Intelligent Optimization Method and System for Server Hardware Resources Based on Adaptive Neural Network

Abstract

The present invention relates to the technical field of server hardware resource optimization. More specifically, it relates to an intelligent optimization method and system for server hardware resources based on an adaptive neural network, including obtaining the hardware resource information and system performance metrics of the server; constructing a topological resource mapping based on the hardware resource information and system performance metrics; executing a group theory resource scheduling algorithm according to the topological resource mapping; performing matrix resource state prediction based on the results of the group theory resource scheduling algorithm; executing number theory resource allocation optimization according to the results of the matrix resource state prediction; updating the weights of the adaptive neural network based on the results of the number theory resource allocation optimization; and outputting an optimized server hardware resource configuration scheme. By introducing the topological resource mapping, this method can comprehensively and accurately represent the complex relationships between multi-dimensional resources, providing a solid foundation for subsequent optimization decisions; the application of the group theory resource scheduling algorithm makes the resource scheduling operation more flexible and systematic.

Description

Technical Field

[0001] The present invention relates to the technical field of server hardware resource optimization. More specifically, it relates to an intelligent optimization method and system for server hardware resources based on an adaptive neural network. Background Art

[0002] With the rapid development of cloud computing and big data technologies, the scale and complexity of data centers have been continuously increasing, and the efficient management and optimization of server hardware resources have become an increasingly severe challenge. Traditional server resource management methods mainly rely on static configuration and simple threshold-triggered mechanisms, making it difficult to cope with the complex and changing load conditions in modern data centers.

[0003] In recent years, machine learning technologies have been widely applied in the field of server resource optimization. Some researchers have proposed resource allocation methods based on prediction models, which analyze historical data to predict future resource requirements. However, these methods often only consider one or a few resource dimensions and are difficult to comprehensively grasp the complex relationships between server resources. Other researchers have tried to use deep learning models to optimize resource allocation, but these models usually require a large amount of training data and computing resources and face challenges in real-time performance and interpretability in practical applications.

[0004] In addition, most existing resource optimization methods adopt isolated optimization strategies and lack systematic consideration of the mutual influence between resources. For example, simply optimizing CPU utilization may lead to bottlenecks in memory or network bandwidth. At the same time, these methods usually have difficulty in balancing multiple objectives such as resource utilization, energy efficiency, and service quality and often fall short in practical applications.

[0005] Another common problem is that existing methods are difficult to adapt to rapidly changing load environments. The workload in a data center may fluctuate violently due to factors such as time, user behavior, and special events, and static optimization strategies are difficult to adjust in a timely manner to adapt to these changes. Although some adaptive methods have been proposed, they often respond sluggishly and are difficult to make quick and accurate decisions in complex and changing environments.

[0006] Facing these challenges, there is an urgent need for an intelligent server resource optimization method that can comprehensively consider multi-dimensional resources, balance multiple optimization objectives, and adapt to dynamic environments. The present invention precisely addresses this need and proposes an intelligent optimization method and system for server hardware resources based on an adaptive neural network. Summary of the Invention

[0007] The present invention proposes an intelligent optimization method and system for server hardware resources based on an adaptive neural network, aiming to solve the following main problems existing in the prior art: incomplete resource representation, single optimization objective, insufficient adaptability, opaque decision-making process, etc.

[0008] The present invention provides an intelligent optimization method for server hardware resources based on an adaptive neural network, including:

[0009] An acquisition step, including:

[0010] Acquire the hardware resource information and system performance metrics of the server;

[0011] A processing step, including:

[0012] Based on the hardware resource information and system performance metrics, construct a topological resource mapping;

[0013] According to the topological resource mapping, execute a group theory resource scheduling algorithm;

[0014] Based on the result of the group theory resource scheduling algorithm, perform matrix resource state prediction;

[0015] According to the result of the matrix resource state prediction, execute number theory resource allocation optimization;

[0016] Based on the result of the number theory resource allocation optimization, update the weights of the adaptive neural network;

[0017] An output step, including:

[0018] Output an optimized server hardware resource configuration plan.

[0019] Preferably, the acquisition of the hardware resource information and system performance metrics of the server specifically includes:

[0020] Acquire the CPU usage rate, memory occupancy rate, disk I / O rate, and network traffic;

[0021] Acquire the application response time, system throughput, and server power consumption.

[0022] Preferably, the construction of the topological resource mapping specifically includes:

[0023] Map the hardware resource information to a high-dimensional topological space;

[0024] Use spherical harmonic functions as topological basis functions;

[0025] Calculate the topological mapping function.

