Cloud computing system based on distributed multi-task request

Abstract

The present invention relates to the field of cloud computing system design for multi-task requests, and in particular to a cloud computing system based on distributed multi-task requests. The present invention provides a task statistics module for storing historical task data executed by a user terminal and task data newly created by the user terminal; a task parsing module for calling the data stored in the task statistics module, analyzing jump information between tasks to obtain jump sensitivity features, and parsing jump response characterization parameters based on the jump sensitivity features to identify the timeliness of the jump response of the user terminal; and a resource allocation module, which is respectively connected to the task statistics module and the task parsing module, and is used to adaptively allocate resources to the user terminal according to the timeliness of the jump response, thereby ensuring the timely response of jumps between tasks and the normal execution of various task processes under the premise of reasonable allocation of computing resources.

Description

Technical Field

[0001] The present invention relates to the field of cloud computing system design for multi-task requests, and in particular to a cloud computing system based on distributed multi-task requests. Background Art

[0002] Due to the combined effects of factors such as the growth of computing demand, the uneven distribution of computing resources, and the need for load balancing, distributed cloud computing is adopted and appropriate resource allocation methods are selected to improve system performance and efficiency by comprehensively considering task characteristics, system status, and resource limitations. Therefore, the allocation of cloud computing resources is crucial to ensuring efficient task execution and system performance optimization.

[0003] Chinese patent publication number: CN117370032A, discloses a method for optimizing resource allocation of a cloud computing server, including: collecting usage data of various resources of a virtual machine, obtaining expected values ​​of the resource requirements of the virtual machine based on the average value of all usage data of various resources during the virtual machine's working hours, correcting the expected values ​​based on the first requirements of the resource usage data to obtain corrected expected values ​​of the resource requirements; estimating the resource utilization rate of the server based on the corrected expected values ​​of the resource requirements of all enabled virtual machines in the server and the total amount of various resources of the server, and allocating virtual machine resources based on the estimated resource utilization rate of the server and the corrected expected values ​​of the resource requirements of the virtual machines.

[0004] However, the prior art still has the following problems:

[0005] When jumping between tasks, since the user side executes multiple tasks or processes in parallel, there may be insufficient computing resources on the user side, which may easily lead to abnormal situations such as incomplete storage of the current task content, too long response time required to jump from the current task to the next task, and too slow execution of the next task. Summary of the Invention

[0006] To this end, the present invention provides a cloud computing system based on distributed multi-task requests to overcome the problems in the prior art where, when jumping between tasks occurs, the user end may have insufficient current computing resources due to the user end executing multiple tasks or processes in parallel, which may easily lead to abnormal situations such as incomplete storage of the current task content, too long response time required for jumping from the current task to the next task, and too slow execution of the next task.

[0007] To achieve the above objectives, the present invention provides a cloud computing system based on distributed multi-task requests, which includes:

[0008] The task statistics module is used to store the historical task data executed by the user end and the task data newly created by the user end;

[0009] A task parsing module, connected to the task statistics module, is used to call the data stored in the task statistics module, analyze the jump information between tasks, determine other tasks that have a jump relationship with the newly created task on the user side, obtain jump sensitivity features, and parse jump response characterization parameters based on the jump sensitivity features to identify the timeliness of the jump response of the user side. The jump sensitivity features include the response time required for task jump and the CPU load of the user side;

[0010] The resource allocation module is connected to the task statistics module and the task analysis module respectively, and is used to allocate resources to the user terminal according to the timeliness of the jump response, including:

[0011] Improve the number of service nodes allocated to the user end, determine a newly created task on the user end, analyze a task execution degree characterizing parameter based on the user end response characteristics of the task jump operation in the historical task data stored in the task statistics module, determine whether the current resources meet the allocation criteria based on the task execution degree characterizing parameter, and adjust the number of service nodes allocated to the user end based on the task execution degree characterizing parameter;

[0012] Or, adjusting the number of service nodes allocated to the user terminal according to the jump response characterization parameter;

[0013] The user-side response characteristics include the preservation completeness of the task content and the execution time of the jump task.

