Resource scheduling management method and device based on cloud edge collaboration, medium and equipment

Abstract

The invention provides a resource scheduling management method and device based on cloud edge collaboration, a medium and equipment, and the method comprises the steps: monitoring the resource use condition of each piece of computing equipment in real time, and the types of the computing equipment comprise a physical machine and an edge device; selecting the type of the computing device for deploying the virtual machine based on a load balancing principle according to the resource use condition of each computing device and the user service requirement; selecting the position and resource configuration information of the computing device for deploying the virtual machine from the computing device of the selected type according to the performance requirement of the user; deploying a virtual machine according to the position and the resource configuration information, and monitoring the resource use condition of the virtual machine in real time; and dynamically adjusting the allocation condition of the computing resources and the storage resources of the virtual machine according to the resource use condition monitored for the virtual machine. The resource utilization rate, the performance and the flexibility of the whole system can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud-edge collaboration, and in particular, to a resource scheduling and management method, device, medium, and equipment based on cloud-edge collaboration. Background Art

[0002] With the increasingly widespread application of container technology in public clouds, private clouds, hybrid clouds, and other fields, the latest container technology has been widely used in the field of cloud computing due to its many advantages such as excellent performance and ease of use. Cloud computing is a mass-participation computing model based on the Internet that provides dynamic and scalable computing resources in the form of services to demanders. Cloud computing is mainly divided into three service types: Infrastructure as a Service, Platform as a Service, and Software as a Service. Edge computing is a distributed computing architecture that decentralizes computing tasks to devices close to the data source. The purpose of edge computing is to reduce response time, improve stability, and at the same time reduce network pressure and energy consumption on the central cloud.

[0003] Cloud-edge collaboration tightly combines cloud computing and edge computing. The edge nodes are responsible for real-time data collection, processing, and preliminary analysis, while the cloud data center is responsible for large-scale computing tasks such as data analysis, machine learning, and model training. In this way, cloud-edge collaboration gives full play to the network advantages of the edge side and the elastic computing power of cloud computing. However, in cloud-edge collaboration environments, such as industrial Internet, intelligent transportation, intelligent manufacturing, etc., higher requirements are placed on the real-time performance and reliability of the network, and traditional cloud computing solutions may not be able to meet these requirements. In addition, network management in cloud-edge collaboration also faces some challenges, including fast fault location and handling, which requires that the customer's network data packets can be forwarded to the target address for location and troubleshooting through traffic mirroring.

[0004] With the rapid development of cloud computing and edge computing, the traditional data center architecture has faced many challenges, such as low resource utilization, low data processing efficiency, and inability to meet real-time requirements. As an integrated solution that integrates functions such as computing, storage, and network, the hyper-converged all-in-one machine has become one of the important ways to improve the efficiency and flexibility of data centers by simplifying and integrating hardware infrastructure.

[0005] However, with the diversification and increase of computing and data processing requirements, a single hyper-converged all-in-one machine solution can no longer fully meet the diverse application scenario requirements. Especially in an environment involving cloud computing and edge computing working together, how to effectively manage and schedule physical machine and virtual machine resources to achieve optimal performance, reliability, and efficiency has become one of the current research hotspots. Summary of the Invention

[0006] To address at least one of the above technical problems, an embodiment of the present invention provides a resource scheduling and management method, apparatus, medium, and device based on cloud-edge collaboration.

[0007] According to a first aspect, the resource scheduling and management method based on cloud-edge collaboration provided by an embodiment of the present invention includes:

[0008] Real-time monitoring of the resource usage of each computing device, where the types of computing devices include physical machines and edge devices;

[0009] Based on the resource usage of each computing device and user service requirements, select the type of computing device for deploying virtual machines based on the principle of load balancing;

[0010] According to the performance requirements of the user, select the location and resource configuration information of the computing device for deploying virtual machines among the selected types of computing devices;

[0011] Deploy virtual machines according to the location and resource configuration information, and real-time monitor the resource usage of the virtual machines;

[0012] According to the monitored resource usage of the virtual machines, dynamically adjust the allocation of computing resources and storage resources of the virtual machines.