[0026] Preferably, the execution of the group theory resource scheduling algorithm specifically includes:

[0027] Define a resource scheduling group;

[0028] Based on the resource scheduling group, transform the topological mapping;

[0029] Generate an optimized topological mapping.

[0030] Preferably, the matrix resource status prediction specifically includes:

[0031] Construct a resource status matrix;

[0032] Define a state transition function based on the Toeplitz matrix;

[0033] Calculate the predicted resource status matrix.

[0034] Preferably, the execution of number theory resource allocation optimization specifically includes:

[0035] Use the Riemann zeta function for resource allocation optimization;

[0036] Calculate the optimal resource allocation scheme.

[0037] Preferably, the update of the weights of the adaptive neural network specifically includes:

[0038] Define a loss function based on the von Neumann entropy;

[0039] Calculate the gradient of the neural network weights;

[0040] Update the neural network weights.

[0041] Preferably, it further includes:

[0042] Based on the optimized server hardware resource configuration scheme, dynamically adjust the hardware resource allocation of the server.

[0043] Preferably, it further includes:

[0044] Periodically repeat the acquisition step, processing step, and output step to achieve continuous optimization.

[0045] The intelligent optimization system for server hardware resources based on an adaptive neural network that executes the method includes:

[0046] An acquisition module for acquiring the hardware resource information and system performance metrics of the server;

[0047] A processing module, including:

[0048] A topology mapping sub-module for constructing a topology resource mapping based on the hardware resource information and system performance metrics;

[0049] A group theory scheduling sub-module for executing a group theory resource scheduling algorithm according to the topology resource mapping;

[0050] A matrix prediction sub-module for performing matrix resource status prediction based on the result of the group theory resource scheduling algorithm;

[0051] A number theory optimization sub-module for performing number theory resource allocation optimization according to the result of the matrix resource state prediction;

[0052] A neural network update sub-module for updating the weights of the adaptive neural network based on the result of the number theory resource allocation optimization;

[0053] An output module for outputting an optimized server hardware resource configuration scheme.

[0054] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0055] First of all, by introducing topological resource mapping, this method can comprehensively and accurately represent the complex relationships between multi-dimensional resources, providing a solid foundation for subsequent optimization decisions. The application of the group theory resource scheduling algorithm makes the resource scheduling operation more flexible and systematic, and can better handle complex scheduling scenarios.

[0056] Secondly, the combination of matrix resource state prediction and number theory resource allocation optimization not only improves the accuracy of resource state prediction, but also realizes the collaborative optimization of multiple objectives. This method can improve resource utilization while taking into account energy efficiency and service quality, achieving an equilibrium improvement of multiple key indicators.

[0057] More importantly, the adaptive neural network weight update mechanism of the present invention endows the system with the ability of continuous learning and self-optimization. This enables this method to quickly adapt to load changes and maintain efficient resource allocation in a dynamic environment. At the same time, by introducing a loss function with quantum concepts, this method considers deeper system state information in the optimization process, improving the accuracy and robustness of decision-making.

[0058] In addition, the method of the present invention realizes the superposition and complementarity of technical effects through the organic combination of various innovative steps. For example, the comprehensive resource representation provided by topological mapping provides a richer information basis for group theory scheduling, and the continuous optimization of the adaptive neural network can continuously improve the quality of topological mapping. This cyclic optimization mechanism enables the system performance to continuously improve over time.

[0059] In summary, the intelligent optimization method and system for server hardware resources based on an adaptive neural network provided by the present invention not only solve many problems existing in the prior art, but also achieve significant improvements in multiple aspects such as resource utilization, energy efficiency, and service quality. This innovative solution provides a new technical path for the intelligent management of data centers and has important theoretical value and broad application prospects. Description of the Drawings

[0060] Figure 1It is the flowchart of the method of the present invention.

[0061] Figure 2 It is the flowchart of obtaining hardware resource information and system performance indicators of the present invention.

[0062] Figure 3 It is the flowchart of constructing topological resource mapping of the present invention.

[0063] Figure 4 It is the flowchart of the group theory resource scheduling algorithm of the present invention.

[0064] Figure 5 It is the flowchart of predicting the matrix resource state of the present invention.

[0065] Figure 6 It is the flowchart of optimizing number theory resource allocation of the present invention.

[0066] Figure 7 It is the flowchart of updating the weights of the adaptive neural network of the present invention.

[0067] Figure 8 It is the logical block diagram of the system of the present invention. Detailed implementation manners

[0068] Please refer to Figure 1-8 , the present invention provides a server hardware resource intelligent optimization method and system based on an adaptive neural network. This method realizes the efficient optimization of server hardware resources.

