Trusted starting method and device of virtual machine and electronic equipment

Abstract

The invention discloses a trusted starting method and device for virtual machines and electronic equipment, and the method comprises the steps: carrying out the grouping processing of each virtual machine, obtaining a plurality of groups, and enabling each group to comprise at least one virtual machine; for each group and the application scene corresponding to each group, setting a trusted measurement range corresponding to each group; when the first virtual machine is started, a current credible index of the first virtual machine is obtained, and the current credible index is determined according to multiple current metric values of the first virtual machine; and when the current credible index is in the credible measurement range of the first group corresponding to the first virtual machine, performing recovery processing on the first virtual machine based on the credible backup data of the first virtual machine. The virtual machine is managed in a group unit, so that the credible starting efficiency of the virtual machine and the running safety of the virtual machine are improved.

Description

Technical Field

[0001] The present application relates to the field of cloud computing technology, and in particular to a trusted startup method of a virtual machine, an electronic device, a storage medium, and a program product. Background Art

[0002] In the related art, during the trusted startup of a virtual machine, the virtual machine's credibility is measured through the virtual trusted platform module (vTPM) standard, the virtual machine is guided to start securely, and a trusted measurement mechanism for the virtual machine is established. Through the virtual trusted platform module corresponding to the virtual machine, the trusted measurement index of this virtual machine startup is compared with the set trusted measurement threshold range to determine the degree of trustworthiness of this virtual machine startup. In the related art, factors such as the application scenario of the virtual machine and the host machine are not taken into account, and the abnormality of the virtual machine is not perceived in advance, which affects the efficiency and security of the virtual machine startup. Summary of the invention

[0003] In view of this, embodiments of the present application provide a trusted startup method for a virtual machine, an electronic device, a storage medium, and a program product.

[0004] The technical solution of the embodiment of the present application is implemented as follows:

[0005] The present invention provides a method for starting a virtual machine in a trusted manner, the method comprising:

[0006] Performing grouping processing on each virtual machine to obtain a plurality of groups, wherein each group includes at least one virtual machine;

[0007] For each of the groups and the application scenarios corresponding to each of the groups, a trust metric range corresponding to each of the groups is set;

[0008] When the first virtual machine is started, obtaining a current trust index of the first virtual machine, wherein the current trust index is determined according to multiple current measurement values ​​of the first virtual machine;

[0009] When the current trust index is within a trust measurement range of a first group corresponding to the first virtual machine, the first virtual machine is restored based on the trusted backup data of the first virtual machine.

[0010] In the above solution, for each group and the application scenario corresponding to each group, setting the trust metric range corresponding to each group includes:

[0011] The following processing is performed for each of the groups:

[0012] Based on the historical credibility measurement range and scenario data of the group, calling a deep learning network to perform prediction processing to obtain a first credibility index;

[0013] Based on the error between the first trust index and the initial trust index, calling the deep learning network to perform iterative optimization processing to obtain multiple second trust indexes, wherein the second trust index obtained in each round of iteration is the input of the next round of iteration;

[0014] The interval formed by the first credibility index and the upper limit value and the lower limit value of the plurality of second credibility indexes is used as the credibility metric range.

[0015] In the above solution, based on the error between the first trust index and the initial trust index, the deep learning network is called to perform iterative optimization processing to obtain multiple second trust indexes, including:

[0016] Based on the error between the first trust index and the initial trust index, calling the deep learning network to perform iterative optimization processing to obtain a first second trust index;

[0017] Based on the error between the i-th second credible index and the i-1-th second credible index, calling the deep learning network to perform iterative optimization processing to obtain the i+1-th second credible index, wherein i is a positive integer greater than or equal to 1, and when i is equal to 1, the i-1-th second credible index is the first credible index;

[0018] When the number of iterations reaches a preset number, or the difference between the second trust indexes obtained by the preset number of optimization processes is less than a first threshold, the iterative optimization process is stopped, and the obtained second trust index is output.

[0019] In the above solution, the restoring the first virtual machine based on the trusted backup data of the first virtual machine includes:

[0020] When the current trust index is lower than the lower limit of the trust measurement range, obtaining the trusted backup data of the first virtual machine;

[0021] Performing detection processing on the trusted backup data to obtain a detection result;

[0022] When the detection result is that the trusted backup data is the latest data, or the detection result is that the trusted backup data is the initial image data, the first virtual machine is restored based on the trusted backup data.

[0023] In the above solution, after the first virtual machine is started, the method further includes:

[0024] Determine a first host machine matching the first virtual machine;

[0025] When the comprehensive trust index of the first host machine does not meet the set threshold of the first group, determining whether the service quality index and the network quality index of the first host machine meet a first condition, wherein the first condition includes: the service quality index is a first preset value, and the network quality index is a second preset value;

[0026] When the first host machine meets the first condition, migrating the first virtual machine carried by the first host machine to a second host machine within the trusted measurement range;

[0027] When the first host machine does not meet the first condition, the first virtual machine is restored based on the trusted backup data of the first virtual machine.

[0028] In the above solution, after determining the first host machine matching the first virtual machine, the method further includes:

[0029] Obtaining the following parameters of the first host machine: hardware security index, trusted platform module index, network quality index, key directory information, service quality index, and average trust index of virtual machines hosted by the first host machine;

[0030] Each of the parameters is multiplied to obtain a comprehensive trust index of the first host machine.

[0031] In the above solution, when the first host machine does not carry any virtual machine, the average trust index of the first host machine is a third preset value; when the virtual machine carried by the first host machine does not belong to any group, the current trust index of the virtual machine is a fourth preset value;

[0032] When the hardware device of the first host machine has no faults, the hardware safety index is a first preset value;

[0033] When the network connectivity rate of the first host machine is greater than a connectivity rate threshold, the network quality indicator is a second preset value;

[0034] When the key directory of the first host machine has not been tampered with, the key directory information is characterized as a first preset value;

[0035] When there is no abnormal process on the first host machine, the service quality indicator is a first preset value.

[0036] In the above solution, when the comprehensive trust index of the first host machine meets the set threshold of the first group, the operating system of the virtual machine is loaded based on the resources of the first host machine;

[0037] The process proceeds to the step of obtaining the current trust index of the first virtual machine.

