A scheduling method and system based on cloud resource computing power allocation

Abstract

The present invention is applicable to the technical field of computing power scheduling, and particularly relates to a scheduling method and system based on cloud resource computing power allocation. The method includes: S100: Obtain the usage objects of cloud resource computing power, extract common tasks, create isolation containers corresponding to the common tasks one by one, receive resource usage requests uploaded by tenants, and connect the resource usage requests to the isolation containers, configure resource limits, and establish a corresponding relationship between the isolation containers and the resource limits; S200: Locate the hardware units for cloud resource computing power allocation. By sorting the isolation containers according to the importance level, the present invention can preferentially ensure the normal operation of the isolation containers with higher importance, reduce the anomalies of the isolation containers, ensure the service quality of the tenants, and through reconstructing the remaining containers, can balance the load, effectively avoid single-point failures, greatly improve the response speed of the hardware units, and ensure the efficient and stable operation of cloud resources.

Description

Technical Field

[0001] The present invention relates to the technical field of computing power scheduling, and particularly to a scheduling method and system based on cloud resource computing power allocation. Background Art

[0002] When performing cloud resource computing power allocation, it is usually necessary to isolate the computing, storage, and network resources between different tenants to ensure that the computing tasks of different tenants do not interfere with each other, and at the same time ensure that the resources such as the computing environment, storage space, and network bandwidth of each tenant are logically separated; and when performing cloud resource computing power allocation, it is also necessary to perform resource balancing, where the goal of resource balancing is to maximize resource utilization and processing capacity, achieve load balancing through dynamic scheduling of resources, and avoid over-concentration of resources, resulting in idle or overloaded resources for some tenants.

[0003] It can be seen that there is a contradictory relationship between resource isolation and balancing; therefore, "how to isolate tasks and balance the loads between different tenants" is the technical problem to be solved by the present invention. Summary of the Invention

[0004] The purpose of the present invention is to provide a scheduling method and system based on cloud resource computing power allocation to solve the problem of "how to isolate tasks and balance the loads between different tenants" proposed in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] A scheduling method based on cloud resource computing power allocation, the method includes:

[0007] S100: Obtain the usage objects of cloud resource computing power, extract the common tasks, create isolation containers corresponding one by one to the common tasks, receive the resource usage requests uploaded by tenants, and connect the resource usage requests to the isolation containers, configure resource limits, and establish the corresponding relationship between the isolation containers and the resource limits;

[0008] S200: Find out the hardware units for cloud resource computing power allocation, form a virtualization management platform, connect the hardware units to the virtualization management platform, read out the resource consumption of the hardware units, and cluster the hardware units into fully loaded units and available units;

[0009] S300: Split the isolation containers into dormant containers and running containers, and judge whether the available units meet the resource consumption of the dormant containers;

[0010] If not, configure the correspondence between the available units and the scheduling containers, activate the pre-built startup policy, determine the importance level of each isolation container, sort all the dormant containers in descending order of the importance level, and successively connect the dormant containers to the available units according to the sorting result, find the remaining containers, and use the startup policy to reconstruct the remaining containers.

[0011] Further, the S100 includes:

[0012] Embed a monitoring mechanism into the usage object to obtain the usage request uploaded by the usage object, where the usage request at least includes: an isolation container item and a time item;

[0013] Generate a record log when the usage request is responded to.

[0014] Further, the S100 further includes:

[0015] Create a cross-reference table, where the cross-reference table includes: an isolation container item and a usage object item;

[0016] Establish the mapping between the tenant, the usage object, and the hardware unit, generate a label using the mapping, and insert the label into the cross-reference table.

[0017] Further, the S200 includes:

[0018] Split the resource consumption to obtain several single items, quantify the single items, and configure weight values corresponding to the single items one by one;

[0019] Integrate the quantified single items and the weight values to obtain a consumption score, compare the consumption score with a preset score threshold, and cluster the hardware units into fully loaded units and available units according to the comparison result.

