Multi-objective optimized intelligent job scheduling system and scheduling method thereof

Abstract

The invention discloses a multi-objective optimized intelligent job scheduling system and a scheduling method thereof, and belongs to the technical field of supercomputers. According to the scheduling method, a Pareto solution set in multiple targets is searched through a simulated annealing algorithm, simulated annealing algorithm search and job scheduling are combined, and an optimal job execution sequence is sought. According to the method, the simulated annealing algorithm and the job scheduling scene demand are combined, and the optimal scheduling mode is searched, so that the job scheduling is more intelligent, and the scheduling efficiency is improved to a certain extent. In the super computer sharing the burst buffer architecture, the burst buffer requirement of the job can be effectively utilized, the backfilling limitation existing in traditional scheduling can be broken through, and the job scheduling efficiency and the system resource utilization rate are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of supercomputers, and particularly relates to an intelligent job scheduling system with multi-objective optimization and its scheduling method. Background Art

[0002] In a high-performance computing (HPC) system, the coordinated utilization of computing resources and I / O (input / output) resources has always been a key factor affecting system efficiency. However, with the rapid growth of computing power, the improvement speed of I / O performance is relatively slow, and the performance gap between the two is becoming increasingly significant. To alleviate this problem, the Burst Buffer technology is introduced as a high-performance intermediate storage layer. It provides a cache layer between computing nodes and storage nodes, significantly improving the efficiency of I / O operations, thereby alleviating the imbalance problem between computing and storage.

[0003] Although the Burst Buffer technology shows significant advantages in improving I / O performance, the finiteness of its resources brings new challenges. Specifically, how to effectively utilize Burst Buffer resources during the job scheduling process while avoiding resource conflicts and fragmentation has become one of the key problems in HPC scheduling optimization. Currently, mainstream scheduling strategies (such as first-come, first-served, FCFS, and its variants combined with backfilling technology) do not fully consider the reservation and optimal management of Burst Buffer resources, resulting in inefficient utilization of resources and a decline in job execution performance.

[0004] However, currently, in the HPC scheduling manager, traditional scheduling strategies are mainly relied on to manage jobs and resource allocation. Its limitation is that the backfilling mechanism usually only focuses on the idle periods of computing resources and lacks fine management of the requirements for other resources such as Burst Buffer. Summary of the Invention

[0005] The object of the present invention is to: in view of the above problems, provide an intelligent job scheduling system with multi-objective optimization and its scheduling method, aiming to improve the limitations of the existing backfilling mechanism and enhance job scheduling efficiency and system resource utilization.

[0006] The technical solution adopted by the present invention is as follows: An intelligent job scheduling method for multi-objective optimization. The scheduling method searches for the Pareto solution set in multiple objectives through the simulated annealing algorithm, combines the search of the simulated annealing algorithm with job scheduling, and seeks the optimal job execution order. The scheduling method includes the following steps: The simulated annealing algorithm uses a random permutation of jobs as the initial solution, takes the initial solution as the starting point of the simulated annealing algorithm, and sets the initial temperature, temperature decay rate, and maximum number of iterations to optimize job scheduling. In each iteration process, the simulated annealing algorithm generates a new solution by locally perturbing the current initial solution. This new solution is the neighborhood solution, and the objective function value of the neighborhood solution is calculated. It is judged whether to accept the new neighborhood solution, and it is judged whether the neighborhood solution can be accepted according to the objective function value of the neighborhood solution. The acceptable neighborhood solutions form the Pareto optimal solution set. When the termination condition of the simulated annealing algorithm is reached, the algorithm is terminated; and the optimal neighborhood solution is returned as the optimal job scheduling solution.

[0007] Further, the initial temperature is the starting temperature of simulated annealing. During the algorithm iteration process, the initial temperature gradually decreases, the range of the neighborhood solution also becomes smaller, and finally it tends to be stable. The temperature decay rate is a parameter that controls the temperature change speed, and the temperature decay rate is less than 1. The maximum number of iterations is set according to specific usage requirements.

[0008] Further, during the iteration process, the local perturbation method of the simulated annealing algorithm is: randomly select two jobs in the initial solution, swap their positions, and form a new scheduling order as the neighborhood solution.

[0009] Further, after the neighborhood solution is generated by the simulated annealing algorithm, the objective function value of the neighborhood solution is calculated. Its objective function value includes three indicators. It is judged whether the current scheduling order is the optimal job scheduling order through each indicator of the objective function value. The indicators are as follows: Total waiting time, which is the sum of the waiting times of all jobs in the queue; Total slowdown ratio , ; Among them, is the job waiting time, is the job running time, is the total slowdown ratio; Total completion time, which is the maximum time when all jobs are completed minus the start time of the earliest job.