[0013] In one embodiment, the method further includes:

[0014] Real-time obtain edge environment data;

[0015] Correspondingly, the step of selecting the type of computing device for deploying virtual machines based on the principle of load balancing according to the resource usage of each computing device and user service requirements includes:

[0016] Based on the resource usage of each computing device, user service requirements, and the edge environment data, select the type of computing device for deploying virtual machines based on the principle of load balancing.

[0017] In one embodiment, the method further includes:

[0018] Predict the load situation of the virtual machines within a preset future time period;

[0019] Correspondingly, the step of dynamically adjusting the allocation of computing resources and storage resources of the virtual machines according to the monitored resource usage of the virtual machines includes: Dynamically adjusting the allocation of computing resources and storage resources of the virtual machines according to the monitored resource usage of the virtual machines and the predicted load situation.

[0020] In one embodiment, the method further includes:

[0021] Dynamically adjust the allocation of computing resources and storage resources of the virtual machine according to the actual operation of the virtual machine.

[0022] In one embodiment, the method further includes:

[0023] Obtain the access behavior data of the user to the data;

[0024] Adjust the storage location and / or caching policy of the data according to the access behavior data.

[0025] In one embodiment, the method further includes:

[0026] Perform regular synchronization processing on the data on the physical machine and the edge device to ensure data consistency between the physical machine and the edge device.

[0027] In one embodiment, the method further includes:

[0028] Perform permission control on the data access request through an access control policy;

[0029] Encrypt the data to be transmitted through an encryption method.

[0030] According to a second aspect, the resource scheduling and management device based on cloud-edge collaboration provided by an embodiment of the present invention includes:

[0031] A first monitoring module, configured to monitor the resource usage of each computing device in real time, where the types of the computing devices include physical machines and edge devices;

[0032] A first selection module, configured to select the type of computing device for deploying a virtual machine based on the resource usage of each computing device and the user service requirements according to the principle of load balancing;

[0033] A virtual machine deployment module, configured to select the location and resource configuration information of the computing device for deploying the virtual machine from the selected type of computing device according to the performance requirements of the user;

[0034] A second monitoring module, configured to deploy the virtual machine according to the location and the resource configuration information, and monitor the resource usage of the virtual machine in real time;

[0035] A dynamic adjustment module, configured to dynamically adjust the allocation of computing resources and storage resources of the virtual machine according to the monitored resource usage of the virtual machine.

[0036] In one embodiment, the device may further include:

[0037] An environment perception module, configured to: obtain edge environment data in real time;

[0038] Correspondingly, the first selection module is specifically configured to: select the type of computing device for deploying the virtual machine based on the resource usage of each computing device, the user service requirements, and the edge environment data according to the principle of load balancing.

[0039] In one embodiment, the device may further include:

[0040] A load prediction module, configured to predict the load condition of the virtual machine within a preset time period in the future;

[0041] Correspondingly, the dynamic adjustment module is specifically configured to: dynamically adjust the allocation of computing resources and storage resources of the virtual machine according to the monitored resource usage and predicted load condition of the virtual machine.

[0042] In one embodiment, the device may further include:

[0043] A dynamic adjustment module, configured to dynamically adjust the allocation of computing resources and storage resources of the virtual machine according to the actual running condition of the virtual machine.

[0044] In one embodiment, the device may further include:

[0045] An access behavior acquisition module, configured to acquire the access behavior data of the user to the data;

[0046] A policy adjustment module, configured to adjust the storage location and / or caching policy of the data according to the access behavior data.

[0047] In one embodiment, the device further includes:

[0048] A data synchronization module, configured to perform regular synchronization processing on the data on the physical machine and the edge device to ensure data consistency between the physical machine and the edge device.

[0049] In one embodiment, the device may further include:

[0050] An access control module, configured to perform access control on data access requests through an access control policy;

[0051] A data encryption module, configured to encrypt the data to be transmitted through an encryption method.

[0052] According to a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed on a computer, the computer is made to execute the method provided in the first aspect.

[0053] According to a fourth aspect, a computing device provided by an embodiment of the present invention includes a memory and a processor. An executable code is stored in the memory. When the processor executes the executable code, the method provided by the first aspect is implemented.

