Object modeling method based on computing network resources

Abstract

The invention discloses an object modeling method based on computing network resources, and relates to the technical field of computers. The object modeling method based on the computing network resources comprises the following steps that S1, resource demand analysis is carried out, wherein the demand of a modeling task for computing and network resources is analyzed; s2, dynamically allocating resources: dynamically allocating calculation and network resources according to a demand analysis result; s3, performing modeling process optimization: performing optimization on the modeling process by applying an optimization algorithm to improve the modeling efficiency and precision; and S4, constructing a unified modeling framework: constructing a universal modeling framework, and realizing object modeling of different fields and different complexities. According to the object modeling method based on the computing network resources, the understanding and design capability of the system is improved, resource allocation and utilization are optimized, the expandability and flexibility of the system are improved, management and maintenance are simplified, and the safety and reliability of the system are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an object modeling method based on computing network resources. Background Art

[0002] The network architecture of a large data center is built with high-performance computers, network devices, storage devices, and related software to provide a stable, efficient, and scalable data processing environment. It usually includes multiple layers and areas, each with its own specific functions and responsibilities. Although the existing large data center network architecture has made significant progress in supporting the growing business needs, it still has some shortcomings.

[0003] Traditional data center network architecture mostly relies on horizontal expansion, that is, responding to business growth by adding more servers and network devices. However, after reaching a certain scale, this approach will face the problems of increased management complexity and decreased resource utilization. Although the traditional hierarchical network architecture improves the flexibility and scalability of the network, it also increases the complexity of management. A variety of network devices of different brands and models may be used in large data centers. There may be problems with the compatibility and interoperability between these devices, which increases the difficulty of management. As the scale of the network expands, the configuration and maintenance of network devices become more and more difficult. As the volume of business increases, the demand for data transmission inside and outside the data center rises sharply, which may lead to insufficient bandwidth and increased latency, which will affect the performance of the application and user experience. In a large data center, different applications and services have different requirements for network resources. If the network architecture cannot effectively balance the traffic distribution, it may cause performance bottlenecks in certain areas or applications. In view of the shortcomings of the prior art, the present invention provides an object modeling method based on computing network resources to solve the above problems. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an object modeling method based on computing network resources to improve system understanding and design capabilities, optimize resource allocation and utilization, enhance system scalability and flexibility, simplify management and maintenance, and enhance system security and reliability.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an object modeling method based on computing network resources, comprising the following steps:

[0006] Step S1: Perform resource requirement analysis: Analyze the requirements of the modeling task for computing and network resources.

[0007] Step S2: Dynamically allocate resources: dynamically allocate computing and network resources based on demand analysis results.

[0008] Step S3: Optimize the modeling process: Use optimization algorithms to tune the modeling process to improve modeling efficiency and accuracy.

[0009] Step S4: Construct a unified modeling framework: Build a universal modeling framework to achieve object modeling in different fields and with different complexity.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Task characteristic analysis. First, clarify the specific requirements of the modeling task, including data volume, model complexity, real-time requirements, data interaction frequency, etc.

[0012] Step S12: Resource demand prediction:

[0013] Computing resource requirements: Predict the required computing resources such as CPU, GPU, and memory based on the data processing volume and algorithm complexity of the task.

[0014] Network resource requirements: Evaluate the data transmission volume, network latency requirements, etc. in the task, and predict the required network resources such as bandwidth and network topology.

[0015] Step S13: Demand quantification: convert the above analysis into specific quantitative indicators, such as the number of CPU cores, memory size (GB), bandwidth (Mbps), etc.

[0016] Preferably, in step S2, the following steps are included:

[0017] Step S21: Real-time monitoring: Establish a resource monitoring system to collect real-time usage of computing resources and network resources, including CPU usage, memory occupancy, network bandwidth utilization, etc.

[0018] Step S22: Demand forecasting model: Use machine learning or statistical methods to establish a resource demand forecasting model to predict resource demand in the future based on historical data and current task status.