[0010] Further, the method for judging whether to accept the new neighborhood solution is: Calculate the difference between the sum of the objective function values of the new neighborhood solution and the previous neighborhood solution as , if , that is, if the objective function value of the new neighborhood solution is better than that of the previous neighborhood solution, then accept the new neighborhood solution; if , that is, if the objective function value of the new neighborhood solution is worse than that of the previous neighborhood solution, then selectively accept the new neighborhood solution according to the calculation of the probability formula; The probability formula is as follows: ; Wherein, is the difference between the objective function values of the new neighborhood solution and the previous neighborhood solution, is the current temperature; The probability formula selectively accepting the new neighborhood solution includes the following judgment: if the random number , then accept the new neighborhood solution, otherwise do not accept the new neighborhood solution; Wherein, the random number randomly generates a random number between 0 and 1, which is used to judge whether to accept a solution with a worse objective function value.

[0011] Furthermore, there is a set of neighborhood solutions in the Pareto optimal solution set, and the Pareto optimal solution set is constructed by judging the dominance relationship between the objective function values; It should be noted that the dominance relationship here means that: in the job scheduling scenario, each neighborhood solution corresponds to a set of objective function values, and these objective function values may represent different indicators of job scheduling, such as the waiting time and completion time of jobs. For any two neighborhood solutions, if one neighborhood solution is not worse than the other neighborhood solution in all the indicators corresponding to the objective functions, and is better than the other neighborhood solution in at least one of the indicators of the objective functions, it is said that this neighborhood solution "dominates" the other neighborhood solution.

[0012] The determination of its dominance relationship includes the following: One-way comparison, when neighborhood solution A is greater than or equal to neighborhood solution B in all objectives, and is greater than neighborhood solution B in at least one objective, then neighborhood solution A dominates neighborhood solution B, where A has dominance and B does not have dominance; Two-way comparison, the new neighborhood solution needs to be compared with all existing neighborhood solutions in the Pareto optimal solution set in a two-way manner. If the new neighborhood solution is not dominated by any existing neighborhood solution in the solution set, add the new neighborhood solution to the Pareto optimal solution set and delete the existing neighborhood solutions in the Pareto optimal solution set that are dominated by the new neighborhood solution; if the new neighborhood solution is directly dominated by any existing neighborhood solution, then discard the new neighborhood solution; When the termination condition of the simulated annealing algorithm is reached, terminate the algorithm to form a Pareto optimal solution set, and all neighborhood solutions in the Pareto optimal solution set do not have dominance.

[0013] Furthermore, there are two termination conditions for the simulated annealing algorithm. One is that when the maximum number of iterations is reached, the algorithm terminates; the other is that when the temperature drops to a set threshold, the algorithm terminates. When the simulated annealing algorithm meets any one of these termination conditions, the algorithm terminates and returns the Pareto optimal solution set.

[0014] Furthermore, after several iterations and temperature decays, the simulated annealing algorithm will finally return a set of non-dominated Pareto optimal solution sets, which is denoted as the optimal job scheduling solution.

[0015] Furthermore, a multi-objective optimization intelligent job scheduling system, the system includes a waiting queue system and a multi-objective optimization system; the waiting queue system submits jobs by users, sends the jobs into the waiting queue for queuing, and the waiting queue automatically queues according to the order in which the jobs enter the system; the multi-objective optimization system optimizes the jobs within the time window in an intelligent scheduling manner to generate the optimal job scheduling solution.

[0016] Furthermore, the multi-objective optimization system extracts the jobs in the waiting queue system for scheduling optimization, and the waiting queue system receives the optimal job scheduling solution generated by the multi-objective optimization system for processing and scheduling.

[0017] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are as follows: The present invention combines the simulated annealing algorithm with the requirements of the job scheduling scenario, searches for the optimal scheduling method, makes the job scheduling more intelligent, and thus improves the scheduling efficiency to a certain extent.

[0018] Moreover, in the supercomputer with a shared burst buffer architecture, the present invention can effectively utilize the burst buffer requirements of jobs, and can break through the backfilling limitations existing in traditional scheduling, improving the job scheduling efficiency and system resource utilization rate. Description of the Drawings

[0019] Figure 1 is the method flow chart of the present invention; Figure 2 is the system framework schematic diagram of the present invention. Detailed Embodiments

[0020] The present invention will be described in detail below with reference to the drawings.

