A server cluster software and hardware integration evaluation system and evaluation method

Abstract

The application relates to the technical field of computers and discloses a server cluster software and hardware integration evaluation system, which comprises a data acquisition module, a cooperative evaluation module and a server cluster software and hardware integration evaluation method.The data acquisition module is used for acquiring hardware performance data and software performance data in a server cluster and performing cleaning and normalization processing on the acquired data; the cooperative evaluation module is used for cooperating with the hardware performance data and the software performance data; and the server cluster software and hardware integration evaluation method comprises the following steps: data acquisition: acquiring hardware performance data and software performance data in a server cluster and performing cleaning and normalization processing on the acquired data.Through software and hardware cooperation modeling, the nonlinear interaction relationship between software and hardware is quantified, the evaluation accuracy is improved, dynamic load prediction and resource optimization are realized by introducing a long short-term memory network, a comprehensive score model is constructed, multi-dimensional performance indexes are uniformly quantified, real-time evaluation and performance bottleneck positioning are realized, and scientific guidance is provided for resource optimization.

Description

Technical Field

[0001] This invention relates to the field of computer technology, specifically to an integrated hardware and software evaluation system and method for server clusters. Background Technology

[0002] Driven by cloud computing, big data, and artificial intelligence technologies, server clusters have become the core infrastructure for high-performance computing and data processing. However, with the expansion of cluster size and increasing complexity, comprehensively evaluating the hardware and software performance of server clusters has become a key challenge for improving system performance and efficiency. In traditional evaluation methods, hardware and software performance are often analyzed separately, resulting in an inability to accurately reflect the overall impact of their synergy on system performance. This separate analysis method leads to shortcomings in the comprehensiveness and scientific rigor of the evaluation.

[0003] There is a complex, non-linear interaction between hardware performance (such as CPU, GPU, memory, and storage) and software performance (such as task response time, throughput, and scheduling efficiency) in a server cluster. For example, when hardware resources are bottlenecked, the execution efficiency of software tasks may be severely affected; conversely, a decrease in software task scheduling efficiency may lead to a decrease in hardware resource utilization. Traditional evaluation methods typically assume that hardware and software performance are independent of each other, lacking quantitative analysis of this synergistic effect, and thus failing to provide effective support for performance optimization and system design.

[0004] Furthermore, the load on server clusters is typically dynamic during actual operation. Especially in cloud computing scenarios, load fluctuations can increase significantly due to changes in task allocation and resource requirements. However, most existing evaluation methods are based on static models, making it difficult to adapt to dynamic load conditions. This evaluation approach often exhibits insufficient robustness under load fluctuations or sudden changes, failing to provide reliable support for real-time resource scheduling.

[0005] On the other hand, existing performance evaluation systems often focus on analyzing single metrics, such as hardware utilization, software response time, or task throughput. However, evaluation of a single metric is often too one-sided and lacks a comprehensive measurement of the overall system performance. In multi-metric analysis, due to the different dimensions and importance of various metrics, existing methods also lack a unified quantitative framework to balance the weights among multiple metrics, making it difficult to fully reflect the system's operating status.

[0006] In view of this, the present invention provides a server cluster hardware and software integrated evaluation system and evaluation method. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides an integrated hardware and software evaluation system and method for server clusters, which solves the problems of difficulty in quantifying the synergistic effect of hardware and software in server clusters, insufficient robustness of dynamic load evaluation, and lack of a unified performance evaluation framework.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a server cluster integrated hardware and software evaluation system, comprising:

[0009] The data acquisition module is used to collect hardware performance data and software performance data from the server cluster, and to clean and normalize the collected data.

[0010] The collaborative evaluation module is used to construct a performance evaluation model of the hardware and software synergy based on the hardware performance data and software performance data, and to calculate the collaborative performance of the system.

[0011] The load prediction module is used to predict future load trends of the server cluster based on collected historical load data.

[0012] The resource optimization and scheduling module is used to dynamically adjust the hardware resource allocation and software task scheduling scheme of the server cluster based on the collaborative performance model and load prediction results.

[0013] The comprehensive evaluation index module is used to construct a comprehensive performance score and output the performance evaluation results based on hardware latency, software response time, and resource utilization.

