Resource pool security management method based on virtualization and containerization

Abstract

The invention relates to the technical field of resource pool management, in particular to a resource pool security management method based on virtualization and containerization. Comprising the steps of constructing a virtual resource pool based on resource equipment parameters; setting a plurality of operation nodes in the virtual resource pool according to the historical task parameters, and generating a first-level resource allocation strategy according to all the operation nodes; setting a task sub-container according to the to-be-executed task, and selecting a to-be-executed node according to the feature parameters of the task sub-container; feedback data packets of all the task sub-containers are obtained according to the preset feedback time node, and whether a correction instruction is generated or not is judged according to all the feedback data packets. All hardware devices are virtualized to construct a virtual resource pool, a plurality of operation nodes are established to adapt to different types of to-be-executed tasks, various operation resources in the virtual resource pool are dynamically allocated according to feature parameters of the operation nodes, and the scheduling and allocation efficiency of the operation resources is improved.

Description

Technical Field

[0001] This application relates to the technical field of resource pool management, and particularly to a resource pool security management method based on virtualization and containerization. Background Art

[0002] A resource pool refers to the abstraction, integration, and centralized management of physical or virtual computing resources (such as CPUs, memory, storage), network resources (such as bandwidth, IP addresses, ports), or other specific functional resources (such as industrial devices, sensor access points, licenses), forming a shared resource set that can be dynamically allocated and recycled on demand.

[0003] With the rapid development of new services such as cloud computing, big data, and the Internet of Things, the demand for operating resources in data centers is increasing day by day. However, the traditional resource pool management method is inefficient and cannot meet the elastic demand. Summary of the Invention

[0004] The purpose of this application is: To solve the above technical problems, this application provides a resource pool security management method based on virtualization and containerization, aiming to improve the operation scheduling efficiency of the resource pool and the utilization rate of operating resources.

[0005] In some embodiments of this application, a resource pool security management method based on virtualization and containerization is provided, including: Construct a virtual resource pool based on resource device parameters; Set multiple operating nodes in the virtual resource pool according to historical task parameters, and generate a first-level resource allocation policy based on all operating nodes; Set task sub-containers according to the tasks to be executed, and select the nodes to be executed according to the characteristic parameters of the task sub-containers; Obtain the feedback data packets of each task sub-container according to the preset feedback time nodes, and determine whether to generate a correction instruction according to all the feedback data packets.

[0006] In some embodiments of this application, when setting multiple operating nodes, it includes: Set multiple task characteristic indicators according to historical task parameters; Generate multiple task categories according to all task characteristic indicators, and establish a task category sequence P, P = (p1, p2…p i …p m ), where p i is the i-th task category; m is the number of task categories; Set p i as the target task category in sequence according to the task category sequence P; Generate the demand evaluation value s of the target task category; s = βi *v i ; where θ1 is the number of demand evaluation indicators; β i is the influence factor of the i-th demand evaluation indicator; vi is the reference value of the i-th demand evaluation indicator in the target task category; Set the number of running nodes of the target task category according to the demand evaluation value v; Set the number of running nodes of each task category in sequence; Establish a running node sequence A, A = (a1, a2... a i ... a n ), where a i is the i-th running node; n is the number of running nodes, and n > m; Set the task category-running node mapping table according to the running node sequence A and the task category sequence P.

[0007] In some embodiments of the present application, when generating a first-level resource allocation strategy according to all running nodes, it includes: Set a i as the target node in sequence according to the running node sequence A; Obtain the feature data packet of the task category corresponding to the target node according to the task category-running node mapping table; Set the running architecture of the target node according to the feature data packet; Generate the resource sub-strategy of the target node; Generate the running architectures and resource sub-strategies of each running node in sequence; Generate a first-level resource allocation strategy according to all resource sub-strategies.