[0020] Further, the S300 includes:

[0021] Embed a load balancing policy into the isolation container to determine the load threshold of each isolation container;

[0022] When the consumption score is greater than the load threshold, activate the load balancing policy.

[0023] Further, the S300 further includes:

[0024] Create a resource management pool and mount the hardware unit to the resource management pool;

[0025] Record the change process of the resource management pool and generate a version snapshot.

[0026] Further, the S300 further includes:

[0027] Determine the deployment location of the edge device and establish a communication link between the edge device and the resource management pool;

[0028] Configure the attributes of the isolation container, select the edge container via the attributes, and connect the edge container and the remaining containers to the edge device.

[0029] Further, the system includes:

[0030] A creation module, configured to obtain the usage object of the cloud resource computing power, extract the common tasks, create isolation containers corresponding to the common tasks one by one, receive the resource usage requests uploaded by the tenants, and connect the resource usage requests to the isolation containers, configure resource limits, and establish the corresponding relationship between the isolation containers and the resource limits;

[0031] A clustering module, configured to find the hardware units for cloud resource computing power allocation, form a virtualization management platform, connect the hardware units to the virtualization management platform, read the resource consumption of the hardware units, and cluster the hardware units into fully loaded units and available units;

[0032] A reconstruction module, configured to split the isolation container into a dormant container and a running container, determine whether the available unit satisfies the resource consumption of the dormant container. If not, configure the corresponding relationship between the available unit and the scheduling container, activate the pre-constructed startup policy, determine the importance of each isolation container, sort all the dormant containers in descending order of the importance, and connect the dormant containers to the available units in sequence according to the sorting result, find the remaining containers, and reconstruct the remaining containers using the startup policy.

[0033] Further, the creation module includes:

[0034] An acquisition unit, configured to embed a monitoring mechanism into the usage object and obtain the usage requests uploaded by the usage object, where the usage requests at least include: isolation container items and time items;

[0035] A generation unit, configured to generate a record log after the usage request is responded to;

[0036] A creation unit, configured to create a comparison table, where the comparison table includes: isolation container items and usage object items;

[0037] An insertion unit, configured to establish the mapping between the tenant, the usage object, and the hardware unit, generate a label using the mapping, and insert the label into the comparison table.

[0038] Further, the clustering module includes:

[0039] A configuration unit, configured to split the resource consumption to obtain a number of single items, quantify the single items, and configure weight values corresponding to the single items one by one;

[0040] A comparison unit, configured to integrate the quantified single items and weight values to obtain a consumption score, compare the consumption score with a preset score threshold, and cluster the hardware units into fully loaded units and available units according to the comparison result.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] By determining common tasks, it is possible to provide data support for resource allocation and scheduling. By creating isolation containers, it is possible to provide an independent running environment for common tasks, minimize the attack surface, and greatly improve the security and stability of the running environment of common tasks. By configuring resource limits, it is possible to effectively avoid cloud resource contention, isolate and prevent malicious behaviors, and further improve the stability of common tasks. By constructing a virtualization management platform, it is possible to maximize the utilization rate of hardware units, thereby improving the utilization efficiency and flexibility of overall resources. By sorting the isolation containers according to the importance level, it is possible to give priority to ensuring the normal operation of the isolation containers with higher importance, reduce isolation container anomalies, and ensure the service quality of tenants. By reconstructing the remaining containers, it is possible to balance the load, effectively avoid single point of failure, greatly improve the response speed of hardware units, and ensure the efficient and stable operation of cloud resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flowchart of a scheduling method based on cloud resource computing power allocation provided by an embodiment of the present invention;

[0044] Figure 2 It is a first sub-flowchart of a scheduling method based on cloud resource computing power allocation provided by an embodiment of the present invention;

[0045] Figure 3 It is a second sub-flowchart of a scheduling method based on cloud resource computing power allocation provided by an embodiment of the present invention;

[0046] Figure 4 It is a third sub-flowchart of a scheduling method based on cloud resource computing power allocation provided by an embodiment of the present invention;

