Cloud desktop dynamic resource optimization method and system

Abstract

The invention discloses a cloud desktop dynamic resource optimization method and system. The cloud desktop dynamic resource optimization method comprises the following steps: S1, monitoring resource use conditions and performance in a cloud desktop, and collecting use condition data of a CPU (Central Processing Unit), a memory and storage and performance data of the cloud desktop in real time; s2, processing and analyzing the collected resource use data and the performance data of the cloud desktops through a data analysis tool, and analyzing a resource demand trend of each cloud desktop and a performance trend of the cloud desktop; s3, predicting the resource demand of the cloud desktop by adopting a prediction algorithm or a machine learning algorithm, and dynamically adjusting resource allocation according to a prediction result; and S4, automatically adjusting resource configuration of the cloud desktop according to an output result of the prediction algorithm or the machine learning algorithm so as to meet real-time requirements of the cloud desktop. Through real-time monitoring, analysis and dynamic adjustment, optimized utilization and load balancing of cloud desktop resources are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud desktop, and in particular to a cloud desktop dynamic resource optimization method and system. Background Art

[0002] With the rapid development of cloud computing technology, cloud desktop, as one of the important applications of cloud computing, has been widely used in various fields such as enterprise, education, government, etc. Cloud desktop transforms traditional physical desktop into virtual desktop in the cloud through virtualization technology. Users can access their desktop environment anytime and anywhere through any supported network device, realizing efficient, convenient and secure office and learning experience.

[0003] However, in a cloud desktop environment, effective resource management and optimization is a key issue. Traditional static resource allocation methods are often pre-allocated based on a fixed number of users and device specifications, and cannot be dynamically adjusted according to the actual usage and needs of users. This leads to waste of resources and low utilization, and also affects the performance and user experience of the cloud desktop.

[0004] In order to solve these problems, researchers and developers are constantly exploring new technical methods and strategies. They try to achieve dynamic resource allocation and optimization by real-time monitoring of cloud desktop resource usage and combining user behavior patterns and demand prediction. At the same time, they are also looking for more efficient and intelligent load balancing algorithms to ensure that cloud desktops can fully utilize resources and meet users' real-time needs.

[0005] However, the existing cloud desktop resource management and optimization system still has some challenges and problems. First, real-time monitoring requires processing a large amount of data. How to ensure the accuracy and real-time nature of the data is a technical problem. Second, user behavior patterns and needs are complex and changeable. How to accurately predict and meet user needs is a challenge. Finally, the performance and security of the cloud desktop are also important factors to consider. How to improve security while ensuring performance is a problem that needs to be solved.

[0006] Therefore, the prior art has defects and needs to be improved. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a cloud desktop dynamic resource optimization method and system to overcome the problems of unreasonable resource allocation, low utilization, limited performance, etc. in the existing cloud desktop environment.

[0008] The technical solution of the present invention is as follows: a cloud desktop dynamic resource optimization method is provided, comprising the following steps.

[0009] S1: Monitors the resource usage and performance of the cloud desktop, and collects CPU, memory, storage usage data and cloud desktop performance data in real time.

[0010] S2: Use data analysis tools to process and analyze the collected resource usage data and cloud desktop performance data to analyze the resource demand trend and performance trend of each cloud desktop.

[0011] S3: Use prediction algorithms or machine learning algorithms to predict the resource requirements of the cloud desktop and dynamically adjust resource allocation based on the prediction results.

[0012] S4: According to the output results of the prediction algorithm or machine learning algorithm, the resource configuration of the cloud desktop is automatically adjusted, including the number of CPU cores, memory size, and storage quota to meet the real-time needs of the cloud desktop.

[0013] Furthermore, in step S1, the usage data is also verified to verify the rationality of the collected CPU, memory, and storage usage data, and to eliminate unreasonable data.

[0014] Furthermore, the cloud desktop dynamic resource optimization method also includes: S5: determining whether the adjusted cloud desktop configuration meets the resource requirements; if not, re-entering step S4 for adjustment; if compliant, regularly monitoring the performance of the adjusted cloud desktop; S6: outputting performance data to determine whether the performance meets the requirements; if not, entering step S2; if compliant, performing load balancing and reallocating cloud desktop resources according to the load balancing strategy to achieve system optimization, and outputting performance data to enter step S1.