[0021] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] As Figure 1 shown, a multi-objective optimization intelligent job scheduling method, which searches for the Pareto solution set in multiple objectives through the simulated annealing algorithm, combines the search of the simulated annealing algorithm with job scheduling, and seeks the optimal job execution order; the scheduling method includes the following steps: The simulated annealing algorithm uses a random permutation of jobs as the initial solution, takes the initial solution as the starting point of the simulated annealing algorithm, and sets the initial temperature, temperature decay rate, and maximum number of iterations to optimize job scheduling; The initial temperature is the starting temperature of simulated annealing. At the beginning, a relatively high value is set for the initial temperature. During the algorithm iteration process, the initial temperature will gradually decrease, and the range of the neighborhood solution will also become smaller, and finally tend to be stable; The temperature decay rate is a parameter that controls the speed of temperature change, and the temperature decay rate is less than 1. Generally, a value less than 1 is used, such as 0.95, indicating that the temperature will gradually decrease after each round of iteration; The maximum number of iterations is set according to specific usage requirements.

[0023] During each iteration process, the simulated annealing algorithm generates a new solution by locally perturbing the current initial solution. This new solution is the neighborhood solution, and the objective function value of the neighborhood solution is calculated; The local perturbation method of the simulated annealing algorithm is: Randomly select two jobs in the initial solution, swap their positions, and form a new scheduling order as the neighborhood solution.

[0024] After the neighborhood solution is generated by the simulated annealing algorithm, the objective function value of the neighborhood solution is calculated. Its objective function value includes three indicators. Whether the current scheduling order is the optimal job scheduling order is judged through each indicator of the objective function value. The indicators are as follows: Total waiting time, which is the sum of the waiting times of all jobs in the queue; Total slowdown ratio , ; Among them, is the job waiting time, is the job running time, is the total slowdown ratio; Total completion time, which is the maximum time when all jobs are completed minus the start time of the earliest job.

[0025] Judge whether to accept the new neighborhood solution, and judge whether the new neighborhood solution can be accepted according to the objective function value of the neighborhood solution; The method for judging whether to accept the new neighborhood solution is: Calculate that the difference between the sum of the objective function values of the new neighborhood solution and the previous neighborhood solution is , if , that is, the objective function value of the new neighborhood solution is better than that of the previous neighborhood solution, then accept the new neighborhood solution; if , that is, the objective function value of the new neighborhood solution is worse than that of the previous neighborhood solution, then selectively accept the new neighborhood solution according to the calculation of the probability formula; The probability formula is as follows: ; Among them, is the difference between the objective function values of the new neighborhood solution and the previous neighborhood solution, is the current temperature; The probability formula for selectively accepting the new neighborhood solution includes the following judgment: if the random number , then accept the new neighborhood solution, otherwise do not accept the new neighborhood solution; Among them, the random number randomly generates a random number between 0 and 1, which is used to judge whether to accept a solution with a worse objective function value.

[0026] Form the Pareto optimal solution set with the acceptable neighborhood solutions. When the termination condition of the simulated annealing algorithm is reached, terminate the algorithm; and return the optimal neighborhood solution as the optimal job scheduling solution.

[0027] There is a group of neighborhood solutions in the Pareto optimal solution set, and the Pareto optimal solution set is constructed by judging the dominance relationship between the objective function values; The determination of its dominance relationship includes the following: One-way comparison. When neighborhood solution A is greater than or equal to neighborhood solution B in all objectives and greater than neighborhood solution B in at least one objective, then neighborhood solution A dominates neighborhood solution B, where A has dominance and B does not have dominance; Two-way comparison. The new neighborhood solution needs to be compared with all existing neighborhood solutions in the Pareto optimal solution set in two ways. If the new neighborhood solution is not dominated by any existing neighborhood solution in the solution set, add the new neighborhood solution to the Pareto optimal solution set and delete the existing neighborhood solutions dominated by the new neighborhood solution in the Pareto optimal solution set; if the new neighborhood solution is directly dominated by any existing neighborhood solution, then discard the new neighborhood solution; When the termination condition of the simulated annealing algorithm is reached, terminate the algorithm, form the Pareto optimal solution set, and all neighborhood solutions in the Pareto optimal solution set do not have dominance.