[0019] Step S23: Dynamically adjust the strategy:

[0020] Computing resource adjustment: Dynamically adjust the number of CPU cores, number of GPUs, memory size, etc. allocated to modeling tasks based on prediction results and current resource usage.

[0021] Network resource adjustment: Adjust network bandwidth allocation, routing strategies, etc. to ensure efficient data transmission and low latency.

[0022] Step S24: Resource pool management: Maintain a shared pool of computing resources and network resources, and dynamically allocate and recycle resources according to task requirements.

[0023] Preferably, in step S3, the following steps are included:

[0024] Step S31: Initialize the population: randomly generate multiple sets of modeling parameter configurations (such as algorithm parameters, data structure configurations, etc.) as the initial population of the genetic algorithm.

[0025] Step S32: Fitness evaluation:

[0026] Modeling efficiency E: By actually running or simulating the modeling process, the time required to complete the modeling task or the amount of data processed is recorded to calculate the modeling efficiency.

[0027] Modeling accuracy A: Evaluate how close the modeling results are to the real data or expected targets and calculate the modeling accuracy.

[0028] Comprehensive fitness: The fitness of each modeling scheme is calculated based on the weighted sum of modeling efficiency and accuracy.

[0029] Step S33: Selection operation: select an excellent modeling scheme from the current population according to the fitness value for subsequent operation.

[0030] Step S34: Crossover and mutation operations:

[0031] Cross: Exchange or combine parameters of the selected modeling schemes to generate a new modeling scheme.

[0032] Mutation: Perform parameter mutation with a small probability on the modeling scheme after crossover or random selection to introduce a new solution space.

[0033] Step S35: Iterative optimization: Repeat the fitness evaluation, selection, crossover and mutation operations until the termination condition is met (such as reaching the maximum number of iterations, the fitness improvement is no longer significant, etc.).

[0034] Preferably, in step S3, an additional optimization strategy is provided, and the additional optimization strategy includes:

[0035] Resource allocation strategy: During the optimization process of the genetic algorithm, the allocation of computing and network resources is dynamically adjusted according to the real-time feedback of the modeling task.

[0036] Parallel computing strategy: Use multi-core CPUs, GPUs, or distributed computing resources to execute multiple independent tasks or steps in the modeling process in parallel.

[0037] Caching strategy: Cache commonly used data and intermediate results to reduce repeated calculations and improve modeling efficiency.

[0038] Preferably, in step S4, the following steps are included:

[0039] Step S41: Modular design: decompose the modeling process into multiple independent modules, such as data preprocessing module, model training module, result evaluation module, etc.

[0040] Step S42: Interface standardization: define the data exchange format and interface standards between modules to ensure compatibility and scalability between different modules.

[0041] Step S43: Domain-independent design: Design a general modeling framework to support object modeling in different domains and with different complexities through parameter configuration and plug-in mechanism.

[0042] Step S44: Framework testing and verification: Test the performance and reliability of the framework in multiple actual modeling tasks, and perform iterative optimization based on feedback.

[0043] Preferably, in step S22, a linear regression model is used to predict resource demand using the following formula:

[0044] y=β0+β1x1+β2x2+...+β n x n

[0045] Among them, y is the predicted resource demand, that is, CPU usage, x1x2...x n are factors that affect resource requirements, namely task complexity, data volume, β0β1β2…β n are model parameters.

[0046] Preferably, in step S32, the modeling efficiency E is calculated by recording the time T (seconds) required to complete the modeling task and the amount of data processed D, and the efficiency E is defined as the reciprocal of the time required to process a unit amount of data;

[0047]

[0048] Modeling accuracyA: Accuracy is usually calculated by comparing the modeling results with the real data or expected targets, using mean square error (MSE);

[0049]

[0050] Among them, y i is the true value, is the predicted value and N is the number of samples.

