Method and device for improving operation efficiency of hyper-converged all-in-one machine based on cloud edge cooperation

Abstract

The invention discloses a method and device for improving the operation efficiency of a hyper-converged all-in-one machine based on cloud edge collaboration, and relates to the technical field of the operation efficiency of hyper-converged all-in-one machines, and the method comprises the steps: deploying a resource monitoring module and an environment perception technology on a physical machine and an edge device, and obtaining resource and environment data; a load balancing algorithm and SLA management are adopted, and the deployment position and resource configuration of the virtual machine are determined according to the load condition and the user requirement; dynamically adjusting resources according to the resource utilization rate and the load prediction by applying a resource adjustment mechanism and a feedback control algorithm; an intelligent data synchronization mechanism and an intelligent migration strategy are designed, and the consistency and the real-time performance of data are ensured by utilizing an intelligent caching technology; through a preset access control strategy and a high-strength encryption algorithm, multi-level security control is implemented, and data transmission and storage security and privacy are protected. The resource utilization rate can be improved, the system performance can be optimized, and diversified application scene requirements can be supported.

Description

Technical Field

[0001] The present invention relates to the technical field of hyper-converged all-in-one machine operating efficiency, and specifically to a method and device for improving the operating efficiency of a hyper-converged all-in-one machine based on cloud-edge collaboration. Background Art

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

[0003] Cloud-edge collaboration closely combines cloud computing and edge computing. It is responsible for real-time data collection, processing and preliminary analysis through edge nodes, while cloud data centers are responsible for large-scale computing tasks such as data analysis, machine learning and model training. In this way, cloud-edge collaboration takes advantage of the network advantages of the edge side and the elastic computing capabilities of cloud computing. However, in the original edge collaboration environment, such as industrial Internet, intelligent transportation, intelligent manufacturing and other fields, higher requirements are placed on the real-time 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 rapid fault location processing, which requires the ability to forward the customer's network data packets to the target address that can be located and troubleshooted through traffic mirroring.

[0004] With the rapid development of cloud computing and edge computing, 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 a solution that integrates computing, storage, and network functions, hyper-converged all-in-one has become one of the important ways to improve data center efficiency and flexibility 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 solution can no longer fully meet the needs of diverse application scenarios. Especially in an environment involving the collaborative work of cloud computing and edge computing, how to effectively manage and schedule physical and virtual machine resources to achieve optimal performance, reliability and efficiency has become one of the hot issues in current research. Summary of the invention

[0006] In view of the requirements and deficiencies in the current technological development, the present invention provides a method and device for improving the operation efficiency of a hyper-converged all-in-one machine based on cloud-edge collaboration. The aim is to solve various 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.

[0007] In a first aspect, the present invention provides a method for improving the operation efficiency of a hyper-converged all-in-one machine based on cloud-edge collaboration. The technical solutions adopted to solve the above technical problems are as follows:

[0008] A method for improving the operation efficiency of a hyper-converged all-in-one machine based on cloud-edge collaboration includes the following steps:

[0009] S1. Deploy a resource monitoring module and environment perception technology on physical machines and edge devices to obtain real-time resource usage and environmental factor data, providing a basis for resource allocation and virtual machine deployment;

[0010] S2. Adopt two strategies of load balancing algorithm and SLA management, and determine the deployment location and resource configuration of virtual machines according to the load conditions of physical machines and edge devices and the user's requirements for performance and availability;

[0011] S3. Apply a resource adjustment mechanism and feedback control algorithm based on real-time monitoring and prediction, and dynamically adjust the computing and storage resource allocation of virtual machines according to resource utilization and load prediction;

[0012] S4. Design an intelligent data synchronization mechanism and migration strategy, and use intelligent caching technology to ensure data consistency and real-time between physical machines and edge devices;

[0013] S5. Adopt multi-level security control measures, and protect the security and privacy of data transmission and storage between physical machines and virtual machines by implementing preset access control policies and using high-strength encryption algorithms.

