A cloud computing resource dynamic scheduling optimization method and system

Abstract

The present invention relates to the field of data processing, and in particular to a method and system for dynamic scheduling optimization of cloud computing resources. The method comprises the following steps: obtaining the tasks currently being processed by each server and recording them as current tasks; obtaining the correlation between each two tasks and the priority of each task; filtering out tasks subject to correlation constraints from the current tasks based on the correlation between each two tasks, and obtaining the subsequent constraint durations of the tasks subject to correlation constraints; constructing a scheduling objective function based on the subsequent constraint durations of the tasks subject to correlation constraints; and maximizing the value of the scheduling objective function to obtain the corresponding subsequent task allocation method, thereby improving the rationality of task allocation.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method and system for dynamic scheduling optimization of cloud computing resources. Background Art

[0002] In the widespread application of cloud computing, dynamic resource scheduling is a key component in ensuring efficient system operation and improving resource utilization. Traditional cloud computing resource scheduling methods often statically allocate resources based on single task attributes (such as task duration and resource requirements), ignoring the potential dependencies and priority differences between tasks, resulting in inefficient resource utilization and delayed task execution.

[0003] As cloud computing applications become increasingly complex, many business processes contain numerous tasks with interdependent relationships. For example, in data analysis scenarios, data modeling and analysis cannot proceed until data cleansing tasks are completed. If these interdependent tasks are not properly scheduled, computing resources may remain idle while waiting for the associated tasks to be assigned, resulting in wasted resources. Existing scheduling strategies typically focus only on allocating currently assigned tasks, ignoring potential future dependent tasks. This makes it impossible to effectively utilize idle resources before dependent tasks are assigned, resulting in a lack of foresight and flexibility in resource scheduling.

[0004] Furthermore, task priority is a key factor influencing system service quality during task scheduling. In scenarios requiring extremely fast response times, such as financial transaction settlement and medical imaging diagnosis, the timely processing of high-priority tasks is crucial to both business success and user health and safety. This makes it difficult to effectively utilize idle resources. If short-duration tasks are assigned solely based on task duration or resource compatibility, there's a high likelihood that high-priority tasks will be unable to access resources in a timely manner due to idle resources being occupied by lower-priority, short-duration tasks.

[0005] To sum up, how to balance task relevance and priority in cloud computing resource scheduling, reasonably utilize idle resources to execute short-term tasks before related tasks are issued, and ensure the timely allocation of high-priority tasks, so as to avoid task execution delays and resource waste caused by priority imbalance, has become a technical problem that needs to be solved urgently. Summary of the Invention

[0006] In order to solve the problem of how to pre-plan related tasks while taking priorities into consideration, the present invention provides a method and system for dynamic scheduling optimization of cloud computing resources.

[0007] In a first aspect, the present invention provides a method for dynamic scheduling and optimization of cloud computing resources, which adopts the following technical solutions:

[0008] A method for dynamic scheduling optimization of cloud computing resources, comprising the steps of:

[0009] Obtain the task that each server is currently processing and record it as the current task;

[0010] According to the historical processing data of the tasks, the correlation between each two tasks and the priority of each task are obtained; according to the correlation between each two tasks, the tasks subject to the correlation constraint are screened out in the current tasks, and the subsequent constraint duration of the tasks subject to the correlation constraint is obtained, and the subsequent constraint duration is the expected interval duration of the tasks with greater correlation than the tasks subject to the correlation constraint; a scheduling objective function is constructed, and the scheduling objective function is negatively correlated with the difference between the required predicted processing duration and the subsequent constraint duration of the subsequent scheduled tasks on the server where the tasks subject to the correlation constraint are located, and is also negatively correlated with the weighted waiting time of all subsequent scheduled tasks; the weighted waiting time is obtained by weighted calculation of the predicted waiting time of all subsequent scheduled tasks using the priority as the weight;

[0011] The value of the scheduling objective function is maximized to obtain the corresponding subsequent task allocation method to achieve task allocation.