[0008] In some embodiments of the present application, when selecting a node to be executed according to the characteristic parameters of the task sub-container, it includes: Set pi as the task category to be compared in sequence according to the task category sequence P; Generate the similarity evaluation value c of the task sub-container and the task category to be compared; Generate the similarity evaluation values of the task sub-container and each task category in sequence, and establish a similarity evaluation value sequence C, C = (c1, c2... c i ... c m ), where c i is the similarity evaluation value of the task sub-container and the i-th task category; Set the task category corresponding to the maximum value c max in the similarity evaluation value sequence C as the first-level task category; Set the running node corresponding to the first-level task category as the first-level node according to the task category-running node mapping table; Generate the load evaluation values of each first-level node; Establish a load evaluation value sequence D, D = (d1, d2…d i …d n2 ), where d i is the load evaluation value of the i-th first-level node; n2 is the number of first-level nodes; Set the first-level node corresponding to the minimum value d min in the load evaluation value sequence D as the to-be-executed node.

[0009] In some embodiments of the present application, when generating the similarity evaluation value c of the task sub-container and the to-be-compared task category, it includes: c = e1 * Q1 * η 1i * w i + e2 * Q2 * η 2i * t i ; Wherein, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; θ2 is the number of resource characteristic indicators; η 1i is the influence factor of the i-th resource characteristic indicator; w i is the similarity of the i-th resource characteristic indicator between the task sub-container and the to-be-compared task category; θ3 is the number of historical evaluation indicators; η 2i is the influence factor of the i-th historical evaluation indicator; t i is the reference value of the i-th historical evaluation indicator.

[0010] In some embodiments of the present application, when establishing the load evaluation value sequence D, it includes: Select the target first-level node in sequence according to all the first-level nodes; Generate the load evaluation value d of the target first-level node; d = α i * j i ; Wherein, θ4 is the number of load evaluation indicators; α i is the influence factor of the i-th load evaluation indicator; j i is the reference value of the i-th load evaluation indicator in the target first-level node.

[0011] In some embodiments of the present application, when judging whether to generate a correction instruction according to all the feedback data packets, it includes: Set a i as the to-be-monitored node in sequence according to the operation node sequence A; Set all the task sub-containers in the to-be-monitored sub-node as the running sub-containers; Set the sequence of running sub - containers Y of the node to be monitored, Y = (y1, y2…y i …y r ), where yi i is the i - th running sub - container of the node to be monitored; r is the number of running sub - containers of the node to be monitored; Set yi i as the target running sub - container in sequence according to the sequence of running sub - containers Y; Obtain the feedback data packet of the target running sub - container at the current feedback time node; Generate the running evaluation value f of the target running sub - container at the current feedback time node; Preset the running evaluation value threshold F1; If f < F1, generate a first - level correction instruction for the target running sub - container at the current feedback time node; Judge whether each running sub - container generates a first - level correction instruction at the current feedback time node in sequence.

[0012] In some embodiments of the present application, when generating the running evaluation value f, it includes: f = g i * u i ; where θ5 is the preset number of running evaluation indicators; gi i is the influence factor of the i - th running evaluation indicator; ui i is the reference value of the i - th running evaluation indicator of the target running sub - container at the current feedback time node.

[0013] In some embodiments of the present application, when judging whether to generate a correction instruction according to all feedback data packets, it further includes: Generate the sequence of running evaluation values F of the node to be monitored at the current feedback time node, F = (f1, f2…f i …f r ), where fi i is the running evaluation value of the i - th running sub - container of the node to be monitored; r is the number of running sub - containers of the node to be monitored; Generate the performance deviation value h of the node to be monitored according to the sequence of running evaluation values F; Set the performance deviation value threshold H1; If h > H1, generate a second - level correction instruction.

[0014] In some embodiments of the present application, when generating the performance deviation value u, it includes: h = e3 * Q3 * λ i * k i + e4 * Q4 * (d' - d'1); Among them, e3 is a preset third weight coefficient; e4 is a preset fourth weight coefficient; Q3 is a preset third fixed coefficient; Q4 is a preset fourth fixed coefficient; θ6 is the number of auxiliary evaluation indicators; λ i is the influence factor of the i-th auxiliary evaluation indicator; k i is the reference value of the i-th auxiliary evaluation indicator generated based on the operation evaluation value sequence; d' is the load evaluation value of the node to be monitored at the current feedback time node; d'1 is the expected load value of the node to be monitored at the current feedback time node.