[0014] The visualization module is used to display hardware performance, software performance, load prediction trends, and comprehensive evaluation results in real time.

[0015] Preferably, the data collected in the data acquisition module includes hardware performance data and software performance data. The hardware performance data includes CPU utilization, GPU utilization, memory utilization, and storage device read / write speed. The software performance data includes task response time, task queuing time, and throughput.

[0016] Preferably, the collaborative evaluation module includes:

[0017] The collaborative model building unit is used to construct a mathematical model of the collaborative effect between hardware and software based on the hardware performance function H(x) and the software performance function S(x).

[0018] The model parameter optimization unit is used to optimize the parameters of the collaborative model based on historical data, including the weight parameters α, β, and γ of the collaborative action function.

[0019] The collaborative performance calculation unit is used to input real-time performance data and calculate the collaborative performance value P(x).

[0020] Preferably, the collaborative performance value of the collaborative performance calculation unit is calculated using the following formula:

[0021] ;

[0022] Where P(x) represents the overall performance of the system, H(x) represents the hardware performance function, S(x) represents the software performance function, and f(H(x),S(x)) represents the hardware-software synergy function. This represents the cumulative effect of synergy, where x represents the input parameter.

[0023] Preferably, the resource optimization and scheduling module includes:

[0024] The objective function construction unit is used to construct the objective function for resource allocation optimization, so as to maximize collaborative performance under the constraint of total resource C;

[0025] The optimization algorithm unit is used to iteratively optimize the resource allocation scheme based on the gradient descent method and update the hardware resource allocation. Software task allocation .

[0026] Preferably, the optimization formula for the objective function in the objective function construction unit is:

[0027] ;

[0028] Where P(x) represents the cooperative performance, and These represent the allocation of hardware resources and software tasks, respectively, with C being the total amount of resources.

[0029] Preferably, the comprehensive evaluation index module calculates the comprehensive performance score using the following formula:

[0030] ;

[0031] in, A normalized score representing hardware latency, software response time, and resource utilization. This indicates the weight of each indicator.

[0032] Preferably, the load prediction module optimizes the prediction model using the following loss function:

[0033] ;

[0034] in, This represents the value of the loss function, and N represents the total number of samples in the training data. This represents the actual load value at time point i. This represents the predicted load value at the i-th time point.

[0035] A method for evaluating the integrated hardware and software of a server cluster includes the following steps:

[0036] Data Acquisition: Collect hardware and software performance data from the server cluster, and clean and normalize the collected data.

[0037] Collaborative evaluation: Construct a performance evaluation model for the collaborative effect of software and hardware, and calculate the collaborative performance of the system based on the model;

[0038] Dynamic load forecasting: Based on historical load data, it uses a long short-term memory network model to predict future load trends;

[0039] Resource optimization scheduling: Based on the collaborative performance model and load prediction results, dynamically adjust the allocation of hardware resources and software tasks;

[0040] Comprehensive performance evaluation: Construct a comprehensive performance index system that integrates hardware performance, software performance, and resource utilization for comprehensive scoring;

[0041] Output: Generates a comprehensive performance evaluation report and provides performance optimization suggestions.

[0042] This invention provides an integrated hardware and software evaluation system and method for server clusters. It offers the following advantages:

[0043] 1. This invention addresses the problem that existing evaluation methods assume independent hardware and software performance and cannot quantify their synergistic effect. It proposes a mathematical model of hardware and software synergy. Through hardware performance function, software performance function, and synergy function, this invention can comprehensively describe the nonlinear interaction between hardware and software performance. The synergistic performance model optimizes parameters α, β, and γ using historical data to accurately quantify the hardware and software synergistic effect, significantly improving the scientific nature and accuracy of the evaluation.

[0044] 2. To address the problem that existing methods struggle to handle dynamic load changes in server clusters, this invention introduces a long short-term memory network model to enable real-time prediction of load change trends. Combined with gradient descent, this invention can dynamically adjust the allocation of hardware resources and software tasks, ensuring maximum collaborative performance during load fluctuations. This solution solves the problem of insufficient robustness in traditional static evaluation and optimization methods, enabling the system to maintain high efficiency in complex operating environments.