[0028] There are two termination conditions for the simulated annealing algorithm. One is that when the maximum number of iterations is reached, the algorithm terminates; the other is that when the temperature drops to the set threshold, the algorithm terminates; when the simulated annealing algorithm meets any one of the termination conditions, the algorithm terminates and returns the Pareto optimal solution set.

[0029] After several iterations and temperature decays, the simulated annealing algorithm will eventually return a set of non-dominated Pareto optimal solution sets, and this Pareto optimal solution set is denoted as the optimal job scheduling solution.

[0030] In the process of job scheduling, this method introduces the resource requirement characteristics of the Burst buffer, effectively allocates and uses the limited burst buffer resources, and uses the simulated annealing algorithm to find the optimal job execution order. Combining with the improved scheduling method, it can be more intelligent and quickly obtain a suitable optimal solution.

[0031] Example 1 As Figure 1 shown, an embodiment of the present invention is a multi-objective optimization intelligent job scheduling method. The scheduling method includes three scheduling plans, which are equivalent to the execution order of jobs: Plan 1: Adopt the first-come-first-served plus backfilling method. Each time backfilling is performed, the negative impact that the backfilled job will delay the first subsequent job needs to be ignored, that is, each time backfilling directly selects the first most suitable job from the subsequent waiting jobs for backfilling; Plan 2: Adopt the shortest job first plus backfilling method. The backfilling mechanism still adopts the method of Plan 1. Note that the entire queue by default has short-running-time jobs arranged in the front; Plan 3: Adopt the custom job order plus backfilling mechanism. The backfilling mechanism adopts the method of Plan 1. The queue order is determined by the custom job order, and the jobs in the front have the qualification for priority backfilling; Based on Plan 3, this embodiment combines the simulated annealing algorithm to search for the Pareto solution set in multiple objectives, combines the search of the simulated annealing algorithm with job scheduling, and seeks the optimal job execution order.

[0032] The scheduling method includes the following steps: 1. Initial stage; Arrange the jobs in a random order as the initial solution. Each job has a job_id, and the execution time, the number of required nodes, and the size of the burst buffer of each job are known. This initial solution is used as the starting point of the algorithm.

[0033] Initial temperature: The starting temperature of simulated annealing, usually set to a relatively high value.

[0034] Temperature decay rate: A parameter that controls the temperature reduction speed. Generally, a value less than 1 is adopted, such as 0.95, indicating that the temperature will gradually decrease after each round of iteration.

[0035] Maximum number of iterations: To avoid excessive running time of the algorithm, a maximum number of iterations is usually set to ensure that the algorithm terminates within a reasonable time.

[0036] 2. Generation of neighborhood solutions; In each iteration, the simulated annealing algorithm generates a new solution by locally perturbing the current solution. The specific perturbation method is as follows: randomly select two jobs in the current solution and swap their positions to form a new scheduling order. This new order is called a "neighborhood solution".

[0037] 3. Calculation of the objective function; Whenever a new neighborhood solution is generated, the objective function value of this solution is calculated. The objective function includes three main indicators: (1) Total waiting time: The sum of the waiting times of all jobs in the queue.

[0038] (2) Total completion time: The maximum time when all jobs are completed minus the start time of the earliest job.

[0039] (3) Total slowdown ratio: ; Among them, is the waiting time of the job, is the running time of the job, is the total slowdown ratio; These objective function values can reflect the quality of the scheduling scheme, and the performance of the current scheduling order is judged by calculating the objective value of each solution.

[0040] 4. Criteria for accepting new solutions; The key part of simulated annealing is how to decide whether to accept a new neighborhood solution. The judgment method is as follows: calculate the difference between the sum of the objective function values of the new neighborhood solution and the previous neighborhood solution as , if , that is, the objective function value of the new neighborhood solution is better than that of the previous neighborhood solution, then accept the new neighborhood solution; if , that is, the objective function value of the new neighborhood solution is worse than that of the previous neighborhood solution, then selectively accept the new neighborhood solution according to the calculation of the probability formula; The probability formula is as follows: ; Among them, is the difference between the objective function values of the new neighborhood solution and the previous neighborhood solution, is the current temperature; The probability formula selectively accepting the new neighborhood solution includes the following judgment: if the random number , then accept the new neighborhood solution, otherwise do not accept the new neighborhood solution; Among them, the random number A random number between 0 and 1 is randomly generated to determine whether to accept a solution with a worse objective function value. This random number is generated by a pseudo-random number generator built into the algorithm, and its generation logic follows the uniform distribution property, that is, each numerical value has an equal probability of occurrence.