[0014] Optionally, step S1 specifically includes:

[0015] Deploy a resource monitoring module on physical machines and edge devices to monitor the utilization of computing resources, storage resources, and network bandwidth in real time;

[0016] Use sensors or network monitoring devices to obtain environmental factor data, which will be used to influence the decision-making of resource allocation and virtual machine deployment.

[0017] Further optionally, step S2 specifically includes:

[0018] A load balancing algorithm based on minimum load or shortest job first, dynamically monitors and analyzes the load conditions of each physical machine and edge device, and deploys virtual machines on physical machines or edge devices according to different loads and service requirements to achieve optimal resource allocation;

[0019] According to the SLA requirements, analyzes the user's expectations for performance and availability, and dynamically adjusts the location and resource configuration of virtual machines to ensure that user requirements are met.

[0020] Further optionally, the specific steps involved in step S3 include:

[0021] Real-time monitors the resource utilization rates of physical machines and edge devices. When the CPU utilization rate exceeds the set threshold or the storage space set threshold, according to the current resource utilization rate and future load prediction, through an automated resource allocation strategy, reallocates idle resources to virtual machines that require more resources;

[0022] Adopts a feedback control algorithm, and adaptively adjusts the resource allocation strategy according to the actual operation of the system to avoid resource bottlenecks and waste of resources.

[0023] Further optionally, the specific steps involved in step S4 include:

[0024] According to the data access pattern and user behavior, formulates an intelligent data synchronization and migration strategy to ensure data consistency and real-time between physical machines and edge devices. At the same time, through regular data backup and migration, ensures data security and reliability;

[0025] Utilizes caching technology, and automatically adjusts the data storage location and caching strategy according to the data access frequency and priority, and stores data with an access frequency higher than the set threshold on edge devices close to users.

[0026] Further optionally, the specific steps involved in step S5 include:

[0027] Implements a role-based access control policy or a policy-based access control policy to precisely control the access permissions of users and devices, and only allows authorized users and devices to access and manage system resources to prevent unauthorized access and data leakage;

[0028] During data transmission and storage, uses a high-strength encryption algorithm to encrypt the data to protect the confidentiality and integrity of the data and prevent the data from being stolen or tampered with by unauthorized third parties.

[0029] In a second aspect, the present invention provides a device for improving the operation efficiency of a hyper-converged all-in-one machine based on cloud-edge collaboration. The technical solution adopted to solve the above technical problems is as follows:

[0030] A device for improving the operation efficiency of a hyper-converged all-in-one machine based on cloud-edge collaboration, which includes:

[0031] A data acquisition module, which is used to obtain resource usage and environmental factor data in real time through resource monitoring modules and environmental perception technologies deployed on physical machines and edge devices, providing a basis for resource allocation and virtual machine deployment;

[0032] An analysis and configuration module, which is used to adopt two strategies of load balancing algorithm and SLA management, and determine the deployment location and resource configuration of virtual machines according to the load conditions of physical machines and edge devices and the user's requirements for performance and availability;

[0033] A dynamic adjustment module, which is used to apply a resource adjustment mechanism and a feedback control algorithm based on real-time monitoring and prediction, and dynamically adjust the computing and storage resource allocation of virtual machines according to resource utilization and load prediction;

[0034] An intelligent cache module, which is used to design an intelligent data synchronization mechanism and migration strategy, and use intelligent cache technology to ensure data consistency and real-time between physical machines and edge devices;

[0035] A data protection module, which is used to adopt multi-level security control measures, and protect the security and privacy of data transmission and storage between physical machines and virtual machines by implementing preset access control policies and using high-strength encryption algorithms.