[0051] Preferably, the comprehensive fitness is the weighted sum of modeling efficiency and accuracy, and the weights are adjusted according to specific needs;

[0052] Fitness=ω E ×E+ω A ×A

[0053] Among them, ω E and ω A are the weights of efficiency and accuracy respectively, and ω E +ω A =1.

[0054] Preferably, in step S23, the network resource construction adopts a network topology structure, including a core layer, a convergence layer and an access layer. The core layer is composed of high-performance routers and switches, which is responsible for high-speed data forwarding. The convergence layer connects the core layer and the access layer to achieve traffic aggregation and distribution. The access layer connects servers and terminal devices to provide network access services.

[0055] The present invention discloses an object modeling method based on computing network resources, which has the following beneficial effects:

[0056] 1. Improve system understanding and design capabilities;

[0057] Through object modeling, complex systems can be divided into multiple objects with clear properties and methods, making the system more modular and easier to understand. This method helps designers better grasp the overall structure of the system and the relationship between each component; the object modeling method supports the division of the system into multiple levels and areas, each of which has its own specific functions and responsibilities. This division helps to simplify the complexity of system design and makes the system easier to manage and maintain.

[0058] 2. Optimize resource allocation and utilization;

[0059] In the object modeling process, it is necessary to conduct a detailed demand analysis of the system's computing resources, network resources, etc. This analysis helps to more accurately predict the various resources required by the system, thereby avoiding waste and shortage of resources; the object model can dynamically reflect the system's resource usage and dynamically adjust resources according to actual conditions. This adjustment helps to improve resource utilization and reduce system operating costs.

[0060] 3. Improve system scalability and flexibility;

[0061] The object modeling method supports dividing the system into multiple independent modules, which interact with each other through well-defined interfaces. This modular design makes the system easier to expand and modify, and can quickly adapt to changes in business needs; in the object model, various resources can be flexibly configured according to the needs of different applications and services. This flexibility helps to meet complex and changing business needs and improve the overall performance of the system.

[0062] 4. Simplify management and maintenance;

[0063] The network architecture based on object modeling can provide a unified management interface, allowing administrators to monitor and manage the entire system more conveniently. This unified management helps reduce management complexity and improve management efficiency; the object model can support automated maintenance processes, such as automatic discovery, automatic configuration, and automatic fault recovery. These automated functions help reduce manual intervention, reduce maintenance costs, and improve system stability and reliability.

[0064] 5. Enhance the security and reliability of the system;

[0065] In the object model, you can define sophisticated permission control policies to ensure that only authorized users can access specific resources and perform specific operations. This permission control helps to enhance the security of the system; the object modeling method supports the construction of fault tolerance and backup mechanisms in the system to ensure that the normal operation of the system can be quickly restored in the event of a failure. This mechanism helps to improve the reliability and availability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0067] Figure 1 It is a schematic diagram of the overall system of the present invention; DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0070] The embodiment of the present invention discloses an object modeling method based on computing network resources. Figure 1 As shown, the following steps are included:

[0071] Step S1: Perform resource requirement analysis: Analyze the requirements of the modeling task for computing and network resources.

[0072] In step S1, the following steps are included:

[0073] Step S11: Task characteristic analysis. First, clarify the specific requirements of the modeling task, including data volume, model complexity, real-time requirements, data interaction frequency, etc.

[0074] Step S12: Resource demand prediction:

[0075] Computing resource requirements: Predict the required computing resources such as CPU, GPU, and memory based on the data processing volume and algorithm complexity of the task.

[0076] Network resource requirements: Evaluate the data transmission volume, network latency requirements, etc. in the task, and predict the required network resources such as bandwidth and network topology.

[0077] Step S13: Demand quantification: convert the above analysis into specific quantitative indicators, such as the number of CPU cores, memory size (GB), bandwidth (Mbps), etc.