[0041] 5. Pareto optimization; In multi-objective optimization problems, there is often no single optimal solution, but a set of optimal solutions, called the Pareto optimal solution set. Therefore, the present invention constructs a Pareto optimal solution set by judging the dominance relationship between objective values. Specifically, if a solution is not inferior to the current solution in all objectives and is superior to the current solution in at least one objective, the current solution is considered a dominant solution. Through such a dominance relationship judgment, the algorithm can gradually construct a Pareto optimal solution set and maintain the non-dominated solutions in the solution set.

[0042] 6. Temperature decay and iteration; After each iteration, the temperature decreases according to the set temperature decay rate, usually set to 0.95. As the temperature gradually decreases, the range of the algorithm to explore new solutions becomes smaller, and finally it tends to be stable and converges to a better solution.

[0043] 7. Termination conditions; There are usually two termination conditions for the simulated annealing algorithm: one is to reach the maximum number of iterations, and the other is that the temperature drops to a certain threshold. The algorithm stops when either of these conditions is met and returns the optimal scheduling solution.

[0044] 8. Output of the final solution; After several iterations and temperature decays, the simulated annealing algorithm will finally return a set of non-dominated solutions (Pareto optimal solution set). These solutions represent the optimal scheduling order under different weight configurations in the multi-objective optimization problem. According to specific requirements, a suitable solution can be selected as the final scheduling order.

[0045] 9. Comprehensively consider the optimization indicators corresponding to the three plans. First, standardize each objective to unify the dimension, and then use the triple weight parameters ( , , ), which correspond to the weight parameters of each optimization indicator respectively, to find the set of optimization indicators with the minimum weighted sum. The corresponding job execution order is the optimal one, where the weight parameters are determined according to specific usage requirements.

[0046] In this embodiment, the resource requirement characteristics of the Burst buffer are introduced in the job scheduling process to effectively allocate and utilize the limited Burst buffer resources. At the same time, the simulated annealing algorithm is used to find the optimal job execution order, and combined with the improved scheduling method, the original job scheduling method becomes more intelligent and can quickly obtain a suitable optimal solution.

[0047] Embodiment 2 As Figure 2 shown, another embodiment of the present invention is a multi-objective optimization intelligent job scheduling system, which includes a waiting queue system and a multi-objective optimization system; The waiting queue system receives jobs submitted by users and sends them into the waiting queue for queuing. The waiting queue automatically queues according to the order in which the jobs enter the system; The multi-objective optimization system performs intelligent scheduling optimization on the jobs within the time window in the mode of the time window to generate the optimal job scheduling solution.

[0048] The multi-objective optimization system extracts the jobs in the waiting queue system for scheduling optimization, and the waiting queue system receives the optimal job scheduling solution generated by the multi-objective optimization system for processing and scheduling.

[0049] Thus, the two systems interact with each other to jointly perform multi-objective optimization intelligent job scheduling, and can effectively utilize the Burst buffer requirements of the jobs in the supercomputer with the Burst buffer architecture, break the backfilling limitations existing in traditional scheduling, and improve job scheduling efficiency and system resource utilization rate.

[0050] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-objective optimization intelligent job scheduling method, characterized in that: The scheduling method searches for the Pareto solution set in multiple objectives by using a simulated annealing algorithm, combines the simulated annealing algorithm search with job scheduling, and seeks the optimal job execution order; the scheduling method comprises the following steps: The simulated annealing algorithm optimizes job scheduling by randomly arranging jobs as the initial solution, taking the initial solution as the starting point of the simulated annealing algorithm, and setting the initial temperature, temperature decay rate and maximum number of iterations; In each iteration, the simulated annealing algorithm generates a new solution by locally perturbing the current initial solution. This new solution is the neighborhood solution, and the objective function value of the neighborhood solution is calculated. Determine whether to accept a new neighborhood solution, and determine whether the neighborhood solution is acceptable based on the objective function value of the neighborhood solution; The acceptable neighborhood solutions are formed into a Pareto optimal solution set. When the termination condition of the simulated annealing algorithm is reached, the algorithm is terminated; and the optimal neighborhood solution is returned as the optimal job scheduling solution.

2. The multi-objective optimization intelligent job scheduling method according to claim 1 is characterized in that: The initial temperature is the starting temperature of simulated annealing. During the algorithm iteration process, the initial temperature will gradually decrease, the range of the neighborhood solution will also become smaller, and finally tend to be stable; the temperature decay rate is a parameter for controlling the temperature change speed, and the temperature decay rate is less than 1; the maximum number of iterations is set according to specific usage requirements.