[0015] Furthermore, all available resources are integrated into a unified resource pool and dynamically allocated according to the needs of the cloud desktop; when a cloud desktop no longer needs certain resources, they are automatically recovered and reallocated to other cloud desktops in need.

[0016] Furthermore, the data analysis tool is matplotlib or excel, and the resource demand trend of each cloud desktop is a line graph with time as the X-axis and resource usage data as the Y-axis; the performance trend of the cloud desktop is a line graph with time as the X-axis and the performance data of the cloud desktop as the Y-axis.

[0017] The present invention monitors the resource usage of cloud desktops in real time and combines prediction algorithms or machine learning algorithms to predict the resource requirements of cloud desktops. The present invention can predict the resource requirements of each cloud desktop in real time and dynamically adjust resource allocation according to the requirements to ensure that each cloud desktop can obtain sufficient resources to support its operation.

[0018] Traditional static resource allocation methods often lead to waste of resources and low utilization rate. The present invention can accurately match resource demand and supply through dynamic resource management, reduce idleness and waste of resources, and thus improve the overall utilization rate of resources.

[0019] Insufficient resources or unreasonable allocation may cause the performance of the cloud desktop to decline, affecting the user experience. The present invention ensures that the cloud desktop always maintains the best performance state by dynamically adjusting resource allocation in real time, providing users with a stable and smooth office and learning experience.

[0020] By improving resource utilization and optimizing performance, the present invention can reduce the operating cost of the cloud desktop. At the same time, through the load balancing strategy, the present invention can also reduce the additional hardware investment and maintenance costs caused by insufficient resources.

[0021] Furthermore, the present invention also provides a cloud desktop dynamic resource optimization system for executing the aforementioned cloud desktop dynamic resource optimization method, including: a data acquisition layer, a data processing layer, a decision analysis layer, and an operation execution layer. The present invention adopts a modular design and is easy to integrate and expand. With the development and changes of the cloud desktop environment, the present invention can flexibly adjust and optimize resource management and optimization strategies to adapt to different application scenarios and needs.

[0022] The data collection layer is responsible for collecting the usage data of various resources in the cloud desktop environment in real time, including the usage of CPU, memory, and disk; the collected data is transmitted to the data processing layer for further processing through the data transmission protocol.

[0023] The data processing layer is responsible for cleaning, integrating and storing the collected data, and establishing a database of cloud desktop resource usage; at the same time, the data processing layer is also responsible for real-time analysis and prediction of data, providing data support for the decision-making analysis layer.

[0024] The decision analysis layer: based on the data provided by the data processing layer, uses machine learning algorithms or predictive analysis algorithms to conduct in-depth analysis and prediction of the usage of cloud desktop resources; based on the analysis results, dynamically generates resource allocation plans and optimization suggestions, and transmits the plans and suggestions to the operation execution layer.

[0025] The operation execution layer automatically or manually adjusts the allocation and configuration of cloud desktop resources according to the solutions and suggestions provided by the decision analysis layer; the operation execution layer interacts with the interface of the cloud desktop management platform to achieve dynamic management and optimization of resources.

[0026] Furthermore, the cloud desktop dynamic resource optimization system uses Java to achieve visualization.

[0027] The present invention has the following technical effects:

[0028] 1. Real-time and accuracy

[0029] Through real-time monitoring, real-time monitoring of various resources in the cloud desktop environment is ensured, so that the system can capture the resource usage status, load conditions and potential performance bottlenecks in real time. At the same time, through advanced data analysis technology, the present invention can accurately predict the demand trend of resources, so as to make optimal decisions on resource allocation in advance. This real-time and accuracy greatly improves the efficiency and accuracy of resource management.

