CN113626164A - Monitoring platform job scheduling method, device, terminal and storage medium - Google Patents
Monitoring platform job scheduling method, device, terminal and storage medium Download PDFInfo
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- 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
- G06F9/4893—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues taking into account power or heat criteria
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- G—PHYSICS
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- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
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Abstract
The invention provides a monitoring platform job scheduling method, device, terminal and storage medium, firstly maintaining jobs to different queues according to job attributes, then calculating the priority of each remaining job in real time according to job parameters in the job execution process, and realizing dynamic adjustment of job priorities, wherein the job parameters comprise job queuing time, job adjustment weight, size of the queue where the job is located, job attributes and job resource limits, namely dynamically adjusting the priority of the job according to the job parameters, selecting proper job to be preferentially executed instead of executing only according to submission time, and in time under the condition of larger job amount, also optimizing the job execution queue to enable the job with execution conditions and to be executed as soon as possible to be preferentially executed and the like without increasing hardware resources, additionally and independently increasing nodes and the like, under the condition of ensuring the normal operation of the monitoring platform, and the execution queue is dynamically optimized, and the resource consumption is effectively reduced.
Description
Technical Field
The invention relates to the field of monitoring platform operation scheduling, in particular to a monitoring platform operation scheduling method, a monitoring platform operation scheduling device, a monitoring platform operation scheduling terminal and a storage medium.
Background
FIFO is one of the earliest classical scheduling algorithms and has been widely used in various scheduling systems. The core of the algorithm carries out job task scheduling execution on all jobs according to the sequence of the submitted time of the jobs. However, in the era of a large-scale data center, a large amount of server resources are uniformly managed by a physical infrastructure monitoring platform, and when the amount of resources is very large (exceeding 1024 nodes), the monitoring platform needs to add hardware configuration or add task scheduling nodes to complete task scheduling, which results in large resource consumption.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method for scheduling operations of a monitoring platform, which effectively reduces hardware resources and resource consumption of an additional individual amplification node while ensuring normal operation of the monitoring platform.
In a first aspect, a technical solution of the present invention provides a monitoring platform job scheduling method, including the following steps:
maintaining the jobs to different queues according to the job attributes;
in the process of executing the operation, calculating the priority of each remaining operation in real time according to the operation parameters; the operation parameters comprise operation queuing time, operation adjusting weight, size of an operation queue, operation attributes and operation resource limitation.
Further, the maintaining the job to different queues according to the job attribute specifically includes:
jobs are maintained to different queues according to job type.
Further, in the process of executing the job, the priority of each remaining job is calculated in real time according to the job parameters, specifically, the priority of each remaining job is calculated according to the following formula:
job priority = job current priority × (job queuing time factor + job current adjustment weight value × (weight factor) + (size of queue where job is located × (job size factor) + (job type value) × (queue type factor) + (job resource limit value) × (resource limit factor) + (job priority offset value);
wherein, the job queuing time factor = job queued time/maximum job queuable time;
the adjustment weight factor = job current adjustment weight value/current adjustment weight value of job with highest current priority;
job size factor = minimum running space required by job request/minimum running space required by job with highest current priority;
the queue type factor = queue priority of the queue where the job is located/queue priority of the queue where the job with the highest current priority is located;
resource limit factor = job current resource limit value/current resource limit value of the highest current priority job.
Further, the job priority offset value is specified when a job is submitted or modified.
Further, the method comprises the following steps:
initially, each job priority is calculated based on the job parameters.
Further, the method comprises the following steps:
and initially, configuring an initial value for the job, wherein the initial value comprises the maximum queuing time of the job, an initial adjustment weight value of the job, the minimum running space required by the job request, a job type value and an initial resource limit value of the job.
Further, the method comprises the following steps:
in the process of executing the job, if the executing job is in a trigger condition waiting trigger state, detecting the execution time required by the next job to be executed;
and if the execution time required by the next job to be executed is less than the threshold value, executing the next job to be executed when the job being executed is in a trigger condition waiting trigger state.
In a second aspect, the present invention provides a monitoring platform job scheduling device, including,
a queue maintenance module: maintaining the jobs to different queues according to the job attributes;
the priority adjusting module: in the process of executing the operation, calculating the priority of each remaining operation in real time according to the operation parameters; the operation parameters comprise operation queuing time, operation adjusting weight, size of an operation queue, operation attributes and operation resource limitation.