[0015] Compared with the prior art, the beneficial effects of a resource pool security management method based on virtualization and containerization in the embodiments of the present application are as follows: By virtualizing all hardware devices to construct a virtual resource pool, and by establishing multiple running nodes to adapt to different types of tasks to be executed, various running resources in the virtual resource pool are dynamically allocated according to the characteristic parameters of each running node, improving the scheduling and allocation efficiency of running resources.

[0016] By containerizing the tasks to be executed and then selecting the corresponding running nodes, the running security of the tasks to be executed is ensured, the running efficiency of each task to be executed is improved, and by periodically collecting the resource consumption data of each task sub-container, the resource allocation strategy is dynamically corrected in a timely manner to ensure the running performance of the task sub-container. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a schematic flowchart of a resource pool security management method based on virtualization and containerization in a preferred embodiment of the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The following combines the drawings and embodiments to further describe in detail the specific implementation manners of the present application. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application.

[0019] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present application.

[0020] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of this application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0021] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0022] As Figure 1 shown, a resource pool security management method based on virtualization and containerization in a preferred embodiment of this application includes: S101: Construct a virtual resource pool based on resource device parameters; S102: Set multiple running nodes in the virtual resource pool according to historical task parameters, and generate a first-level resource allocation policy based on all running nodes; S103: Set task sub-containers according to the tasks to be executed, and select the nodes to be executed according to the characteristic parameters of the task sub-containers; S104: Obtain the feedback data packets of each task sub-container according to the preset feedback time nodes, and determine whether to generate a correction instruction according to all the feedback data packets.

[0023] Specifically, the resource devices include physical resource devices such as CPU, memory, storage, etc. and virtual resources such as networks, etc. By abstracting and virtualizing all resource devices, a virtual resource pool is constructed.

[0024] Specifically, when setting multiple running nodes, it includes: Set multiple task characteristic indicators according to historical task parameters; Generate multiple task categories according to all task characteristic indicators, and establish a task category sequence P, P = (p1, p2... p i ... p m ), where p i is the i-th task category; m is the number of task categories; Set p i as the target task category in sequence according to the task category sequence P; Generate a demand evaluation value s for the target task category; s = β i *v i ; where θ1 is the number of demand evaluation indicators; β i is the influence factor of the i-th demand evaluation indicator; vi is the reference value of the i-th demand evaluation indicator in the target task category; Set the number of running nodes of the target task category according to the demand evaluation value v; Set the number of running nodes of each task category in sequence; Establish a running node sequence A, A = (a1, a2... a i …a n ), where a i is the i-th running node; n is the number of running nodes, and n > m; Set a task category - running node mapping table according to the running node sequence A and the task category sequence P.

[0025] Specifically, the larger the demand evaluation value, the larger the corresponding number of running nodes, and the mapping relationship between the demand evaluation value and the number of running nodes can be set according to historical parameters.

[0026] Specifically, set multiple sub - resource pools in the virtual resource pool based on all resource parameters of the virtual resource pool, and set a single sub - resource pool as a single running node.

[0027] Specifically, historical task parameters refer to all user demand tasks that the system has executed. By analyzing and processing the historical task parameters, multiple task characteristic indicators are generated.

[0028] Specifically, task characteristic indicators include but are not limited to multiple parameters such as business type, running load, and maximum demand for various running resources. By quantifying each task characteristic indicator, multiple value ranges of each task characteristic indicator are generated. Through random combinations of the value ranges, multiple task categories are constructed.

[0029] Specifically, generate a historical data packet of the target task category according to historical task parameters. The historical data packet includes all user demand tasks that have been executed and belong to the target task category. Generate the reference value of each demand evaluation indicator according to the historical data packet. The larger the reference value of each demand evaluation indicator, the more resources the target task category needs to occupy.