[0054] The resource scheduling and management method, device, medium, and device based on cloud-edge collaboration provided by the embodiments of the present invention first monitor the resource usage of each computing device in real time, and then select the type of computing device for deploying a virtual machine based on the resource usage of each computing device and user service requirements according to the principle of load balancing. Then, according to the performance requirements of the user, select the location and resource configuration information of the computing device for deploying the virtual machine among the selected type of computing devices. Furthermore, deploy the virtual machine according to the location and the resource configuration information, and monitor the resource usage of the virtual machine in real time. Thus, according to the monitored resource usage of the virtual machine, dynamically adjust the allocation of the computing resources and storage resources of the virtual machine. It can be seen that the embodiments of the present invention monitor the utilization of various resources in real time through the resource monitoring module deployed on the physical machine and the edge device, and then select the type, location, and resource configuration information of the computing device for deploying the virtual machine, and then deploy the virtual machine. This method can improve resource utilization, optimize system performance, and promote the deep integration of cloud computing and edge computing, laying a technical foundation for the development of future intelligent applications. That is, the embodiments of the present invention can improve the overall resource utilization, performance, and flexibility of the system by combining the advantages of physical machine and virtual machine resources and working collaboratively at the cloud and edge ends. Description of the Drawings

[0055] Figure 1 It is a schematic flowchart of a resource scheduling and management method based on cloud-edge collaboration in an embodiment of the present invention;

[0056] Figure 2 It is a structural block diagram of a resource scheduling and management device based on cloud-edge collaboration in an embodiment of the present invention. Detailed Embodiments

[0057] In a first aspect, an embodiment of the present invention provides a resource scheduling and management method based on cloud-edge collaboration. Refer to Figure 1 , the method includes the following steps S110 to S150:

[0058] S110. Monitor the resource usage of each computing device in real time, where the types of the computing devices include physical machines and edge devices;

[0059] It is understandable that resource awareness is the basis for ensuring the efficient operation of the system. The usage of various resources can be monitored in real time through resource monitoring modules deployed on physical machines and edge devices. For example, the resource monitoring module can be used to obtain the CPU and memory utilization rates of physical machines, the storage resource conditions of edge devices, and the real-time load conditions of network bandwidth in real time.

[0060] In addition to monitoring the resource usage of computing devices, physical factor data such as temperature and humidity of the edge environment can also be obtained through environmental awareness technology, and these factors may affect the decision-making of virtual machine deployment. Specifically, real-time data of the edge environment, including environmental data such as temperature and humidity, can be obtained through sensors or network monitoring devices, and these data can be collectively referred to as edge environment data.

[0061] S120. According to the resource usage of each computing device and the user's service requirements, select the type of computing device for deploying the virtual machine based on the principle of load balancing;

[0062] It is understandable that in order to achieve optimal resource utilization and performance, multiple virtual machine scheduling strategies are adopted in the embodiments of the present invention. First of all, the load balancing algorithm is an important part of it. By dynamically monitoring and analyzing the resource usage and load conditions of each physical machine and edge device, and then according to different resource usage, load conditions and user service requirements, it is decided to deploy the virtual machine on the most suitable node. For example, for tasks with high short-term load, the virtual machine can be selected to be deployed on a physical machine with stronger computing power to obtain higher computing performance; while for data-intensive tasks, edge devices with high-speed storage and network bandwidth can be preferentially selected for deployment to reduce data transmission latency and improve data processing efficiency.

[0063] It can be seen that in this way, it can be decided whether to deploy the virtual machine on a physical machine or on an edge device.

[0064] Among them, the principle of load balancing refers to determining the deployment of the virtual machine on a physical machine or an edge device based on a load balancing algorithm (such as minimum load, shortest job first) to achieve the most optimal allocation of resources as much as possible.

[0065] S130. According to the performance requirements of the user, select the location and resource configuration information of the computing device for deploying the virtual machine among the selected types of computing devices;

[0066] It is understandable that SLA management is also an important consideration in virtual machine scheduling. According to the user's requirements for performance and availability, the location and resource configuration information of the virtual machine can be dynamically adjusted to ensure that the service level requirements agreed upon in the SLA are met. For example, for applications that require real-time data processing and high availability guarantees, the virtual machine can be deployed on edge devices that are close to the user and have low network latency to improve the user experience and service response speed.

