that 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 still processes the abnormality after the 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 feedback adjustment scheme of 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 execution task is improved, the occurrence of breakdown of the target system is avoided, 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 that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a task dynamic scheduling method provided by an embodiment of the present invention;
FIG. 2 is a flowchart of step S3 in a task dynamic scheduling method according to an embodiment of the present invention;
FIG. 3 is a flowchart of step S34 in a task dynamic scheduling method according to an embodiment of the present invention;
FIG. 4 is a flowchart of step S5 in the task dynamic scheduling method according to the embodiment of the present invention;
FIG. 5 is a flowchart for adjusting the running state of a normal task in the task dynamic scheduling method according to the embodiment of the present invention;
FIG. 6 is a schematic diagram of a task dynamic scheduling device 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 following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The task dynamic scheduling method is applied to the server, and the server can be realized by an independent server or a server cluster formed by a plurality of servers. In one embodiment, as shown in fig. 1, a method for dynamically scheduling tasks is provided, including the following steps:
s1: and acquiring a system pressure value of the target system.
In the embodiment of the invention, the system pressure value is used for representing the load condition corresponding to the target system, and the system pressure value is in direct proportion to the load condition corresponding to the target system, namely, 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: comparing the system pressure value with a preset threshold value, and if the system pressure value is larger than the preset threshold value, acquiring all execution tasks in the target system and influence factors corresponding to the execution tasks, wherein the execution tasks comprise the maximum execution time.
In the embodiment of the invention, the system pressure value is compared with the preset threshold value, if the system pressure value is larger than the preset threshold value, 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 are required to be scheduled, and all the current execution tasks of the target system and the influence factors corresponding to each execution task are acquired from the preset database.
The preset database is a database specially used for storing the execution tasks of the target system currently in execution and the influence factors corresponding to each execution task.
It should be emphasized that to further ensure the privacy and security of the execution tasks, the execution tasks may also be stored in a blockchain node.
S3: and for each execution task, performing 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 to execute. And screening out abnormal execution tasks according to the maximum execution time and a preset screening requirement aiming at each execution task, and reserving normal execution tasks as normal tasks.
The preset screening requirement refers to a filtering rule for carrying out abnormal judgment on the execution task in combination with the maximum execution time.
By extracting the normal task, the abnormal task can be effectively filtered, the abnormal condition of the target system caused by the intervention of the abnormal task 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 carrying out 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 factors corresponding to each normal task are imported to a preset computing port, and after the influence factors are detected, the preset computing port calculates the real-time priority corresponding to the influence factors by using a preset real-time priority computing function, namely the real-time priority corresponding to the normal task.
The preset computing port refers to a processing port preset by a user for computing real-time priority, and the port comprises a real-time priority computing function preset by the user.
S5: and carrying out 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 based on a preset time interval.
In the embodiment of the invention, the preset scheduling scheme refers to a scheduling rule that a user presets to schedule tasks in combination with real-time priority. The preset time interval is a time interval set according to the actual requirement of the user, and may specifically be 1 hour or half an hour, which is not limited herein.
Specifically, real-time scheduling processing is performed on each normal task 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 again from a preset log table to serve as a target pressure value.
S6: and comparing the target pressure value with a preset threshold value, and if the target pressure value is smaller than the preset threshold value, completing real-time scheduling processing of the normal task.
Specifically, comparing the target pressure value with a preset threshold, if the target pressure value is smaller than the preset threshold, the real-time scheduling processing mode of the normal tasks aiming at the target system can effectively reduce the system load, namely, the scheduling processing of each normal task is completed.
S7: if the target pressure value is greater than or equal to the preset threshold value, the preset scheduling scheme is sent to the monitoring end, and the adjustment scheme sent by the monitoring end is obtained to perform real-time scheduling processing on the normal task until the target pressure value is smaller than the preset threshold value.
In the embodiment of the invention, the monitoring end is a processing port which can analyze a preset scheduling scheme and output an adjustment scheme according to an analysis result under the condition that the target pressure value is larger than or equal to a preset threshold value.