[0069] First, the method of the present invention includes an acquisition step, a processing step, and an output step. In the acquisition step, the system acquires the hardware resource information and system performance indicators of the server. These information are the basis for subsequent optimization processes.

[0070] Specifically, in an embodiment of the present invention, the acquired hardware resource information includes CPU usage rate, memory occupancy rate, disk I / O rate, and network traffic. These parameters comprehensively reflect the usage status of the server's hardware resources. Preferably, the sampling frequency of the CPU usage rate can be set to 1 time per second to capture the changes in CPU load in a timely manner. The memory occupancy rate can be sampled once every 5 seconds because the memory usage usually changes relatively slowly. The disk I / O rate and network traffic can be sampled once every 10 seconds to balance data accuracy and system overhead.

[0071] At the same time, this method also acquires system performance indicators, including application response time, system throughput, and server energy consumption. These indicators are crucial for evaluating the overall performance of the system. For example, a threshold can be set for the application response time, such as 200 milliseconds, and if it exceeds this value, it is considered that the system performance needs to be optimized. The system throughput can be measured by the number of requests processed per second, and the server energy consumption can be obtained through real-time power consumption monitoring.

[0072] In the processing step, the method of the present invention first constructs a topological resource mapping based on the obtained hardware resource information and system performance metrics. This step is one of the core innovations of the present invention. The topological resource mapping uses a high-dimensional topological space to represent complex resource relationships, and this representation method can better capture the interdependencies and influences between resources.

[0073] In the optimization of server hardware resources, it is crucial to understand and analyze the relationships between various hardware resources (such as CPU, memory, disk I / O, network traffic, etc.). Traditional linear models are difficult to capture these complex relationships, while topology provides tools to describe these complex relationships.

[0074] The process of constructing the topological resource mapping can be described by the following mathematical formula:

[0075] ,

[0076] where, is the topological mapping function, is the hardware resource space, is the target topological space, is the specific resource configuration, is the weight of the i-th basis function, is the i-th topological basis function.

[0077] In this scenario, assume that it is necessary to analyze the relationship between CPU usage and memory occupancy. By using spherical harmonic functions as basis functions, these variables can be mapped into a high-dimensional space. For example, if represents a combination of CPU usage and memory occupancy at a specific time point, then can capture the non-linear relationship between these two variables. This mapping helps to identify potential bottlenecks or redundancies, thereby guiding the reallocation of resources.

[0078] Suppose on a web server, it is found that there is a certain periodic correlation between CPU usage and memory occupancy. By applying the above formula, this correlation can be better understood, and the load balancing strategy can be adjusted according to the analysis results to improve the overall performance.

[0079] The present invention preferably uses spherical harmonic functions as topological basis functions, and their definition is as follows:

[0080] ,

[0081] where, is the associated Legendre polynomial, and are integer parameters, satisfying . is a spherical harmonic function, is the polar angle, is the azimuthal angle, is the complex exponential function (where is the imaginary unit).

[0082] The advantage of using spherical harmonic functions is that they form an orthogonal basis on the sphere and can efficiently represent any function on the sphere, which is very useful for describing the multi-dimensional characteristics of server resources.

[0083] Through this topological mapping, the hardware resource state of the server can be mapped into a high-dimensional space, thus better capturing the complex relationships between resources. For example, the correlation between CPU usage and memory occupancy can be better represented in this high-dimensional space.

[0084] Resource scheduling is one of the key issues in server management. Group theory provides a new perspective to understand and optimize the resource scheduling process.

[0085] Next, this method executes the group theory resource scheduling algorithm according to the constructed topological resource mapping. The introduction of the group theory resource scheduling algorithm is another innovation point of the present invention. It regards resource scheduling operations as elements on the group, thus enabling more flexible and systematic resource scheduling.

[0086] The group theory resource scheduling algorithm can be represented by the following mathematical formula:

[0087] ,

[0088] where, is the resource scheduling group, is a specific scheduling operation, represents the group action. is the group action mapping, is the topological resource mapping.

[0089] In practical applications, if it is necessary to balance the use of CPU and memory among multiple server nodes, this problem can be regarded as a rotation operation in a two-dimensional space. For example, when the CPU usage on a certain node reaches the peak, an appropriate rotation matrix can be calculated to reallocate tasks to other nodes to avoid overload.

[0090] Consider a distributed database system where data is distributed across multiple nodes. By applying the group theory resource scheduling algorithm, query tasks can be dynamically reallocated according to the current load conditions of each node, ensuring that the workload of all nodes is as uniform as possible, and improving the response speed and stability of the system.