[0030] Specifically, demand evaluation indicators include but are not limited to multiple parameters related to resource requirements such as average task execution duration, total task volume, and task execution frequency. The value of the influence factor of each demand evaluation indicator is set according to its correlation with resource requirements. The greater the correlation, the larger the value of the corresponding influence factor, and its specific value range can be set according to historical parameters.

[0031] It can be understood that in the above embodiments, multiple task categories are set based on the analysis of historical task parameters, and the corresponding number of running nodes is dynamically adjusted according to the demand evaluation values of each task category, so as to improve the running efficiency of all tasks to be executed.

[0032] In the preferred embodiment of the present application, when generating the first-level resource allocation policy according to all running nodes, it includes: Set a i as the target node according to the running node sequence A; Obtain the characteristic data packet of the task category corresponding to the target node according to the task category-running node mapping table; Set the running architecture of the target node according to the characteristic data packet; Generate the resource sub-policy of the target node; Generate the running architecture and resource sub-policies of each running node in turn; Generate the first-level resource allocation policy according to all resource sub-policies.

[0033] Specifically, the characteristic data packet includes the expected demand for various running resources by the task category and the required running architecture. Set the running architecture corresponding to the task node according to the characteristic data packet, so as to improve the task execution efficiency of the target node.

[0034] Specifically, different task categories may not have the same demand for various types of resources. For example, for a task category with low running load but requiring timely data interaction, the occupancy of its cpu resources can be appropriately reduced, and the occupancy rate of network resources can be increased.

[0035] Specifically, the expected demand for various types of resources by the task category is set according to the actual consumption of various resources in its historical data packet. The greater the consumption, the greater the corresponding expected demand. Generate the resource sub-policy of the target node according to all expected demands, and schedule all resources in the virtual resource pool according to all resource sub-policies.

[0036] Specifically, when selecting the node to be executed according to the characteristic parameters of the task sub-container, it includes: Set pi as the task category to be compared in turn according to the task category sequence P; Generate the similarity evaluation value c of the task sub-container and the task category to be compared; Generate the similarity evaluation values of the task sub-container and each task category in turn, and establish the similarity evaluation value sequence C, C=(c1, c2…c i …c m ), where c i is the similarity evaluation value of the task sub-container and the i-th task category; Set the maximum value c in the similarity evaluation value sequence C max The corresponding task category is the first-level task category; Set the running node corresponding to the first-level task category as the first-level node according to the task category - running node mapping table; Generate the load evaluation value of each first-level node; Establish a load evaluation value sequence D, D = (d1, d2…d i …d n2 ), where d i is the load evaluation value of the i-th first-level node; n2 is the number of first-level nodes; Set the minimum value d in the load evaluation value sequence D min The corresponding first-level node is the node to be executed.

[0037] Specifically, the larger the similarity evaluation value, the more suitable the running node corresponding to the task category to be compared is for running the current task sub-container. By analyzing all the runs corresponding to the task category to be compared, the task sub-container is placed in the most idle running node for running.

[0038] Specifically, the task sub-container can share all the running resources of the running node.

[0039] Specifically, when generating the similarity evaluation value c of the task sub-container and the task category to be compared, it includes: c = e1 * Q1 * η 1i * w i + e2 * Q2 * η 2i * t i ; Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ2 is the number of resource characteristic indicators; η 1i is the influence factor of the i-th resource characteristic indicator; w i is the similarity of the i-th resource characteristic indicator between the task sub-container and the task category to be compared; θ3 is the number of historical evaluation indicators; η 2i is the influence factor of the i-th historical evaluation indicator; t i is the reference value of the i-th historical evaluation indicator.

[0040] Specifically, the resource characteristic indicators include, but are not limited to, the demand ratio of the task sub-container for various running resources and the demand ratio of the task category to be compared for various running resources. The closer the demand ratios are, the greater the corresponding similarity.

[0041] Specifically, the greater the degree of dependence of the task category on the resources represented by the resource feature indicators, the greater the impact factor of the resource feature indicators.