[0014] Furthermore, the task parsing module is used to analyze the jump information between tasks to obtain jump-sensitive features including:

[0015] Used to determine the jump instructions issued by each task;

[0016] for confirming other tasks that have a jump relationship with the task according to the jump instruction of the task;

[0017] It is used to extract the average response time and average CPU load required for task jump when the task jumps to each task with a jump relationship.

[0018] Furthermore, the task parsing module is used to parse jump response characterization parameters according to jump sensitivity features, including:

[0019]

[0020] In formula (1), M represents the jump response characterization parameter, T represents the average response time required for task jump, T0 represents the response time threshold required for task jump, P represents the average CPU load of the user end after executing the jump, P0 represents the CPU load threshold of the user end, α represents the response time weight coefficient required for jump, and β represents the CPU load weight coefficient.

[0021] Furthermore, the task analysis module is used to identify the timeliness of the jump response of the user terminal, including:

[0022] If the jump response characterization parameter is greater than or equal to the jump response characterization parameter threshold, the jump response of the user terminal is determined to be of a weak timeliness category;

[0023] If the jump response characterization parameter is less than the jump response characterization parameter threshold, the jump response of the user terminal is determined to be of a strong timeliness category.

[0024] Furthermore, the resource allocation module is used to allocate resources to the user terminal according to the timeliness of the jump response, including:

[0025] If the jump response of the user terminal is of a weak timeliness category, the number of service nodes allocated to the user terminal is increased, a newly created task of the user terminal is determined, and based on the user terminal response characteristics of the jump operation of the task in the historical task data stored in the task statistics module, a task execution degree characterization parameter is analyzed, and whether the current resources meet the allocation criteria is determined based on the task execution degree characterization parameter, so as to adjust the number of service nodes allocated to the user terminal based on the task execution degree characterization parameter;

[0026] If the jump response of the user terminal is of a strong timeliness category, the number of service nodes allocated to the user terminal is adjusted according to the jump response characterization parameter.

[0027] Furthermore, the resource allocation module is used to analyze the parameters representing the degree of task execution, including:

[0028] Used to determine the tasks newly created by the user;

[0029] To call the task stack information before the task jump is completed and the task stack information after the task jump is completed in the task statistics module, and calculate the overlap of the task stack information;

[0030] To confirm the overlap as the preservation completeness of the task content;

[0031] The preservation integrity of the task content is used as the first feature;

[0032] used to calculate the ratio of the execution time threshold to the execution time of the task as the second feature;

[0033] The sum of the first feature and the second feature is determined as a parameter representing the degree of task execution.

[0034] Furthermore, the resource allocation module is used to determine whether the current resources meet the allocation criteria, including:

[0035] If the task execution degree characterization parameter is greater than or equal to the task execution degree characterization parameter threshold, it is determined that the current resource meets the allocation criteria;

[0036] If the task execution degree characterization parameter is less than the task execution degree characterization parameter threshold, it is determined that the current resources do not meet the allocation criteria.

[0037] Furthermore, the resource allocation module is used to determine whether to adjust the number of service nodes allocated to the user terminal, including:

[0038] If the current resources do not meet the allocation criteria, the number of service nodes allocated to the user terminal is adjusted based on the task execution degree characterization parameter.

[0039] Furthermore, the resource allocation module is used to adjust the number of service nodes allocated to the user terminal based on the task execution degree characterization parameter.

[0040] The number of service node allocations is increased, and the amount of increase in the number of allocations is negatively correlated with the parameter representing the degree of task execution.

[0041] Furthermore, the resource allocation module is used to adjust the number of service nodes allocated to the user terminal according to the jump response characterization parameter, including:

[0042] The number of allocated service nodes is increased, and the amount of increase in the number of allocated service nodes is positively correlated with the jump response characterization parameter.

[0043] Compared with the prior art, the present invention sets up a task statistics module to store historical task data executed by the user terminal and newly created task data of the user terminal; a task analysis module to call the data stored in the task statistics module, analyze the jump information between tasks to obtain jump sensitivity features, and analyze the jump response characterization parameters based on the jump sensitivity features to identify the timeliness of the jump response of the user terminal; a resource allocation module, which is respectively connected to the task statistics module and the task analysis module, to adaptively allocate resources to the user terminal according to the timeliness of the jump response, and then, under the premise of reasonable allocation of computing resources, ensure the timely response of jumps between tasks and the normal execution of various task processes.