[0038] The present application provides a trusted startup device for a virtual machine, the device comprising:

[0039] A threshold setting module, used for grouping each virtual machine to obtain a plurality of groups, wherein each group includes at least one virtual machine;

[0040] The threshold setting module is further used to set a trust metric range corresponding to each group and an application scenario corresponding to each group;

[0041] A startup module, configured to obtain a current trust index of the first virtual machine when the first virtual machine is started, wherein the current trust index is determined according to multiple current measurement values ​​of the first virtual machine;

[0042] The recovery module is configured to recover the first virtual machine based on the trusted backup data of the first virtual machine when the current trusted index is within the trusted measurement range of the first group corresponding to the first virtual machine.

[0043] An embodiment of the present application also provides an electronic device, comprising: a processor and a memory for storing a computer program that can be run on the processor, wherein the processor is used to execute the steps in the trusted startup method of the above-mentioned virtual machine when running the computer program.

[0044] An embodiment of the present application further provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned trusted startup method of the virtual machine are implemented.

[0045] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps in the above-mentioned trusted startup method of a virtual machine.

[0046] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps in the above-mentioned trusted startup method of a virtual machine.

[0047] The embodiment of the present application obtains multiple groups by grouping each virtual machine, and manages the trusted measurement range of the virtual machine in combination with the application scenario in groups, so that the trusted measurement range is adaptive to the virtual machines in different application scenarios, and the accuracy of fault detection in the trusted startup process of the virtual machine is improved. When the virtual machine exceeds the trusted measurement range, the virtual machine is restored through the backup data to restore the virtual machine to the trusted startup state. The abnormality in the startup phase is perceived in advance, and the abnormal startup is used as a sign to trigger the execution of self-healing recovery, and the virtual machine is restored to the trusted startup state, which improves the security of the virtual machine operation and the startup efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A system architecture diagram of a trusted startup method for a virtual machine provided in an embodiment of the present application;

[0049] Figure 2 A first flow chart of a trusted startup method for a virtual machine provided in an embodiment of the present application;

[0050] Figure 3 A second flow chart of the trusted startup method of a virtual machine provided in an embodiment of the present application;

[0051] Figure 4 A third flow chart of the trusted startup method of a virtual machine provided in an embodiment of the present application;

[0052] Figure 5 A fourth flow chart of the trusted startup method of a virtual machine provided in an embodiment of the present application;

[0053] Figure 6 A fifth flow chart of the trusted startup method of a virtual machine provided in an embodiment of the present application;

[0054] Figure 7 A schematic diagram of the hardware composition structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0057] Before further describing the embodiments of the present application in detail, the nouns and terms involved in the embodiments of the present application are explained. The nouns and terms involved in the embodiments of the present application are subject to the following interpretations.

[0058] 1) Virtual machine metrics: usually refers to a series of parameters that quantify various performance indicators in a virtual machine environment. These metrics may include but are not limited to the CPU usage, memory usage, disk I / O read / write speed, network traffic, etc.

[0059] 2) Trust Metric Index (Virtual Machine Trust Metric Index): It is a quantitative indicator used to evaluate the overall trustworthiness of a virtual machine in terms of security, stability, performance, and compliance at a specific point in time. This index is usually calculated from a series of sub-indexes or metrics, each of which represents the performance of the virtual machine in a specific aspect. In the embodiment of the present application, the trust metric index is obtained based on the failure rate during the virtual machine startup process, and the trustworthiness of the virtual machine startup is determined by comparing the trust metric index of this virtual machine startup with the set trust metric threshold range.

[0060] 3) Host Machine: In virtualization technology, it refers to the physical computer that runs virtualization software. It provides the hardware resources and software environment required to run one or more virtual machines (Guest Machines). On the host machine, virtualization software (such as VMware, VirtualBox, KVM, etc.) can simulate a complete computer system, allowing multiple operating systems to run independently on the same physical machine.

[0061] 4) Cloud platform: Also known as cloud computing platform, it is an Internet-based service platform that provides computing resources, storage resources, network resources, and various other service resources. Users can access and use these resources on demand through the Internet. Cloud platforms are usually established and operated by cloud service providers to provide flexible and scalable IT services to individual users, enterprises, and other organizations. In the embodiment of the present application, the cloud platform is a platform that provides virtual machine services. It relies on the host machine as its physical basis, and virtualizes the resources of the host machine into multiple virtual machines through virtualization technology to provide users with flexible and on-demand resource services. Users can create and manage virtual machines on the cloud platform, and the cloud platform is responsible for handling the physical hardware and maintenance behind the virtual machines.

[0062] At present, comprehensive protection and building a solid digital economy security system are important foundations for the stable development of the digital economy, among which trusted computing is an important cornerstone of security and trustworthiness. The new generation of trusted computing solutions must comply with the national Trusted Cryptography Module (TCM) standard and be compatible with the international Trusted Platform Module (TPM) 2.0 standard. The trusted computing chip is the hardware's trusted measurement root, trusted storage root, and trusted authentication root. The trusted computing chip is called to measure and verify that the device's hardware and software environment is not tampered with by Trojans, viruses, and human maliciousness, protect the confidentiality and privacy of data stored in the device or cloud, and realize secure storage. It also serves as a trusted root for identity authentication to support the identity authentication and authorization of service objects and permissions for various cloud services.

[0063] Trusted chips are widely embedded in computers around the world, but with the popularization of cloud computing, information security and protection issues have received more extensive attention. The trust chain technology of trusted computing and the architecture and concept of trust relationship transmission, combined with virtualization technology, form the virtual trusted platform module (vTPM) standard, which supports the transformation of the entire cloud service architecture and enables cloud computing to upgrade to a new security architecture.

[0064] In the related art, for the trusted startup of virtual machines, the virtual trusted platform module (vTPM) standard is used to measure the trustworthiness of virtual machines, guide the virtual machines to start safely, and establish a trusted measurement mechanism for virtual machines at the method, system, storage medium and device levels. Through the vTPM module corresponding to the virtual machine, the address sequence and content change records of memory operations during the startup of the virtual machine are collected, and the measurement values ​​of the virtual machine add-ons are measured. The integrity of the virtual hardware is verified according to the measurement extension value and the benchmark measurement value. If it is complete, the virtual machine is guided to start. If it is not complete, the virtual machine cannot start. According to the failure ratio during the startup of the virtual machine, the trusted measurement index is obtained, and the trusted measurement index of this virtual machine startup is compared with the set trusted measurement threshold range to determine the trustworthiness of this virtual machine startup. However, the related art has the following defects: the measurement database (a database system for storing and managing measurement data) is divided according to each operating system supported by the system, and the trusted measurement library is established according to the type of operating system. It is difficult to meet the performance of multiple scenarios under the same system. Only trusted measurement is provided, but no self-healing recovery method is provided for abnormalities such as non-trusted.