[0012] The present invention takes into account the relevance of tasks, and leaves time for tasks subject to relevance constraints to wait for the issuance of related tasks, thereby preventing the processing of related tasks from being interrupted and improving the continuity of task processing. Furthermore, when considering the relevance constraints to arrange tasks, the difference between the required predicted processing time of subsequent arranged tasks and the subsequent constraint time on the server where the task subject to the relevance constraints is located is introduced, thereby limiting the arrangement of tasks within the constraint time, so that the server can be effectively utilized before waiting for the issuance of the related tasks, while also preventing the related tasks from being interrupted. Furthermore, when considering the relevance constraints to arrange tasks, a weighted waiting time is also introduced, so that when arranging tasks, not only the relevance of tasks but also the priority of tasks are considered, effectively preventing only considering the relevance of tasks and preventing some high-priority tasks from being prioritized, thereby improving the rationality of task arrangement.

[0013] Preferably, the construction of the scheduling objective function includes:

[0014] ;

[0015] in, represents the subsequent constraint duration of the i-th task subject to the associated constraint, It represents the predicted processing time required for the jth task to be subsequently scheduled on the server where the ith task subject to the association constraint is located. Indicates the preset anti-zero coefficient, Indicates the number of tasks subsequently scheduled on the server where the i-th task subject to the associated constraint is located. represents the number of tasks subject to the association constraint, Indicates the predicted waiting time of the zth subsequent scheduled task, Indicates the priority of the zth subsequent scheduled task, U indicates the number of subsequent scheduled tasks, Indicates the server occupancy rate. Represents the scheduling objective function.

[0016] When constructing the scheduling objective function, the present invention introduces the difference between the subsequent constraint duration and the predicted processing duration of the subsequent scheduled task, so that the processing time of the task arrangement within the constraint duration is as close as possible to the constraint duration, thereby preventing the subsequent scheduled task processing time from being too long, resulting in the interruption of the processing process of the related task, or preventing the subsequent scheduled task processing time from being too short, resulting in the server being idle; further, when constructing the objective function, the priority and predicted waiting time are introduced to enable the priority situation to be considered when arranging tasks, thereby preventing only focusing on the arrangement problem of related tasks when arranging tasks, and causing high-priority tasks to be arranged later, thereby improving the rationality of task arrangement.

[0017] Preferably, the step of filtering out tasks subject to association constraints from the current tasks according to the association between each two tasks includes:

[0018] Obtain the time interval between the task subject to the associated constraint and other tasks based on the task's historical processing data, and obtain the duration of the task subject to the associated constraint;

[0019] If the correlation between the current task and other tasks is greater than the preset correlation threshold, the current task is determined to be a task suspected of being subject to correlation constraints;

[0020] If the time interval between the task suspected of being subject to association constraints and other tasks is greater than the time duration of the current task, the task suspected of being subject to association constraints will be determined as a task subject to association constraints.

[0021] On the contrary, a task suspected to be subject to association constraints is not a task subject to association constraints.

[0022] The present invention screens out tasks suspected of being subject to association constraints by comparing their association with a preset threshold, and then screens out tasks that have exceeded the issuance time interval by comparing the issuance time interval with the issuance duration, thereby preventing unnecessary waiting caused by the associated tasks not being issued.

[0023] Preferably, the subsequent constraint duration of the task subject to the association constraint includes:

[0024] The other tasks with the greatest correlation with the task subject to the correlation constraint are recorded as target tasks;

[0025] The difference between the release time interval and the released duration is used as the subsequent constraint duration of the task subject to the associated constraint.

[0026] The present invention obtains a relatively accurate assignment time of the associated task by excluding the assignment time of the current task by subtracting the assignment time interval from the assignment time.

[0027] Preferably, the obtaining of the correlation between each two tasks based on the historical processing data of the tasks includes:

[0028] Determine whether there is a data inflow and outflow relationship between the two tasks. If there is a data inflow and outflow relationship, set the basic value of the correlation between the two tasks to 1, otherwise set it to 0;

[0029] Set a time window and use the Apriori algorithm to calculate the confidence of the association rules between each two tasks based on the data in the time window;

[0030] The correlation between the two tasks is obtained by multiplying the confidence of the association rule by the correlation basis value.

[0031] The present invention comprehensively and accurately reflects the relevance of tasks based on whether there is a data inflow and outflow relationship between tasks and the confidence of the association rules of the tasks.