[0044] In particular, the present invention analyzes jump response characterization parameters based on jump sensitive features. During the task jump process, the current existing resources of the user end need to meet the different execution processes of each task. If the existing resources are insufficient, it may affect the process and execution efficiency of tasks that are not allocated resources. For example, the response time required for the process from the end of the current task to the start of the execution process of the next task to be jumped is too long, and the CPU load of the user end is too high after the jump instruction is issued. Therefore, the present application calculates the jump response characterization parameters through the response time required for task jump and the CPU load to characterize the response degree of the user end after executing the jump instruction, and provides data support for the subsequent identification of the timeliness of the jump response of the user end, so as to adaptively allocate resources to the user end, and then, under the premise of reasonable allocation of computing resources, ensure the timely response of jumps between tasks and the normal execution of various task processes.

[0045] In particular, the present invention pre-increases the number of service nodes allocated to the user end to meet the normal execution and jump response of several existing tasks in the task statistics module. On the premise of ensuring the stability of the current user end in executing several tasks, it further analyzes the response status and resource demand level of the user end after creating a new task.

[0046] In particular, the present invention identifies the timeliness of a user-side jump response. For example, a user-side jump response of the weak timeliness category indicates that the user-side responds slowly to a jump operation of any task and that the subsequent jump task execution process is abnormal, reducing the efficiency of task execution. Therefore, the present application uses historical task data stored in the task statistics module to determine the relationship between the newly created task of the user-side and other existing tasks that may potentially undergo a jump operation. It estimates the response characteristics presented by the user-side when a potential jump operation occurs and analyzes a task execution degree characterization parameter to characterize the completeness of the current task content and the execution degree of the next task to be jumped. Furthermore, it determines whether the current resources of the user-side meet the allocation criteria and whether they can meet the different execution processes of the current task and the next task to be jumped. The task execution degree characterization parameter is calculated based on the completeness of the current task content and the execution time of the jump task, and the number of service nodes allocated to the user-side is adjusted. Furthermore, under the premise of reasonable allocation of computing resources, slow task execution and system overload are avoided, and timely response to jumps between tasks and the normal execution of various task processes are ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A functional module diagram of a cloud computing system based on distributed multi-task requests according to an embodiment of the invention;

[0048] Figure 2 A logic decision diagram for identifying the timeliness of a jump response of a user terminal according to an embodiment of the invention;

[0049] Figure 3 A logical decision diagram for determining whether the current resources meet the allocation criteria according to the embodiment of the invention

[0050] Figure 4 This is a logic decision diagram for determining whether to adjust the number of service nodes allocated to a user terminal according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0052] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0053] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the term "connection" should be understood in a broad sense. For example, it can refer to a fixed connection, a detachable connection, or an integral connection; it can refer to a mechanical connection or an electrical connection. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0054] See also Figures 1 to 4 As shown, Figure 1 This is a functional module diagram of a cloud computing system based on distributed multi-task requests according to an embodiment of the present invention. Figure 2 A logical decision diagram for identifying the timeliness of a jump response of a user terminal according to an embodiment of the invention. Figure 3 A logical decision diagram for determining whether current resources meet allocation criteria according to an embodiment of the invention. Figure 4 This is a logical decision diagram for determining whether to adjust the number of service nodes allocated to a user terminal according to an embodiment of the invention. The cloud computing system based on distributed multi-task requests according to an embodiment of the invention includes:

[0055] The task statistics module is used to store the historical task data executed by the user end and the task data newly created by the user end;

[0056] A task parsing module, connected to the task statistics module, is used to call the data stored in the task statistics module, analyze the jump information between tasks, determine other tasks that have a jump relationship with the newly created task on the user side, obtain jump sensitivity features, and parse jump response characterization parameters based on the jump sensitivity features to identify the timeliness of the jump response of the user side. The jump sensitivity features include the response time required for task jump and the CPU load of the user side;