[0065] In the related art, for the self-healing and repair of cloud platforms, the initial resource backup pool and the cloud platform self-healing and repair mechanism are created to meet the high-performance business requirements. The cloud platform initial resource backup pool is created, and fault detection is enabled. When the cloud platform crashes or fails, the cloud platform self-healing and repair method is used to restore the business data of the abnormal cloud platform, find the initial cloud platform image file of the abnormal cloud platform, and restore the initial state data of the cloud platform to enable it to go online again to execute business. The cloud platform that is back online sends a synchronization request to any homogeneous cloud platform in the cloud cluster, and then the homogeneous cloud platform sends a synchronization request feedback to synchronize the cloud platform data and business; the cloud platform that is back online generates synchronization information based on the synchronization request feedback to restore business data. However, the related art has the following defects: the virtual machine fault detection is enabled, and the virtual machine is migrated to the heterogeneous cloud platform as a migration identifier, which only covers the crash or failure of the cloud platform, and does not perceive the abnormality in the startup phase in advance, resulting in low virtual machine startup efficiency; the virtual machine abnormality caused by the host machine failure is not covered, and there is a technical limitation that the host machine cannot be migrated and can only be self-healed and backed up and rebuilt.

[0066] In view of this, an embodiment of the present application provides a trusted startup method for a virtual machine, which divides the virtual machines into trusted groups based on the group dimension. Each group contains one or more virtual machines, and each group independently sets a trusted measurement threshold range to meet the scenario-based trusted index requirements. During the virtual machine startup and system loading stages, trusted measurements are performed to perceive abnormalities in the startup stage in advance, and abnormal startup is used as a sign to trigger self-healing recovery, thereby restoring the virtual machine to a trusted startup state.

[0067] To facilitate understanding of the trusted startup method of a virtual machine provided in the embodiment of the present application, refer to Figure 1 , Figure 1 A system architecture diagram of a trusted startup method for a virtual machine provided in an embodiment of the present application; the system of the trusted startup method for a virtual machine includes: a trusted startup module 101, a trusted measurement module 102, a self-healing recovery module 103, a trusted threshold recommendation module 104, a trusted threshold display module 106, and a trusted threshold setting module 105. The trusted startup module 101 is used to perform task distribution, trusted call, and loading functions of the virtual machine; the trusted measurement module 102 uses the host machine and the virtual machine as the main body to perform trusted measurement; the self-healing recovery module 103 also uses the host machine and the virtual machine as the main body, uses migration or evacuation means to use the host machine as the main body to perform trusted recovery of the carried virtual machine, and uses system rollback means to use the virtual machine as the main body to perform trusted recovery of the virtual machine itself; the trusted threshold setting module 105 supports setting, resetting, and modifying modules; the trusted threshold display module 106 provides and displays the trusted measurement threshold range and corresponding results of each user group; the trusted threshold recommendation module 104 uses a deep learning network to provide a recommended trusted measurement threshold range based on the scene, so that the new group can refer to and set the initial value.

[0068] Figure 5 A flow chart of a trusted startup method of a virtual machine provided in an embodiment of the present application is shown as follows: Figure 5 As shown, the trusted startup method of the virtual machine can be implemented through steps 501 to 504, which are described in detail below.

[0069] Step 501, grouping each virtual machine to obtain multiple groups.

[0070] Here, each group includes at least one virtual machine.

[0071] For example, in the embodiment of the present application, the group is the management unit of the virtual machine. When the user uses the virtual machine service, each user can independently have one or more trusted groups, but each trusted group independently sets the trusted measurement threshold range. A trusted group is a collection of one or more virtual machines, and these virtual machines in the same group have the same trusted measurement threshold range. The trusted measurement threshold range is a division of the trustworthiness of the group, consisting of a minimum value and a maximum value. Only when the current trusted measurement result falls within this range can it be judged as a trusted state.

[0072] Step 502: for each group and the application scenario corresponding to each group, a trust metric range corresponding to each group is set.

[0073] For example, an application scenario refers to a scenario of a business performed by a virtual machine in a group. When the business functions performed by the virtual machine are different, the application scenario is also different. The deep learning model can be called based on the data corresponding to the application scenario and the historical trust metric range of the group to determine the trust metric range corresponding to each group.

[0074] In some embodiments, reference Figure 6 , step 502 can be implemented by following steps 5021 to 5023, which are described in detail below.

[0075] In step 5021, the following processing is performed for each group: based on the historical trust metric range and scenario data of the group, a deep learning network is called to perform prediction processing to obtain a first trust index.

[0076] For example, the deep learning network can be a convolutional neural network (CNN) or a feedforward neural network, which is pre-trained based on a sample of a historical trust metric range and scenario data, and can predict a trust index when the virtual machine is started normally based on the data to be processed. The first trust index is a trust metric index when the virtual machine is started normally.

[0077] In an embodiment of the present application, the calculation of the recommended trust metric range is achieved by the following methods: determining the number of hidden layers and the number of nodes in each layer in the deep learning network, performing scenario-based threshold selection in the process of calculating the recommended threshold, and selecting a more suitable deep learning network from multiple scenario combinations in combination with the actual operation of the cloud platform's trusted system to achieve scenario combination analysis and recommended trust metric threshold range optimization.

[0078] In step 5022, based on the error between the first trust index and the initial trust index, a deep learning network is called to perform iterative optimization processing to obtain multiple second trust indexes.

[0079] Here, the second trust index obtained in each round of iteration is the input of the next round of iteration.

[0080] For example, the error between the first trust index and the initial trust index refers to the reconstruction error, which refers to the difference or loss between the reconstructed data and the original input by reconstructing (or restoring) the input data through the learned model. Iterative optimization processing refers to the process of predicting a new trust index based on the trust index already output by the deep learning network.

[0081] The reconstruction error is positively correlated with the credibility metric, which is derived as follows. The reconstruction error is defined as the following formula (1):

[0082]

[0083] Where m and n are data dimensions, N is the total number of parameters, F is the trusted index after optimization, T is the original trusted index, and F can be represented by the following formula (2):

[0084]

[0085] Where T = P(X 0 ), represents the product of multiple metric values ​​of the virtual machine in the original state. Substituting T into formula (1), we get the following formula (3):

[0086]

[0087] According to Err∝E(x,h), we can get That is, the credible reconstruction error can reflect the overall characteristics of the credible index recommendation system.