[0067] Among them, SLA management: According to the requirements of the service level agreement (SLA), adjust the location and resource configuration of the virtual machine to ensure that the user's expectations for performance and availability are met. The full spelling of SLA is Service Level Agreement, which is an agreement reached between the service provider and the customer regarding the service quality, specifying the specific standards and commitments that the service provider needs to achieve.

[0068] S140. Deploy the virtual machine according to the location and the resource configuration information, and monitor the resource usage of the virtual machine in real time;

[0069] It can be seen that after determining the type, location, and resource configuration information of the computing device on which the virtual machine is deployed, the virtual machine can be deployed, and after the virtual machine is deployed, the resource usage of the virtual machine can be monitored in real time.

[0070] S150. Dynamically adjust the allocation of the computing resources and storage resources of the virtual machine according to the monitored resource usage of the virtual machine.

[0071] It can be seen that the allocation of various resources of the virtual machine can be dynamically adjusted according to the monitored resource usage of the virtual machine in real time.

[0072] In one embodiment, the method may further include:

[0073] Obtain edge environment data in real time;

[0074] Correspondingly, the step of selecting the type of computing device for deploying the virtual machine based on the resource usage of each computing device and the user's business requirements and the principle of load balancing includes:

[0075] Select the type of computing device for deploying the virtual machine based on the resource usage of each computing device, the user's business requirements, and the edge environment data according to the principle of load balancing.

[0076] It can be seen that when selecting the type of computing device for deploying the virtual machine, the edge environment data can also be considered. If the temperature or other environmental data in the edge environment is not suitable for the deployment of the virtual machine, the virtual machine needs to be deployed on a physical machine to further ensure the availability and stability after the virtual machine is deployed.

[0077] In one embodiment, the method may further include:

[0078] Predict the load condition of the virtual machine within a preset future time period;

[0079] Correspondingly, the dynamically adjusting the allocation of computing resources and storage resources of the virtual machine according to the monitored resource usage condition of the virtual machine includes: dynamically adjusting the allocation of computing resources and storage resources of the virtual machine according to the monitored resource usage condition and the predicted load condition of the virtual machine.

[0080] It can be understood that with the changes in the workload and the continuous evolution of user requirements, dynamic resource allocation is the key to ensuring the efficient operation of the system. The embodiments of the present invention may adopt a resource adjustment mechanism based on real-time monitoring and prediction, and dynamically adjust the allocation of computing resources and storage resources of the virtual machine according to the currently monitored resource utilization rate and the future load prediction.

[0081] For example, when the CPU utilization rate exceeds the threshold or the storage space is close to saturation, through an automated resource allocation strategy, the idle resources are reallocated to the virtual machines that need more computing or storage resources. Among them, the resource adjustment mechanism refers to: dynamically adjusting the allocation of computing resources and storage resources of the virtual machine according to the real-time monitored resource utilization rate and the load prediction, so as to adapt to the changes in the workload.

[0082] In one embodiment, the method may further include:

[0083] Dynamically adjust the allocation of computing resources and storage resources of the virtual machine according to the actual running condition of the virtual machine.

[0084] It can be seen that this is a feedback control algorithm, which adaptively adjusts the resource allocation strategy according to the actual running condition to avoid resource bottlenecks and improve resource utilization efficiency.

[0085] In one embodiment, the method may further include:

[0086] Obtain the access behavior data of the user for the data;

[0087] Adjust the storage location and / or caching policy of the data according to the access behavior data.

[0088] It can be seen that the storage location and caching policy of data can be dynamically adjusted according to access behavior data. Specifically, the storage location and caching policy of data can be automatically adjusted according to the access frequency and priority of the data, so as to improve the overall data processing efficiency and user experience of the system. For example, frequently accessed data can be stored on edge devices close to users to reduce data access latency. Using the caching policy can reduce data access latency and improve the overall response speed and user experience of the system.