[0057] The resource allocation module is connected to the task statistics module and the task analysis module respectively, and is used to allocate resources to the user terminal according to the timeliness of the jump response, including:

[0058] Improve the number of service nodes allocated to the user end, determine a newly created task on the user end, analyze a task execution degree characterizing parameter based on the user end response characteristics of the task jump operation in the historical task data stored in the task statistics module, determine whether the current resources meet the allocation criteria based on the task execution degree characterizing parameter, and adjust the number of service nodes allocated to the user end based on the task execution degree characterizing parameter;

[0059] Or, adjusting the number of service nodes allocated to the user terminal according to the jump response characterization parameter;

[0060] The user-side response characteristics include the preservation completeness of the task content and the execution time of the jump task.

[0061] Specifically, there is no limitation on the specific structure of the task statistics module, which may be a database for storing information, and the historical task data may be stored in the database, which will not be described in detail.

[0062] Specifically, there is no limitation on the specific structure of the task statistics module, task analysis module, resource allocation module and the modules. They themselves or each unit therein can be composed of logical components or a combination of logical components, and the logical components include field programmable processors, computers or microprocessors in computers.

[0063] Specifically, the task parsing module is used to analyze the jump information between tasks to obtain jump sensitive features including:

[0064] Used to determine the jump instructions issued by each task;

[0065] for confirming other tasks that have a jump relationship with the task according to the jump instruction of the task;

[0066] It is used to extract the average response time and average CPU load required for task jump when the task jumps to each task with a jump relationship.

[0067] It is understandable that if a jump occurs between two tasks, then there is a jump relationship between the two tasks.

[0068] Specifically, the task parsing module is used to parse jump response characterization parameters based on jump sensitivity features, including:

[0069]

[0070] In formula (1), M represents the jump response characterization parameter, T represents the average response time required for task jump, T0 represents the response time threshold required for task jump, P represents the average CPU load of the user end after executing the jump, P0 represents the CPU load threshold of the user end, α represents the response time weight coefficient required for jump, and β represents the CPU load weight coefficient.

[0071] The response time required for task jump is the response time from the end of the current task to the start of the execution of the jumped task.

[0072] In this embodiment, the time from the end of the current task process to the start of the execution process of the next task to be jumped to is regarded as the jump response process. The response time threshold T0 required for task jump and the user-side CPU load threshold P0 are pre-measured. The historical task data executed by the user-side are obtained, and the jump instruction data issued between several tasks are recorded. Combined with the response time data required for task jump and the CPU load data of the user-side after executing the jump instruction, the average response time ΔT required for jump between several tasks and the average CPU load ΔP of the user-side are solved. It is set that T0 = r1×ΔT, P0 = r2×ΔP, r1 is the first precision coefficient, r2 is the second precision coefficient, 1.05<r1<1.15, 0.95<r1<1.05, α is 0.55, and β is 0.45.

[0073] The present invention analyzes jump response characterization parameters based on jump sensitive features. During the task jump process, the current existing resources of the user end need to meet the different execution processes of each task. If the existing resources are insufficient, it may affect the process and execution efficiency of tasks that are not allocated resources. For example, the response time required for the process from the end of the current task to the start of the execution process of the next task to be jumped is too long, and the CPU load of the user end is too high after the jump instruction is issued. Therefore, the present application calculates the jump response characterization parameters through the response time required for the task jump and the CPU load to characterize the response degree of the user end after executing the jump instruction, and provides data support for the subsequent identification of the timeliness of the jump response of the user end, so as to adaptively allocate resources to the user end, and then, under the premise of reasonable allocation of computing resources, ensure the timely response of the jump between tasks and the normal execution of various task processes.

[0074] Specifically, the task analysis module is used to identify the timeliness of the jump response of the user terminal, including:

[0075] If the jump response characterization parameter is greater than or equal to the jump response characterization parameter threshold, the jump response of the user terminal is determined to be of a weak timeliness category;

[0076] If the jump response characterization parameter is less than the jump response characterization parameter threshold, the jump response of the user terminal is determined to be of a strong timeliness category.