[0088] In some embodiments, step 5022 may be implemented by:

[0089] Based on the error between the first trust index and the initial trust index, the deep learning network is called to perform iterative optimization processing to obtain the first second trust index. Based on the error between the i-th second trust index and the i-1-th second trust index, the deep learning network is called to perform iterative optimization processing to obtain the i+1-th second trust index, where i is a positive integer greater than or equal to 1, and when i is equal to 1, the i-1-th second trust index is the first trust index. When the number of iterations reaches a preset number, or the difference between the second trust indexes obtained by the preset number of optimization processes is less than a first threshold, the iterative optimization process is stopped, and the obtained second trust index is output.

[0090] The reconstruction error of each layer in the deep learning network can be represented as Among them, x i is the output of the i-th optimization process, that is, the input of the i+1-th optimization process, x i-1 is the input of the i-th optimization process, that is, the output of the i-1-th optimization process, and N is the data volume of the overall trusted group of the cloud platform. When the trusted reconstruction error is used to select the number of trusted index layers, when the error drops to the set threshold, the original network structure is retained, otherwise an additional layer of network structure is added.

[0091] During the iteration process, according to the historical trust metric threshold range data and scenario correspondence of all trusted groups of cloud platform users, the corresponding network structure layer of the deep learning network is constructed, and each trusted group is used as a dimension to obtain the single-layer reconstruction error value of this round of optimization. The single-layer reconstruction error is analyzed. If the comprehensive trust index predicted by the network structure layer gradually decreases with the increase of the number of cycles, and the network structure layer has reached the set performance threshold, then this network structure is retained.

[0092] If the comprehensive trust index decreases before reaching the performance threshold, it means that there are too many bad network layers in the deep learning network at this moment. The lower network should be eliminated and reverse feedback parameter optimization should be used to rebuild the network structure layer. If repeated adjustments of a single scenario cannot achieve the optimization of the comprehensive trust index, it means that the scenario system is single and other scenarios need to be added to the combination to rebuild the network structure layer. If the performance of the network structure layer in adjacent cycles is significantly optimized and has reached the set performance threshold, it means that the number of network nodes is too low, resulting in dimensionality reduction errors. This network structure layer is an unstable network structure. Other parameters should be added to the combination to rebuild the network structure layer. Using the above algorithm, calculate the second trust index until the optimization threshold is reached or the overall trust index has not been significantly improved after multiple rounds of optimization, then increase the optimization interval period to enter transient stable operation.

[0093] In step 5023, the interval formed by the first credibility index and the upper limit value and the lower limit value of the plurality of second credibility indexes is used as the credibility measurement range.

[0094] For example, the trust metric range is an interval consisting of an upper limit value and a lower limit value. The smallest trust index is selected as the lower limit value and the highest trust index is selected as the upper limit value. The trust index greater than or equal to the smallest trust index and less than or equal to the highest trust index is taken as the trust metric range.

[0095] In the embodiment of the present application, the virtual machines are divided into multiple groups according to the trustworthiness metric of the virtual machines, and each group independently sets a trustworthiness metric threshold range to complete group-level trustworthy unified management, thereby improving the management efficiency of the virtual machines and the efficiency of the trusted startup of the virtual machines.

[0096] In some embodiments, the trust metric range of each group can also be set in the following manner:

[0097] (1) Users set the trusted metric range. The user manually specifies the trusted metric threshold range, which is applicable to professional trusted scenarios with centralized operation and maintenance cloud platforms or site reliability engineering (SRE) engineers. SRE engineers can set their own trusted business division standards. For example, the confidentiality project group must be divided into 0.95 or above, general projects must be divided into 0.8 or above, business operation and maintenance monitoring cloud platforms and fault handling cloud platforms must be divided into 0.75 or above, and super administrators must be divided into 0.5 or above. Subsequently, SRE engineers will divide their own virtual machines into different groups according to their own standards, thus entering the trusted closed-loop management at the virtual machine level.

[0098] (2) The system default recommended value is used as the trusted measurement range for each group. This is applicable to the application scenario where the cloud platform applies for trusted management for new users. The cloud platform calculates the entire network service to obtain real-time recommended values, and regularly updates and pushes them to users. Users can choose whether to accept the recommendation (the first time using the cloud platform, the initial threshold is notified after the recommendation is mandatory, and it is not optional) and enter the trusted closed-loop management.

[0099] In the embodiment of the present application, the user is provided with the functions of querying, setting, resetting and modifying the trusted metric threshold range during the application process. The setting of the trusted metric threshold range supports manual specification or using the system default recommended value, or the trusted metric threshold range is automatically filled in by the system recommendation according to the scenario. The recommended trusted metric range is formed by the cloud platform background calling algorithm analysis, and the recommended threshold is displayed in real time on the trusted metric threshold range management page, which is convenient for users to refer to and set and modify the trusted group threshold.

[0100] Continue to refer Figure 5 , step 503, when the first virtual machine is started, obtain the current trust index of the first virtual machine.

[0101] Here, the current trust index is determined according to the current multiple measurement values ​​of the first virtual machine. The first virtual machine is any virtual machine, and the first group is the group to which the first virtual machine belongs.

[0102] As can be seen from the above, the integrity of the virtual hardware is verified based on the measurement extension value and the benchmark measurement value. If it is complete, the virtual machine is booted, and if it is not complete, the virtual machine cannot be started. The trust measurement index is obtained based on the failure ratio during the virtual machine startup process. The current trust index of the first virtual machine is also the trust measurement index of the first virtual machine.

[0103] Step 504 : When the current trust index is within the trust measurement range of the first group corresponding to the first virtual machine, restore the first virtual machine based on the trusted backup data of the first virtual machine.

[0104] For example, the trusted backup data refers to data pre-backed up for the first virtual machine system, or image data of the virtual machine.

[0105] In some embodiments, step 504 can be implemented in the following manner: when the current trust index is lower than the lower limit of the trust measurement range, the trusted backup data of the first virtual machine is obtained; the trusted backup data is detected and processed to obtain a detection result; when the detection result is that the trusted backup data is the latest data, or the detection result is that the trusted backup data is the initial mirror data, the first virtual machine is restored based on the trusted backup data.

[0106] For example, determine whether the current trusted backup data is mirror data, or whether the trusted backup data is within the trusted measurement threshold range. If it is the initial image of the platform, the trust index is 100%, which can be used for recovery processing. If it is the latest local backup data previously made for this virtual machine, it can also be used for recovery processing.

[0107] In some embodiments, when the trust index of a single virtual machine exceeds a trust metric threshold range, it supports triggering a self-healing recovery mechanism in the form of reconstruction or forced restart.