[0036] Optionally, the involved data acquisition module deploys resource monitoring modules on physical machines and edge devices to monitor the utilization of computing resources, storage resources, and network bandwidth in real time; the data acquisition module uses sensors or network monitoring devices to obtain edge environment data, and the edge environment data will be used to influence the decision-making of resource allocation and virtual machine deployment;

[0037] The analysis and configuration module is based on a load balancing algorithm of minimum load or shortest job first, dynamically monitors and analyzes the load conditions of each physical machine and edge device, and deploys virtual machines on physical machines or edge devices according to different loads and business requirements to achieve optimal resource allocation. At the same time, the analysis and configuration module analyzes the user's expectations for performance and availability according to SLA requirements, and dynamically adjusts the location and resource configuration of virtual machines to ensure meeting user needs.

[0038] Further optionally, the involved dynamic adjustment module monitors the resource utilization of physical machines and edge devices in real time. When the CPU utilization exceeds the set threshold or the storage space set threshold, according to the current resource utilization and future load prediction, through an automated resource allocation strategy, the idle resources are reallocated to virtual machines that need more resources. At the same time, the dynamic adjustment module adopts a feedback control algorithm, and adaptively adjusts the resource allocation strategy according to the actual operation of the system to avoid resource bottlenecks and waste of resources;

[0039] The intelligent caching module formulates intelligent data synchronization and migration strategies based on data access patterns and user behaviors, ensuring data consistency and real-time between the physical machine and the edge device. Meanwhile, through regular data backup and migration, it guarantees data security and reliability. Also, using caching technology, according to the access frequency and priority of data, it automatically adjusts the storage location and caching strategy of data, storing data with an access frequency higher than the set threshold on the edge device closer to the user.

[0040] Further optionally, the involved data protection module implements role-based access control policies or policy-based access control policies, precisely controlling the access permissions of users and devices, only allowing authorized users and devices to access and manage system resources, and preventing unauthorized access and data leakage;

[0041] During data transmission and storage, the data protection module uses high-strength encryption algorithms to encrypt data, protecting data confidentiality and integrity, and preventing data from being stolen or tampered with by unauthorized third parties.

[0042] A method and device for improving the operation efficiency of a hyper-converged all-in-one machine based on cloud-edge collaboration according to the present invention has the beneficial effects compared with the prior art as follows:

[0043] 1. Through the resource monitoring module and environmental perception technology, the present invention can obtain resource usage and environmental factor data in real time, providing a basis for resource allocation and virtual machine deployment. By using load balancing algorithms and SLA management strategies, resource allocation is made more reasonable, improving resource utilization; based on the resource adjustment mechanism and feedback control algorithm for real-time monitoring and prediction, the computing and storage resource allocation of virtual machines is dynamically adjusted, avoiding resource bottlenecks and waste, and enhancing the overall system performance; the intelligent data synchronization mechanism, migration strategy, and intelligent caching technology ensure data consistency and real-time between the physical machine and the edge device. Meanwhile, multi-level security control measures protect the security and privacy of data transmission and storage;

[0044] 2. The present invention can solve the problem of computing power collaboration between cloud computing and edge computing, optimize resource utilization, and through the deep integration of cloud, network, and edge computing resources, can effectively respond 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;

[0045] 3. The present invention aims to address numerous challenges faced by traditional data center architectures in the face of cloud computing and edge computing requirements, particularly issues such as low resource utilization, inefficient data processing, and inability to meet real-time requirements. It aims to 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, laying a technical foundation for the development of future intelligent applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] FIG. Figure 1 is a flowchart of the method according to Embodiment 1 of the present invention;

[0047] FIG. Figure 2 is a block diagram of module connections according to Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] To make the technical solutions, technical problems to be solved, and technical effects of the present invention clearer and more understandable, the following describes the technical solutions of the present invention clearly and completely in conjunction with specific embodiments.