[0077] The jump response characterization parameter M0 is selected in the range [1.21,1.36].

[0078] Specifically, the resource allocation module is used to allocate resources to the user terminal according to the timeliness of the jump response, including:

[0079] If the jump response of the user terminal is of a weak timeliness category, the number of service nodes allocated to the user terminal is increased, a newly created task of the user terminal is determined, and based on the user terminal response characteristics of the jump operation of the task in the historical task data stored in the task statistics module, a task execution degree characterization parameter is analyzed, and whether the current resources meet the allocation criteria is determined based on the task execution degree characterization parameter, so as to adjust the number of service nodes allocated to the user terminal based on the task execution degree characterization parameter;

[0080] If the jump response of the user terminal is of a strong timeliness category, the number of service nodes allocated to the user terminal is adjusted according to the jump response characterization parameter.

[0081] The present invention increases the number of service nodes allocated to the user end in advance to meet the normal execution and jump response of several existing tasks in the task statistics module. On the premise of ensuring the stability of the current user end in executing several tasks, it further analyzes the response status and resource demand level of the user end after creating a new task.

[0082] Specifically, the resource allocation module is used to analyze the task execution level characterization parameters, including:

[0083] Used to determine the tasks newly created by the user;

[0084] To call the task stack information before the task jump is completed and the task stack information after the task jump is completed in the task statistics module, and calculate the overlap of the task stack information;

[0085] To confirm the overlap as the preservation completeness of the task content;

[0086] The preservation integrity of the task content is used as the first feature;

[0087] used to calculate the ratio of the execution time threshold to the execution time of the task as the second feature;

[0088] The sum of the first feature and the second feature is determined as a parameter representing the degree of task execution.

[0089] It is understandable that there are multiple records of task completion jumps in the historical task data. Therefore, the overlap is the average overlap of the task stack information before and after the task completion jumps, and the execution duration is the average execution duration of the multiple tasks.

[0090] In this embodiment, the task execution program characterization parameter is determined according to the following formula:

[0091]

[0092] Where C represents the parameter representing the degree of task execution, W represents the preservation completeness of the current task content, X0 represents the execution time threshold, and X represents the execution time of the jump task.

[0093] The execution time threshold X0 is obtained from the pre-measurement point. The execution time data of several tasks after jumping are obtained, and the average execution time ΔX is solved. It is set that X0 = z × ΔX, where z is the offset coefficient, 1.05<r1<1.15.

[0094] The execution duration is the time it takes from the start of the jump task to the completion of the execution after the jump is completed.

[0095] Specifically, the information covered by the task itself includes stack information, memory mapping, and task status. Among them, the stack information saves information such as local variables, function parameters, and return addresses. The stack information content before the current task jumps stored in the task statistics module is compared with the stack information content after the current task jumps. The overlap between the two versions of the stack information content is identified to determine the completeness of the current task content.

[0096] Specifically, during the task jump process, saving the current task content is a key process to ensure that the task can maintain its status unchanged and the integrity of the task content when it is resumed later, ensuring that the task can be smoothly connected and continued.

[0097] Specifically, the resource allocation module is used to determine whether the current resources meet the allocation criteria, including:

[0098] If the task execution degree characterization parameter is greater than or equal to the task execution degree characterization parameter threshold, it is determined that the current resources meet the allocation criteria.

[0099] If the task execution degree characterization parameter is less than the task execution degree characterization parameter threshold, it is determined that the current resources do not meet the allocation criteria.

[0100] The task execution program characterization parameter C0 is selected in the interval [1.83,1.94].

[0101] Specifically, the resource allocation module is used to determine whether to adjust the number of service nodes allocated to the user terminal, including:

[0102] If the current resources do not meet the allocation criteria, the number of service nodes allocated to the user terminal is adjusted based on the task execution degree characterization parameter.

[0103] Specifically, the resource allocation module is used to adjust the number of service nodes allocated to the user terminal based on the task execution degree characterization parameter.

[0104] The number of service node allocations is increased, and the amount of increase in the number of allocations is negatively correlated with the parameter representing the degree of task execution.