3. The multi-objective optimization intelligent job scheduling method according to claim 1 is characterized in that: During the iteration process, the local perturbation mode of the simulated annealing algorithm is: Randomly select two jobs in the initial solution, exchange their positions, and form a new scheduling order as the neighborhood solution.

4. The multi-objective optimization intelligent job scheduling method according to claim 3 is characterized in that: After the neighborhood solution is generated, the simulated annealing algorithm calculates the objective function value of the neighborhood solution. The objective function value includes three indicators. The indicators of the objective function value are used to determine whether the current scheduling sequence is the optimal job scheduling sequence. The indicators are as follows: Total waiting time, which is the sum of the waiting time of all jobs in the queue; Total reduction ratio , ； in, is the job waiting time, is the job running time, is the total reduction ratio; Total completion time, the maximum completion time of all jobs minus the start time of the earliest job.

5. The multi-objective optimization intelligent job scheduling method according to claim 1 is characterized in that: The method for determining whether to accept a new neighborhood solution is: Calculate the total difference of the objective function value between the new neighborhood solution and the previous neighborhood solution as ,like , that is, the objective function value of the new neighborhood solution is better than the objective function value of the previous neighborhood solution, then the new neighborhood solution is accepted; if , that is, if the objective function value of the new neighborhood solution is worse than that of the previous neighborhood solution, the new neighborhood solution is selectively accepted according to the probability formula; The probability formula is as follows: ； in, is the difference between the objective function value of the new neighborhood solution and the previous neighborhood solution, is the current temperature; The probability formula selectively accepts new neighborhood solutions including the following judgments: If the random number , then the new neighborhood solution is accepted, otherwise the new neighborhood solution is not accepted; Among them, the random number A random number between 0 and 1 is randomly generated to determine whether to accept a solution with a worse objective function value.

6. The multi-objective optimization intelligent job scheduling method according to claim 1 is characterized in that: The Pareto optimal solution set includes a set of neighborhood solutions, and the Pareto optimal solution set is constructed by judging the dominance relationship between the objective function values; The determination of the dominance relationship includes the following: One-way comparison: when the neighborhood solution A is greater than or equal to the neighborhood solution B in all objectives, and greater than the neighborhood solution B in at least one objective, then the neighborhood solution A dominates the neighborhood solution B, where A is dominant and B is not dominant. Bidirectional comparison: the new neighborhood solution needs to be compared with all existing neighborhood solutions in the Pareto optimal solution set. If the new neighborhood solution is not dominated by any existing neighborhood solution in the solution set, the new neighborhood solution is added to the Pareto optimal solution set, and the existing neighborhood solutions dominated by the new neighborhood solution in the Pareto optimal solution set are deleted; if the new neighborhood solution is directly dominated by any existing neighborhood solution, the new neighborhood solution is discarded; When the termination condition of the simulated annealing algorithm is reached, the algorithm is terminated to form a Pareto optimal solution set, and all neighborhood solutions in the Pareto optimal solution set are not dominant.

7. The multi-objective optimization intelligent job scheduling method according to claim 1 is characterized in that: The simulated annealing algorithm has two termination conditions: one is that the algorithm terminates when the maximum number of iterations is reached; the other is that the algorithm terminates when the temperature drops to a set threshold; when the simulated annealing algorithm meets any one of the termination conditions, the algorithm terminates and returns the Pareto optimal solution set.

8. The multi-objective optimization intelligent job scheduling method according to claim 7 is characterized in that: After several iterations and temperature decay, the simulated annealing algorithm will eventually return a set of non-dominated Pareto optimal solutions, which are recorded as the optimal job scheduling solution.

9. A multi-objective optimization intelligent job scheduling system, using a multi-objective optimization intelligent job scheduling method according to any one of claims 1 to 8, characterized in that: The system includes a waiting queue system and a multi-objective optimization system; The waiting queue system is a system where users submit jobs and send them to the waiting queue for queuing. The waiting queue automatically queues jobs according to the order in which they enter the system. The multi-objective optimization system performs intelligent scheduling optimization on the jobs within the time window according to the time window mode to generate the optimal job scheduling solution.

10. The multi-objective optimization intelligent job scheduling system according to claim 9, characterized in that: The multi-objective optimization system extracts jobs from the waiting queue system for scheduling optimization, and the waiting queue system receives the optimal job scheduling solution generated by the multi-objective optimization system for processing and scheduling.

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