[0047] Figure 5 It is a block diagram of the composition of a scheduling system based on cloud resource computing power allocation provided by an embodiment of the present invention;

[0048] Figure 6 It is a block diagram of the composition of an establishment module in a scheduling system based on cloud resource computing power allocation provided by an embodiment of the present invention;

[0049] Figure 7 This is the block diagram of the clustering module in the scheduling system for cloud resource computing power allocation provided by the embodiments of the present invention;

[0050] Figure 8 This is the block diagram of the reconstruction module in the scheduling system for cloud resource computing power allocation provided by the embodiments of the present invention. Detailed implementation manners

[0051] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying 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.

[0052] In Embodiment 1, Figure 1 The implementation process of the scheduling method for cloud resource computing power allocation provided by the embodiments of the present invention is shown, and the details are as follows:

[0053] S100: Obtain the usage objects of cloud resource computing power, extract common tasks, create isolation containers corresponding to the common tasks one by one, receive the resource usage requests uploaded by tenants, and connect the resource usage requests to the isolation containers, configure resource limits, and establish the corresponding relationship between the isolation containers and the resource limits.

[0054] Determine the usage objects of cloud resource computing power, where the usage objects can be computing clusters, virtual machines or other specific programs using cloud resources, etc.; extract specific tasks from the usage objects, determine the trigger frequency of the tasks, and divide the tasks into common tasks and non-common tasks according to the trigger frequency; create corresponding isolation containers for each common task, receive the resource usage requests uploaded by tenants, analyze the resource requirements in the resource usage requests (such as CPU, memory, storage, network bandwidth, etc. requirements), and connect the resource usage requests to the corresponding isolation containers.

[0055] Configure resource limits according to the resource requirements in each isolation container, such as setting the maximum number of CPU cores, memory limits, and storage space of the isolation containers, etc., to ensure that the isolation containers will not exceed the predetermined resource range during operation, and avoid resource abuse or conflicts, where the resource limits are pre-determined by professionals.

[0056] S200: Find out the hardware units for cloud resource computing power allocation, form a virtualization management platform, connect the hardware units to the virtualization management platform, read out the resource consumption of the hardware units, and cluster the hardware units into fully loaded units and available units.

[0057] Locate the hardware units for cloud resource computing power allocation. The hardware units are the devices on which the objects of operation are used. Integrate all the hardware units to form a virtualization management platform. Through software virtualization technology, abstract the hardware units into virtual resources so as to flexibly allocate and schedule them to different objects of use. For example, computing resources such as CPUs and memories can be divided into multiple virtual instances, and storage devices can be virtualized into multiple storage volumes; integrate all the virtual resources to generate a virtualization management platform.

[0058] Through the virtualization management platform, conduct real-time monitoring of all the hardware units to obtain the resource consumption data of each hardware unit, including CPU usage rate, memory occupancy, and storage space usage, etc., and based on the resource consumption data, identify which hardware units are in a fully loaded state, that is, the resource consumption is close to or reaches its maximum carrying capacity, and which hardware units are in an available state, that is, the resource consumption is low and there is still a certain margin, so as to determine the fully loaded units and available units.

[0059] S300: Split the isolation container into a dormant container and a running container, and determine whether the available unit can meet the resource consumption of the dormant container;

[0060] If not, configure the corresponding relationship between the available unit and the scheduling container, activate the pre-built startup policy, determine the importance level of each isolation container, sort all the dormant containers in descending order of the importance level, and according to the sorting result, connect the dormant containers to the available units in sequence, find the remaining containers, and use the startup policy to reconstruct the remaining containers.

[0061] According to the running status of the isolation container, divide the isolation container into a dormant container and a running container. If the available unit can meet the resource consumption of the dormant container, that is, the available unit can process all the dormant containers, then activate the pre-built startup policy. The startup policy is: after the dormant container starts, according to the corresponding relationship between the isolation container and the common tasks, connect the dormant container to the corresponding available unit; determine the importance level of each common task, sort all the common tasks in descending order of the importance level, and at the same time determine the sorting of the running container and the dormant container; connect all the dormant containers to the available units in sequence in descending order of the importance level.