[0078] Step S1 can clearly understand the specific requirements of the modeling task for computing and network resources through task characteristic analysis and resource demand prediction, providing a basis for subsequent resource allocation and optimization. Avoid resource waste caused by unreasonable resource allocation during the modeling process, especially in large data centers, where resource costs are high and reasonable resource allocation is particularly important. Through demand analysis, performance bottlenecks caused by insufficient resources can be foreseen and avoided, ensuring the smooth progress of modeling tasks.

[0079] Step S2: Dynamically allocate resources: dynamically allocate computing and network resources according to the demand analysis results. Step S2 includes the following steps:

[0080] Step S21: Real-time monitoring: Establish a resource monitoring system to collect real-time usage of computing resources and network resources, including CPU usage, memory occupancy, network bandwidth utilization, etc.

[0081] Step S22: Demand forecasting model: Use machine learning or statistical methods to establish a resource demand forecasting model to predict resource demand in the future based on historical data and current task status.

[0082] Step S23: Dynamically adjust the strategy:

[0083] Computing resource adjustment: Dynamically adjust the number of CPU cores, number of GPUs, memory size, etc. allocated to modeling tasks based on prediction results and current resource usage.

[0084] Network resource adjustment: Adjust network bandwidth allocation, routing strategies, etc. to ensure efficient data transmission and low latency.

[0085] Step S24: Resource pool management: Maintain a shared pool of computing resources and network resources, and dynamically allocate and recycle resources according to task requirements.

[0086] Step S2 can significantly improve resource utilization by dynamically adjusting resource allocation, especially in large data centers with large load changes. The dynamic adjustment strategy can quickly respond to changes in task requirements and reduce task waiting time caused by resource adjustment delays.

[0087] Step S3: Optimize the modeling process: Use the optimization algorithm to tune the modeling process to improve the modeling efficiency and accuracy. In step S3, the following steps are included:

[0088] Step S31: Initialize the population: randomly generate multiple sets of modeling parameter configurations (such as algorithm parameters, data structure configurations, etc.) as the initial population of the genetic algorithm.

[0089] Step S32: Fitness evaluation:

[0090] Modeling efficiency E: By actually running or simulating the modeling process, the time required to complete the modeling task or the amount of data processed is recorded to calculate the modeling efficiency.

[0091] Modeling accuracy A: Evaluate how close the modeling results are to the real data or expected targets and calculate the modeling accuracy.

[0092] Comprehensive fitness: The fitness of each modeling scheme is calculated based on the weighted sum of modeling efficiency and accuracy.

[0093] Step S33: Selection operation: select an excellent modeling scheme from the current population according to the fitness value for subsequent operation.

[0094] Step S34: Crossover and mutation operations:

[0095] Cross: Exchange or combine parameters of the selected modeling schemes to generate a new modeling scheme.

[0096] Mutation: Perform parameter mutation with a small probability on the modeling scheme after crossover or random selection to introduce a new solution space.

[0097] Step S35: Iterative optimization: Repeat the fitness evaluation, selection, crossover and mutation operations until the termination condition is met (such as reaching the maximum number of iterations, the fitness improvement is no longer significant, etc.).

[0098] The optimization algorithm in step S3 can significantly improve modeling efficiency and reduce modeling time. Through fitness evaluation and iterative optimization, the modeling accuracy can be continuously improved to ensure the accuracy of the modeling results.

[0099] In step S3, an additional optimization strategy is provided, and the additional optimization strategy includes:

[0100] Resource allocation strategy: During the optimization process of the genetic algorithm, the allocation of computing and network resources is dynamically adjusted according to the real-time feedback of the modeling task.

[0101] Parallel computing strategy: Use multi-core CPUs, GPUs, or distributed computing resources to execute multiple independent tasks or steps in the modeling process in parallel.

[0102] Caching strategy: Cache commonly used data and intermediate results to reduce repeated calculations and improve modeling efficiency.