In a third aspect, a technical solution of the present invention provides a terminal, including:
a processor;
a memory for storing instructions for execution by the processor;
wherein the processor is configured to perform any of the methods described above.
In a fourth aspect, the invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the method of any one of the above.
The invention provides a monitoring platform job scheduling method, device, terminal and storage medium, firstly maintaining jobs to different queues according to job attributes, then calculating the priority of each remaining job in real time according to job parameters in the job execution process, and realizing dynamic adjustment of job priorities, wherein the job parameters comprise job queuing time, job adjustment weight, size of the queue where the job is located, job attributes and job resource limits, namely dynamically adjusting the priority of the job according to the job parameters, selecting proper job to be preferentially executed instead of executing only according to submission time, and in time under the condition of larger job amount, also optimizing the job execution queue to enable the job with execution conditions and to be executed as soon as possible to be preferentially executed and the like without increasing hardware resources, additionally and independently increasing nodes and the like, under the condition of ensuring the normal operation of the monitoring platform, and the execution queue is dynamically optimized, and the resource consumption is effectively reduced.
Drawings
The following explains the english terms to which the present invention relates:
FIFO: first In First Out.
For a clearer explanation of the embodiments or technical solutions of the prior art of the present application, the drawings needed for the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a monitoring platform job scheduling method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a monitoring platform job scheduling apparatus according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of a terminal according to a third embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the disclosure, the following detailed description will be given with reference to the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. 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 application.
FIFO is one of the earliest classical scheduling algorithms and has been widely used in various scheduling systems. The core of the algorithm carries out job task scheduling execution on all jobs according to the sequence of the submitted time of the jobs. However, in the era of a large-scale data center, a large amount of server resources are uniformly managed by a physical infrastructure monitoring platform, and when the amount of resources is very large (exceeding 1024 nodes), the monitoring platform needs to add hardware configuration or add task scheduling nodes to complete task scheduling, which results in large resource consumption.
Therefore, the embodiment provides a job scheduling method for a monitoring platform, which calculates job priorities in real time, optimizes an execution queue, so that jobs having execution conditions and needing to be executed as soon as possible are executed preferentially, and the like, without increasing hardware resources, separately expanding nodes, and the like, and under the condition of ensuring normal operation of the monitoring platform, dynamically optimizes the execution queue, thereby effectively reducing resource consumption.
As shown in fig. 1, the method for scheduling a monitoring platform job provided by this embodiment includes the following steps.
S1, the job is maintained to different queues according to the job attribute.
And considering that the task urgency degrees of different attributes are different, the jobs are maintained in different queues according to the job attributes.
In some specific embodiments, the jobs are maintained in different queues according to the job types, that is, the job attribute of this embodiment is specifically the job type, one queue represents to maintain one type of job, and different queues have different priorities, such as low priority of a hardware asset acquisition task, high priority of a performance data acquisition task, a monitoring patrol task (periodic priority dynamic adjustment), and the like.
S2, calculating the priority of each residual job in real time according to the job parameters in the job execution process; the operation parameters comprise operation queuing time, operation adjusting weight, size of an operation queue, operation attributes and operation resource limitation.
Note that a job with a higher priority is executed with higher priority.
In this embodiment, when a certain job (serial scheduling in which only one job is executed at a time) or certain jobs (parallel scheduling in which a plurality of jobs are executed at a time) are executed, priorities of all remaining jobs waiting to be executed are recalculated, and the remaining jobs are queued to form a reasonable execution layout. Specifically, the priority of the job is calculated according to job parameters, wherein the job parameters comprise job queuing time, job adjustment weight, size of a queue where the job is located, job attributes and job resource limitation. The task adjustment weight defines a basic priority, and is initially set as needed, for example, if a task needs to be executed earlier, the task adjustment weight is set to be larger, and if the task needs to be executed later, the task adjustment weight is set to be smaller. The size of the queue of the job refers to the number of jobs in the queue. The job resource limitation refers to CPU, memory, network, storage, and keyboard required for job execution (for example, if a job needs manual entry of yes, it should be guaranteed that the keyboard is in an idle state and is executable).