[0091] In practical applications, the special orthogonal group SO(n) can be selected as the resource scheduling group, which is defined as:

[0092] ,

[0093] Here, represents the set of n×n real matrices, is the identity matrix, represents the determinant of matrix A, is the set of n×n real matrices.

[0094] The advantage of using the SO(n) group is that it can represent rotational transformations in an n-dimensional space, which is very similar to the operation of resource scheduling. For example, when it is necessary to balance the load between the CPU and memory, it can be regarded as a rotation operation in a two-dimensional space.

[0095] By executing the group theory resource scheduling algorithm, an optimized topological mapping can be obtained . This optimized mapping reflects the new state after resource scheduling and provides a basis for subsequent optimization steps.

[0096] In summary, the method of the present invention realizes the efficient representation and flexible scheduling of server hardware resources by innovatively introducing the concepts of topology and group theory. Compared with traditional resource optimization methods, this method can better capture the complex relationships between resources, thereby achieving more accurate resource optimization. In practical applications, this method can significantly improve the resource utilization rate of the server, reduce resource waste, and enhance the overall performance of the system. After the group theory resource scheduling algorithm is executed, the method of the present invention further performs matrix resource state prediction based on the result. This step makes full use of the advantages of matrix theory and can more accurately predict the future resource state.

[0097] To achieve more accurate resource prediction and scheduling, it is very important to use historical data for state prediction. Specifically, the matrix resource state prediction can be expressed by the following mathematical formula:

[0098] ,

[0099] Where, is the resource state matrix at time t, is the state transition function, is the optimized topological mapping.

[0100] In this method, by constructing a Toeplitz matrix , it can effectively utilize past data points to predict future resource requirements. For example, when predicting the CPU usage rate in the next 5 minutes, considering the data of the previous 10 time points can help capture potential trends and patterns.

[0101] Suppose we want to predict the disk I / O rate of a certain server in the future for a period of time. By collecting the I / O rate data once every 10 seconds in the past period of time, constructing the corresponding Toeplitz matrix, and using the above formula for prediction. This enables taking measures in advance, such as increasing the cache size or adjusting the read / write strategy, to cope with the upcoming peak load.

[0102] In a preferred embodiment of the present invention, is defined as a special function based on the Toeplitz matrix:

[0103] ,

[0104] Here, is the Toeplitz matrix generated by . The selection of the Toeplitz matrix is based on its special structural characteristics, which can effectively capture the temporal correlation of the resource state. For example, when predicting the CPU usage rate, the usage rate at the current moment is usually closely related to the usage rates at the previous several moments, and this relationship can be well represented by the Toeplitz matrix.

[0105] Preferably, when constructing the Toeplitz matrix in the method of the present invention, the data of the most recent 10 time points can be selected for use. The selection of this value is based on experience, which can capture sufficient historical information without introducing excessive computational overhead.

[0106] Resource allocation should not only consider the current demand but also anticipate possible future changes. Number theory provides a unique perspective for optimization.

[0107] Next, according to the result of the matrix resource state prediction, the method performs number-theoretic resource allocation optimization. This step innovatively introduces concepts in number theory, especially the Riemann zeta function, to optimize resource allocation. The number-theoretic resource allocation optimization can be represented by the following mathematical formula:

[0108]

[0109] where, is the optimized resource allocation, is the Riemann zeta function, is the complex parameter, is the matrix 's diagonal element vector. is the resource allocation vector to be optimized and belongs to the integer vector space , represents the L2 norm (Euclidean norm) of the vector.

[0110] Suppose there is a data center with three server nodes, and it is necessary to allocate CPU cores, memory capacity, and disk / O bandwidth. The current prediction results show that the future resource requirements are as follows: Number of cores: 5; Memory capacity: 16GB; Disk I / O bandwidth: 100MB / s;

[0111] These requirements can be represented as a vector .

[0112] Now, it is necessary to find an integer vector , such that is as close as possible to [5, 16, 100]. Suppose is selected, then . At this time, we need to find an integer vector , for example , such that . In this way, according to the current resource prediction and historical data, the resource allocation plan can be dynamically adjusted to ensure the efficient operation of the system.

[0113] In the case of resource constraints, choosing appropriate parameters can make the Riemann zeta function exhibit different characteristics, thus simulating different resource allocation strategies. For example, when is close to 1, the function value increases rapidly, indicating that resources are very scarce; while when resources are sufficient, a larger value can be selected to make the function value approach 1.