[0032] Preferably, before maximizing the value of the scheduling objective function and obtaining the corresponding subsequent task allocation method, a solution space needs to be constructed first. The method for constructing the solution space includes:

[0033] For any task to be assigned, a server is selected as the target server according to any selection method, and a target server is assigned to each task to be assigned. The correspondence between all tasks to be assigned and servers is regarded as a feasible solution, and the space formed by all feasible solutions is regarded as the solution space.

[0034] Preferably, the method for obtaining the predicted waiting time includes:

[0035] Get the predicted processing time of each task, get the pending tasks arranged before each task and record them as predecessor pending tasks, add the cumulative predicted processing time of all predecessor pending tasks before each task and add the predicted remaining processing time of the current task on the corresponding server to obtain the predicted waiting time of each task.

[0036] Preferably, the method of maximizing the value of the scheduling objective function to obtain the corresponding subsequent task allocation method includes:

[0037] Make the scheduling objective function take the maximum value, use the optimization algorithm to solve the optimal solution of the scheduling objective function, and obtain the task allocation method corresponding to the optimal solution.

[0038] Preferably, the task allocation is achieved by:

[0039] Assign servers to each task to be assigned according to the task allocation method corresponding to the optimal solution.

[0040] In a second aspect, the present invention provides a cloud computing resource dynamic scheduling optimization system, which adopts the following technical solutions:

[0041] A cloud computing resource dynamic scheduling optimization system includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned cloud computing resource dynamic scheduling optimization method is implemented.

[0042] By adopting the above technical solution, the above-mentioned cloud computing resource dynamic scheduling optimization method is generated into a computer program and stored in a memory to be loaded and executed by a processor, thereby making a terminal device based on the memory and the processor for easy use.

[0043] The present invention has the following technical effects:

[0044] The present invention takes into account the relevance of tasks and leaves time for tasks subject to relevance constraints to wait for the issuance of related tasks, thereby preventing the processing of related tasks from being interrupted and improving the consistency of task processing;

[0045] Furthermore, when considering associated constraints for task scheduling, the difference between the required predicted processing time for subsequent scheduled tasks and the subsequent constraint time on the server where the task subject to the associated constraints is located is introduced, thereby limiting the task scheduling within the constraint time, so that the server can be effectively utilized before waiting for the associated tasks to be issued, and at the same time, preventing the associated tasks from being interrupted.

[0046] Furthermore, when considering the associated constraints for task scheduling, weighted waiting time is introduced, so that when scheduling tasks, not only the association of tasks but also the priority of tasks are considered, which effectively prevents only considering the association of tasks and preventing some high-priority tasks from being scheduled first, thereby improving the rationality of task scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flowchart of a method in a cloud computing resource dynamic scheduling optimization method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The embodiment of the present invention discloses a method for dynamically scheduling and optimizing cloud computing resources, referring to Figure 1 , including steps S1 to S3:

[0049] S1: Obtain the task that each server is currently processing and record it as the current task.

[0050] Specifically, the task being processed by each server at the current moment is obtained and recorded as the current task.

[0051] S2: Obtain the correlation between every two tasks and the priority of each task based on the historical processing data of the tasks; filter out the tasks subject to correlation constraints in the current tasks based on the correlation between every two tasks, and obtain the subsequent constraint duration of the tasks subject to correlation constraints, where the subsequent constraint duration is the expected interval duration of the tasks with greater correlation than the tasks subject to correlation constraints; construct a scheduling objective function, which is negatively correlated with the difference between the required predicted processing duration and the subsequent constraint duration of the subsequent scheduled tasks on the server where the tasks subject to correlation constraints are located, and is also negatively correlated with the weighted waiting time of all subsequent scheduled tasks; the weighted waiting time is obtained by weighted calculation of the predicted waiting time of all subsequent scheduled tasks using priority as the weight.

[0052] It should be noted that in order to solve the problem of pre-planning related tasks while taking into account the priority, it is necessary to first obtain the relevance and priority of the tasks.

[0053] S20: Obtain the correlation between every two tasks and the priority of each task based on the historical processing data of the tasks.