[0062] After the connection is completed, find the remaining containers that have not been processed, integrate the load balancing policy into the startup policy, use the startup policy and all the hardware units to process the remaining containers, and balance the load.

[0063] If the available units can meet the resource consumption of the dormant container, after receiving the resource usage request uploaded by the tenant, directly start the corresponding dormant container and process it using the available units.

[0064] In Embodiment 2, Figure 2 The implementation process of the scheduling method based on cloud resource computing power allocation provided by the embodiment of the present invention is shown. The following details S100 as follows:

[0065] S101: Embed a monitoring mechanism into the usage object to obtain the usage request uploaded by the usage object, where the usage request at least includes: an isolated container item and a time item.

[0066] Integrate a task monitoring tool in each usage object. The task monitoring tool can track and record the resource consumption situation, task execution status, system health status, etc. of the usage object in real time. After receiving the usage request uploaded by the usage object, split out the isolated container item and the time item from it. The isolated container item refers to the identifier corresponding to the isolated container that needs to be enabled, and the time item is the time when the usage request is generated.

[0067] S102: Generate a record log when the usage request is responded to.

[0068] When the usage request is responded to by the hardware unit, generate a record log to record the entire process of the usage request from generation, being responded to, and starting the corresponding isolated container.

[0069] In Embodiment 3, Figure 2 The implementation process of the scheduling method based on cloud resource computing power allocation provided by the embodiment of the present invention is shown. The following continues to detail S100 as follows:

[0070] S103: Create a comparison table, where the comparison table includes: an isolated container item and a usage object item.

[0071] Create a comparison table to configure the corresponding relationship between the isolated container and the usage object; by constructing the corresponding relationship, the source corresponding to the ongoing task can be quickly determined, so as to better perform load balancing and prediction.

[0072] S104: Establish a mapping between the tenant, the usage object, and the hardware unit, and use the mapping to generate a label and insert the label into the comparison table.

[0073] Determine the mapping between the tenant, the usage object, and the hardware unit. By constructing the mapping, better resource management can be performed, and at the same time, it is convenient for fault troubleshooting; generate a label using the mapping and insert the label into the comparison table.

[0074] In Embodiment 4, Figure 3The implementation process of the scheduling method based on cloud resource computing power allocation provided by the embodiments of the present invention is shown. The following details S200 as follows:

[0075] S201: Split the resource consumption to obtain several single items, quantify the single items, and configure weight values corresponding to the single items one by one.

[0076] The resource consumption is split to obtain single items such as CPU usage rate single item, memory occupancy single item, disk single item, and network bandwidth single item, etc. Each single item is quantified and given a weight value.

[0077] S202: Integrate the quantified single items and weight values to obtain consumption scores, compare the consumption scores with a preset score threshold, and cluster the hardware units into fully loaded units and available units according to the comparison results.

[0078] Multiply the quantization value of each single item by the weight value and perform weighting to obtain the consumption score of each hardware unit. If the consumption score is greater than the score threshold, the corresponding hardware unit is defined as a fully loaded unit; otherwise, it is defined as an available unit.

[0079] In Embodiment 5, Figure 4 The implementation process of the scheduling method based on cloud resource computing power allocation provided by the embodiments of the present invention is shown. The following details S300 as follows:

[0080] S301: Embed a load balancing policy into the isolation container to determine the load threshold of each isolation container.

[0081] S302: When the consumption score is greater than the load threshold, activate the load balancing policy.

[0082] A load threshold is set for each hardware unit. The load balancing policy is: if the consumption score is greater than the load threshold, the part exceeding the load threshold is transferred to other hardware units.

[0083] In Embodiment 6, Figure 4 The implementation process of the scheduling method based on cloud resource computing power allocation provided by the embodiments of the present invention is shown. The following continues to detail S300 as follows:

[0084] S303: Create a resource management pool and mount the hardware units to the resource management pool.