Specifically, if the target pressure value is greater than or equal to the preset threshold, it means that the scheduling processing scheme for the execution of the target system cannot effectively reduce the system load, and the preset scheduling scheme for the normal task is sent to the monitoring end according to the preset sending mode, after receiving the information, the monitoring end can 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 scheduling processing is performed for the normal task according to the adjustment scheme until the target pressure value is less than the preset threshold.
The preset sending mode may specifically be in the form of mail, which is not limited herein.
In this embodiment, when a system pressure value of a target system is greater than a preset threshold value, all execution tasks and influence factors corresponding to the execution tasks in the target system are obtained; performing exception 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 using the influence factors to obtain the real-time priority corresponding to each normal task; carrying out 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 based on a preset time interval; if the target pressure value is smaller than the preset threshold value, completing real-time scheduling processing of the normal task; if the target pressure value is greater than or equal to the preset threshold value, the preset scheduling scheme is sent to the monitoring end, and the adjustment scheme sent by the monitoring end is obtained to perform real-time scheduling processing on the normal task until the target pressure value is smaller than the preset threshold value. Whether the target system is abnormal or not can be identified by means of identifying the system pressure value, so that real-time scheduling processing can be performed later in time, and the stability of the target system is ensured; the abnormal task can be effectively filtered in the mode of performing abnormal judgment on the execution task in the real-time scheduling process, the abnormal condition of the target system caused by the intervention of the abnormal task in the subsequent scheduling processing process is avoided, and therefore the processing efficiency and the safety of the target system can be improved; the importance of the normal tasks can be analyzed in real time by calculating the real-time priority corresponding to each normal task, so that 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 still processes the abnormality after the 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 feedback adjustment scheme of 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 execution task is improved, the occurrence of breakdown of the target system is avoided, and the working efficiency of a user is further improved.
In one embodiment, the execution tasks are stored in the blockchain, as shown in fig. 2, in step S3, that is, for each execution task, the execution task is subjected to abnormal judgment according to the maximum execution time, and the extracting the normal task includes the following steps:
s31: and acquiring the historical execution time corresponding to the execution task from a preset historical library for each execution task.
In the embodiment of the invention, each execution task has a corresponding task id, the task id is compared with the history id in the preset history library, and if the task id is the same as the history id, the execution time corresponding to the history id is obtained as the history execution time of the execution task corresponding to the task id.
The preset history library is a database specially used for storing different history ids and execution times corresponding to the history ids, and the history ids which are the same as the task ids are needed to exist.
S32: based on the historical execution time and the maximum execution time, abnormal execution tasks are removed through the Laida criterion, and normal execution tasks are reserved as initial tasks.
In the embodiment of the invention, the Laida criterion means that a group of detection data is firstly assumed to contain only random errors, standard deviation is obtained by calculation, a section is determined according to a certain probability, and the errors exceeding the section are considered to be not random errors but coarse errors, and the data containing the errors should be removed.
Specifically, an abnormal execution task is screened out as an abnormal task according to the formula (1), the abnormal task is eliminated, and a normal execution task is reserved as an initial task.
|vb|= |xb-x| >3σ formula (1)
Where vb is the error, xb is the maximum execution time, x is the historical execution time, and σ is the preset standard deviation.
If vb is greater than 3σ, it indicates that the execution task corresponding to the error is an abnormal task.
S33: the current execution state and the estimated execution state of the initial task are obtained.
In the embodiment of the invention, the current execution state and the current execution time of the initial task are acquired 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 acquired by utilizing the mode that the task id is matched with the basic id in the preset pre-estimated library, and the execution state corresponding to the execution time identical to the current execution time is selected from all the execution states according to the current execution time to serve as the pre-estimated execution state.
The preset pre-estimated library is a database 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 a preset rule. The preset rule refers to a rule preset by a user to determine a normal task according to different comparison results.