[0054] Preferably, as an example, obtaining the relevance between every two tasks and the priority of each task based on the historical processing data of the tasks includes:

[0055] Determine whether there is a data inflow and outflow relationship between the two tasks. If there is a data inflow and outflow relationship, set the basic value of the correlation between the two tasks to 1, otherwise set it to 0;

[0056] Set a time window of preset length and use the Apriori algorithm to calculate the confidence of the association rule between each two tasks based on the data in the time window;

[0057] The correlation between the two tasks is obtained by multiplying the confidence of the association rule by the correlation basis value.

[0058] The priority of each task is set manually, or the priority of the task is set using a priority setting network, where the priority setting network can use an existing network, such as an XGBoost network.

[0059] It's understandable that only when there's a data flow between two tasks, and they frequently occur together within a short timeframe, can they be considered interconnected. In other words, the processing of one task requires the data of the other, and the two tasks cannot be processed independently. Therefore, these tasks should be assigned to the same server, and there shouldn't be too many other tasks between them to ensure continuity between them.

[0060] It should be noted that the use of the Apriori algorithm to calculate the confidence of the association rule between each two tasks is an existing technology and will not be described in detail here.

[0061] S21: Filter out tasks subject to association constraints from the current tasks according to the association between every two tasks, and obtain subsequent constraint durations of the tasks subject to association constraints.

[0062] It's important to note that sometimes the current task's dependent tasks haven't been issued yet. To prevent subsequent dependent tasks from being interrupted due to processor idleness, it's necessary to predict whether there will be any dependent tasks after the current task. This allows for some time to be reserved for tasks with a high probability of being dependent. First, historical data must be analyzed to predict whether the current task has any dependent tasks and to set an appropriate reserved time based on the historical intervals between the two tasks being issued.

[0063] Preferably, as an example, tasks subject to association constraints are screened out from the current tasks according to the association between each two tasks, and subsequent constraint durations of the tasks subject to association constraints are obtained, including:

[0064] Obtain the time interval between the task subject to the associated constraint and other tasks based on the task's historical processing data, and obtain the duration of the task subject to the associated constraint;

[0065] If the correlation between the current task and other tasks is greater than the preset correlation threshold, the current task is determined to be a task suspected of being subject to correlation constraints;

[0066] If the time interval between the task suspected of being subject to association constraints and other tasks is greater than the time duration of the current task, the task suspected of being subject to association constraints will be determined as a task subject to association constraints.

[0067] On the contrary, a task suspected to be subject to association constraints is not a task subject to association constraints.

[0068] The other tasks with the greatest correlation with the task subject to the correlation constraint are recorded as target tasks;

[0069] The difference between the release time interval and the released duration is used as the subsequent constraint duration of the task subject to the associated constraint.

[0070] It's important to note that the time interval between the task subject to the constraint and the target task is predicted based on historical data analysis. The time interval between the two tasks is the time it took to complete the task. The difference between the time interval and the time interval reflects the predicted time it will take to complete the task. Therefore, by setting the predicted time interval after each task subject to the constraint, we reserve some time for the task, preventing interruptions between the two tasks.

[0071] It should be added that the method for obtaining the time interval between the task subject to the association constraint and other tasks based on the historical processing data of the task includes:

[0072] Record the other tasks being analyzed as research tasks;

[0073] In the historical processing data of the task, the task that is the same as the task subject to the association constraint is obtained and recorded as the benchmark task, and the task that is the same as the research task is obtained and recorded as the reference task;

[0074] The time interval between each benchmark task and the nearest reference task is obtained, and the average of the time intervals between all benchmark tasks and the nearest reference task is used as the time interval between the task subject to the association constraint and other tasks.

[0075] S22: Construct a scheduling objective function.

[0076] It's important to note that if the server isn't running while waiting for associated tasks to be delivered, resources will be wasted. Therefore, inserting short-duration tasks while waiting for associated tasks to be delivered can effectively improve resource utilization. Furthermore, since tasks generally have certain priorities, forcing low-priority, short-duration tasks to the front to ensure resource utilization will cause higher-priority tasks to wait longer. Therefore, when scheduling tasks while waiting for associated tasks to be delivered, it's important to consider not only resource utilization but also task priorities.