[0114] In a cloud computing environment, according to the current number and type of user requests, dynamically adjust the CPU cores and memory capacity allocated to each virtual machine. By adjusting the parameters of the Riemann zeta function , it is possible to flexibly respond to changes in resource requirements at different times, maximizing resource utilization while ensuring service quality.

[0115] The effectiveness of all the above algorithms depends on high-quality data input. The data acquisition steps include real-time monitoring of various server metrics (such as CPU usage, memory occupancy, etc.) and regular sampling of these data. These data are not only used for current resource optimization decisions but also provide a basis for future predictions. Through fine data processing and analysis, it is possible to ensure more accurate resource scheduling, reduce unnecessary resource waste, and improve the overall system efficiency.

[0116] The definition of the Riemann zeta function is as follows:

[0117] ,

[0118] is the Riemann zeta function, is a complex parameter in the form of , where and are respectively the real part and the imaginary part of is the complex number the real part of , is a positive integer for each term in the summation, is the specific expression for each term, which can be written as or .

[0119] The reason for the method of the present invention to choose to use the Riemann zeta function lies in its unique mathematical properties. In particular, when takes different values, exhibits different characteristics, which can be used to simulate different resource allocation strategies. For example, when is close to 1, the function value increases rapidly, which can be used to represent the situation of resource shortage; while when is large, the function value approaches 1, which can represent the situation of sufficient resources.

[0120] Preferably, in one embodiment of the present invention, the value range of can be set between 1.1 and 2. The selection of this range is based on a large number of experimental results and can achieve good optimization effects in most cases.

[0121] After completing the optimization of number theory resource allocation, the method of the present invention updates the weights of the adaptive neural network based on the optimization results. This step is the core of the entire method, which ensures that the optimization process can continuously adapt to the changing environment. The update of the adaptive neural network weights can be represented by the following mathematical formula:

[0122] ,

[0123] where, is the neural network weight at time t, is the learning rate, is the loss function, is the gradient of the loss function with respect to the weights, is the loss function.

[0124] Suppose there is a data center that needs to allocate resources such as CPU cores, memory capacity, and disk I / O bandwidth. The current prediction results show that the future resource requirements are as follows: Number of CPU cores: 5; Memory capacity: 16 GB; Disk I / O bandwidth: 100 MB / s;

[0125] These requirements can be represented as a vector .

[0126] Suppose the weight matrix of the current neural network is:

[0127]

[0128] These weights need to be updated according to the loss function . Suppose the current gradient of the loss function is:

[0129]

[0130] The learning rate is , then the new weight matrix is calculated as follows:

[0131]

[0132] Substitute the values:

[0133]

[0134] The calculation result:

[0135]

[0136] In this process, as the optimized resource allocation vector, represents the optimal allocation of each resource, and guides the adjustment direction of the weights. By continuously updating the weights, the neural network can finally predict and optimize resource allocation more accurately.

[0137] An innovation of the present invention is that it introduces the von Neumann entropy in quantum mechanics to define the loss function:

[0138] ,

[0139] Here, is the density matrix, is the Hamiltonian, is the inverse temperature parameter, is the regularization parameter. Tr is the von Neumann entropy, used to quantify the uncertainty of the system, is the matrix and The square of the Frobenius norm between them is used to measure the difference between two matrices. is the exponential form of the Hamiltonian used to describe the energy distribution of the system.

[0140] An adaptive neural network (ANN) can continuously adjust its weights according to feedback in a dynamic environment to optimize resource allocation and improve system performance. This ability makes the ANN very suitable for the intelligent optimization of server hardware resources.

[0141] By using the von Neumann entropy in quantum mechanics as part of the loss function, the complexity and uncertainty of the system can be better captured. This helps to achieve more precise optimization in the resource scheduling process. For example, in a high-load data center, the status of resources such as CPU and memory is monitored in real time, and the weights of the neural network are adjusted according to these statuses to achieve optimal resource allocation.

[0142] Suppose a data center needs to dynamically adjust the resource allocation of each virtual machine according to the current workload. By applying the above formula, the weights of the neural network can be continuously updated according to the actual resource usage, so as to more accurately predict future resource requirements and make corresponding adjustments. For example, if the CPU usage rate of a certain virtual machine suddenly rises, the system can increase the CPU allocation of this virtual machine by adjusting the weights to prevent overload.

[0143] Preferably, in the method of the present invention, the learning rate can be set to 0.01, and this value can usually achieve a good balance between the convergence speed and stability. The inverse temperature parameter can be set to 1, and this value can provide sufficient quantum effects in most cases. The regularization parameter can be set to 0.001 to prevent overfitting.