[0049] Embodiment 1:

[0050] In conjunction with FIG. Figure 1 , this embodiment proposes a method for improving the operation efficiency of a hyper-converged all-in-one machine based on cloud-edge collaboration, which includes the following steps:

[0051] S1. Deploy a resource monitoring module and environmental perception technology on physical machines and edge devices to obtain resource usage and environmental factor data in real time, providing a basis for resource allocation and virtual machine deployment, specifically including:

[0052] Deploy a resource monitoring module on physical machines and edge devices to monitor the utilization of computing resources (CPU, memory), storage resources (hard disk, flash memory), and network bandwidth in real time;

[0053] Use sensors or network monitoring devices to obtain environmental factor data, which will be used to influence decisions on resource allocation and virtual machine deployment.

[0054] S2. Adopt two strategies of load balancing algorithm and SLA management to determine the deployment location and resource configuration of virtual machines according to the load conditions of physical machines and edge devices and the user's requirements for performance and availability, specifically including:

[0055] Based on the minimum load or shortest job first load balancing algorithm, dynamically monitor and analyze the load conditions of each physical machine and edge device. According to different loads and business requirements, deploy virtual machines on physical machines or edge devices to achieve optimal resource allocation. For example, for tasks with high short-term loads, the system can choose to deploy virtual machines on physical machines with stronger computing capabilities to obtain higher computing performance. 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.

[0056] According to the SLA requirements, analyze the user's expectations for performance and availability, and dynamically adjust the location and resource configuration of virtual machines to ensure that user needs are met. For example, for applications that require real-time data processing and high availability guarantees, the system can deploy virtual machines on edge devices close to users with low network latency to enhance the user experience and service response speed.

[0057] Among them, SLA, that is, Service Level Agreement, is a contract signed between service providers and customers to clarify the rights and obligations of both parties in the service provision process, and details the key indicators such as service quality, performance, and availability, as well as the corresponding assessment criteria and liability for breach of contract.

[0058] S3. Apply a resource adjustment mechanism and feedback control algorithm based on real-time monitoring and prediction. According to resource utilization and load prediction, dynamically adjust the computing and storage resource allocation of virtual machines, specifically including:

[0059] Real-time monitor the resource utilization of physical machines and edge devices. When the CPU utilization exceeds the set threshold or the storage space set threshold, according to the current resource utilization and future load prediction, through an automated resource allocation strategy, reallocate idle resources to virtual machines that need more resources.

[0060] Adopt a feedback control algorithm to adaptively adjust the resource allocation strategy according to the actual operation of the system to avoid resource bottlenecks and waste of resources.

[0061] S4. Design an intelligent data synchronization mechanism and migration strategy, and use intelligent caching technology to ensure data consistency and real-time between physical machines and edge devices, specifically including:

[0062] According to the data access pattern and user behavior, formulate an intelligent data synchronization and migration strategy to ensure data consistency and real-time between physical machines and edge devices. At the same time, through regular data backup and migration, ensure the security and reliability of data.

[0063] Using cache technology, automatically adjust the storage location and cache policy of data according to the access frequency and priority of the data, and store the data with an access frequency higher than the set threshold on the edge device close to the user.

[0064] S5. Adopt multi-level security control measures to protect the security and privacy of data transmission and storage between physical machines and virtual machines by implementing preset access control policies and using the high-strength encryption algorithm AES, specifically including:

[0065] Implement role-based access control policies or policy-based access control policies to precisely control the access rights of users and devices, and only allow authorized users and devices to access and manage system resources to prevent unauthorized access and data leakage;

[0066] During the data transmission and storage process, use a high-strength encryption algorithm to encrypt the data to protect the confidentiality and integrity of the data and prevent the data from being stolen or tampered with by unauthorized third parties.