In the embodiment, the method of screening the initial task from the execution tasks according to the historical execution time and the maximum execution time can effectively remove part of data which does not accord with the user setting standard, avoid subsequent calculation of redundant data and improve the calculation efficiency of the target system; and then the normal task is screened out from the initial tasks according to the current execution state and the estimated execution state, so that abnormal tasks can be effectively filtered, abnormal conditions of the target system caused by intervention of the abnormal tasks in the subsequent scheduling processing process are avoided, and the processing efficiency and the safety of the target system can be improved.
In one embodiment, as shown in fig. 3, in step S34, comparing the current execution state with the estimated execution state, and determining the normal task according to the comparison result includes the following steps:
S341: and comparing the current execution state with the estimated execution state.
Specifically, the current execution state and the estimated execution state are compared.
S342: 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 a task priority.
In the embodiment of the present invention, according to the comparison manner 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 the normal task, and the normal task includes the task priority.
The task priority refers to a sequence for indicating execution of tasks.
S343: 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 of step S341, if the current execution state is different from the estimated execution state, it is identified whether the current execution state is a delay 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 to be a normal task.
It should be noted that, if the current execution state is delayed, it means that the initial task corresponding to the current execution state is due to insufficient system resources or abnormal conditions, so that an early warning needs to be sent out to increase the priority thereof, thereby preventing starvation.
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 secondarily identified, so that inaccuracy in primary identification caused by errors can be avoided, and the accuracy of acquiring a normal task is improved; and finally, the task priority corresponding to the initial task in the delay state is improved and is determined to be a normal task, and early warning can be effectively sent out to improve the priority of the task, so that starvation phenomenon is prevented, the accuracy of acquiring the normal task is ensured, and the accuracy of subsequent real-time scheduling processing is further improved.
In one embodiment, the impact factors include urgency, execution value and balance factors, and in step S4, performing real-time priority calculation on the normal tasks by using the impact factors to obtain real-time priorities corresponding to each normal task includes the following steps:
s41: according to the formula (2), calculating the real-time priority corresponding to each normal task:
p=ds (1-a) +wr a formula (2)
Where p is real-time priority, ds is urgency, a is equalization factor, and wr is execution value.
Specifically, the real-time priority corresponding to each normal task is utilized in formula (2).
Further, the degree of urgency ds in the formula (2) may be specifically calculated by the formula (3), and the execution value wr may be calculated by the formula (4).
Wherein t is s Maximum execution time corresponding to normal task, (d) i -systemime) is a preset absolute deadline for the normal task.
The preset absolute deadline means that a normal task must obtain a meaningful result within the deadline, otherwise the normal task is considered to be failed to execute.
It should be noted that ds and t s The real-time priority corresponding to the normal task is higher as the maximum execution time corresponding to the normal task is larger in proportion; ds and d i The systemime is inversely proportional, meaning that the smaller the preset absolute deadline for a normal task, the higher the real-time priority for the normal task.
Preferably, ds has a value in the range of (0, 1).
Wherein e is a base number of natural logarithms, u is a preset attenuation coefficient corresponding to a normal task, t is a difference value between the current execution time of the normal task and the maximum execution time, 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 coefficient is different, there are different attenuation speeds, and if the preset absolute deadline is 500, the different preset attenuation coefficients will divide the normal task into 3 categories, namely, critical, sensitive and insensitive.
Wherein, the value range of the critical u is as follows: and u is more than or equal to 4 and less than or equal to 6, which means that the normal tasks have mandatory requirements on the preset absolute deadline, and the normal tasks are quickly attenuated once the normal tasks exceed the preset absolute deadline as long as the normal tasks are finished well before the preset absolute deadline.
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 means that the normal tasks are more sensitive to the completion time, and the corresponding benefits are different along with the different completion times.
The value range of the insensitive u is as follows: u=0, indicating that such normal tasks are more relaxed with respect to the preset absolute deadline requirement and the benefit value is substantially fixed.