[0105] In this embodiment, optionally,

[0106] Compare the task execution degree characterization parameter C with the first task execution degree characterization parameter comparison threshold C1 and the second task execution degree characterization parameter comparison threshold C2,

[0107] If C>C2, the allocation quantity increase amount is determined to be the first allocation quantity increase amount a1, and a1=[0.27a0] is set;

[0108] If C1≤C≤C2, then the allocation quantity increase amount is determined to be the second allocation quantity increase amount a2, and a2=[0.33a0] is set;

[0109] If C<C1, the allocation quantity increase amount is determined to be the third allocation quantity increase amount a3, and a3=[0.41a0] is set;

[0110] Where a0 represents the number of initially allocated service nodes, C1 = 0.7C0, and C2 = 0.9C0.

[0111] Regarding the initial number of service nodes to be allocated, those skilled in the art can determine it based on the performance indicators, hardware resources, cloud service quotas, and task requirements of the user end, which will not be elaborated here.

[0112] The present invention identifies the timeliness of a user-side jump response. For example, a user-side jump response of a weak timeliness category indicates that the user-side responds slowly to a jump operation of any task and that the subsequent jump task execution process is abnormal, thereby reducing task execution efficiency. Therefore, the present application uses historical task data stored in a task statistics module to determine the relationship between the newly created task of the user-side and other existing tasks that may potentially undergo a jump operation. It estimates the response characteristics presented by the user-side when a potential jump operation occurs, analyzes a task execution degree characterization parameter to characterize the completeness of the current task content and the execution degree of the next task to be jumped. Furthermore, it determines whether the current resources of the user-side meet allocation standards and can meet the different execution processes of the current task and the next task to be jumped. The task execution degree characterization parameter is calculated based on the completeness of the current task content and the execution time of the jump task, and the number of service nodes allocated to the user-side is adjusted. Furthermore, under the premise of reasonable allocation of computing resources, slow task execution and system overload are avoided, and timely response to jumps between tasks and the normal execution of various task processes are ensured.

[0113] Specifically, the resource allocation module is used to adjust the number of service nodes allocated to the user terminal according to the jump response characterization parameter, including:

[0114] The number of allocated service nodes is increased, and the amount of increase in the number of allocated service nodes is positively correlated with the jump response characterization parameter.

[0115] In this embodiment, optionally,

[0116] The jump response characterization parameter M is compared with the first jump response characterization parameter comparison threshold M1 and the second jump response characterization parameter comparison threshold M2.

[0117] If M>M2, the allocation quantity increase amount is determined to be the first allocation quantity increase amount a1, and a1=[0.43a0] is set;

[0118] If M1≤M≤M2, then the allocation quantity increase amount is determined to be the second allocation quantity increase amount a2, and a2=[0.36a0] is set;

[0119] If M<M1, the allocation quantity increase amount is determined to be the third allocation quantity increase amount a3, and a3=[0.28a0] is set;

[0120] Where a0 represents the number of initially allocated service nodes, M1 = 0.75M0, M2 = 0.85M0.

[0121] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A cloud computing system based on distributed multi-task requests, characterized in that: include: The task statistics module is used to store the historical task data executed by the user end and the task data newly created by the user end; A task parsing module, connected to the task statistics module, is used to call the data stored in the task statistics module, analyze the jump information between tasks, determine other tasks that have a jump relationship with the newly created task on the user side, obtain jump sensitivity features, and parse jump response characterization parameters based on the jump sensitivity features to identify the timeliness of the jump response of the user side. The jump sensitivity features include the response time required for task jump and the CPU load of the user side; The resource allocation module is connected to the task statistics module and the task analysis module respectively, and is used to allocate resources to the user terminal according to the timeliness of the jump response. include, Improve the number of service nodes allocated to the user end, determine a newly created task on the user end, analyze a task execution degree characterizing parameter based on the user end response characteristics of the task jump operation in the historical task data stored in the task statistics module, determine whether the current resources meet the allocation criteria based on the task execution degree characterizing parameter, and adjust the number of service nodes allocated to the user end based on the task execution degree characterizing parameter; Or, adjusting the number of service nodes allocated to the user terminal according to the jump response characterization parameter; The user-side response characteristics include the preservation completeness of the task content and the execution time of the jump task.