[0108] Rebuilding a virtual machine is initiated by the user of the virtual machine. The user manually specifies to change to the initial trusted image set by the system (supports changing the operating system type, such as changing from CentOS to Euler, etc.), or performs system trusted recovery through the local system backup. The trust index of the virtual machine returning to the initial trusted image is 100%. For the virtual machine using the local system backup, the trust index is measured in real time during the system loading process and evaluated to see if it is higher than the trusted group threshold setting. If it does not meet the requirements, it will naturally return to select the next backup for system trusted rollback operation. The advantage is that it is self-controllable and supports changing the operating system type. The disadvantage is that it requires real-time human attention and the SRE personnel must have the ability to predict risks.

[0109] The forced restart (hard-reboot) of the virtual machine is a function selected by the virtual machine user. The cloud platform automatically collects, analyzes, monitors, and rolls back to complete a series of trusted recovery processes. The self-healing recovery mechanism is triggered by forced restart. The operating system of the current virtual machine is measured in real time during the system loading process to evaluate whether it is higher than the trusted group threshold setting. If it does not meet the requirements, it will naturally return to the most recent trusted backup for the system trusted rollback operation. If it meets the threshold requirements, the current trusted system will be backed up for the next backup and recovery. The advantage is that it is set once, automatically recovered, and no long-term manual attention is required. However, it does not support changing the operating system type and can only roll back to an earlier backup. In the event of an unavoidable failure, manual intervention is still required.

[0110] In the embodiment of the present application, it is proposed to manage virtual machines in groups, and each group independently sets a trusted metric threshold range to complete group-level trusted management. The management efficiency of virtual machines is improved, so that the trusted metric threshold range of the virtual machine matches the group to which it belongs and the corresponding application scenario, thereby improving the startup efficiency and startup security of the virtual machine. According to the business attributes of the virtual machine group, a trusted trusted metric threshold range is adaptively recommended. According to the trusted metric results of the host machine to which the virtual machine is distributed and the virtual machine loading stage, early perception of abnormalities is achieved, and the abnormal startup is used as a self-healing recovery sign to automatically restore the virtual machine to a trusted startup state, thereby improving the security of virtual machine startup.

[0111] In some embodiments, after the first virtual machine is started, a first host machine that matches the first virtual machine is determined. When the comprehensive trust index of the first host machine does not meet the set threshold of the first group, it is determined whether the service quality index and the network quality index of the first host machine meet the first condition, wherein the first condition includes: the service quality index is a first preset value, and the network quality index is a second preset value. When the first host machine meets the first condition, the first virtual machine carried by the first host machine is migrated to a second host machine within the trust measurement range. When the first host machine does not meet the first condition, the first virtual machine is restored based on the trusted backup data of the first virtual machine.

[0112] For example, the first host machine matched by the first virtual machine is a physical electronic device used to provide a physical basis for the first virtual machine. The comprehensive trust index is a trust index obtained by multiplying multiple parameters of the first host machine, which can be used to characterize whether the host machine has a fault. When the comprehensive trust index of the host machine does not meet the set threshold of the first group, it means that the host machine is not sufficient to provide a physical basis for the first virtual machine, and it is necessary to determine whether to replace the host machine based on the network quality and service quality of the host machine. The first preset value is 1, and the second preset value is 1. The following will explain the principles of obtaining service quality indicators and network quality indicators. If the first condition is met, it means that the core service quality of the host machine is normal and the network quality allows, then the migration strategy is used first to replace the host machine; if the first condition is not met, it means that a business blocking exception is encountered, or a hardware failure causes the virtual machine to be unable to migrate, and the cloud platform enables self-healing backup and reconstruction for the hosted virtual machine.

[0113] In some embodiments, after determining the first host machine that matches the first virtual machine, the parameters of the first host machine are obtained, and each parameter is multiplied to obtain the comprehensive trust index of the first host machine. The comprehensive trust index of the host machine is denoted as F(x),

[0114] When the comprehensive trust index F(x) of the host machine is lower than the threshold set by the cloud platform, and the proportion of these hosts that need self-healing recovery is within the controllable proportion of the cloud platform, the self-healing recovery mechanism is enabled. The controllable proportion of the cloud platform is manually set by the cloud platform administrator according to the scale, type, and business continuity requirements of the cloud platform. For example, if the proportion of standby machines in a large central resource pool is 5%, the controllable proportion of the cloud platform is set to no more than 5%. If the overall resources of a medium-sized dedicated resource pool are small but the proportion of standby machines for full coverage of scenarios is 10%, the controllable proportion of the cloud platform is set to no more than 10%. Exceeding the proportion of standby machines is judged to exceed the controllable proportion of the cloud platform. If a core switch failure occurs and a batch of host machines lose connection or packet loss occurs, a hardware-level network device failure is also judged to exceed the controllable proportion of the cloud platform.

[0115] In some embodiments, the parameters of the first host include the following:

[0116] (1) Hardware safety index. That is, the general hardware health index. The hardware safety index is determined by the parameters of the hardware devices obtained by real-time monitoring of the general hardware health of the host machine. The hardware devices include power supply, CPU, memory, fan, etc. When the hardware device of the first host machine has no faults, the hardware safety index is the first preset value. For example: the hardware safety index is recorded as x 1 , only supports setting to 0 or 1. Any hardware failure will be recorded as 0.

[0117] (2) Trusted Platform Module (TPM) Index. The Trusted Platform Module Index is obtained using the TPM chip installed on the host. According to the international TPM2.0 standard, it protects the confidentiality and privacy of data stored on the device or in the cloud to achieve secure storage, and serves as the root of trust for identity authentication to support the authentication and authorization of cloud service objects and permissions. The Trusted Platform Module Index is denoted as x 2 , which is the real-time output value measured by the system TPM.

[0118] (3) Network quality index. The network quality index is obtained by real-time monitoring of the communication quality of the management network, storage network and business network. When the network connectivity rate of the first host is greater than the connectivity rate threshold, the network quality index is the second preset value (1). For example, the network quality index is marked as x 3 , which is the real-time output value of the connectivity rate of the system's regular network fluctuation monitoring. If the connectivity rate of the storage network and business network is lower than 50%, this value is immediately set to 0.

[0119] (4) Key directory information. The key directory information is determined by monitoring the host machine to ensure that it is not tampered with by Trojans, viruses, or human malicious tampering. When the key directory of the first host machine is not tampered with, the key directory information is represented by the first preset value. For example, the key directory information is recorded as x 4 , only supports setting to 0 or 1. If any directory is read-only or has insufficient free space, this value is set to 0.