[0067] Embodiment 2:

[0068] Combined with the attached Figure 2 , this embodiment proposes a device for improving the operation efficiency of a hyper-converged all-in-one machine based on cloud-edge collaboration, which includes:

[0069] A data acquisition module, which is used to obtain resource usage and environmental factor data in real time through resource monitoring modules and environmental perception technologies deployed on physical machines and edge devices, providing a basis for resource allocation and virtual machine deployment;

[0070] An analysis and configuration module, which is used to adopt two strategies of load balancing algorithm and SLA management, and determine the deployment location and resource configuration of virtual machines according to the load conditions of physical machines and edge devices and the user's requirements for performance and availability;

[0071] A dynamic adjustment module, which is used to apply a resource adjustment mechanism and a feedback control algorithm based on real-time monitoring and prediction, and dynamically adjust the computing and storage resource allocation of virtual machines according to resource utilization and load prediction;

[0072] An intelligent cache module, which is used to design an intelligent data synchronization mechanism and migration strategy, and use intelligent cache technology to ensure data consistency and real-time between physical machines and edge devices;

[0073] A data protection module, which is used to adopt multi-level security control measures to protect the security and privacy of data transmission and storage between physical machines and virtual machines by implementing preset access control policies and using high-strength encryption algorithms.

[0074] In this embodiment, the data acquisition module deploys a resource monitoring module on the physical machine and the edge device to monitor the utilization of computing resources, storage resources, and network bandwidth in real time; the data acquisition module uses sensors or network monitoring devices to obtain edge environment data, which will be used to influence the decisions on resource allocation and virtual machine deployment.

[0075] According to the SLA requirements, analyze the user's expectations for performance and availability, and dynamically adjust the location and resource configuration of the virtual machine to ensure that the user's needs are met.

[0076] In this embodiment, the involved analysis and configuration module dynamically monitors and analyzes the load conditions of each physical machine and edge device based on the load balancing algorithm of minimum load or shortest job first. According to different loads and service requirements, the virtual machine is deployed on the physical machine or edge device to achieve optimal resource allocation. For example, for tasks with high short-term loads, the system can choose to deploy the virtual machine on a physical machine with stronger computing power to obtain higher computing performance; for data-intensive tasks, an edge device with high-speed storage and network bandwidth can be preferentially selected for deployment to reduce data transmission latency and improve data processing efficiency. At the same time, the analysis and configuration module analyzes the user's expectations for performance and availability according to the SLA requirements, and dynamically adjusts the location and resource configuration of the virtual machine to ensure that the user's needs are met. For example, for applications that require real-time data processing and high availability guarantees, the system can deploy the virtual machine on an edge device close to the user with low network latency to enhance the user experience and service response speed.

[0077] In this embodiment, the involved dynamic adjustment module monitors the resource utilization of the physical machine and the edge device in real time. When the CPU utilization exceeds the set threshold or the storage space set threshold, according to the current resource utilization and future load prediction, through an automated resource allocation strategy, the idle resources are reallocated to the virtual machines that need more resources. At the same time, the dynamic adjustment module adopts a feedback control algorithm to adaptively adjust the resource allocation strategy according to the actual operation of the system to avoid resource bottlenecks and waste of resources.

[0078] In this embodiment, the involved intelligent caching module formulates intelligent data synchronization and migration strategies according to the data access pattern and user behavior to ensure data consistency and real-time between the physical machine and the edge device. At the same time, through regular data backup and migration, the security and reliability of the data are guaranteed. At the same time, using caching technology, according to the access frequency and priority of the data, the storage location and caching strategy of the data are automatically adjusted, and the data with an access frequency higher than the set threshold is stored on the edge device close to the user.

[0079] In this embodiment, the data protection module implements role-based access control policies or policy-based access control policies to precisely control the access rights of users and devices, only allowing authorized users and devices to access and manage system resources, and preventing unauthorized access and data leakage. During data transmission and storage, the data protection module uses high-strength encryption algorithms to encrypt data, protecting the confidentiality and integrity of the data and preventing the data from being stolen or tampered with by unauthorized third parties.