[0030] 2. Improved resource utilization

[0031] Through dynamic resource allocation technology, the present invention can automatically adjust the resource allocation ratio and priority according to the user's real-time needs and the system load. This dynamic resource allocation strategy can ensure that each cloud desktop can obtain sufficient resources to support its operation, while avoiding waste and idleness of resources. Compared with the traditional static resource allocation method, the cloud desktop dynamic resource management and optimization system can significantly improve resource utilization and reduce operation and maintenance costs.

[0032] 3. Load balancing and performance improvement

[0033] Through load balancing, the present invention can evenly distribute user requests to multiple servers for processing, thereby ensuring the stability and efficiency of the system. This load balancing strategy can not only reduce the load pressure of a single server, but also improve the processing capacity and response speed of the entire system. At the same time, the present invention can also dynamically adjust the request allocation strategy according to the load situation and processing capacity of the server, further improving the performance and stability of the system.

[0034] 4. Improved Operation and Maintenance Efficiency

[0035] Through the automated operation technology, automatic configuration and management of resources are realized; this automated operation mode can reduce manual intervention and error rate, reduce the burden and management cost of operation and maintenance personnel. At the same time, the present invention also provides a visual interface and a friendly operation experience, so that operation and maintenance personnel can more conveniently perform resource management and optimization operations. This improvement in operation and maintenance efficiency not only improves the maintainability of the system, but also enables the system to better adapt to changing business needs.

[0036] 5. Improved security

[0037] The cloud desktop dynamic resource management and optimization system has comprehensive security measures. Through encrypted transmission, access control, data backup and other technical means, the system can ensure the integrity and confidentiality of data and prevent data leakage and illegal access. At the same time, the system can also monitor and audit user operations to promptly discover and handle potential security risks. This improvement in security enables the cloud desktop dynamic resource management and optimization system to better meet users' needs for data security and privacy protection.

[0038] In summary, the cloud desktop dynamic resource management and optimization system achieves optimal resource utilization and load balancing through real-time monitoring, intelligent analysis, dynamic adjustment and other technical means. This technical effect not only improves the performance and stability of the system, but also reduces the operation and maintenance costs and management difficulty. At the same time, the system also has comprehensive security measures to ensure the security and privacy of data. Therefore, the cloud desktop dynamic resource management and optimization system is an efficient, intelligent and secure cloud desktop resource management solution.

[0039] By adopting the above scheme, the present invention provides a cloud desktop dynamic resource optimization method and system, which realizes the optimal utilization and load balancing of cloud desktop resources through real-time monitoring, analysis and dynamic adjustment. The present invention has the characteristics of real-time, intelligent, dynamic, scalable and safe, and can meet the management and optimization needs of cloud desktop environments of different scales and requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0041] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0042] See also Figure 1 The present invention provides a cloud desktop dynamic resource optimization method, comprising the following steps.

[0043] S1: Monitors the resource usage and performance of the cloud desktop, and collects CPU, memory, storage usage data and cloud desktop performance data in real time.

[0044] S2: Use data analysis tools to process and analyze the collected resource usage data and cloud desktop performance data to analyze the resource demand trend and performance trend of each cloud desktop.

[0045] S3: Use prediction algorithms or machine learning algorithms to predict the resource requirements of the cloud desktop and dynamically adjust resource allocation based on the prediction results.

[0046] S4: According to the output results of the prediction algorithm or machine learning algorithm, the resource configuration of the cloud desktop is automatically adjusted, including the number of CPU cores, memory size, and storage quota to meet the real-time needs of the cloud desktop.

[0047] In this embodiment, step S1 also includes verifying the usage data, verifying the rationality of the collected CPU, memory, and storage usage data, and eliminating unreasonable data.

[0048] In this embodiment, the cloud desktop dynamic resource optimization method also includes: S5: determining whether the adjusted cloud desktop configuration meets the resource requirements; if not, re-entering step S4 for adjustment; if compliant, regularly monitoring the performance of the adjusted cloud desktop; S6: outputting performance data to determine whether the performance meets the requirements; if not, entering step S2; if compliant, performing load balancing and reallocating cloud desktop resources according to the load balancing strategy to achieve system optimization, and outputting performance data to enter step S1.