2. The cloud computing system based on distributed multi-task request according to claim 1, characterized in that: The task parsing module is used to analyze the jump information between tasks to obtain jump sensitive features including: Used to determine the jump instructions issued by each task; for confirming other tasks that have a jump relationship with the task according to the jump instruction of the task; It is used to extract the average response time and average CPU load required for task jump when the task jumps to each task with a jump relationship.

3. The cloud computing system based on distributed multi-task request according to claim 1, characterized in that: The task parsing module is used to parse jump response characterization parameters according to jump sensitivity features, including: In formula (1), M represents the jump response characterization parameter, T represents the average response time required for task jump, T0 represents the response time threshold required for task jump, P represents the average CPU load of the user end after executing the jump, P0 represents the CPU load threshold of the user end, α represents the response time weight coefficient required for jump, and β represents the CPU load weight coefficient.

4. The cloud computing system based on distributed multi-task request according to claim 1, characterized in that: The task analysis module is used to identify the timeliness of the jump response of the user terminal, including: If the jump response characterization parameter is greater than or equal to the jump response characterization parameter threshold, the jump response of the user terminal is determined to be of a weak timeliness category; If the jump response characterization parameter is less than the jump response characterization parameter threshold, the jump response of the user terminal is determined to be of a strong timeliness category.

5. The cloud computing system based on distributed multi-task request according to claim 1, characterized in that: The resource allocation module is used to allocate resources to the user terminal according to the timeliness of the jump response, including: If the jump response of the user terminal is of a weak timeliness category, the number of service nodes allocated to the user terminal is increased, a newly created task of the user terminal is determined, and based on the user terminal response characteristics of the jump operation of the task in the historical task data stored in the task statistics module, a task execution degree characterization parameter is analyzed, and whether the current resources meet the allocation criteria is determined based on the task execution degree characterization parameter, so as to adjust the number of service nodes allocated to the user terminal based on the task execution degree characterization parameter; If the jump response of the user terminal is of a strong timeliness category, the number of service nodes allocated to the user terminal is adjusted according to the jump response characterization parameter.

6. The cloud computing system based on distributed multi-task request according to claim 1, characterized in that: The resource allocation module is used to analyze the task execution level characterization parameters, including: Used to determine the tasks newly created by the user; To call the task stack information before the task jump is completed and the task stack information after the task jump is completed in the task statistics module, and calculate the overlap of the task stack information; To confirm the overlap as the preservation completeness of the task content; The preservation integrity of the task content is used as the first feature; used to calculate the ratio of the execution time threshold to the execution time of the task as the second feature; The sum of the first feature and the second feature is determined as a parameter representing the degree of task execution.

7. The cloud computing system based on distributed multi-task request according to claim 1, characterized in that: The resource allocation module is used to determine whether the current resources meet the allocation criteria, including: If the task execution degree characterization parameter is greater than or equal to the task execution degree characterization parameter threshold, it is determined that the current resource meets the allocation criteria; If the task execution degree characterization parameter is less than the task execution degree characterization parameter threshold, it is determined that the current resources do not meet the allocation criteria.

8. The cloud computing system based on distributed multi-task request according to claim 1, characterized in that: The resource allocation module is used to determine whether to adjust the number of service nodes allocated to the user terminal, including: If the current resources do not meet the allocation criteria, the number of service nodes allocated to the user terminal is adjusted based on the task execution degree characterization parameter.

9. The cloud computing system based on distributed multi-task request according to claim 1, characterized in that: The resource allocation module is used to adjust the number of service nodes allocated to the user terminal based on the task execution degree characterization parameter, The number of service node allocations is increased, and the amount of increase in the number of allocations is negatively correlated with the parameter representing the degree of task execution.

10. The cloud computing system based on distributed multi-task request according to claim 1, characterized in that: The resource allocation module is used to adjust the number of service nodes allocated to the user terminal according to the jump response characteristic parameter, including: The number of allocated service nodes is increased, and the amount of increase in the number of allocated service nodes is positively correlated with the jump response characterization parameter.

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