[0045] 3. To address the lack of a unified quantitative indicator system in existing methods, this invention proposes a comprehensive scoring model based on multi-dimensional performance indicators through a comprehensive evaluation indicator module. By normalizing and weighting key indicators such as hardware latency, software response time, and resource utilization, a unified comprehensive scoring system is formed. This system can not only quantify the overall performance of the server cluster in real time, but also accurately locate performance bottlenecks, providing scientific guidance for resource optimization and system upgrades. Attached Figure Description

[0046] Figure 1 This is a framework diagram of the system of the present invention;

[0047] Figure 2 This is a framework diagram of the collaborative evaluation module of the present invention;

[0048] Figure 3 This is a framework diagram of the resource optimization and scheduling module of the present invention;

[0049] Figure 4 This is a flowchart of the method of the present invention. Detailed Implementation

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Please see the appendix Figure 1 This invention provides an integrated hardware and software evaluation system for server clusters, comprising:

[0052] The data acquisition module is used to collect hardware performance data and software performance data from the server cluster, and to clean and normalize the collected data.

[0053] The collaborative evaluation module is used to construct a performance evaluation model of hardware and software synergy based on hardware and software performance data, and to calculate the collaborative performance of the system.

[0054] The load prediction module is used to predict future load trends of the server cluster based on collected historical load data.

[0055] The resource optimization and scheduling module is used to dynamically adjust the hardware resource allocation and software task scheduling scheme of the server cluster based on the collaborative performance model and load prediction results.

[0056] The comprehensive evaluation index module is used to construct a comprehensive performance score and output the performance evaluation results based on hardware latency, software response time, and resource utilization.

[0057] The visualization module is used to display hardware performance, software performance, load prediction trends, and comprehensive evaluation results in real time.

[0058] Specifically, the data acquisition module is the foundation of the entire system, used to collect hardware and software performance data from the server cluster in real time. Hardware performance data includes CPU and GPU utilization, memory usage, and disk read / write speeds, which are obtained through low-level monitoring tools such as IPMI, SNMP, or Redfish. Software performance data is collected through task monitoring tools, mainly recording task response time, throughput, and task queuing time.

[0059] To ensure data accuracy, the collected data underwent cleaning and normalization. Data cleaning included filtering outliers and filling in missing values ​​to eliminate interference during monitoring. Normalization mapped all performance metrics to the [0,1] interval, enabling subsequent mathematical modeling to directly handle heterogeneous data.

[0060] The collected performance data is processed by the collaborative evaluation module to construct a performance evaluation model for the synergy between software and hardware. System performance is quantified using the following formula:

[0061] ;

[0062] Here, H(x) and S(x) represent hardware and software performance, respectively, and f(H(x),S(x)) is a synergistic function used to capture the complex nonlinear interaction between hardware and software performance.

[0063] The synergistic effect function is obtained by fitting historical performance data, and its form can be expressed as:

[0064] ;

[0065] α, β, and γ are key parameters reflecting the synergistic effect. When optimizing the model, these parameters are fitted using the least squares method to ensure the model accurately describes the synergistic effect of the hardware and software. Finally, this module inputs real-time performance data into the model to calculate the overall synergistic performance P(x).

[0066] The load forecasting module predicts the load change trend of the server cluster within a future time window by performing time series analysis on historical load data. This system uses a Long Short-Term Memory (LSTM) network for forecasting, whose powerful time-dependent modeling capabilities can capture complex load change patterns.

[0067] The model takes a sequence of past load data as input and outputs predicted future load values. To optimize the model's predictive performance, mean squared error is chosen as the loss function.

[0068] ;

[0069] By continuously optimizing the model parameters using gradient descent, the predicted value is reduced. Compared with the true value The error between them.

[0070] Based on the collaborative performance model and load prediction results, the resource optimization and scheduling module dynamically adjusts the allocation of hardware resources and software tasks to ensure maximum system performance under dynamic load conditions. This module establishes the following optimization objectives:

[0071] ;

[0072] in, and These represent the allocation of hardware resources and software tasks, respectively, with C being the total resource capacity.

[0073] The optimization process uses gradient descent for iterative updates:

[0074] ;

[0075] Through real-time monitoring and optimization, the system can dynamically allocate resources when the load fluctuates, balancing hardware utilization and software execution efficiency.