[0077] Preferably, as an example, constructing a scheduling objective function includes:

[0078] ;

[0079] in, represents the subsequent constraint duration of the i-th task subject to the associated constraint, It represents the predicted processing time required for the jth task to be subsequently scheduled on the server where the ith task subject to the association constraint is located. Indicates the preset anti-zero coefficient, Indicates the number of tasks subsequently scheduled on the server where the i-th task subject to the associated constraint is located. represents the number of tasks subject to the association constraint, Indicates the predicted waiting time of the zth subsequent scheduled task, Indicates the priority of the zth subsequent scheduled task, U indicates the number of subsequent scheduled tasks, Indicates the server utilization rate.

[0080] It is understandable that It reflects the difference between the processing time of the tasks scheduled in the process of waiting for the associated tasks to be issued and the subsequent constraint time. The larger the value, the greater the difference between the processing time of the tasks scheduled in the process of waiting for the associated tasks to be issued and the subsequent constraint time. Therefore, the tasks scheduled in the process of waiting for the associated tasks to be issued either have a long running time, resulting in a more serious situation where the associated tasks cannot be processed in a connected manner; or have a short running time, resulting in low resource utilization of the server, and thus the more unreasonable this scheduling method is. It reflects the weighted waiting time based on priority. The larger the value, the more high-priority tasks have longer waiting times, and thus the more unreasonable the task arrangement is. The larger the value, the higher the server utilization rate, and thus the more reasonable the task arrangement method.

[0081] It should be added that the method for obtaining the predicted processing time includes:

[0082] The tasks that are identical to each task in the historical processing data of the tasks are recorded as analysis tasks, and the average processing time of all analysis tasks is used as the predicted processing time of each task.

[0083] It should be further added that the method for obtaining the predicted waiting time includes:

[0084] Get the predicted processing time for each task. Obtain the pending tasks that precede each task as predecessor pending tasks. Add the predicted processing times of all preceding pending tasks to the predicted remaining processing time of the current task on the corresponding server to obtain the predicted waiting time for each task. The predicted remaining processing time is equal to the predicted processing time of the current task minus the processed time.

[0085] S3: Maximize the value of the scheduling objective function to obtain the corresponding subsequent task allocation method to achieve task allocation.

[0086] Preferably, as an example, maximizing the value of the scheduling objective function to obtain the corresponding subsequent task allocation method to achieve task allocation includes:

[0087] The scheduling objective function is maximized, and an optimization algorithm is used to solve the scheduling objective function to obtain an optimal solution. The task allocation method corresponding to the optimal solution is then determined. Each task to be assigned is assigned to a server according to the task allocation method corresponding to the optimal solution. Existing optimization algorithms can be genetic algorithms or simulated annealing algorithms, and this embodiment does not specifically limit this.

[0088] It can be understood that by maximizing the value of the scheduling objective function, the task arrangement is made most reasonable, and thus the tasks are arranged according to the most appropriate task arrangement method.

[0089] Before finding the optimal solution, it is necessary to clarify the solution space of the scheduling objective function, so the construction method of the solution space needs to be explained below.

[0090] It should be added that the solution space is constructed as follows:

[0091] For any task to be assigned, a server is selected as the target server according to any selection method, and a target server is assigned to each task to be assigned. The corresponding relationship between all tasks to be assigned and servers is regarded as a feasible solution.

[0092] According to different selection methods, the target servers selected for each task to be assigned are different. By using different selection methods to select the target servers, several feasible solutions are obtained, and the space formed by all feasible solutions is used as the solution space.

[0093] An embodiment of the present invention further discloses a cloud computing resource dynamic scheduling optimization system, including a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, a cloud computing resource dynamic scheduling optimization method according to the present invention is implemented.

[0094] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0095] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory, dynamic random access memory, static random access memory, enhanced dynamic random access memory, high bandwidth memory, hybrid memory cube, etc., or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium may be part of, accessible to, or connectable to the device.