In this embodiment, the real-time priority corresponding to each normal task can be rapidly and accurately calculated through the formula (2), so that the accuracy and the processing efficiency of the subsequent real-time scheduling processing of the normal task according to the real-time priority are improved.
In an embodiment, the target system includes k execution platforms for executing normal tasks, and each execution platform includes a resource remaining value, as shown in fig. 4, in step S5, real-time scheduling processing is performed on each normal task according to a real-time priority and a preset scheduling scheme, and re-acquiring a system pressure value of the target system as a target pressure value based on a preset time interval includes the following steps:
s51: and comparing the resource remaining value contained in each execution platform with a preset normal value.
In the embodiment of the invention, since the target system comprises k execution platforms for executing normal tasks and each execution platform comprises a resource remaining value, the resource remaining value contained by each execution platform is compared with a preset normal value.
S52: and if the resource remaining value is greater than or equal to the preset normal value, selecting the execution platform with the maximum resource remaining value as the target platform.
In the embodiment of the present invention, according to the comparison manner in step S51, if the resource remaining value is greater than or equal to the preset normal value, the resource remaining value of each execution platform is compared, and the execution platform with the largest resource remaining value is selected as the target platform.
The resource remaining value is used for representing the current load condition of the execution platform, and the resource remaining value is inversely proportional to the current load condition of the execution platform, namely, the larger the resource remaining value is, the lighter the current load condition of the execution platform is.
It should be noted that, the target platform is a dedicated platform for executing a normal task, and when the target platform is selected, in order to use existing resources uniformly, an execution platform with a larger resource remaining value is preferably selected.
In addition, if the execution platform is selected from the viewpoint of saving the execution platform resources, the execution platform with heavy load condition can be selected, namely, the execution platform with smaller resource remaining value is selected as the target platform.
S53: and if the resource remaining value is smaller than the preset normal value, transmitting all the execution platforms and the resource remaining value of each execution platform to the auditing user.
In the embodiment of the present invention, according to the comparison manner in step S51, if the resource remaining value is smaller than the preset normal value, all execution platforms and the resource remaining value of each execution platform are sent to the auditing user.
S54: and acquiring an execution platform fed back by the auditing user as a target platform.
In the embodiment of the invention, the auditing user is a user which is specially used for analyzing the execution platform and the resource residual value of each execution platform, when the auditing user receives the execution platform and the resource residual value of each execution platform, the auditing user analyzes according to the actual situation, selects one execution platform from the execution platforms for feedback, and when the execution platform 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 a system pressure value of the target system as a target pressure value based on a preset time interval.
Specifically, all normal tasks are ordered according to the sequence of the real-time priority, scheduling processing is carried out on the target platform one by one according to the order, namely, after one normal task is executed, a second normal task is executed, and after a preset time interval passes, the system pressure value of the target system is obtained again from a preset log table to serve as a target pressure value.
Further, when the real-time priority is the same, the normal task with the earlier execution time is scheduled with priority.
It should be noted that if the normal task fails to be continuously scheduled on the target platform, the preset execution platform is selected again as the target platform for scheduling, so as to improve the robustness of the system.
In this embodiment, the manner of comparing the resource remaining value included in each execution platform with the preset normal value can screen out an appropriate target platform according to analysis under different conditions, ensure that the normal task can stably run on the target platform, avoid the situation that the real-time scheduling processing fails due to resource congestion, thereby effectively improving the accuracy of the real-time scheduling processing, avoiding the situation that the target system breaks down, and further improving the working efficiency of the user.
In one embodiment, the normal task includes an operation state, where the operation state includes an original state and a scheduling state, as shown in fig. 5, after step S6, the task dynamic scheduling method further includes:
s8: if the target pressure value is smaller than the preset restarting value, the running states of all the current normal tasks of the target system are obtained 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 value set according to the actual requirement of the user and used for recovering the original state of the normal task.