[0144] It should be noted that the method of the present invention also includes dynamically adjusting the hardware resource allocation of the server based on the optimized server hardware resource configuration scheme. This step ensures that the optimization results can be actually applied to the server system. For example, if the optimization result shows that an application requires more CPU resources, the system will correspondingly increase the CPU time slice allocated to this application.

[0145] In addition, the method of the present invention also includes periodically repeating the acquisition step, the processing step, and the output step to achieve continuous optimization. Preferably, this period can be set to 5 minutes. The choice of this time interval is based on the consideration of the server load change speed, which can not only respond to load changes in a timely manner but also avoid introducing additional system overhead due to overly frequent optimization.

[0146] Generally speaking, the method of the present invention realizes the intelligent optimization of server hardware resources by innovatively combining the concepts of topology, group theory, matrix theory, number theory, and quantum mechanics. This method can not only accurately capture the complex relationships between resources but also adapt to the dynamically changing environment, thereby achieving more efficient and precise resource allocation. In practical applications, this method can significantly improve the resource utilization rate of the server, reduce resource waste, enhance the overall system performance, and provide strong technical support for the efficient operation of the data center. The present invention also provides an intelligent optimization system for server hardware resources based on an adaptive neural network. The design of this system aims to implement each step of the aforementioned method, thereby achieving the efficient optimization of server hardware resources.

[0147] Specifically, this system includes an acquisition module 1, a processing module 2, and an output module 3. These modules work together to complete the intelligent optimization process of server hardware resources.

[0148] The acquisition module 1 is responsible for acquiring the hardware resource information and system performance metrics of the server. In a preferred embodiment of the present invention, the acquisition module 1 may include multiple sensor sub-modules, such as a CPU usage rate sensor sub-module 11, a memory occupancy rate sensor sub-module 12, a disk I / O rate sensor sub-module 13, and a network traffic sensor sub-module 14. These sensor sub-modules are respectively responsible for collecting the corresponding hardware resource information. At the same time, the acquisition module 1 may also include a performance metric acquisition sub-module 15 for collecting system performance metrics such as application response time, system throughput, and server energy consumption.

[0149] The processing module 2 is the core of this system. It contains multiple sub-modules, and each sub-module corresponds to a key step in the method. Specifically, the processing module 2 includes a topology mapping sub-module 21, a group theory scheduling sub-module 22, a matrix prediction sub-module 23, a number theory optimization sub-module 24, and a neural network update sub-module 25.

[0150] The topology mapping sub-module 21 is used to construct a topology resource mapping based on the hardware resource information and system performance metrics provided by the acquisition module 1. In an embodiment of the present invention, the topology mapping sub-module 21 may use a high-performance GPU for parallel computing to accelerate the construction process of the topology mapping. Preferably, an NVIDIA Tesla V100 GPU can be used, which has 5120 CUDA cores and can efficiently handle large-scale matrix operations.

[0151] The group theory scheduling sub-module 22 is responsible for performing the group theory resource scheduling algorithm according to the topological resource mapping. In a preferred embodiment of the present invention, the group theory scheduling sub-module 22 may adopt a distributed computing architecture and utilize the computing power of multiple servers to process group theory operations in parallel. For example, the Apache Spark distributed computing framework can be used, which can effectively process large-scale data sets and complex mathematical operations.

[0152] Based on the results of the group theory resource scheduling algorithm, the matrix prediction sub-module 23 performs matrix resource state prediction. In an embodiment of the present invention, the matrix prediction sub-module 23 may adopt a dedicated matrix operation accelerator, such as Google's Tensor Processing Unit (TPU). The TPU is specifically optimized for large-scale matrix operations and can significantly improve the speed and efficiency of matrix prediction.

[0153] According to the results of the matrix resource state prediction, the number theory optimization sub-module 24 performs number theory resource allocation optimization. In a preferred embodiment of the present invention, the number theory optimization sub-module 24 may adopt a quantum annealing algorithm to solve the optimization problem. For example, the D-Wave quantum annealer can be used, which can find an approximate optimal solution to complex optimization problems in a relatively short time.

[0154] The neural network update sub-module 25 is responsible for updating the weights of the adaptive neural network based on the results of the number theory resource allocation optimization. In an embodiment of the present invention, the neural network update sub-module 25 may adopt an FPGA (Field-Programmable Gate Array) to accelerate the training and update process of the neural network. For example, the Intel Stratix 10 FPGA can be used, which can provide a floating-point operation performance of up to 10 TFLOPS and is very suitable for the rapid update of neural networks.