[0089] In one embodiment, the method may further include:

[0090] Regularly synchronize the data on the physical machine and the edge device to ensure data consistency between the physical machine and the edge device.

[0091] It can be understood that in the cloud-edge collaboration environment, effective data management and migration strategies are the keys to ensuring data consistency and real-time performance. Through the data synchronization mechanism and migration strategy of the present invention, data synchronization and consistency between the physical machine and the edge device can be ensured. Through regular data backup and migration, data consistency and real-time performance between the physical machine and the edge device are ensured.

[0092] In one embodiment, the method may further include:

[0093] Perform permission control on data access requests through an access control policy;

[0094] Encrypt the data to be transmitted through an encryption method.

[0095] It can be understood that in the cloud-edge collaboration environment, protecting the security and privacy of user data is of crucial importance. The embodiments of the present invention adopt multi-level security control measures to ensure the security of data transmission and storage between the physical machine and the virtual machine.

[0096] Among them, implement a strict access control policy, only allowing authorized users and devices to access and manage system resources, protecting data transmission and storage between the physical machine and the virtual machine, and preventing unauthorized access and data leakage. For example, through a role-based access control policy or a policy-based access control policy, the access permissions of users and devices can be precisely controlled to prevent unauthorized access and data leakage.

[0097] Among them, use a high-strength encryption algorithm to encrypt the data to protect the confidentiality and integrity of the data during data transmission and storage. For example, encrypt sensitive data through encryption algorithms such as AES (Advanced Encryption Standard) to prevent the data from being stolen or tampered with by unauthorized third parties during transmission and storage.

[0098] The embodiments of the present invention mainly relate to the following technologies: (1) Monitoring the resource usage and perceiving the edge environment data in the cloud-edge collaboration environment; (2) Supporting virtual machine scheduling strategies in the cloud-edge collaboration environment, including load balancing algorithms and SLA management algorithms. (3) Supporting dynamic resource allocation in the cloud-edge collaboration environment, including resource adjustment mechanisms and adaptive adjustments. (4) Supporting data management and migration in the cloud-edge collaboration environment, including data synchronization mechanisms and intelligent caching. (5) Supporting security and privacy protection in the cloud-edge collaboration environment, including access control policies and encryption technologies.

[0099] The embodiments of the present invention introduce key technologies such as resource perception and scheduling, virtual machine scheduling strategies, dynamic resource allocation, data management, and security protection. First, the resource monitoring module deployed on physical machines and edge devices monitors the utilization of various resources in real time, and uses environment perception technology to obtain edge environment data, providing a basis for resource allocation and virtual machine deployment decisions. Secondly, the load balancing algorithm and SLA management strategy are adopted to dynamically adjust the deployment location and resource configuration of virtual machines to optimize resource utilization and meet user requirements. Further, through the dynamic resource allocation mechanism and intelligent caching technology, according to real-time load and prediction, the allocation of computing and storage resources is adjusted to improve the response speed and data processing efficiency. Finally, to ensure data security and privacy protection, strict access control policies and high-strength data encryption technologies are implemented to effectively prevent unauthorized access and data leakage.

[0100] The embodiments of the present invention aim to solve many challenges faced by traditional data center architectures in the face of cloud computing and edge computing requirements, especially problems such as low resource utilization, low data processing efficiency, and inability to meet real-time requirements. The embodiments of the present invention can improve resource utilization, optimize system performance, support diverse application scenario requirements, enhance system security, and promote the deep integration of cloud computing and edge computing through the hybrid deployment of physical machines and virtual machines, laying a technical foundation for the development of future intelligent applications. That is, by combining the advantages of physical machine and virtual machine resources and working together at the cloud and edge ends, the embodiments of the present invention can improve the overall resource utilization, performance, and flexibility of the system.

[0101] It can be seen that through the implementation of the present invention, the computing power coordination problem between cloud computing and edge computing can be solved to optimize resource utilization. By deeply integrating the computing resources of the cloud, network, and edge, this method effectively responds to the multi-level deployment and flexible scheduling of computing, storage, and network resources required by future services, making the data traffic mirroring between the cloud and the edge more efficient and flexible.