[0042] Specifically, the historical evaluation indicators include, but are not limited to, whether the current task sub-container has been executed in the historical data packet of the task category to be compared, the number of executions, the historical average execution duration, and other parameters for determining whether the task to be executed corresponding to the current task sub-container is a historical execution task.

[0043] Specifically, all parameters in the model are normalized by presetting a first fixed coefficient and a second fixed coefficient, so that each parameter in the model is within the same value range.

[0044] Specifically, when establishing the load evaluation value sequence D, it includes: Select the target first-level node in sequence according to all the first-level nodes; Generate the load evaluation value d of the target first-level node; d = α i *j i ; where θ4 is the number of load evaluation indicators; α i is the impact factor of the i-th load evaluation indicator; j i is the reference value of the i-th load evaluation indicator in the target first-level node.

[0045] Specifically, the load evaluation indicators include, but are not limited to, load value, the number of tasks to be executed in the running node, the average waiting time of each task to be executed, and other parameter values related to the running state.

[0046] Specifically, the greater the degree of association between the load evaluation indicator and the running state, the greater the value of the corresponding impact factor.

[0047] It can be understood that in the above embodiments, by containerizing the tasks to be executed and then selecting the corresponding running nodes, the running safety of the tasks to be executed is ensured, and the running efficiency of each task to be executed is improved.

[0048] In the preferred embodiment of the present application, when determining whether to generate a correction instruction according to all the feedback data packets, it includes: Set a i as the node to be monitored in sequence according to the running node sequence A; Set all the task sub-containers in the node to be monitored as running sub-containers; Set the running sub-container sequence Y of the node to be monitored, Y = (y1, y2…y i …y r ), where y iis the i-th running sub-container of the node to be monitored; r is the number of running sub-containers of the node to be monitored; Set y in sequence according to the sequence of running sub-container numbers Y i is the target running sub-container; Obtain the feedback data packet of the target running sub-container at the current feedback time node; Generate the running evaluation value f of the target running sub-container at the current feedback time node; Preset the running evaluation value threshold F1; If f < F1, generate a first-level correction instruction for the target running sub-container at the current feedback time node; Judge in sequence whether each running sub-container generates a first-level correction instruction at the current feedback time node.

[0049] Specifically, the running evaluation value threshold can be set according to historical parameters.

[0050] Specifically, the first-level correction instruction refers to dynamically correcting the resource occupancy of the target running sub-container to ensure the running efficiency of the target running sub-container, and its correction process is to reallocate the resources inside the running node.

[0051] Specifically, when generating the running evaluation value f, it includes: f = g i *u i ; where θ5 is the number of preset running evaluation indicators; g i is the influence factor of the i-th running evaluation indicator; u i is the reference value of the i-th running evaluation indicator of the target running sub-container at the current feedback time node.

[0052] Specifically, the larger the running evaluation value, the higher the overall running performance of the running sub-container.

[0053] It can be understood that in the above embodiments, by periodically collecting the resource consumption data of each task sub-container, the resource allocation strategy is dynamically corrected in a timely manner to ensure the running performance of the task sub-container.

[0054] In the preferred embodiment of this application, when judging whether to generate a correction instruction according to all feedback data packets, it further includes: Generate the sequence of running evaluation values F of the node to be monitored at the current feedback time node, F = (f1, f2... f i ... f r ), where f i is the running evaluation value of the i-th running sub-container of the node to be monitored; r is the number of running sub-containers of the node to be monitored; Generate the performance deviation value h of the node to be monitored according to the operation evaluation value sequence F; Set the performance deviation value threshold H1; If h > H1, generate a secondary correction instruction.

[0055] Specifically, the performance deviation value threshold can be set according to historical parameters. The larger the performance deviation value is, the lower the overall operation efficiency of the node to be monitored is.

[0056] Specifically, the secondary correction instruction refers to reallocating the resources in the virtual resource pool to ensure the operation efficiency within each operation node.