[0080] In summary, by using the method and device for improving the operation efficiency of a hyper-converged all-in-one machine based on cloud-edge collaboration of the present invention, resource utilization can be improved, resource bottlenecks and waste can be avoided, the needs of users for performance and availability can be met, user satisfaction can be enhanced, resource allocation and service policies can be flexibly adjusted according to user needs and business scenarios, the adaptability and flexibility of the system can be improved, and many challenges faced by traditional data center architectures in the face of cloud computing and edge computing requirements can be solved, especially problems such as low resource utilization, low data processing efficiency, and inability to meet real-time requirements.

[0081] The above specific application examples have elaborated in detail the principles and implementation manners of the present invention. These embodiments are only used to help understand the core technical content of the present invention. Based on the above specific embodiments of the present invention, any improvements and modifications made by those skilled in the art of this technical field without departing from the principles of the present invention shall fall within the scope of patent protection of the present invention.

Claims

1. A method for improving the operating efficiency of a hyper-converged appliance based on cloud-edge collaboration, characterized in that: The steps include: S1. Deploy resource monitoring modules and environment perception technologies on physical machines and edge devices to obtain resource usage and environmental factor data in real time, providing a basis for resource allocation and virtual machine deployment. S2, using load balancing algorithm and SLA management strategies, determines the deployment location and resource allocation of virtual machines based on the load of physical machines and edge devices and user requirements for performance and availability; S3, using resource adjustment mechanisms based on real-time monitoring and prediction and feedback control algorithms to dynamically adjust the computing and storage resource allocation of virtual machines according to resource utilization and load prediction; S4. Design intelligent data synchronization mechanisms and migration strategies, and use intelligent caching technology to ensure data consistency and real-time performance between physical machines and edge devices; S5. Adopt multi-level security control measures to protect the security and privacy of data transmission and storage between physical machines and virtual machines by implementing preset access control policies and using high-strength encryption algorithms.

2. The method for improving the operating efficiency of a hyper-converged appliance based on cloud-edge collaboration according to claim 1 is characterized in that: The step S1 specifically includes: Deploy resource monitoring modules on physical machines and edge devices to monitor the utilization of computing resources, storage resources, and network bandwidth in real time; Sensors or network monitoring devices are used to obtain environmental factor data, which will be used to influence decisions on resource allocation and virtual machine deployment.

3. The method for improving the operating efficiency of a hyper-converged appliance based on cloud-edge collaboration according to claim 2 is characterized in that: The step S2 specifically includes: Based on the load balancing algorithm that prioritizes the minimum load or the shortest job, dynamically monitor and analyze the load of each physical machine and edge device, and deploy virtual machines on physical machines or edge devices according to different loads and business requirements to achieve optimal resource allocation; According to SLA requirements, analyze user expectations for performance and availability, and dynamically adjust the location and resource configuration of virtual machines to ensure that user needs are met.

4. The method for improving the operating efficiency of a hyper-converged appliance based on cloud-edge collaboration according to claim 3 is characterized in that: The step S3 specifically includes: Monitor the resource utilization of physical machines and edge devices in real time. When the CPU utilization exceeds the set threshold or the storage space threshold, the idle resources are reallocated to the virtual machines that need more resources through the automated resource allocation strategy based on the current resource utilization and future load forecast. By adopting feedback control algorithm, the resource allocation strategy is adaptively adjusted according to the actual operation of the system to avoid resource bottlenecks and waste of resources.

5. The method for improving the operating efficiency of a hyper-converged appliance based on cloud-edge collaboration according to claim 4 is characterized in that: The step S4 specifically includes: Develop intelligent data synchronization and migration strategies based on data access patterns and user behaviors to ensure data consistency and real-time performance between physical machines and edge devices. At the same time, ensure data security and reliability through regular data backup and migration. Using caching technology, the data storage location and caching strategy are automatically adjusted according to the data access frequency and priority, and data with access frequency higher than the set threshold is stored on edge devices close to the user.