The priority of the operation is dynamically adjusted in real time according to the operation parameters, so that the execution queue can be optimized, and the operation with the execution condition and needing to be executed as soon as possible can be preferentially executed.
In a serial implementation example, for example, there are A, B, C, D four queues, there are four jobs a1, a2, a3, and a4 in the current a queue from high to low in priority, five jobs B1, B2, B3, B4, and B5 in the B queue from high to low in priority, three jobs C1, C2, and C3 in the C queue from high to low in priority, and four jobs D1, D2, D3, and D4 in the D queue from high to low in priority. And c1 in a1, b1, c1 and d1 has the highest priority, after the currently executed job is executed, c1 is executed, and the rest other actions are reordered in the c1 execution process.
For example, there are A, B, C, D four queues, for example, there are four a1, a2, a3, and a4 jobs in the a queue, five B1, B2, B3, B4, and B5 jobs in the B queue, three C1, C2, and C3 jobs in the C queue, and four D1, D2, D3, and D4 jobs in the D queue. The four jobs with the highest priority in the jobs are a1, a2, c1 and d1, after the four jobs currently being executed are executed, the four jobs a1, a2, c1 and d1 are executed, and the rest other actions are reordered in the process of executing a1, a2, c1 and d 1.
The job scheduling method for the monitoring platform provided by this embodiment is to maintain jobs in different queues according to job attributes, then calculate priorities of remaining jobs in real time according to job parameters during job execution, and implement dynamic adjustment of job priorities, where the job parameters include job queuing time, job adjustment weight, job queue size, job type and job resource restriction, that is, dynamically adjust the priority of jobs according to the job parameters, select appropriate job to be executed preferentially, rather than executing only according to submission time, and in time under the condition of large job volume, also optimize job execution queue, so that jobs with execution conditions and to be executed as soon as possible are executed preferentially, and without increasing hardware resources and additionally increasing nodes individually, and under the condition of ensuring normal operation of the monitoring platform, and the execution queue is dynamically optimized, and the resource consumption is effectively reduced.
In some specific embodiments, during the execution of the job, the priority of each remaining job is calculated in real time according to the job parameter, specifically, the priority of each remaining job is calculated according to the following formula, it should be noted that the job parameter of this embodiment includes a job attribute, and the job attribute is specifically taken as a job type:
job priority = job current priority × job queuing time factor + job current adjustment weight value × adjustment weight factor + job-located queue size × job size factor + job type value × queue type factor + job resource limit value × resource limit factor + job priority offset value.
Wherein, the job queuing time factor = job queued time/maximum job queuable time;
the adjustment weight factor = job current adjustment weight value/current adjustment weight value of job with highest current priority;
job size factor = minimum running space required by job request/minimum running space required by job with highest current priority;
the queue type factor = queue priority of the queue where the job is located/queue priority of the queue where the job with the highest current priority is located;
resource limit factor = job current resource limit value/current resource limit value of the highest current priority job.
In some embodiments, the job priority offset value is specified when a job is submitted or modified, with particular values being integers between-16383 and 16384.
It should be noted that, when the next calculation of the work resource limit value is performed, the work resource limit value is the last calculated work resource limit value x resource limit factor. When the current adjustment weight value of the operation is calculated next time, the current adjustment weight value of the operation is the current adjustment weight value of the operation calculated last time, and the weight factor is adjusted.
According to the formula, the job priority is readjusted on the premise of not influencing the running job, and the adjustment of the job priority can realize the following functions: 1) scheduling the job meeting the resource limitation in priority, for example, if the task has high priority and has an execution condition, scheduling the job for execution in priority; 2) if the priority is not adjusted, the next operation may not be executed because the memory resource requirement is not met, and the policy optimizes the priority, so that the next executable operation can use the existing free resources, and the execution of the operation does not influence the execution time of the operation. In other words, the scheduling policy allows low priority jobs to be run ahead of time without affecting the earliest startup time of high priority jobs. 3) An important condition of this scheduling policy is the run-time limit of the job, so the job run-time estimate provided by the user when submitting the job has a very important impact on whether the job can be recalled. The monitoring platform can periodically scan the job queue according to the priority order, search the callback time and dynamically adjust according to the monitoring job priority.