[0102] In a second aspect, the embodiments of the present invention provide a resource scheduling and management device based on cloud-edge collaboration. Refer to Figure 2 , the device 100 includes:

[0103] The first monitoring module 110 is used to monitor the resource usage of each computing device in real time. The types of the computing devices include physical machines and edge devices;

[0104] The first selection module 120 is used to select the type of computing device for deploying a virtual machine based on the resource usage of each computing device and the user service requirements, according to the principle of load balancing;

[0105] The virtual machine deployment module 130 is used to select the location and resource configuration information of the computing device for deploying the virtual machine among the selected type of computing devices according to the performance requirements of the user;

[0106] The second monitoring module 140 is used to deploy the virtual machine according to the location and the resource configuration information, and monitor the resource usage of the virtual machine in real time;

[0107] The dynamic adjustment module 150 is used to dynamically adjust the allocation of the computing resources and storage resources of the virtual machine according to the monitored resource usage of the virtual machine.

[0108] In one embodiment, the device may further include:

[0109] The environment perception module is used to: obtain edge environment data in real time;

[0110] Correspondingly, the first selection module is specifically used to: select the type of computing device for deploying the virtual machine according to the resource usage of each computing device, the user service requirements, and the edge environment data, based on the principle of load balancing.

[0111] In one embodiment, the device may further include:

[0112] The load prediction module is used to predict the load condition of the virtual machine within a preset time period in the future;

[0113] Correspondingly, the dynamic adjustment module is specifically used to: dynamically adjust the allocation of the computing resources and storage resources of the virtual machine according to the monitored resource usage of the virtual machine and the predicted load condition.

[0114] In one embodiment, the device may further include:

[0115] The dynamic adjustment module is used to dynamically adjust the allocation of the computing resources and storage resources of the virtual machine according to the actual running condition of the virtual machine.

[0116] In one embodiment, the device may further include:

[0117] The behavior acquisition module is used to acquire the access behavior data of the user to the data;

[0118] A policy adjustment module, configured to adjust the storage location and / or caching policy of data according to the access behavior data.

[0119] In one embodiment, the device further includes:

[0120] A data synchronization module, configured to perform regular synchronization processing on the data on the physical machine and the edge device to ensure data consistency between the physical machine and the edge device.

[0121] In one embodiment, the device further includes:

[0122] An access control module, configured to perform access control on data access requests through an access control policy;

[0123] A data encryption module, configured to encrypt the data to be transmitted through an encryption method.

[0124] It can be understood that the explanations, specific implementation manners, beneficial effects, examples, etc. of the relevant content in the device provided by the embodiments of the present invention can refer to the corresponding parts in the method provided in the first aspect, and will not be elaborated here.

[0125] In a third aspect, an embodiment of the present invention provides a computer-readable medium, on which computer instructions are stored. When the computer instructions are executed by a processor, the processor executes the method provided in the first aspect.

[0126] Specifically, a system or device equipped with a storage medium can be provided. On the storage medium, software program codes for implementing the functions of any one of the above embodiments are stored, and the computer (or CPU or MPU) of the system or device reads and executes the program codes stored in the storage medium.

[0127] In this case, the program code read from the storage medium itself can implement the functions of any one of the above embodiments. Therefore, the program code and the storage medium storing the program code constitute a part of the present invention.

[0128] Embodiments of the storage medium for providing program codes include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Optionally, the program codes can be downloaded from a server computer through a communication network.

[0129] In addition, it should be clear that not only can part or all of the actual operations be completed by executing the program code read by a computer, but also by the operating system etc. operating on the computer based on the instructions of the program code, thereby implementing the functions of any one of the above embodiments.

[0130] In addition, it can be understood that the program code read from the storage medium is written into the memory provided in the expansion board inserted into the computer or into the memory provided in the expansion module connected to the computer, and then based on the instructions of the program code, the CPU etc. installed on the expansion board or the expansion module execute part and all of the actual operations, thereby implementing the functions of any one of the above embodiments.

[0131] It can be understood that for the explanations, specific implementation manners, beneficial effects, examples, etc. of the content in the computer-readable medium provided by the embodiments of the present invention, reference can be made to the corresponding parts in the method provided in the first aspect, and details are not described herein again.