[0085] Integrate all cloud resource computing power to create a resource management pool, which is deployed in a virtualization management platform and is mainly used to uniformly manage and allocate all hardware units.

[0086] S304: Record the change process of the resource management pool and generate a version snapshot.

[0087] Record the change process of the resource management pool, and embed a version control mechanism in the resource management pool. The version control mechanism is mainly used to track and record the status updates or configuration changes of the resource management pool. The resource management pool can monitor operations such as the addition, removal, status change, or attribute update of hardware units in real time, record these operations in detail in the form of logs, and define the obtained logs as version snapshots.

[0088] In Embodiment 7, Figure 4 The implementation process of the scheduling method based on cloud resource computing power allocation provided by the embodiment of the present invention is shown. The following further details S300 as follows:

[0089] S305: Determine the deployment location of the edge device, and establish a communication link between the edge device and the resource management pool.

[0090] Determine the deployment location of the edge device, and after the deployment is completed, connect the edge device to the resource management pool.

[0091] S306: Configure the attributes of the isolation container, select the edge container via the attributes, and connect the edge container and the remaining containers to the edge device.

[0092] Configure the attributes of the isolation container, where the attributes are task type, resource requirements, priority, data traffic characteristics, and latency sensitivity, etc.; and select the isolation container suitable for running in the edge device according to the attributes, determine it as the edge container, and connect the edge container and the remaining containers to the edge device; in other words, connect the tasks suitable for running in the edge device and the tasks beyond the cloud resource computing power to the edge device for processing.

[0093] Figure 5 The block diagram of the composition structure of the scheduling system based on cloud resource computing power allocation provided by the embodiment of the present invention is shown. The scheduling system 1 based on cloud resource computing power allocation includes:

[0094] A creation module 11, configured to obtain the usage objects of cloud resource computing power, extract common tasks, create isolation containers corresponding to the common tasks one by one, receive the resource usage requests uploaded by tenants, and connect the resource usage requests to the isolation containers, configure resource limits, and establish the corresponding relationship between the isolation containers and the resource limits;

[0095] A clustering module 12, configured to find the hardware units for cloud resource computing power allocation, form a virtualization management platform, connect the hardware units to the virtualization management platform, read the resource consumption of the hardware units, and cluster the hardware units into fully loaded units and available units;

[0096] The reconstruction module 13 is configured to determine ongoing real-time tasks based on the fully loaded units, divide the isolation containers into dormant containers and running containers, determine whether the available units meet the resource consumption of the dormant containers. If not, configure the corresponding relationship between the available units and the scheduling containers, activate a pre-built startup policy, determine the importance level of each isolation container, sort all the dormant containers in descending order of the importance level, and successively connect the dormant containers to the available units according to the sorting result. Search for the remaining containers and use the startup policy to reconstruct the remaining containers.

[0097] Figure 6 The composition structure block diagram of the scheduling system provided by the embodiment of the present invention based on cloud resource computing power allocation is shown. The establishment module 11 includes:

[0098] An acquisition unit 111, configured to embed a monitoring mechanism into the usage object and acquire a usage request uploaded by the usage object, where the usage request at least includes: an isolation container item and a time item;

[0099] A generation unit 112, configured to generate a record log after the usage request is responded to;

[0100] A creation unit 113, configured to create a comparison table, where the comparison table includes: an isolation container item and a usage object item;

[0101] An insertion unit 114, configured to establish a mapping between the tenant, the usage object, and the hardware unit, generate a label using the mapping, and insert the label into the comparison table.

[0102] Figure 7 The composition structure block diagram of the scheduling system provided by the embodiment of the present invention based on cloud resource computing power allocation is shown. The clustering module 12 includes:

[0103] A configuration unit 121, configured to split the resource consumption to obtain a number of single items, quantify the single items, and configure weight values corresponding to the single items one by one;

[0104] A comparison unit 122, configured to integrate the quantified single items and weight values to obtain a consumption score, compare the consumption score with a preset score threshold, and cluster the hardware units into fully loaded units and available units according to the comparison result.