[0155] The output module 3 is used to output the optimized server hardware resource configuration scheme. In a preferred embodiment of the present invention, the output module 3 may include a visualization sub-module 31 and an execution sub-module 32. The visualization sub-module 31 is responsible for intuitively presenting the optimization results to the system administrator in the form of charts or animations, while the execution sub-module 32 is responsible for automatically applying the optimization scheme to the server system.

[0156] It should be noted that the system may further include a feedback module 4. The feedback module 4 is responsible for collecting the optimized system performance data and feeding these data back to the acquisition module 1 to form a closed-loop optimization system. This design enables the system to continuously learn and improve, thereby achieving continuous performance optimization.

[0157] Preferably, the system can adopt a modular design, and each module communicates through standardized interfaces. This design enables the system to have good scalability and maintainability. For example, if a more advanced optimization algorithm emerges in the future, only the corresponding sub-module needs to be replaced, without changing the overall structure of the system.

[0158] Generally speaking, the intelligent optimization system of server hardware resources based on the adaptive neural network provided by the present invention realizes the efficient and intelligent optimization of server hardware resources through the collaborative work of each functional module. This system can not only accurately capture the complex relationships between resources but also adapt to the dynamically changing environment, thereby achieving more efficient and precise resource allocation. In practical applications, such a system can significantly improve the resource utilization rate of the data center, reduce energy waste, enhance the overall operation efficiency, and provide strong technical support for the intelligent management of modern data centers.

[0159] To verify the superiority of the intelligent optimization method and system of server hardware resources based on the adaptive neural network of the present invention, a group of tests was designed, including one embodiment and two comparative examples. These experiments were carried out in a large data center environment involving 1000 servers and observed continuously for 30 days.

[0160] Example 1 adopted the method of the present invention, including topological resource mapping, group theory resource scheduling, matrix resource state prediction, number theory resource allocation optimization, and adaptive neural network weight update.

[0161] Comparative example 1 adopted the traditional threshold-based resource allocation method, which triggered resource reallocation when the resource utilization rate exceeded the preset threshold.

[0162] Comparative example 2 adopted a simple machine learning method, using a linear regression model to predict resource requirements and allocate them.

[0163] The following five indicators were selected to evaluate the performance of each method:

[0164] 1. Average resource utilization rate: Data was collected once per minute using system monitoring tools, and the average value over 30 days was taken.

[0165] 2. Energy efficiency: Calculate the number of tasks processed per kilowatt-hour of electrical energy.

[0166] 3. Service quality (SLA achievement rate): Statistically analyze the proportion of requests that meet the service level agreement within 30 days.

[0167] 4. System response time: Record the average response time of all requests.

[0168] 5. Optimization algorithm execution time: Record the average time-consuming for each resource optimization decision.

[0169] The test results are shown in Table 1 as follows:

[0170] Table 1. Comparison of Test Results between Example 1 and Comparative Examples 1 and 2

[0171] Index Example 1 Comparative Example 1 Comparative Example 2 Average resource utilization rate 78.5% 62.3% 70.1% Energy efficiency (number of tasks / kWh) 1250 950 1050 SLA achievement rate 99.7% 95.2% 97.8% System response time (ms) 85 150 120 Optimization algorithm execution time (s) 2.5 0.5 1.2

[0172] It can be seen from the test results in Table 1 that the method of the present invention (Example 1) is significantly superior to the traditional method (Comparative Example 1) and the simple machine learning method (Comparative Example 2) in most indicators.

[0173] In terms of resource utilization, the method of the present invention reaches 78.5%, which is 16.2 and 8.4 percentage points higher than those of Comparative Example 1 and Comparative Example 2 respectively. This indicates that the method of the present invention can utilize server resources more effectively and reduce resource waste. High resource utilization means that the data center can process the same number of tasks with less hardware, thereby reducing hardware costs and energy consumption.

[0174] The energy efficiency index shows that the method of the present invention can process 1250 tasks per kilowatt-hour, which is increased by 31.6% and 19% compared with Comparative Example 1 and Comparative Example 2 respectively. This result directly reflects the advantages of this method in energy conservation and emission reduction, which is beneficial to reducing the operation cost and environmental impact of the data center.

[0175] In terms of service quality, the method of the present invention reaches an SLA achievement rate of 99.7%, which is much higher than the other two methods. This means that adopting this method can provide users with more stable and reliable services, which helps to improve user satisfaction and corporate reputation.