[0076] To comprehensively quantify system performance, a comprehensive performance score is introduced into the comprehensive evaluation index module. :

[0077] ;

[0078] in, It is a normalized score of various performance indicators (such as hardware latency, software response time, and resource utilization). These are weights, used to reflect the importance of different indicators.

[0079] By calculating a comprehensive score, the module can pinpoint performance bottlenecks. For example, when a certain score is too low (such as resource utilization being below 50%), the module will prompt the user to make optimization adjustments.

[0080] Finally, the visualization module presents the system's operational status in real-time using charts, including hardware performance, software performance, load prediction trends, and comprehensive evaluation results. Users can view the following information using Grafana or similar tools:

[0081] Real-time usage of hardware resources (CPU / GPU utilization, memory usage).

[0082] The execution efficiency of software tasks (task throughput, response time).

[0083] Future load change trends compared to current load.

[0084] Overall performance score and bottleneck indicators.

[0085] In addition, users can customize the analysis view and export reports containing performance evaluations and optimization suggestions, providing a scientific basis for cluster management.

[0086] The data collected in the data acquisition module includes hardware performance data and software performance data. Hardware performance data includes CPU utilization, GPU utilization, memory usage, and storage device read / write speed. Software performance data includes task response time, task queuing time, and throughput.

[0087] Please see the appendix Figure 2 The collaborative evaluation module includes:

[0088] The collaborative model building unit is used to construct a mathematical model of the collaborative effect between hardware and software based on the hardware performance function H(x) and the software performance function S(x).

[0089] The model parameter optimization unit is used to optimize the parameters of the collaborative model based on historical data, including the weight parameters α, β, and γ of the collaborative action function.

[0090] The collaborative performance calculation unit is used to input real-time performance data and calculate the collaborative performance value P(x).

[0091] The collaborative performance value of the collaborative performance calculation unit is calculated using the following formula:

[0092] ;

[0093] Where P(x) represents the overall performance of the system, H(x) represents the hardware performance function, S(x) represents the software performance function, and f(H(x),S(x)) represents the hardware-software synergy function. This represents the cumulative effect of synergy, where x represents the input parameter.

[0094] Specifically, regarding the above, the collaborative evaluation module is one of the core modules of the server cluster integrated hardware and software evaluation system. Its main function is to quantify the complex interaction between hardware and software performance based on a mathematical model of hardware and software synergy, and to calculate the system's collaborative performance value through real-time performance data, providing a scientific basis for subsequent load prediction and resource optimization scheduling. The collaborative evaluation module consists of a collaborative model construction unit, a model parameter optimization unit, and a collaborative performance calculation unit. These units cooperate with each other to complete the modeling, parameter optimization, and dynamic calculation of performance values ​​for the synergy between hardware and software performance.

[0095] In this embodiment, the collaborative model construction unit is used to establish a mathematical model of the synergistic effect between hardware and software. The core objective is to quantify and express the independent and synergistic effects of hardware and software performance using mathematical formulas. The basic formula for the system performance model is:

[0096] ;

[0097] Where: P(x) represents the overall performance of the system, which is the overall performance of hardware performance H(x) and software performance S(x) and their synergistic effect; H(x) is the hardware performance function, reflecting the operating status of hardware resources (such as CPU, GPU, memory, etc.); S(x) is the software performance function, describing the execution efficiency of software tasks (such as response time, throughput, etc.). This is a hardware / software interaction function used to quantify the nonlinear interaction between hardware and software performance; The cumulative effect of synergy represents the overall contribution of hardware and software synergy to overall performance as the input x (such as load and time) changes.

[0098] The first part of the formula, H(x)·S(x), represents the direct product of hardware and software performance, quantifying their independent contributions; the second part... This captures the complex synergistic relationship between hardware and software performance.

[0099] In this embodiment, the model parameter optimization unit is used to determine the cooperative action function. The specific form and parameter values ​​are specified. To accurately describe the nonlinear interaction between hardware and software, the cooperative function takes the following form:

[0100] ;

[0101] Where α, β, and γ are weighting parameters that describe the nonlinear contribution of hardware performance, the nonlinear contribution of software performance, and the interaction contribution between hardware and software, respectively.

[0102] The optimization process for model parameters is based on historical data, and the parameters are fitted using the least squares method:

[0103] ;

[0104] in: This represents the actual measured system performance value;

[0105] This represents the performance value predicted by the model.