[0105] Figure 8 The composition structure block diagram of the scheduling system provided by the embodiment of the present invention based on cloud resource computing power allocation is shown. The reconstruction module 13 includes:

[0106] A determination unit 131, configured to embed a load balancing policy into the isolation container and determine the load threshold for each isolation container;

[0107] An activation unit 132, configured to activate the load balancing policy when the consumption score is greater than the load threshold;

[0108] A mounting unit 133, configured to create a resource management pool and mount the hardware unit into the resource management pool;

[0109] A recording unit 134, configured to record the change process of the resource management pool and generate a version snapshot;

[0110] An establishment unit 135, configured to determine the deployment location of the edge device and establish a communication link between the edge device and the resource management pool;

[0111] An access unit 136, configured to configure the attributes of the isolation container, select the edge container based on the attributes, and access the edge container and the remaining containers to the edge device.

[0112] Among them, the establishment module 11 is mainly configured to complete step S100, the clustering module 12 is mainly configured to complete step S200, and the reconstruction module 13 is mainly configured to complete step S300;

[0113] The acquisition unit 111 is mainly configured to complete step S101, the generation unit 112 is mainly configured to complete step S102, the creation unit 113 is mainly configured to complete step S103, and the insertion unit 114 is mainly configured to complete step S104;

[0114] The configuration unit 121 is mainly configured to complete step S201, and the comparison unit 122 is mainly configured to complete step S202;

[0115] The determination unit 131 is mainly configured to complete step S301, the activation unit 132 is mainly configured to complete step S302, the mounting unit 133 is mainly configured to complete step S303, the recording unit 134 is mainly configured to complete step S304, the establishment unit 135 is mainly configured to complete step S305, and the access unit 136 is mainly configured to complete step S306.

[0116] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0117] All functions that can be achieved by the scheduling method based on cloud resource computing power allocation are completed by a computer device. The computer device includes one or more processors and one or more memories. At least one program code is stored in the one or more memories, and the program code is loaded and executed by the one or more processors to implement the functions of the scheduling method based on cloud resource computing power allocation.

[0118] The processor fetches instructions from the memory one by one, analyzes the instructions, and then completes corresponding operations according to the requirements of the instructions, generating a series of control commands to make all parts of the computer act automatically, continuously and coordinately, becoming an organic whole, and realizing the input of the program, the input of data, and the operation and output of results. All arithmetic operations or logical operations generated in this process are completed by the arithmetic unit; the memory includes a read-only memory, and the read-only memory is used to store computer programs. A protection device is provided outside the memory.

[0119] Exemplarily, a computer program can be divided into one or more modules. One or more modules are stored in the memory and executed by the processor to complete the present invention. One or more modules can be a series of computer program instruction segments capable of completing specific functions, and these instruction segments are used to describe the execution process of the computer program in the terminal device.

[0120] Those skilled in the art can understand that the description of the above service device is only an example and does not constitute a limitation on the terminal device. It may include more or fewer components than the above description, or combine some components, or different components. For example, it may include input and output devices, network access devices, buses, etc.

[0121] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it cannot be understood as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

[0122] The above is only a preferred embodiment of the present invention and is 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 scheduling method based on cloud resource computing power allocation, characterized in that: The method comprises: S100: Obtain the usage objects of cloud resource computing power, extract common tasks, create isolated containers corresponding to the common tasks, receive resource usage requests uploaded by tenants, connect the resource usage requests to the isolated containers, configure resource restrictions, and establish a corresponding relationship between the isolated containers and resource restrictions; S200: Find out the hardware units for cloud resource computing power allocation, build a virtualization management platform, connect the hardware units to the virtualization management platform, read out the resource consumption of the hardware units, and cluster the hardware units into fully loaded units and available units; The S200 includes: Splitting the resource consumption to obtain a number of individual items, quantifying the individual items, and configuring weight values ​​corresponding to the individual items one by one; Integrate the quantified single items and weight values ​​to obtain a consumption score, compare the consumption score with a preset score threshold, and cluster the hardware units into fully loaded units and available units according to the comparison result; S300: dividing the isolated container into a dormant container and a running container, and determining whether the available unit satisfies resource consumption of the dormant container; If not, configure the correspondence between the available units and the scheduling containers, activate the pre-built startup strategy, determine the importance of each of the isolated containers, sort all the dormant containers in descending order of importance, and connect the dormant containers to the available units in sequence according to the sorting results, find out the remaining containers, and reconstruct the remaining containers using the startup strategy.