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, the running state of the normal task corresponding to the scheduling state is adjusted 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 states are adjusted to be original states. By adjusting the running state, the target system can recover 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 number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
In an embodiment, a task dynamic scheduling device is provided, where the task dynamic scheduling device corresponds to the task dynamic scheduling method in the above embodiment one by one. As shown in fig. 6, the task dynamic scheduling device includes a first acquisition module 61, a second acquisition module 62, an abnormality judgment module 63, a calculation module 64, a scheduling module 65, a first scheduling completion module 66, and a second scheduling completion module 67. The functional modules are described in detail as follows:
a first acquiring module 61, configured to acquire 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 obtain all execution tasks in the target system and influence factors corresponding to the execution tasks if the system pressure value is greater than the preset threshold, where the execution tasks include a maximum execution time; it should be emphasized that, to further ensure the privacy and security of the execution tasks, the execution tasks may also be stored in a node of a blockchain;
An abnormality judgment module 63, configured to perform abnormality judgment on the execution task according to the maximum execution time for each execution task, and extract a normal task;
the calculation module 64 is configured to perform real-time priority calculation on the normal tasks by using the influence factors, so as 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 re-acquire a system pressure value of the target system as a target pressure value based on a preset time interval;
the first scheduling completion module 66 is configured to compare the target pressure value with a preset threshold, and complete real-time scheduling processing of the normal task if the target pressure value is less than the preset threshold;
the second scheduling completion module 67 is configured to send a preset scheduling scheme to the monitoring end if the target pressure value is greater than or equal to the preset threshold, and acquire an adjustment 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.
Further, the abnormality determination module 63 includes:
the third acquisition sub-module is used for acquiring the historical execution time corresponding to the execution task from a preset historical library aiming at each execution task;
The initial task determination submodule is used for removing abnormal execution tasks through a Laida criterion based on the historical execution time and the maximum execution time, and reserving normal execution tasks as initial tasks;
the fourth acquisition sub-module is used for acquiring the current execution state and the estimated execution state of the initial task;
and the comparison sub-module is used for comparing the current execution state with the estimated execution state and determining a normal task according to a comparison result.
Further, the comparing sub-module includes:
the state comparison unit is used for comparing the current execution state with the estimated execution state;
the state comparison same unit is used for determining an initial task corresponding to the current execution state as a normal task if the current execution state is the same as the estimated execution state, wherein the normal task comprises a task priority;
the state comparison unit is used for comparing the current execution state with the estimated execution state, and 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 a normal task.
Further, the calculation module 64 includes:
the real-time priority computing sub-module is used for computing 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 real-time priority, ds is urgency, a is equalization factor, and wr is execution value.
Further, the scheduling module 65 includes:
the numerical comparison sub-module is used for comparing the resource remaining value contained in each execution platform with a preset normal value;
the first numerical comparison sub-module is used for selecting an execution platform with the largest resource remaining value as a target platform if the resource remaining value is larger than or equal to a preset normal value;
the second numerical value comparison sub-module is used for sending all the execution platforms and the resource remaining value of each execution platform to the auditing user if the resource remaining value is smaller than the preset normal value;
a fifth acquisition sub-module, configured to acquire an execution platform fed back by the auditing user as a target platform;
and the target platform scheduling sub-module is used for scheduling all normal tasks on the target platform according to the real-time priority sequence, and re-acquiring a system pressure value of the target system as a target pressure value based on a preset time interval.
Further, the task dynamic scheduling device further comprises:
the running state acquisition module is used for acquiring the running states of all current normal tasks of the target system from a preset state library if the target pressure value is smaller than a preset restarting value;
and the running state adjusting module is used for adjusting the running state of the normal task corresponding to the scheduling state to 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 block diagram of a computer device 90 in one 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 coupled to each other via a system bus. It should be noted that only computer device 90 having components 91-93 is shown in FIG. 7, but it should be understood that not all of the illustrated components need be implemented and that more or fewer components may alternatively be implemented. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory 91 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, 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, which are provided on the computer device 90. Of course, the memory 91 may also include both an internal memory unit and an external memory device of the computer device 90. In this embodiment, the memory 91 is generally used to store an operating system and various application software installed on the computer device 90, such as program codes of the task dynamic scheduling method. Further, the memory 91 may 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 (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 92 is generally used to control the overall operation of the computer device 90. In this embodiment, the processor 92 is configured to execute a program code stored in the memory 91 or process data, for example, a program code for executing the task dynamic scheduling method.