[0176] The system response time is an important indicator to measure the user experience. The method of the present invention controls the average response time within 85 milliseconds, which is 65 milliseconds and 35 milliseconds faster than those of Comparative Example 1 and Comparative Example 2 respectively. This significant performance improvement can directly improve the user experience, especially for applications with high real-time requirements.

[0177] It should be noted that the method of the present invention is slightly inferior to the other two methods in optimizing the algorithm execution time. This is because this method involves more complex calculation processes, including topology mapping, group theory scheduling, etc. However, considering the significant performance improvement brought by this method, this slightly increased calculation time is completely acceptable. Moreover, with the continuous improvement of hardware performance and the further optimization of the algorithm, this gap is expected to be further narrowed.

[0178] Generally speaking, the experimental results of this group strongly prove the superiority of the method of the present invention. It not only performs excellently in terms of resource utilization rate and energy efficiency, but also can significantly improve the system response speed while ensuring high service quality. This comprehensive performance improvement fully reflects the innovative value of the present invention that integrates advanced concepts such as topology, group theory, matrix theory, number theory, and quantum mechanics.

[0179] It is particularly worth mentioning that the method of the present invention performs excellently in a dynamic and complex data center environment, which benefits from its core mechanism of adaptive neural network. Traditional methods often have difficulty coping with rapidly changing load conditions, while this method can better adapt to environmental changes and make more intelligent resource allocation decisions through continuous learning and optimization.

[0180] The experimental results of this group not only verify the innovation of the present invention in theory, but also prove its great potential in practical applications. By adopting this method, data center operators can significantly improve resource utilization efficiency, reduce energy consumption, and at the same time provide better services for users. This undoubtedly has important practical significance for the data center industry that is currently facing challenges such as resource tension, high energy consumption, and growing user demands.

[0181] It should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent optimization method for server hardware resources based on an adaptive neural network, characterized in that Including: The obtaining step includes: Obtaining the hardware resource information and system performance metrics of the server; The processing step includes: Based on the hardware resource information and system performance metrics, constructing a topological resource mapping; According to the topological resource mapping, executing a group theory resource scheduling algorithm; Based on the result of the group theory resource scheduling algorithm, performing matrix resource state prediction; According to the result of the matrix resource state prediction, executing number theory resource allocation optimization; Based on the result of the number theory resource allocation optimization, updating the weights of the adaptive neural network; The output step includes: Outputting an optimized server hardware resource configuration plan; The specific construction of the topological resource mapping includes: Mapping the hardware resource information to a high-dimensional topological space; Using spherical harmonic functions as topological basis functions; Calculating the topological mapping function; The specific execution of the group theory resource scheduling algorithm includes: Defining a resource scheduling group; Based on the resource scheduling group, performing a transformation on the topological mapping; Generating an optimized topological mapping; The specific performance of the matrix resource state prediction includes: Constructing a resource state matrix; Defining a state transition function based on the Toeplitz matrix; Calculating the predicted resource state matrix; The specific execution of the number theory resource allocation optimization includes: Using the Riemann zeta function for resource allocation optimization; Calculating the optimal resource allocation plan; The specific update of the weights of the adaptive neural network includes: Defining a loss function based on the von Neumann entropy; Calculating the gradient of the neural network weights; Updating the neural network weights.

2. The method according to claim 1, wherein The specific obtaining of the hardware resource information and system performance metrics of the server includes: Obtaining the CPU usage rate, memory occupancy rate, disk I / O rate, and network traffic; Obtaining the application response time, system throughput, and server energy consumption.

3. The method according to claim 1, characterized in that It also includes: Based on the optimized server hardware resource configuration plan, dynamically adjusting the hardware resource allocation of the server.

4. The method according to claim 1, characterized in that, It also includes: Periodically repeating the execution of the obtaining step, processing step, and output step to achieve continuous optimization.

5. An intelligent optimization system for server hardware resources based on an adaptive neural network that executes the method according to any one of claims 1-4, characterized in that, Including: An obtaining module for obtaining the hardware resource information and system performance metrics of the server; A processing module, including: A topological mapping sub-module for constructing a topological resource mapping based on the hardware resource information and system performance metrics; A group theory scheduling sub-module for executing a group theory resource scheduling algorithm according to the topological resource mapping; A matrix prediction sub-module for performing matrix resource state prediction based on the result of the group theory resource scheduling algorithm; A number theory optimization sub-module for executing number theory resource allocation optimization according to the result of the matrix resource state prediction; A neural network update sub-module for updating the weights of the adaptive neural network based on the result of the number theory resource allocation optimization; An output module for outputting an optimized server hardware resource configuration plan.

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