Claims

1. A cloud computing resource dynamic scheduling optimization method, characterized in that: Including steps: Obtain the task that each server is currently processing and record it as the current task; According to the historical processing data of the tasks, the correlation between each two tasks and the priority of each task are obtained; according to the correlation between each two tasks, the tasks subject to correlation constraints are screened out in the current tasks, and the subsequent constraint duration of the tasks subject to correlation constraints is obtained, and the subsequent constraint duration is the expected interval duration of the tasks with greater correlation than the tasks subject to correlation constraints; according to the correlation between each two tasks, the tasks subject to correlation constraints are screened out in the current tasks, including: according to the historical processing data of the tasks, the time interval between the tasks subject to correlation constraints and other tasks is obtained, and the duration of the tasks subject to correlation constraints has been obtained; if the correlation between the current task and other tasks is greater than the preset correlation threshold, the current task is determined to be a task suspected of being subject to correlation constraints; if the time interval between the tasks suspected of being subject to correlation constraints and other tasks is greater than the duration of the current task has been issued, the task suspected of being subject to correlation constraints is determined to be a task subject to correlation constraints; otherwise, the task suspected of being subject to correlation constraints is not a task subject to correlation constraints; The subsequent constraint duration of the task subject to the association constraint includes: obtaining other tasks that are most relevant to the task subject to the association constraint as target tasks; and taking the difference between the time interval for issuing tasks and the issued duration as the subsequent constraint duration of the task subject to the association constraint; Construct a scheduling objective function. The scheduling objective function is negatively correlated with the difference between the predicted processing time required for subsequent scheduled tasks and the subsequent constraint time on the server where the task subject to the associated constraint resides. It is also negatively correlated with the weighted waiting time of all subsequent scheduled tasks. The weighted waiting time is calculated by weighting the predicted waiting time of all subsequent scheduled tasks using their priorities as weights. Constructing a scheduling objective function includes: ； in, represents the subsequent constraint duration of the i-th task subject to the associated constraint, It represents the predicted processing time required for the jth task to be subsequently scheduled on the server where the ith task subject to the association constraint is located. Indicates the preset anti-zero coefficient, Indicates the number of tasks subsequently scheduled on the server where the i-th task subject to the associated constraint is located. represents the number of tasks subject to the association constraint, Indicates the predicted waiting time of the zth subsequent scheduled task, Indicates the priority of the zth subsequent scheduled task, U indicates the number of subsequent scheduled tasks, Indicates the server occupancy rate; Methods for obtaining the predicted waiting time include: Get the predicted processing time of each task, get the pending tasks arranged before each task and record them as the predecessor pending tasks, add the cumulative predicted processing time of all the predecessor pending tasks before each task and the predicted remaining processing time of the current task on the corresponding server to get the predicted waiting time of each task; The value of the scheduling objective function is maximized to obtain the corresponding subsequent task allocation method to achieve task allocation.

2. A cloud computing resource dynamic scheduling optimization method according to claim 1, characterized in that: The step of obtaining the correlation between two tasks based on the historical processing data of the tasks includes: Determine whether there is a data inflow and outflow relationship between the two tasks. If there is a data inflow and outflow relationship, set the basic value of the correlation between the two tasks to 1, otherwise set it to 0; Set a time window and use the Apriori algorithm to calculate the confidence of the association rules between each two tasks based on the data in the time window; The correlation between the two tasks is obtained by multiplying the confidence of the association rule by the correlation basis value.

3. The method for dynamic scheduling and optimization of cloud computing resources according to claim 1, characterized in that: Before maximizing the value of the scheduling objective function and obtaining the corresponding subsequent task allocation method, a solution space must be constructed. The method for constructing the solution space includes: For any task to be assigned, a server is selected as the target server according to any selection method, and a target server is assigned to each task to be assigned. The correspondence between all tasks to be assigned and servers is regarded as a feasible solution, and the space formed by all feasible solutions is regarded as the solution space.

4. The method for dynamic scheduling and optimization of cloud computing resources according to claim 1, characterized in that: The method of maximizing the value of the scheduling objective function to obtain the corresponding subsequent task allocation includes: Make the scheduling objective function take the maximum value, use the optimization algorithm to solve the optimal solution of the scheduling objective function, and obtain the task allocation method corresponding to the optimal solution.

5. A cloud computing resource dynamic scheduling optimization method according to claim 4, characterized in that: The method of achieving task allocation includes: Assign servers to each task to be assigned according to the task allocation method corresponding to the optimal solution.

6. A cloud computing resource dynamic scheduling optimization system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a cloud computing resource dynamic scheduling optimization method according to any one of claims 1 to 5 is implemented.

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