The network interface 93 may include a wireless network interface or a wired network interface, the network interface 93 typically being used to establish communication connections between the computer device 90 and other electronic devices.
The present application also provides another embodiment, namely, provides a computer readable storage medium, where an execution task information recording program is stored, where the execution task information recording program can be executed by at least one processor, so that the at least one processor executes the steps of any one of the task dynamic scheduling methods described above.
It should be emphasized that to further ensure the privacy and security of the execution tasks, the execution tasks may also be stored in nodes of a blockchain
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), including several instructions for causing a computer device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method described in the embodiments of the present application.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
Finally, it should be noted that the above-described embodiments are merely some, but not all, embodiments of the present application, and that the preferred embodiments of the present application are shown in the drawings and do not limit the scope of the patent. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a more thorough understanding of the present disclosure. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing, or equivalents may be substituted for elements thereof. All equivalent structures made by the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the protection scope of the application.
Claims (9)
1. The task dynamic scheduling method is characterized by comprising the following steps of:
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 larger than the preset threshold value, acquiring all execution tasks in the target system and influence factors corresponding to the execution tasks, wherein the execution tasks comprise maximum execution time;
For each execution task, acquiring the historical execution time corresponding to the execution task from a preset historical library;
based on the historical execution time and the maximum execution time, eliminating the abnormal execution task through a Laida criterion, and reserving the normal execution task as an initial task;
acquiring a current execution state and current execution time of an initial task according to a task ID of the initial task, matching the task ID with a basic ID in a preset pre-estimated library, acquiring all execution states corresponding to the basic ID which is successfully matched and the execution time corresponding to each execution state, and selecting the execution state corresponding to the execution time identical to the current execution time from all the execution states as the pre-estimated execution state according to the current execution time;
comparing the current execution state with the estimated execution state, and determining a normal task according to a comparison result;
carrying out real-time priority calculation on the normal tasks by using the influence factors to obtain real-time priorities corresponding to the normal tasks;
carrying out 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 based on a preset time interval;
Comparing the target pressure value with a preset threshold value, and if the target pressure value is smaller than the preset threshold value, completing real-time scheduling processing of the normal task;
if the target pressure value is greater than or equal to a preset threshold value, a preset scheduling scheme is sent to a monitoring end, and an adjustment scheme sent by the monitoring end is obtained to perform real-time scheduling processing on the normal task until the target pressure value is smaller than the preset threshold value;
wherein, based on the historical execution time and the maximum execution time, rejecting the abnormal execution task by using a Laida criterion, and reserving the normal execution task as an initial task comprises the following steps:
screening out abnormal execution tasks as abnormal tasks according to the following formula, eliminating the abnormal tasks, and reserving normal execution tasks as initial tasks;
the method comprises the following steps of (i) vb (i= |xb-x (i) 3 sigma), wherein vb is an error, xb is a maximum execution time, x is a historical execution time, and sigma is a preset standard deviation;
if vb is greater than 3σ, it indicates that the execution task corresponding to the error is an abnormal task.
2. The method of task dynamic scheduling according to claim 1, wherein the step of comparing the current execution state with the estimated execution state and determining a 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.
3. The method for dynamically scheduling tasks according to claim 1, wherein the influencing factors include an emergency degree, an execution value and an equalizing factor, and the step of calculating the real-time priority of the normal tasks by using the influencing factors to obtain the real-time priority corresponding to each of the normal tasks comprises:
and 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.