[0049] In this embodiment, all available resources are integrated into a unified resource pool and dynamically allocated according to the needs of the cloud desktop; when a cloud desktop no longer needs certain resources, they are automatically recovered and reallocated to other cloud desktops in need.

[0050] In this embodiment, the data analysis tool is matplotlib or excel, and the resource demand trend of each cloud desktop is a line graph with time as the X-axis and resource usage data as the Y-axis; the performance trend of the cloud desktop is a line graph with time as the X-axis and the performance data of the cloud desktop as the Y-axis.

[0051] The present invention monitors the resource usage of cloud desktops in real time and combines prediction algorithms or machine learning algorithms to predict the resource requirements of cloud desktops. The present invention can predict the resource requirements of each cloud desktop in real time and dynamically adjust resource allocation according to the requirements to ensure that each cloud desktop can obtain sufficient resources to support its operation.

[0052] Traditional static resource allocation methods often lead to waste of resources and low utilization rate. The present invention can accurately match resource demand and supply through dynamic resource management, reduce idleness and waste of resources, and thus improve the overall utilization rate of resources.

[0053] Insufficient resources or unreasonable allocation may cause the performance of the cloud desktop to decline, affecting the user experience. The present invention ensures that the cloud desktop always maintains the best performance state by dynamically adjusting resource allocation in real time, providing users with a stable and smooth office and learning experience.

[0054] By improving resource utilization and optimizing performance, the present invention can reduce the operating cost of the cloud desktop. At the same time, through the load balancing strategy, the present invention can also reduce the additional hardware investment and maintenance costs caused by insufficient resources.

[0055] The present invention also provides a cloud desktop dynamic resource optimization system for executing the aforementioned cloud desktop dynamic resource optimization method, including: a data acquisition layer, a data processing layer, a decision analysis layer, and an operation execution layer. The present invention adopts a modular design and is easy to integrate and expand. With the development and changes of the cloud desktop environment, the present invention can flexibly adjust and optimize resource management and optimization strategies to adapt to different application scenarios and needs.

[0056] The data collection layer is responsible for collecting the usage data of various resources in the cloud desktop environment in real time, including the usage of CPU, memory, and disk; the collected data is transmitted to the data processing layer for further processing through the data transmission protocol.

[0057] The data processing layer is responsible for cleaning, integrating and storing the collected data, and establishing a database of cloud desktop resource usage; at the same time, the data processing layer is also responsible for real-time analysis and prediction of data, providing data support for the decision-making analysis layer.

[0058] The decision analysis layer: based on the data provided by the data processing layer, uses machine learning algorithms or predictive analysis algorithms to conduct in-depth analysis and prediction of the usage of cloud desktop resources; based on the analysis results, dynamically generates resource allocation plans and optimization suggestions, and transmits the plans and suggestions to the operation execution layer.

[0059] The operation execution layer automatically or manually adjusts the allocation and configuration of cloud desktop resources according to the solutions and suggestions provided by the decision analysis layer; the operation execution layer interacts with the interface of the cloud desktop management platform to achieve dynamic management and optimization of resources.

[0060] In this embodiment, the cloud desktop dynamic resource optimization system uses Java to achieve visualization.

[0061] The present invention can monitor the usage of various resources in a cloud desktop environment in real time, and analyze and predict future resource demands and usage trends in real time.

[0062] The prediction algorithm or machine learning algorithm of the present invention predicts the resource demand of the cloud desktop, performs in-depth analysis and prediction on resource usage, and realizes intelligent management and optimization of resources.

[0063] The present invention can dynamically adjust the allocation and configuration of resources according to the real-time needs of users and the load conditions of the system, thereby ensuring full utilization of resources and load balancing.

[0064] The present invention adopts modular design, which is easy to integrate and expand. With the development and change of the cloud desktop environment, the system can flexibly adjust and optimize the management strategy.

[0065] The present invention has perfect security measures, can ensure the integrity and confidentiality of data, and prevent data leakage and illegal access.