[0120] (5) Service quality index. The service quality index is obtained by real-time monitoring of the service quality of computing services, network services, virtualization services, etc. When there is no abnormal process on the first host machine, the service quality index is the first preset value. For example, the service quality index is marked as x 5 , only supports setting to 0 or 1. If any service heartbeat monitoring is abnormal, the process verification mechanism is enabled to confirm the process abnormality. This value is set to 0, otherwise it remains at 1.

[0121] (6) The average trust index of the virtual machines carried by the first host. The trust measurement index of each virtual machine on the host must be reported to the cloud platform, and the cloud platform calculates the average trust index of the virtual machines carried by each host. When the first host does not carry any virtual machine, the average trust index of the first host is the third preset value; when the virtual machine carried by the first host does not belong to any group, the current trust index of the virtual machine is the fourth preset value. For example: if the carried virtual machines are not in a group with a trust index turned on, the trust index of this virtual machine is 100%; if there is no virtual machine on the current host, the average trust index is 100%. For example: the average trust index of the virtual machines carried by the first host is recorded as x 6 , which is the real-time calculation result of the average trust index of the virtual machines carried by each host.

[0122] In the embodiment of the present application, trust metrics of two dimensions, virtual machines and host machines, are provided. With respect to the trust metrics of virtual machines, virtual machines are divided into multiple groups, and each group independently sets a trust metric threshold range to complete group-level trust unified management, and the trust index after the trust metric of each virtual machine participates in the calculation of the comprehensive trust index of the host machine. With respect to the trust metric of the host machine, the trust metric of the host machine is comprehensively performed. The trust startup efficiency and startup security of the virtual machine are improved. With respect to the virtual machine, the distribution of virtual machines is distributed and adjusted in real time according to the trust index of the host machine, so that the virtual machine is always preferentially distributed to the host machine with higher trust. The trust metric results in the system loading stage complete the early perception of abnormalities, and use the abnormal startup as a flag for executing self-healing recovery, and automatically roll back the virtual machine to a trusted state. With respect to the host machine, the trust index of the host machine is tracked regularly. Once the trust metric threshold range is exceeded, the virtual machine is preferentially migrated to the trusted host machine. If migration is not possible, the hosted virtual machine is rebuilt through self-healing backup.

[0123] In some embodiments, when the comprehensive trust index of the first host machine meets the set threshold of the first group, the operating system of the virtual machine is loaded based on the resources of the first host machine; and the process proceeds to step 503 .

[0124] In some embodiments, the trusted startup method of the virtual machine provided in the embodiments of the present application can be applied to public cloud platforms and private cloud platforms to realize trusted startup of virtual machines at the IaaS level, and perform self-healing recovery for non-trusted loading scenarios, providing dual trusted protection from both the virtual machine itself and the host machine, meeting trusted computing technology and application requirements.

[0125] The following is an explanation of the trusted startup method of the virtual machine provided in the embodiment of the present application with reference to Figure 2 , Figure 2 A first flow chart of a trusted startup method for a virtual machine provided in an embodiment of the present application.

[0126] In step S1, trusted threshold management.

[0127] When the user selects the trusted threshold management function in the cloud platform, step S1 is executed.

[0128] In step S2, the threshold value is changed.

[0129] When the user selects to change the threshold in the trusted threshold management function, the process proceeds from step S1 to step S2.

[0130] In step S3, it is determined whether a threshold is recommended.

[0131] When the user selects the recommended threshold, the trusted measurement range recommended by the trusted startup method of the virtual machine provided by the cloud platform according to the embodiment of the present application is used, and the process proceeds to step S4. When the user chooses not to recommend the threshold, the process proceeds to step S6, and the default or specified threshold is used, that is, the default trusted measurement range preset by the system is used, or the trusted measurement range is manually specified by the user. For example: it supports the use of a default value of 95% of the threshold or a trusted measurement range manually specified by the user as greater than 50% and less than or equal to 100%.

[0132] In step S4, the scene of the virtual machine is set.

[0133] In step S5, thresholds are allocated corresponding to different scenarios.

[0134] For example, according to the usage requirements of the virtual machines, the user can set the business scenarios of the virtual machine groups, and the cloud platform allocates the trusted measurement range according to the different scenarios of the groups corresponding to the virtual machines.

[0135] After both step S5 and step S6, step S7 is executed to distribute tasks to the virtual machines.

[0136] For example, the distributed task may be a distributed virtual machine creation or reconstruction task.

[0137] In step S8, it is determined whether the host machine is trustworthy.

[0138] When the host machine is not trustworthy, execute step S9 to replace the host machine. Determine the host machine corresponding to the virtual machine, use real-time scheduling, add the real-time comprehensive trust index of the host machine as a scheduling weight factor to the scheduler, and if the current host machine scheduled does not meet the set threshold range of the current trusted cluster, replace it with another host machine, so as to initialize the virtual machine or adjust it to another host machine that meets the threshold range of the host machine trust metric.

[0139] When the host machine is trustworthy, step S10 is executed to load the virtual machine, and step S11 is executed to measure the virtual machine. The host machine actually creates and rebuilds the virtual machine, loads the operating system in the virtual machine after the creation is completed, and uses the vTPM device to perform trust measurement.

[0140] In step S12, it is determined whether the virtual machine has an abnormal index.

[0141] That is, whether the trust index of the virtual machine is an abnormal index is determined according to the measurement result, and the default trust index of the virtual machine created for the first time is 100%.

[0142] When the judgment result of step S12 is yes, step S13 is executed to perform backup rollback.

[0143] Backup rollback means that if the current measurement result is not within the trusted measurement threshold, it will automatically roll back to the last trusted backup. If it is always abnormally trusted, it will loop back to the initial image of the cloud platform.

[0144] In some embodiments, if the trust index is lower than the threshold range, the self-healing recovery mechanism needs to be enabled. If it is higher than the threshold, only an alarm prompt is given. The current trust index is high. Whether to increase the upper limit of the trust index threshold range, or replace the virtual machine with a higher trust value to another group. Some businesses will have a sudden change in trust due to sudden changes in business peaks. After the business falls back, the trust may return to above the average level. Therefore, for this phenomenon, only the lower limit is abnormally self-healed, and only an alarm prompt is given if it exceeds the upper limit.

[0145] When the judgment result of step S12 is no, step S14 is executed to generate a credible backup.