[0103] Step S4: Construct a unified modeling framework: Build a general modeling framework to realize object modeling in different fields and with different complexities, decompose the modeling process into multiple independent modules for easy management and maintenance, define a unified data exchange format and interface standards to ensure compatibility and scalability between different modules, design a general modeling framework to support object modeling in different fields and with different complexities, improve the reusability and flexibility of the framework, and modular design and interface standardization make the framework easy to expand and can easily integrate new modeling algorithms or modules.

[0104] In step S4, the following steps are included:

[0105] Step S41: Modular design: decompose the modeling process into multiple independent modules, such as data preprocessing module, model training module, result evaluation module, etc.

[0106] Step S42: Interface standardization: define the data exchange format and interface standards between modules to ensure compatibility and scalability between different modules.

[0107] Step S43: Domain-independent design: Design a general modeling framework to support object modeling in different domains and with different complexities through parameter configuration and plug-in mechanism.

[0108] Step S44: Framework testing and verification: Test the performance and reliability of the framework in multiple actual modeling tasks, and perform iterative optimization based on feedback.

[0109] In step S22, a linear regression model is used to predict resource demand using the following formula:

[0110] y=β0+β1x1+β2x2+...+β n x n

[0111] Among them, y is the predicted resource demand, that is, CPU usage, x1x2...x n are factors that affect resource requirements, namely task complexity, data volume, β0β1β2…β nare model parameters.

[0112] In step S32, the modeling efficiency E is calculated by recording the time T (seconds) required to complete the modeling task and the amount of data processed D. The efficiency E is defined as the reciprocal of the time required to process a unit amount of data;

[0113]

[0114] Modeling accuracyA: Accuracy is usually calculated by comparing the modeling results with the real data or expected targets, using mean square error (MSE);

[0115]

[0116] Among them, y i is the true value, is the predicted value and N is the number of samples.

[0117] The comprehensive fitness is the weighted sum of modeling efficiency and accuracy, and the weights are adjusted according to specific needs;

[0118] Fitness=ω E ×E+ω A ×A

[0119] Among them, ω E and ω A are the weights of efficiency and accuracy respectively, and ω E +ω A =1.

[0120] In step S23, the network resource construction adopts a network topology structure, including a core layer, a convergence layer and an access layer. The core layer is composed of high-performance routers and switches, which are responsible for high-speed data forwarding. The convergence layer connects the core layer and the access layer to achieve traffic aggregation and distribution. The access layer connects servers and terminal devices to provide network access services.

[0121] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0122] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An object modeling method based on computing network resources, characterized in that: The steps include: Step S1: Perform resource requirement analysis: Analyze the requirements of the modeling task for computing and network resources; Step S2: Dynamically allocate resources: dynamically allocate computing and network resources according to the demand analysis results; Step S3: Optimize the modeling process: Use optimization algorithms to tune the modeling process to improve modeling efficiency and accuracy; Step S4: Construct a unified modeling framework: Build a universal modeling framework to achieve object modeling in different fields and with different complexity.

2. The object modeling method based on computing network resources according to claim 1 is characterized in that: In step S1, the following steps are included: Step S11: Task characteristic analysis, first, clarify the specific requirements of the modeling task; Step S12: Resource demand prediction: Computing resource requirements: predict the required CPU, GPU, and memory computing resources based on the data processing volume and algorithm complexity of the task; Network resource requirements: Evaluate the data transmission volume and network delay requirements in the task, and predict the required network resources; Step S13: Demand quantification: convert the above analysis into specific quantitative indicators.

3. The object modeling method based on computing network resources according to claim 1 is characterized in that: In step S2, the following steps are included: Step S21: Establish a resource monitoring system to collect the usage of computing resources and network resources in real time; Step S22: Establish a resource demand prediction model to predict resource demand in the future based on historical data and current task status; Step S23: Computing resource adjustment: dynamically adjust the number of CPU cores, number of GPUs, and memory size allocated to the modeling task according to the prediction results and current resource usage; Adjust network bandwidth allocation and routing strategies to ensure efficient data transmission and low latency; Step S24: Maintain a shared pool of computing resources and network resources, and dynamically allocate and recycle resources according to task requirements.