[0132] In a fourth aspect, an embodiment of this specification provides a computing device, including a memory and a processor. An executable code is stored in the memory. When the processor executes the executable code, the method in any one of the embodiments in this specification is implemented.

[0133] It can be understood that for the explanations, specific implementation manners, beneficial effects, examples, etc. of the content in the computing device provided by the embodiments of the present invention, reference can be made to the corresponding parts in the method provided in the first aspect, and details are not described herein again.

[0134] The various embodiments in this specification are all described in a progressive manner. The same or similar parts among the various embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments. In particular, for the apparatus embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0135] Those skilled in the art should be able to realize that in the above one or more examples, the functions described in the present invention can be implemented by hardware, software, add-ons, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium.

[0136] The specific embodiments described above further elaborate on the object, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solution of the present invention shall be included within the protection scope of the present invention.

Claims

1. A resource scheduling and management method based on cloud-edge collaboration, characterized in that including: Real-time monitoring of the resource usage of each computing device, where the types of the computing devices include physical machines and edge devices; Based on the resource usage of each computing device and user service requirements, selecting the type of computing device for deploying virtual machines based on the principle of load balancing; According to the performance requirements of the user, selecting the location and resource configuration information of the computing device for deploying virtual machines among the selected types of computing devices; Deploying virtual machines according to the location and the resource configuration information, and real-time monitoring the resource usage of the virtual machines; Dynamically adjusting the allocation of computing resources and storage resources of the virtual machines according to the monitored resource usage of the virtual machines.

2. The method according to claim 1, wherein Also including: Real-time obtaining of edge environment data; Correspondingly, the step of selecting the type of computing device for deploying virtual machines based on the resource usage of each computing device and user service requirements, based on the principle of load balancing, includes: Selecting the type of computing device for deploying virtual machines based on the resource usage of each computing device, user service requirements, and the edge environment data, based on the principle of load balancing.

3. The method according to claim 1, wherein Also including: Predicting the load situation of the virtual machines within a preset future time period; Correspondingly, the step of dynamically adjusting the allocation of computing resources and storage resources of the virtual machines according to the monitored resource usage of the virtual machines includes: Dynamically adjusting the allocation of computing resources and storage resources of the virtual machines according to the monitored resource usage of the virtual machines and the predicted load situation.

4. The method according to claim 1, wherein Also including: Dynamically adjusting the allocation of computing resources and storage resources of the virtual machines according to the actual running situation of the virtual machines.

5. The method according to claim 1, characterized in that, Also including: Obtaining user access behavior data for data; Adjusting the storage location and / or caching policy of the data according to the access behavior data.

6. The method according to claim 1, characterized in that, Also including: Performing regular synchronization processing on the data on the physical machines and the edge devices to ensure data consistency between the physical machines and the edge devices.

7. The method according to claim 1, wherein Also including: Controlling the permissions of data access requests through an access control policy; Encrypting the data to be transmitted through an encryption method.

8. A resource scheduling and management device based on cloud-edge collaboration, characterized in that, including: A first monitoring module for real-time monitoring of the resource usage of each computing device, where the types of the computing devices include physical machines and edge devices; A first selection module for selecting the type of computing device for deploying virtual machines based on the resource usage of each computing device and user service requirements, based on the principle of load balancing; A virtual machine deployment module for selecting the location and resource configuration information of the computing device for deploying virtual machines among the selected types of computing devices according to the performance requirements of the user; A second monitoring module for deploying virtual machines according to the location and the resource configuration information, and real-time monitoring the resource usage of the virtual machines; A dynamic adjustment module for dynamically adjusting the allocation of computing resources and storage resources of the virtual machines according to the monitored resource usage of the virtual machines.

9. A computer-readable storage medium, characterized in that, Stored thereon is a computer program, which when executed on a computer, causes the computer to execute the method described in any one of claims 1 to 7.

10. A computing device, characterized in that, It includes a memory and a processor. Executable code is stored in the memory. When the processor executes the executable code, the method described in any one of claims 1 to 7 is implemented.

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