[0146] That is, when the trust index of the virtual machine is within the normal range, trusted backup data can be generated based on the data of the virtual machine. And after step S14, step S15 is executed to report the trust index of a single machine. The trust index of the virtual machine is uploaded to the cloud platform. For example: the trust measurement index of the virtual machine is reported to the cloud platform, and the cloud platform calculates the average trust index of the virtual machines hosted on each host machine. If the hosted virtual machines are not in the group with the trust index turned on, the trust index of this virtual machine is 100%. If there is no virtual machine on the current host machine, the average trust index is 100%.

[0147] In some embodiments, the user may also select virtual machine lifecycle management and proceed to step S22.

[0148] In step S23, the virtual machine is rebuilt.

[0149] When the user chooses to rebuild the virtual machine in the virtual machine lifecycle management function, step S26 is executed after step 23 to determine whether there is a trusted image or backup. If the virtual machine does not have a trusted image or backup data, the process ends. If there is backup data, the process goes to step S8 to determine whether the host machine is trustworthy, and the host machine executes the reconstruction of the virtual machine.

[0150] In step S24, a trusted group is created or joined.

[0151] When the user chooses to create or join a trusted group in the virtual machine lifecycle management function, if it is the first time to create a new virtual machine, or move an existing virtual machine to a trusted group, the trusted metric threshold range is set based on the group as the basic unit. If the trusted metric threshold range of the group is added or changed, go to step S3.

[0152] In step S25, the virtual machine is restarted.

[0153] When the user selects to restart the virtual machine in the virtual machine lifecycle management function, step S25 is executed, and the restart of the virtual machine is executed by the host machine, and the process proceeds to step S8 to determine whether the host machine is trustworthy.

[0154] In some embodiments, reference Figure 3 , Figure 3 A second flow chart of the trusted startup method for a virtual machine provided in an embodiment of the present application.

[0155] In step S21, the host machine is trusted.

[0156] Assuming that the user selects the host trust measurement function, step S21 is executed. The host trust measurement is performed periodically, including the host's general hardware health index, TPM measurement index, network quality, key directory changes, core service quality and other factors.

[0157] After step S21, step S17 is executed to determine the comprehensive trust index of the host machine, and after step S17, step S18 is executed to determine whether the host machine has an abnormal index. The trust index of the host machine is compared with the set host machine trust metric threshold range.

[0158] When the judgment result of step S18 is yes, execute step S19 to migrate the virtual machine or step S20 to evacuate. Assuming that the migration or evacuation is completed, the execution of step S9 to replace the host machine is realized. If the core service quality is normal and the network quality allows, the migration strategy is used first; if a business blocking anomaly is encountered, or a hardware failure causes the virtual machine to be unable to migrate, the cloud platform enables self-healing backup reconstruction for the hosted virtual machine.

[0159] When the judgment result of step S18 is no, execute step S15 to report the single trust index. Upload the comprehensive trust index of the host machine and the trust index of the virtual machine on the host machine to the cloud platform. The cloud platform can determine the average trust index of the host machine based on the trust index of the virtual machine on the host machine. After step S16, execute step S17 periodically and cyclically.

[0160] In some embodiments, reference Figure 4 , Figure 4 A third flow chart of the trusted startup method of a virtual machine provided in an embodiment of the present application;

[0161] In step S1, trusted threshold management.

[0162] In step S27, the threshold of the virtual machine is queried.

[0163] When the user selects the trusted threshold management function and selects the function of querying the threshold of the virtual machine, the trusted index and other parameters of the virtual machine are queried based on the information of the virtual machine input by the user.

[0164] In step S28, the query result is displayed.

[0165] In step S29, the backup is exported.

[0166] For example, the query result includes: the current trust metric threshold range, trust metric result, and information about trustworthy backup data. The user can choose to back up and export the query result.

[0167] In the embodiment of the present application, a trusted display module and an export module are provided to the user, and the user can query the trusted status of the virtual machine in real time, or export the current trusted backup for next use. In addition, a trusted measurement threshold range setting, reset, and modification module is provided, and the user can independently choose whether to use the host machine trusted and virtual machine trusted mechanisms, and can set the virtual machine trusted measurement threshold range by himself, which can meet the requirements of different business scenarios.

[0168] The embodiment of the present application also provides a trusted startup device for a virtual machine, which corresponds to the trusted startup method for the virtual machine, and each step in the embodiment of the trusted startup method for the virtual machine is also fully applicable to the embodiment of the present device. The device includes:

[0169] A threshold setting module, used for grouping each virtual machine to obtain a plurality of groups, wherein each group includes at least one virtual machine;

[0170] The threshold setting module is also used to set the trust measurement range corresponding to each group and the application scenario corresponding to each group;

[0171] A startup module, configured to obtain a current trust index of the first virtual machine when the first virtual machine is started, wherein the current trust index is determined according to multiple current measurement values ​​of the first virtual machine;

[0172] The recovery module is used to recover the first virtual machine based on the trusted backup data of the first virtual machine when the current trusted index is within the trusted measurement range of the first group corresponding to the first virtual machine.

[0173] It should be noted that: the trusted boot device of the virtual machine provided in the above embodiment only uses the division of the above program modules as an example when performing the trusted boot of the virtual machine. In actual applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the processing described above. In addition, the trusted boot device of the virtual machine provided in the above embodiment and the trusted boot method embodiment of the virtual machine belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0174] Based on the hardware implementation of the above program modules and in order to implement the method of the embodiment of the present application, the embodiment of the present application also provides an electronic device. Figure 7 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 7 As shown, the electronic equipment includes:

[0175] Communication interface 701, capable of exchanging information with other devices such as network devices;

[0176] The processor 702 is connected to the communication interface 701 to implement information exchange with other devices and is used to execute the method provided by one or more of the above technical solutions when running the computer program. The computer program is stored in the memory 703.

[0177] Of course, in actual application, the various components in the electronic device are coupled together through the bus system 704. It can be understood that the bus system 704 is used to realize the connection and communication between these components. In addition to the data bus, the bus system also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Figure 7 Various buses are labeled as bus system 704 .

[0178] The memory 703 in the embodiment of the present application is used to store various types of data to support the operation of the computer device. Examples of such data include: any computer program used to operate on the electronic device.

[0179] It can be understood that the memory 703 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAM bus random access memory (DRRAM, Direct Rambus Random Access Memory).The memories described in the embodiments of the present application are intended to include, but are not limited to, these and any other suitable types of memories.