[0066] In summary, the cloud desktop dynamic resource management and optimization system achieves optimal resource utilization and load balancing through real-time monitoring, intelligent analysis, dynamic adjustment and other technical means. This technical effect not only improves the performance and stability of the system, but also reduces the operation and maintenance costs and management difficulty. At the same time, the system also has comprehensive security measures to ensure the security and privacy of data. Therefore, the cloud desktop dynamic resource management and optimization system is an efficient, intelligent and secure cloud desktop resource management solution.

[0067] In summary, the present invention provides a cloud desktop dynamic resource optimization method and system, which realizes the optimal utilization and load balancing of cloud desktop resources through real-time monitoring, analysis and dynamic adjustment. The present invention has the characteristics of real-time, intelligent, dynamic, scalable and safe, and can meet the management and optimization needs of cloud desktop environments of different scales and requirements.

[0068] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A cloud desktop dynamic resource optimization method, characterized in that: The following steps are involved: S1: Monitors the resource usage and performance of the cloud desktop, and collects CPU, memory, storage usage data and cloud desktop performance data in real time; S2: Use data analysis tools to process and analyze the collected resource usage data and cloud desktop performance data to analyze the resource demand trend and performance trend of each cloud desktop; S3: Use prediction algorithms or machine learning algorithms to predict the resource requirements of cloud desktops and dynamically adjust resource allocation based on the prediction results; S4: According to the output results of the prediction algorithm or machine learning algorithm, the resource configuration of the cloud desktop is automatically adjusted, including the number of CPU cores, memory size, and storage quota to meet the real-time needs of the cloud desktop.

2. A cloud desktop dynamic resource optimization method according to claim 1, characterized in that: In step S1, the usage data is also verified to verify the rationality of the collected CPU, memory, and storage usage data, and to eliminate unreasonable data.

3. A cloud desktop dynamic resource optimization method according to claim 1, characterized in that: Also includes: S5: Determine whether the adjusted cloud desktop configuration meets resource requirements; If not, re-enter step S4 for adjustment. If yes, regularly monitor the performance of the adjusted cloud desktop. S6: Output performance data to determine whether the performance meets the requirements; if not, proceed to step S2; if it meets the requirements, perform load balancing and reallocate cloud desktop resources according to the load balancing strategy to achieve system optimization, and output performance data to proceed to step S1.

4. A cloud desktop dynamic resource optimization method according to claim 1, characterized in that: All available resources are integrated into a unified resource pool and dynamically allocated according to the needs of the cloud desktop; when a cloud desktop no longer needs certain resources, they are automatically recovered and reallocated to other cloud desktops in need.

5. A cloud desktop dynamic resource optimization method according to claim 1, characterized in that: The data analysis tool is matplotlib or excel, the resource demand trend of each cloud desktop is a line graph with time as the X-axis and resource usage data as the Y-axis; the performance trend of the cloud desktop is a line graph with time as the X-axis and the performance data of the cloud desktop as the Y-axis.

6. A cloud desktop dynamic resource optimization system, characterized in that: The method for dynamic resource optimization of a cloud desktop according to any one of claims 1 to 5 comprises: a data collection layer, a data processing layer, a decision analysis layer and an operation execution layer; The data collection layer is responsible for collecting the usage data of various resources in the cloud desktop environment in real time, including the usage of CPU, memory, and disk; the collected data is transmitted to the data processing layer for further processing through the data transmission protocol; The data processing layer is responsible for cleaning, integrating and storing the collected data and establishing a database of cloud desktop resource usage. At the same time, the data processing layer is also responsible for real-time analysis and prediction of data to provide data support for the decision-making analysis layer. The decision analysis layer: based on the data provided by the data processing layer, uses machine learning algorithms or predictive analysis algorithms to conduct in-depth analysis and prediction of the use of cloud desktop resources; based on the analysis results, dynamically generates resource allocation plans and optimization suggestions, and transmits the plans and suggestions to the operation execution layer; The operation execution layer: automatically or manually adjusts the allocation and configuration of cloud desktop resources according to the solutions and suggestions provided by the decision analysis layer; the operation execution layer interacts with the interface of the cloud desktop management platform to achieve dynamic management and optimization of resources.

7. A cloud desktop dynamic resource optimization system according to claim 6, characterized in that: Visualization is achieved using Java.

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