In some embodiments, each job priority is initially calculated also from the job parameters, i.e. according to the above formula. Correspondingly, at the beginning, the initial value configuration is carried out on the job, and the initial value comprises the maximum queuing time of the job, the initial adjustment weight value of the job, the minimum running space required by the job request, the type value of the job and the initial resource limit value of the job.
In some embodiments, to improve the execution efficiency, the following steps are further performed:
in the process of executing the job, if the executing job is in a trigger condition waiting trigger state, detecting the execution time required by the next job to be executed;
and if the execution time required by the next job to be executed is less than the threshold value, executing the next job to be executed when the job being executed is in a trigger condition waiting trigger state.
That is, the next job is executed during the time when the job waiting trigger condition (such as the keyboard input segment) is being executed, and it should be noted that the execution time of the next job is short so as not to affect the execution of the executing job. In addition, the next job is selected next job with the re-adjusted priority, and the above steps may not be performed until the priority is not re-adjusted.
An embodiment is provided below to further illustrate the present invention, and as shown in fig. 2, the embodiment includes the following steps.
S101, initially, maintaining the jobs to different queues according to the job types.
And S102, configuring initial values for the job, wherein the initial values comprise the maximum queuing time of the job, the initial adjustment weight value of the job, the minimum running space required by the job request, the type value of the job and the initial resource limit value of the job.
S103, calculate each job priority according to the following formula from the configured initial values.
Job priority = job current priority × job queuing time factor + job current adjustment weight value × adjustment weight factor + job-located queue size × job size factor + job type value × queue type factor + job resource limit value × resource limit factor + job priority offset value.
Wherein, the job queuing time factor = job queued time/maximum job queuable time;
the adjustment weight factor = job current adjustment weight value/current adjustment weight value of job with highest current priority;
job size factor = minimum running space required by job request/minimum running space required by job with highest current priority;
the queue type factor = queue priority of the queue where the job is located/queue priority of the queue where the job with the highest current priority is located;
resource limit factor = job current resource limit value/current resource limit value of the highest current priority job.
S104, calculating the priority of each residual job in real time according to the job parameters in the job execution process;
the priority of each remaining job is calculated specifically according to the following formula:
job priority = job current priority × job queuing time factor + job current adjustment weight value × adjustment weight factor + job-located queue size × job size factor + job type value × queue type factor + job resource limit value × resource limit factor + job priority offset value.
Wherein, the job queuing time factor = job queued time/maximum job queuable time;
the adjustment weight factor = job current adjustment weight value/current adjustment weight value of job with highest current priority;
job size factor = minimum running space required by job request/minimum running space required by job with highest current priority;
the queue type factor = queue priority of the queue where the job is located/queue priority of the queue where the job with the highest current priority is located;
resource limit factor = job current resource limit value/current resource limit value of the highest current priority job.
S105, in the process of executing the job, if the executing job is in a trigger condition waiting trigger state, detecting the execution time required by the next job to be executed; and if the execution time required by the next job to be executed is less than the threshold value, executing the next job to be executed when the job being executed is in a trigger condition waiting trigger state.
Example two
The embodiment provides a monitoring platform job scheduling device, which is used for implementing the monitoring platform job scheduling method.
As shown in fig. 3, the monitoring platform job scheduling apparatus provided in this embodiment includes the following functional modules.
The queue maintenance module 101: maintaining the jobs to different queues according to the job attributes;
the priority adjustment module 102: in the process of executing the operation, calculating the priority of each remaining operation in real time according to the operation parameters; the operation parameters comprise operation queuing time, operation adjusting weight, size of an operation queue, operation attributes and operation resource limitation.
In some embodiments, the queue maintenance module 101 maintains jobs to different queues based on job type.
In some embodiments, the priority adjustment module 102 calculates the priority of each remaining job in real time according to the job parameters, specifically, calculates the priority of each remaining job according to the following formula:
job priority = job current priority × job queuing time factor + job current adjustment weight value × adjustment weight factor + job-located queue size × job size factor + job type value × queue type factor + job resource limit value × resource limit factor + job priority offset value.