[0180] The method disclosed in the above embodiment of the present application can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, a DSP, or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiment of the present application, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory, and the processor reads the program in the memory and completes the steps of the above method in combination with its hardware.

[0181] Optionally, when the processor 702 executes the program, the corresponding processes implemented by the electronic device in each method of the embodiments of the present application are implemented, which will not be described in detail here for the sake of brevity.

[0182] In an exemplary embodiment, the present application also provides a storage medium, namely a computer storage medium, specifically a computer-readable storage medium, for example, including a first memory storing a computer program, and the computer program can be executed by a processor of a computer device to complete the aforementioned method steps. The computer-readable storage medium can be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface storage, optical disk, or CD-ROM.

[0183] In the several embodiments provided in the present application, it should be understood that the disclosed devices, computer equipment and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0184] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0185] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0186] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium, which, when executed, executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, disks or optical disks.

[0187] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present application can be essentially or partly embodied in the form of a software product that contributes to the relevant technology. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0188] In an exemplary embodiment, the embodiment of the present application further provides a computer program product, including a computer program, which can be executed by the processor 702 of the electronic device to complete the steps described in the method of the embodiment of the present application applied to the remote management terminal.

[0189] In an exemplary embodiment, the embodiment of the present application further provides a computer program product, including a computer program, which can be executed by the processor 702 of the electronic device to complete the steps described in the method applied to the terminal in the embodiment of the present application.

[0190] It should be noted that: "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0191] In addition, the technical solutions described in the embodiments of the present application can be combined arbitrarily without conflict.

[0192] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A trusted startup method for a virtual machine, characterized in that: The method comprises: Performing grouping processing on each virtual machine to obtain a plurality of groups, wherein each group includes at least one virtual machine; For each of the groups and the application scenarios corresponding to each of the groups, a trust metric range corresponding to each of the groups is set; When the first virtual machine is started, obtaining a current trust index of the first virtual machine, wherein the current trust index is determined according to multiple current measurement values ​​of the first virtual machine; When the current trust index is within a trust measurement range of a first group corresponding to the first virtual machine, the first virtual machine is restored based on the trusted backup data of the first virtual machine.

2. The method according to claim 1, characterized in that The step of setting a trust metric range corresponding to each group and the application scenario corresponding to each group includes: The following processing is performed for each of the groups: Based on the historical credibility measurement range and scenario data of the group, calling a deep learning network to perform prediction processing to obtain a first credibility index; Based on the error between the first trust index and the initial trust index, calling the deep learning network to perform iterative optimization processing to obtain multiple second trust indexes, wherein the second trust index obtained in each round of iteration is the input of the next round of iteration; The interval formed by the first credibility index and the upper limit value and the lower limit value of the plurality of second credibility indexes is used as the credibility metric range.

3. The method according to claim 2, characterized in that Based on the error between the first trust index and the initial trust index, calling the deep learning network to perform iterative optimization processing to obtain multiple second trust indexes, including: Based on the error between the first trust index and the initial trust index, calling the deep learning network to perform iterative optimization processing to obtain a first second trust index; Based on the error between the i-th second credible index and the i-1-th second credible index, calling the deep learning network to perform iterative optimization processing to obtain the i+1-th second credible index, wherein i is a positive integer greater than or equal to 1, and when i is equal to 1, the i-1-th second credible index is the first credible index; When the number of iterations reaches a preset number, or the difference between the second trust indexes obtained by the preset number of optimization processes is less than a first threshold, the iterative optimization process is stopped, and the obtained second trust index is output.

4. The method according to claim 1, characterized in that: The restoring the first virtual machine based on the trusted backup data of the first virtual machine includes: When the current trust index is lower than the lower limit of the trust measurement range, obtaining the trusted backup data of the first virtual machine; Performing detection processing on the trusted backup data to obtain a detection result; When the detection result is that the trusted backup data is the latest data, or the detection result is that the trusted backup data is the initial image data, the first virtual machine is restored based on the trusted backup data.

5. The method according to claim 1, characterized in that After the first virtual machine is started, the method further includes: Determine a first host machine matching the first virtual machine; When the comprehensive trust index of the first host machine does not meet the set threshold of the first group, determining whether the service quality index and the network quality index of the first host machine meet a first condition, wherein the first condition includes: the service quality index is a first preset value, and the network quality index is a second preset value; When the first host machine meets the first condition, migrating the first virtual machine carried by the first host machine to a second host machine within the trusted measurement range; When the first host machine does not meet the first condition, the first virtual machine is restored based on the trusted backup data of the first virtual machine.

6. The method according to claim 5, characterized in that After determining the first host machine that matches the first virtual machine, the method further includes: Obtaining the following parameters of the first host machine: hardware security index, trusted platform module index, network quality index, key directory information, service quality index, and average trust index of virtual machines hosted by the first host machine; Each of the parameters is multiplied to obtain a comprehensive trust index of the first host machine.

7. The method according to claim 6, characterized in that When the first host machine does not host any virtual machine, the average trust index of the first host machine is a third preset value; when the virtual machine hosted by the first host machine does not belong to any group, the current trust index of the virtual machine is a fourth preset value; When the hardware device of the first host machine has no faults, the hardware safety index is a first preset value; When the network connectivity rate of the first host machine is greater than a connectivity rate threshold, the network quality indicator is a second preset value; When the key directory of the first host machine has not been tampered with, the key directory information is characterized as a first preset value; When there is no abnormal process on the first host machine, the service quality indicator is a first preset value.

8. The method according to claim 5, characterized in that The method further comprises: When the comprehensive trust index of the first host machine meets the set threshold of the first group, loading the operating system of the virtual machine based on the resources of the first host machine; The process proceeds to the step of obtaining the current trust index of the first virtual machine.

9. A trusted boot device for a virtual machine, characterized in that: The device comprises: A threshold setting module, used for grouping each virtual machine to obtain a plurality of groups, wherein each group includes at least one virtual machine; The threshold setting module is further used to set a trust metric range corresponding to each group and an application scenario corresponding to each group; A startup module, configured to obtain a current trust index of the first virtual machine when the first virtual machine is started, wherein the current trust index is determined according to multiple current measurement values ​​of the first virtual machine; The recovery module is configured to recover the first virtual machine based on the trusted backup data of the first virtual machine when the current trusted index is within the trusted measurement range of the first group corresponding to the first virtual machine.

10. An electronic device, characterized in that: include: A processor and a memory for storing a computer program that can be executed on the processor, wherein: The processor is used to execute the steps of the trusted startup method of the virtual machine according to any one of claims 1 to 8 when running a computer program.

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