[0057] Specifically, when generating the performance deviation value u, it includes: h = e3 * Q3 * λ i * k i + e4 * Q4 * (d' - d'1); Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; θ6 is the number of auxiliary evaluation indicators; λ i is the influence factor of the i-th auxiliary evaluation indicator; k i is the reference value for generating the i-th auxiliary evaluation indicator based on the operation evaluation value sequence; d' is the load evaluation value of the node to be monitored at the current feedback time node; d'1 is the expected load value of the node to be monitored at the current feedback time node.

[0058] Specifically, Specifically, normalize all the parameters in the model through the preset third fixed coefficient and fourth fixed coefficient, so that each parameter in the model is within the same value range.

[0059] According to the first concept of the present application, virtualize all hardware devices to construct a virtual resource pool, and establish multiple operation nodes to adapt to different types of tasks to be executed. According to the characteristic parameters of each operation node, dynamically allocate various operation resources in the virtual resource pool to improve the scheduling and allocation efficiency of operation resources.

[0060] According to the second concept of the present application, after containerizing the tasks to be executed, select the corresponding operation nodes to ensure the operation safety of the tasks to be executed, improve the operation efficiency of each task to be executed, and dynamically correct the resource allocation strategy in a timely manner by periodically collecting the resource consumption data of each task sub-container to ensure the operation performance of the task sub-container.

[0061] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present application, several improvements and substitutions can be made, and these improvements and substitutions should also be regarded as the protection scope of the present application.

Claims

1. A resource pool security management method based on virtualization and containerization, characterized in that Including: Construct a virtual resource pool based on resource device parameters; Set multiple running nodes in the virtual resource pool according to historical task parameters, and generate a first-level resource allocation policy based on all running nodes; Set task sub-containers according to the tasks to be executed, and select nodes to be executed according to the characteristic parameters of the task sub-containers; Obtain feedback data packets of each task sub-container according to the preset feedback time nodes, and determine whether to generate a correction instruction according to all feedback data packets.

2. The resource pool security management method based on virtualization and containerization according to claim 1, wherein When setting multiple running nodes, including: Set multiple task characteristic indicators according to historical task parameters; Generate multiple task categories according to all task characteristic indicators, and establish a task category sequence P, P = (p1, p2…p i …p m ), where p i is the i-th task category; m is the number of task categories; Set p successively according to the task category sequence P i as the target task category; Generate a demand evaluation value s for the target task category; s = β i *v i ; Among them, θ1 is the number of demand evaluation indicators; β i is the influence factor of the i-th demand evaluation indicator; vi is the reference value of the i-th demand evaluation indicator in the target task category; Set the number of running nodes for the target task category according to the demand evaluation value v; Set the number of running nodes for each task category in turn; Establish a sequence of operating nodes A, A = (a1, a2…a i …a n ), where a i is the i-th operating node; n is the number of operating nodes, and n > m; Set a task category-running node mapping table according to the running node sequence A and the task category sequence P.

3. The resource pool security management method based on virtualization and containerization according to claim 2, wherein When generating a first-level resource allocation policy based on all running nodes, including: Set a according to the running node sequence A in turn i as the target node; Obtain the characteristic data packets of the task categories corresponding to the target nodes according to the task category-running node mapping table; Set the running architecture of the target nodes according to the characteristic data packets; Generate resource sub-policies for the target nodes; Generate the running architectures and resource sub-policies of each running node in turn; Generate a first-level resource allocation policy according to all resource sub-policies.

4. The security management method for a resource pool based on virtualization and containerization according to claim 3, characterized in that, When selecting nodes to be executed according to the characteristic parameters of the task sub-containers, including: Set pi as the task category to be compared in turn according to the task category sequence P; Generate a similarity evaluation value c between the task sub-container and the task category to be compared; Generate task sub - containers and similarity evaluation values for each task category in sequence, and establish a sequence C of similarity evaluation values, C=(c1, c2…c i …c m ), where c i is the similarity evaluation value between the task sub - container and the i - th task category; Set the maximum value c in the similarity evaluation value sequence C max The corresponding task category is the primary task category; Set the running nodes corresponding to the first-level task categories as first-level nodes according to the task category-running node mapping table; Generate load evaluation values for each first-level node; Establish a load evaluation value sequence D, D = (d1, d2…d i …d n2 ), where d i is the load evaluation value of the i-th first-level node; n2 is the number of first-level nodes; Set the minimum value d in the load evaluation value sequence D min The corresponding first-level node is the node to be executed.