Wherein, the job queuing time factor = job queued time/maximum job queuable time;
the adjustment weight factor = job current adjustment weight value/current adjustment weight value of job with highest current priority;
job size factor = minimum running space required by job request/minimum running space required by job with highest current priority;
the queue type factor = queue priority of the queue where the job is located/queue priority of the queue where the job with the highest current priority is located;
resource limit factor = job current resource limit value/current resource limit value of the highest current priority job.
In some embodiments, the job priority offset value is specified when a job is submitted or modified.
In some embodiments, the priority adjustment module 102 is further configured to initially calculate a respective job priority based on the job parameters.
Correspondingly, the device further comprises an initial value configuration module 103, which is used for performing initial value configuration on the job at the initial time. The initial values comprise the maximum queuing time of the job, the initial adjustment weight value of the job, the minimum running space required by the job request, a job type value and an initial resource limiting value of the job.
In some embodiments, the apparatus further comprises the following functional modules:
the execution time detection module 104: in the process of executing the job, if the executing job is in a trigger condition waiting trigger state, detecting the execution time required by the next job to be executed;
the job execution module 105: and if the execution time required by the next job to be executed is less than the threshold value, executing the next job to be executed when the job being executed is in a trigger condition waiting trigger state.
The monitoring platform job scheduling apparatus of this embodiment is used to implement the foregoing monitoring platform job scheduling method, so that a specific implementation manner of the apparatus can be seen in the foregoing embodiment section of the monitoring platform job scheduling method, and therefore, reference may be made to descriptions of corresponding respective section embodiments for a specific implementation manner thereof, and a description thereof will not be further provided herein.
The invention provides a monitoring platform job scheduling device, firstly, jobs are maintained to different queues according to job attributes, then, in the job execution process, the priorities of the remaining jobs are calculated in real time according to job parameters, and the dynamic adjustment of the job priorities is realized, wherein the job parameters comprise job queuing time, job adjustment weight, job queue size, job type and job resource limitation, namely, the job priorities are dynamically adjusted according to the job parameters, a proper job is selected for priority execution instead of execution only according to submission time, the job execution queue can be optimized under the condition of larger job amount, so that jobs with execution conditions and needing to be executed as soon as possible can be executed preferentially, hardware resources do not need to be increased, nodes are additionally and separately expanded, and the like, under the condition of ensuring the normal operation of a monitoring platform, the execution queue is dynamically optimized, effectively reducing resource consumption.
EXAMPLE III
Fig. 4 is a schematic structural diagram of a terminal device 300 according to an embodiment of the present invention, where the terminal device 300 may be used to execute the monitoring platform job scheduling method according to the embodiment of the present invention.
Specifically, the terminal device 300 performs at least the following steps:
s1, maintaining the jobs to different queues according to the job attributes;
s2, calculating the priority of each residual job in real time according to the job parameters in the job execution process; the operation parameters comprise operation queuing time, operation adjusting weight, size of a queue where the operation is located, operation type and operation resource limitation.
Among them, the terminal apparatus 300 may include: a processor 310, a memory 320, and a communication unit 330. The components communicate via one or more buses, and those skilled in the art will appreciate that the architecture of the servers shown in the figures is not intended to be limiting, and may be a bus architecture, a star architecture, a combination of more or less components than those shown, or a different arrangement of components.
The memory 320 may be used for storing instructions executed by the processor 310, and the memory 320 may be implemented by any type of volatile or non-volatile storage terminal or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The executable instructions in memory 320, when executed by processor 310, enable terminal 300 to perform some or all of the steps in the method embodiments described below.
The processor 310 is a control center of the storage terminal, connects various parts of the entire electronic terminal using various interfaces and lines, and performs various functions of the electronic terminal and/or processes data by operating or executing software programs and/or modules stored in the memory 320 and calling data stored in the memory. The processor may be composed of an Integrated Circuit (IC), for example, a single packaged IC, or a plurality of packaged ICs connected with the same or different functions. For example, the processor 310 may include only a Central Processing Unit (CPU). In the embodiment of the present invention, the CPU may be a single operation core, or may include multiple operation cores.
A communication unit 330, configured to establish a communication channel so that the storage terminal can communicate with other terminals. And receiving user data sent by other terminals or sending the user data to other terminals.
Example four
The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and the program may include some or all of the steps in the embodiments provided by the present invention when executed.