4. The method for dynamically scheduling tasks according to claim 1, wherein the target system comprises k execution platforms for executing the normal tasks, and each execution platform comprises a resource remaining value, wherein k is a positive integer greater than 1, the step of 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 based on a preset time interval comprises:
comparing the resource remaining value contained in each execution platform with a preset normal value;
if the resource remaining value is larger than or equal to a preset normal value, selecting an execution platform with the largest resource remaining value as a target platform;
if the resource remaining value is smaller than a preset normal value, transmitting all the execution platforms and the resource remaining 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 a system pressure value of the target system as a target pressure value based on a preset time interval.
5. The method for dynamically scheduling tasks according to claim 1, wherein the normal tasks include an operation state, the operation state includes an original state and a scheduling state, the comparing the target pressure value with a preset threshold value, and if the target pressure value is smaller than the preset threshold value, the method for dynamically scheduling tasks further comprises, after the step of completing the real-time scheduling process for the normal tasks:
if the target pressure value is smaller than a preset restarting value, acquiring the current running states of all the normal tasks of the target system from a preset state library;
and 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 an original state.
6. A task dynamic scheduling device, characterized in that the task dynamic scheduling device comprises:
the first acquisition module is used for acquiring a system pressure value of the target system;
the second acquisition module is used for comparing the system pressure value with a preset threshold value, and acquiring all execution tasks in the target system and influence factors corresponding to the execution tasks if the system pressure value is larger than the preset threshold value, wherein the execution tasks comprise the maximum execution time;
The abnormality judging module is used for acquiring the historical execution time corresponding to each execution task from a preset historical library, eliminating the abnormal execution task by pulling an arrival criterion based on the historical execution time and the maximum execution time, reserving the normal execution task as an initial task, acquiring the current execution state and the current execution time of the initial task according to the task ID of the initial task, matching the task ID with a basic ID in a preset pre-estimated library, acquiring all the execution states corresponding to the basic ID successfully matched and the execution time corresponding to each execution state, selecting the execution state corresponding to the execution time identical to the current execution time from all the execution states according to the current execution time as a pre-estimated execution state, comparing the current execution state with the pre-estimated execution state, and determining a normal task according to a comparison result;
the calculation module is used for calculating the real-time priority of 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 carrying out 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 based on 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 real-time scheduling processing of the normal task;
the second scheduling completion module is used for sending a preset scheduling scheme to a 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;
the abnormality judging module is further used for screening abnormal execution tasks to be used as abnormal tasks according to the following formula, eliminating the abnormal tasks and reserving the normal execution tasks to be used as initial tasks;
the method comprises the following steps of (i) vb (i= |xb-x (i) 3 sigma), wherein vb is an error, xb is a maximum execution time, x is a historical execution time, and sigma is a preset standard deviation;
if vb is greater than 3σ, it indicates that the execution task corresponding to the error is an abnormal task.
7. The task dynamic scheduling device according to claim 6, wherein the abnormality determination module includes:
the third acquisition sub-module is used for acquiring the historical execution time corresponding to each execution task from a preset historical library aiming at each execution task;
An initial task determination submodule, configured to reject the abnormal execution task by using a rada criterion based on the historical execution time and the maximum execution time, and reserve the normal execution task as an initial task;
the fourth acquisition sub-module is used for acquiring the current execution state and the estimated execution state of the initial task;
and the comparison sub-module is used for comparing the current execution state with the estimated execution state and determining the normal task according to a comparison result.
8. 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 task dynamic scheduling method according to any one of claims 1 to 5 when the computer program is executed.
9. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the task dynamic scheduling method according to any one of claims 1 to 5.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9400682B2 (en) * | 2012-12-06 | 2016-07-26 | Hewlett Packard Enterprise Development Lp | Ranking and scheduling of monitoring tasks |
-
2020
- 2020-06-24 CN CN202010591152.9A patent/CN111694652B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
|---|
| "云平台的任务优先级调度研究";程邺华;《中国优秀硕士学位论文全文数据库 (信息科技辑)》;20160615;第I139-81页 * |
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