5. The resource pool security management method based on virtualization and containerization according to claim 4, characterized in that When generating a similarity evaluation value c between the task sub-container and the task category to be compared, including: c = e1 * Q1 * η 1i * w i + e2 * Q2 * η 2i * t i ; Among them, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; θ2 is the number of resource feature indicators; η 1i is the influence factor of the i-th resource feature indicator; w i is the similarity of the i-th resource feature indicator in the task sub-container and the task category to be compared; θ3 is the number of historical evaluation indicators; η 2i is the influence factor of the i-th historical evaluation indicator; t i is the reference value of the i-th historical evaluation indicator.

6. The resource pool security management method based on virtualization and containerization according to claim 5, wherein When establishing a load evaluation value sequence D, including: Select target first-level nodes in turn according to all first-level nodes; Generate a load evaluation value d for the target first-level node; d= α i *j i ; Among them, θ4 is the number of load evaluation indicators; α i is the influence factor of the i-th load evaluation indicator; j i is the reference value of the i-th load evaluation indicator in the target first-level node.

7. The resource pool security management method based on virtualization and containerization according to claim 6, wherein When determining whether to generate a correction instruction according to all feedback data packets, including: Set a according to the operation node sequence A in turn i as the node to be monitored; Set all task sub-containers in the sub-nodes to be monitored as running sub-containers; Set the sequence of running sub - containers of the node to be monitored as Y, Y = (y1, y2…y i …y r ), where y i is the i - th running sub - container of the node to be monitored; r is the number of running sub - containers of the node to be monitored; Set y according to the running sub-container sequence Y in turn i as the target running sub-container; Obtain the feedback data packet of the target running sub-container at the current feedback time node; Generate a running evaluation value f for the target running sub-container at the current feedback time node; Preset a running evaluation value threshold F1; If f < F1, generate a first-level correction instruction for the target running sub-container at the current feedback time node; Judge in turn whether each running sub-container generates a first-level correction instruction at the current feedback time node.

8. The security management method for resource pool based on virtualization and containerization according to claim 7, characterized in that When generating a running evaluation value f, including: f = g i * u i ; Among them, θ5 is the number of preset operation evaluation indicators; g i is the influence factor of the i-th operation evaluation indicator; u i is the reference value of the i-th operation evaluation indicator of the target operation sub-container at the current feedback time node.

9. The resource pool security management method based on virtualization and containerization according to claim 8, wherein, When determining whether to generate a correction instruction according to all feedback data packets, it also includes: Generate the operation evaluation value sequence F of the node to be monitored at the current feedback time node, F = (f1, f2... f i ... f r ), where f i is the operation evaluation value of the i-th operation sub-container of the node to be monitored; r is the number of operation sub-containers of the node to be monitored; Generate a performance deviation value h for the sub-nodes to be monitored according to the running evaluation value sequence F; Set a performance deviation value threshold H1; If h > H1, generate a second-level correction instruction.

10. The method for secure management of a resource pool based on virtualization and containerization according to claim 9, wherein When generating a performance deviation value u, including: h = e3 * Q3 * λ i * k i + e4 * Q4 * (d' - d'1); Among them, e3 is a preset third weight coefficient; e4 is a preset fourth weight coefficient; Q3 is a preset third fixed coefficient; Q4 is a preset fourth fixed coefficient; θ6 is the number of auxiliary evaluation indicators; λ i is the influence factor of the i-th auxiliary evaluation indicator; k i is the reference value for generating the i-th auxiliary evaluation indicator based on the operation evaluation value sequence; d' is the load evaluation value of the node to be monitored at the current feedback time node; d'1 is the expected load value of the node to be monitored at the current feedback time node.

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