[0106] Parameter optimization is performed using gradient descent, with parameters updated in each iteration.

[0107] ;

[0108] Where: η is the learning rate, used to control the step size of parameter updates; Let be the loss function, and be the sum of squared prediction errors for historical data.

[0109] Through the above optimization process, we can ensure that the synergy function can accurately reflect the interaction effect between hardware and software performance.

[0110] In this embodiment, the function of the collaborative performance calculation unit is based on the real-time collected hardware performance data H(x) and software performance data S(x), combined with the optimized collaborative action function. The overall performance value P(x) of the dynamic calculation system is calculated using the following formula:

[0111] ;

[0112] The hardware performance data H(x) and software performance data S(x) are collected in real time during system runtime and input into the collaborative evaluation module for analysis. Collaborative action function. The integral part is calculated using numerical methods, and its cumulative effect quantifies the contribution of the hardware and software synergy under the current load conditions.

[0113] In practice, the collaborative performance calculation unit combines the collaborative action function parameters α, β, and γ obtained from the aforementioned optimization to dynamically solve the integral part using numerical methods. Real-time input of hardware and software performance data ensures that the system can quickly respond to load changes and outputs the comprehensive performance P(x) as a key result for performance evaluation.

[0114] Please see the appendix Figure 3 The resource optimization and scheduling module includes:

[0115] The objective function construction unit is used to construct the objective function for resource allocation optimization, so as to maximize collaborative performance under the constraint of total resource C;

[0116] The optimization algorithm unit is used to iteratively optimize the resource allocation scheme based on the gradient descent method and update the hardware resource allocation. Software task allocation .

[0117] The optimization formula for the objective function in the objective function construction unit is:

[0118] ;

[0119] Where P(x) represents the cooperative performance, and These represent the allocation of hardware resources and software tasks, respectively, with C being the total amount of resources.

[0120] Specifically, regarding the above, the resource optimization and scheduling module in the server cluster integrated hardware and software evaluation system is used to dynamically optimize the allocation of hardware resources and software tasks to improve the overall performance of the system under dynamic load conditions. Through the collaborative work of the objective function construction unit and the optimization algorithm unit, the resource allocation scheme is adjusted in real time. In the optimization of the objective function, the goal is to maximize collaborative performance, and iterative calculations are performed using gradient descent to ensure that the optimal allocation strategy is obtained under the constraint of total resource quantity.

[0121] In this embodiment, the objective function construction unit is used to define the mathematical objective function for resource allocation optimization. The core objective is to maximize the overall collaborative performance P(x) of the system under the constraint of a given total resource amount C. The specific objective function formula is as follows:

[0122] ;

[0123] Where: P(x) represents the cooperative performance of the system, which is calculated by the cooperative evaluation module; The amount of hardware resources allocated reflects the usage of hardware resources at time t; The allocation amount for software tasks reflects the scheduling ratio of software tasks at time t; C is the total amount of resources, representing the upper limit of resources currently available to the system.

[0124] Constraints on resource allocation in the objective function formula The goal is to ensure that the allocation of hardware resources and software tasks does not exceed the total resource capacity, while the optimization objective maxP(x) aims to improve the contribution of hardware and software synergy to system performance through reasonable allocation.

[0125] This unit is also responsible for dynamically incorporating the performance calculation results P(x) of the collaborative evaluation module into the objective function, providing performance feedback to the optimization algorithm unit to guide the adjustment of resource allocation.

[0126] In this embodiment, the optimization algorithm unit is responsible for iteratively optimizing resource allocation using gradient descent based on the objective function, and finally outputting hardware resources. and software tasks The optimal allocation strategy.

[0127] 1. Iterative update using gradient descent

[0128] The optimization process is based on the following iterative formula:

[0129] ;

[0130] in: and These represent the hardware resources and software task allocation amounts for the k-th iteration, respectively; η is the learning rate, which controls the step size for each iteration. and These are the partial derivatives of the overall collaborative performance P(x) with respect to the amount of hardware resources allocated and the amount of software tasks allocated, respectively, reflecting the marginal impact of the current resource allocation on system performance.