4. The object modeling method based on computing network resources according to claim 1 is characterized in that: In step S3, the following steps are included: Step S31: randomly generating multiple sets of modeling parameter configurations as the initial population of the genetic algorithm; Step S32: Perform fitness evaluation: By simulating the modeling process, the time required to complete the modeling task or the amount of data processed is recorded, and the modeling efficiency is calculated; Evaluate the closeness of modeling results to expected goals and calculate modeling accuracy; The fitness of each modeling scheme is calculated based on the weighted sum of modeling efficiency and accuracy; Step S33: Select an excellent modeling scheme from the current population according to the fitness value for subsequent operations; Step S34: Crossover and mutation operations: Crossover: Exchange or combine the parameters of the selected modeling schemes to generate a new modeling scheme; Mutation: Perform parameter mutation with a small probability on the modeling scheme after crossover or random selection to introduce a new solution space; Step S35: Iterative optimization: Repeat the fitness evaluation, selection, crossover and mutation operations until the termination condition is met.

5. The object modeling method based on computing network resources according to claim 4 is characterized in that: In step S3, an additional optimization strategy is provided, and the additional optimization strategy includes: Resource allocation strategy, during the optimization process of the genetic algorithm, dynamically adjusts the allocation of computing and network resources according to the real-time feedback of the modeling task; Parallel computing strategy, using multi-core CPU, GPU, and distributed computing resources to execute multiple independent tasks in the modeling process in parallel; Caching strategy: cache commonly used data and intermediate results to reduce repeated calculations and improve modeling efficiency.

6. The object modeling method based on computing network resources according to claim 1 is characterized in that: In step S4, the following steps are included: Step S41: decomposing the modeling process into multiple independent modules; Step S42: define the data exchange format and interface standards between modules to ensure compatibility and scalability between different modules; Step S43: design a general modeling framework to support object modeling in different fields and with different complexity through parameter configuration and plug-in mechanism; Step S44: Test the performance and reliability of the framework in multiple actual modeling tasks, and perform iterative optimization based on feedback.

7. The object modeling method based on computing network resources according to claim 3 is characterized in that: In step S22, a linear regression model is used to predict resource demand using the following formula: y=β0+β1x1+β2x2+...+β n x n Among them, y is the predicted resource demand, that is, CPU usage, x1x2...x n are factors that affect resource requirements, namely task complexity, data volume, β0β1β2…β n are model parameters.

8. The object modeling method based on computing network resources according to claim 1 is characterized in that: In step S32, the modeling efficiency E is calculated by recording the time T required to complete the modeling task and the amount of data processed D. The efficiency E is defined as the reciprocal of the time required to process a unit amount of data; The modeling accuracy A is calculated by comparing the difference between the modeling results and the real data or expected targets, using the mean square error MSE; Among them, y i is the true value, is the predicted value and N is the number of samples.

9. The object modeling method based on computing network resources according to claim 8 is characterized in that: The comprehensive fitness is the weighted sum of modeling efficiency and accuracy, and the weights are adjusted according to specific needs; Fitness=ω E ×E+ω A ×A Among them, ω E and ω A are the weights of efficiency and accuracy respectively, and ω E +ω A =1.

10. The object modeling method based on computing network resources according to claim 3 is characterized in that: In step S23, the network resource construction adopts a network topology structure, including a core layer, a convergence layer and an access layer. The core layer is composed of high-performance routers and switches, and is responsible for high-speed data forwarding; the convergence layer connects the core layer and the access layer to achieve traffic aggregation and distribution; the access layer connects servers and terminal devices to provide network access services.

Images