6. The method for improving the operating efficiency of a hyper-converged appliance based on cloud-edge collaboration according to claim 5 is characterized in that: The step S5 specifically includes: Implement role-based access control policies or policy-based access control policies to precisely control the access rights of users and devices, allowing only authorized users and devices to access and manage system resources, and preventing unauthorized access and data leakage; During the data transmission and storage process, high-strength encryption algorithms are used to encrypt data to protect the confidentiality and integrity of the data and prevent the data from being stolen or tampered with by unauthorized third parties.

7. A device for improving the operating efficiency of a hyper-converged integrated machine based on cloud-edge collaboration, characterized in that: It includes: The data acquisition module is used to obtain resource usage and environmental factor data in real time through resource monitoring modules and environment perception technologies deployed on physical machines and edge devices, providing a basis for resource allocation and virtual machine deployment; The analysis and configuration module is used to adopt two strategies, load balancing algorithm and SLA management, to determine the deployment location and resource configuration of virtual machines according to the load of physical machines and edge devices and the user's requirements for performance and availability; Dynamic adjustment module, which uses resource adjustment mechanism based on real-time monitoring and prediction and feedback control algorithm to dynamically adjust the computing and storage resource allocation of virtual machines according to resource utilization and load prediction; Intelligent caching module, which is used to design intelligent data synchronization mechanisms and migration strategies, and use intelligent caching technology to ensure data consistency and real-time performance between physical machines and edge devices; The data protection module is used to adopt multi-level security control measures to protect the security and privacy of data transmission and storage between physical machines and virtual machines by implementing preset access control policies and using high-strength encryption algorithms.

8. The device for improving the operating efficiency of a hyper-converged integrated machine based on cloud-edge collaboration according to claim 7, characterized in that: The data acquisition module deploys resource monitoring modules on physical machines and edge devices to monitor the utilization of computing resources, storage resources and network bandwidth in real time; the data acquisition module uses sensors or network monitoring devices to obtain edge environment data, which will be used to influence decisions on resource allocation and virtual machine deployment; The analysis and configuration module dynamically monitors and analyzes the load conditions of each physical machine and edge device based on a load balancing algorithm that prioritizes the minimum load or the shortest job, and deploys virtual machines on physical machines or edge devices according to different loads and business requirements to achieve optimal resource allocation. At the same time, the analysis and configuration module analyzes user expectations for performance and availability according to SLA requirements, and dynamically adjusts the location and resource configuration of virtual machines to ensure that user needs are met.

9. The device for improving the operating efficiency of a hyper-converged integrated machine based on cloud-edge collaboration according to claim 8, characterized in that: The dynamic adjustment module monitors the resource utilization of physical machines and edge devices in real time. When the CPU utilization exceeds the set threshold or the storage space set threshold, the idle resources are reallocated to virtual machines that need more resources through an automated resource allocation strategy based on the current resource utilization and future load forecast. At the same time, the dynamic adjustment module uses a feedback control algorithm to adaptively adjust the resource allocation strategy based on the actual operation of the system to avoid resource bottlenecks and waste of resources. The intelligent cache module formulates intelligent data synchronization and migration strategies according to data access patterns and user behaviors to ensure data consistency and real-time performance between physical machines and edge devices, and at the same time, ensures data security and reliability through regular data backup and migration. At the same time, the cache technology is used to automatically adjust the data storage location and cache strategy according to the data access frequency and priority, and store data with access frequency higher than the set threshold on edge devices close to users.

10. The device for improving the operating efficiency of a hyper-converged integrated machine based on cloud-edge collaboration according to claim 9, characterized in that: The data protection module implements a role-based access control strategy or a policy-based access control strategy to accurately control the access rights of users and devices, allowing only authorized users and devices to access and manage system resources, and preventing unauthorized access and data leakage; During the data transmission and storage process, the data protection module uses a high-intensity encryption algorithm to encrypt the data to protect the confidentiality and integrity of the data and prevent the data from being stolen or tampered with by unauthorized third parties.

Images