Specifically, the program executes at least the following steps:
s1, maintaining the jobs to different queues according to the job attributes;
s2, calculating the priority of each residual job in real time according to the job parameters in the job execution process; the operation parameters comprise operation queuing time, operation adjusting weight, size of a queue where the operation is located, operation type and operation resource limitation.
The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a Random Access Memory (RAM).
Those skilled in the art will readily appreciate that the techniques of the embodiments of the present invention may be implemented as software plus a required general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied in the form of a software product, where the computer software product is stored in a storage medium, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like, and the storage medium can store program codes, and includes instructions for enabling a computer terminal (which may be a personal computer, a server, or a second terminal, a network terminal, and the like) to perform all or part of the steps of the method in the embodiments of the present invention.
The same and similar parts in the various embodiments in this specification may be referred to each other. Especially, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant points can be referred to the description in the method embodiment.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The above disclosure is only for the preferred embodiments of the present invention, but the present invention is not limited thereto, and any non-inventive changes that can be made by those skilled in the art and several modifications and amendments made without departing from the principle of the present invention shall fall within the protection scope of the present invention.
Claims (10)
1. A monitoring platform job scheduling method is characterized by comprising the following steps:
maintaining the jobs to different queues according to the job attributes;
in the process of executing the operation, calculating the priority of each remaining operation in real time according to the operation parameters; the operation parameters comprise operation queuing time, operation adjusting weight, size of an operation queue, operation attributes and operation resource limitation.
2. The monitoring platform job scheduling method according to claim 1, wherein the maintaining of jobs to different queues according to job attributes specifically includes:
jobs are maintained to different queues according to job type.
3. The monitoring platform job scheduling method according to claim 2, wherein in the job execution process, the priority of each remaining job is calculated in real time according to the job parameters, specifically, the priority of each remaining job is calculated according to the following formula:
job priority = job current priority × (job queuing time factor + job current adjustment weight value × (weight factor) + (size of queue where job is located × (job size factor) + (job type value) × (queue type factor) + (job resource limit value) × (resource limit factor) + (job priority offset value);
wherein, the job queuing time factor = job queued time/maximum job queuable time;
the adjustment weight factor = job current adjustment weight value/current adjustment weight value of job with highest current priority;
job size factor = minimum running space required by job request/minimum running space required by job with highest current priority;
the queue type factor = queue priority of the queue where the job is located/queue priority of the queue where the job with the highest current priority is located;
resource limit factor = job current resource limit value/current resource limit value of the highest current priority job.
4. The supervisory platform job scheduling method according to claim 3, wherein the job priority offset value is specified when a job is submitted or modified.
5. The method for supervisory platform job scheduling according to claim 4, further comprising the steps of:
initially, each job priority is calculated based on the job parameters.
6. The method for supervisory platform job scheduling according to claim 5, further comprising the steps of:
and initially, configuring an initial value for the job, wherein the initial value comprises the maximum queuing time of the job, an initial adjustment weight value of the job, the minimum running space required by the job request, a job type value and an initial resource limit value of the job.
7. The method for supervisory platform job scheduling according to claim 6, further comprising the steps of:
in the process of executing the job, if the executing job is in a trigger condition waiting trigger state, detecting the execution time required by the next job to be executed;
and if the execution time required by the next job to be executed is less than the threshold value, executing the next job to be executed when the job being executed is in a trigger condition waiting trigger state.
8. A monitoring platform operation scheduling device is characterized by comprising,
a queue maintenance module: maintaining the jobs to different queues according to the job attributes;
the priority adjusting module: in the process of executing the operation, calculating the priority of each remaining operation in real time according to the operation parameters; the operation parameters comprise operation queuing time, operation adjusting weight, size of an operation queue, operation attributes and operation resource limitation.
9. A terminal, comprising:
a processor;
a memory for storing instructions for execution by the processor;
wherein the processor is configured to perform the method of any one of claims 1-7.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2023231899A1 (en) * | 2022-05-31 | 2023-12-07 | 华为技术有限公司 | Communication scheduling method and apparatus, and related device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2023231899A1 (en) * | 2022-05-31 | 2023-12-07 | 华为技术有限公司 | Communication scheduling method and apparatus, and related device |
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