2. The scheduling method based on cloud resource computing power allocation according to claim 1 is characterized in that: The S100 includes: Embed a monitoring mechanism into the usage object to obtain a usage request uploaded by the usage object, wherein the usage request at least includes: an isolation container item and a time item; When the usage request is responded to, a record log is generated.

3. The scheduling method based on cloud resource computing power allocation according to claim 1 is characterized in that: The S100 further includes: Creating a comparison table, wherein the comparison table includes: an isolation container item and a usage object item; A mapping between the tenant, the usage object and the hardware unit is established, a label is generated using the mapping, and the label is inserted into a comparison table.

4. The scheduling method based on cloud resource computing power allocation according to claim 1 is characterized in that: The S300 includes: Embed a load balancing strategy into the isolated container to determine a load threshold for each isolated container; When the consumption score is greater than a load threshold, the load balancing strategy is activated.

5. The scheduling method based on cloud resource computing power allocation according to claim 1 is characterized in that: The S300 further includes: Creating a resource management pool, and mounting the hardware unit into the resource management pool; The change process of the resource management pool is recorded, and a version snapshot is taken.

6. The scheduling method based on cloud resource computing power allocation according to claim 1 is characterized in that: The S300 further includes: Determine the deployment location of the edge device and establish a communication link between the edge device and the resource management pool; The properties of the isolation container are configured, an edge container is selected based on the properties, and the edge container and the remaining containers are connected to the edge device.

7. A scheduling system based on cloud resource computing power allocation, characterized in that: The system comprises: Establish a module for obtaining the use objects of cloud resource computing power, extracting common tasks, creating isolated containers corresponding to the common tasks, receiving resource usage requests uploaded by tenants, and connecting the resource usage requests to the isolated containers, configuring resource restrictions, and establishing a corresponding relationship between the isolated containers and resource restrictions; The clustering module is used to find the hardware units for cloud resource computing power allocation, build a virtualization management platform, connect the hardware units to the virtualization management platform, read the resource consumption of the hardware units, and cluster the hardware units into fully loaded units and available units; The clustering module comprises: A configuration unit, used for splitting the resource consumption to obtain a plurality of individual items, quantifying the individual items, and configuring weight values ​​corresponding to the individual items one by one; A comparison unit, used to integrate the quantified single items and weight values ​​to obtain a consumption score, compare the consumption score with a preset score threshold, and cluster the hardware units into fully loaded units and available units according to the comparison result; A reconstruction module is used to divide the isolated container into a dormant container and a running container, determine whether the available unit meets the resource consumption of the dormant container, and if not, configure the correspondence between the available unit and the scheduling container, activate the pre-built startup strategy, determine the importance of each of the isolated containers, sort all the dormant containers in descending order of importance, and according to the sorting results, connect the dormant containers to the available units in turn, find out the remaining containers, and use the startup strategy to reconstruct the remaining containers.

8. The scheduling system based on cloud resource computing power allocation according to claim 7 is characterized in that: The establishment module includes: An acquisition unit, configured to embed a monitoring mechanism into the usage object, and acquire a usage request uploaded by the usage object, wherein the usage request at least includes: an isolation container item and a time item; A generating unit, configured to generate a record log after the use request is responded to; A creating unit, used for creating a comparison table, wherein the comparison table includes: an isolation container item and a use object item; The insertion unit is used to establish a mapping between the tenant, the usage object and the hardware unit, generate a label using the mapping, and insert the label into the comparison table.

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