[0131] 2. Iterative optimization process

[0132] The specific process of the optimization algorithm unit is as follows:

[0133] Initialization: Sets the initial resource allocation scheme. and Ensure that the initial values ​​satisfy the constraints. .

[0134] Performance feedback: Calculate the collaborative performance P(x) under the current allocation scheme, and update the partial derivative value based on the performance calculation results.

[0135] Gradient update: Adjust resource allocation based on the gradient descent formula to generate a new allocation scheme. and .

[0136] Iteration termination condition: Check the convergence of the objective function; if the performance improvement is insufficient (i.e., ... If ϵ is the set precision threshold, then the iteration stops; otherwise, the next round of updates continues.

[0137] Output the optimal allocation strategy: When the algorithm converges, output the current hardware resources and software task allocation.

[0138] 3. Numerical stability of the optimization algorithm

[0139] To improve the numerical stability of the optimization process, the optimization algorithm unit dynamically adjusts the learning rate η. During gradient descent, η is increased to accelerate convergence when performance improvement is slow; and η is decreased to avoid oscillations when performance fluctuations are large.

[0140] The comprehensive evaluation index module calculates the overall performance score using the following formula:

[0141] ;

[0142] in, A normalized score representing hardware latency, software response time, and resource utilization. This indicates the weight of each indicator.

[0143] Specifically, the comprehensive evaluation index module is used to quantitatively analyze the overall performance of the server cluster, combining multiple dimensions such as hardware performance, software performance, and resource utilization to calculate a comprehensive performance score. Through a scientific scoring model and weight allocation, the comprehensive evaluation index module can output performance results that reflect the current state of the system and provide a basis for performance optimization.

[0144] Overall performance score The calculation formula is as follows:

[0145] ;

[0146] in: This represents the normalized score of the i-th performance indicator, which specifically includes key performance parameters such as hardware latency, software response time, and resource utilization. The weight of the i-th performance indicator reflects its importance to the overall system performance; m represents the total number of performance indicators.

[0147] This formula combines various performance indicators The weighted sum is used as the overall performance score of the system. The weight The settings can be flexibly adjusted according to specific scenarios and user needs.

[0148] In this embodiment, the normalized score calculation of the performance index is performed.

[0149] Since different performance indicators (such as hardware latency, software response time, and resource utilization) have significantly different dimensions and value ranges, it is necessary to normalize the original indicators and map them to a unified interval (usually [0,1]) to facilitate calculation.

[0150] The normalization formula is:

[0151]

[0152] Where: the actual value is the currently measured performance index value; the minimum value is the best value of the index in the historical record; and the maximum value is the worst value of the index in the historical record.

[0153] Normalized The closer the value is to 1, the closer the performance is to the historical best state; the closer the value is to 0, the closer the performance is to the best state.

[0154] In this embodiment, weight These are key parameters affecting the overall performance score. Different system scenarios and user needs have different focuses on performance metrics, therefore, it is necessary to allocate weights appropriately. Common weighting methods include:

[0155] Even distribution:

[0156] All performance metrics should be assigned the same weight:

[0157] ;

[0158] This is suitable for scenarios where the indicators are of equal importance.

[0159] 2. Experience settings:

[0160] Based on experience and practical needs, assign greater weight to metrics that are of higher importance. For example, in scenarios with high responsiveness requirements, software response time can be given a higher weight.

[0161] Dynamic adjustment

[0162] By leveraging historical performance data or user feedback, weights are dynamically adjusted to optimize the applicability of the overall score. The specific formula is:

[0163] ;

[0164] in, This represents the importance factor of the i-th indicator.

[0165] In this embodiment, the calculation process for the comprehensive performance score is as follows:

[0166] Data input: Collect real-time performance data of the system, including hardware latency, software response time, and resource utilization.

[0167] Indicator Normalization: Normalize the raw performance data to generate normalized scores. .

[0168] Weighted calculation: according to the formula Calculate the overall performance score .

[0169] Output results: Output the overall performance score of the system and generate a performance evaluation report based on the score.

[0170] The comprehensive evaluation metrics module can evaluate the overall performance of the server cluster in real time. Specific applications include:

[0171] Performance bottleneck analysis: By observing various aspects The scoring system can quickly identify weaknesses in hardware, software, or resource utilization.

[0172] Optimization recommendations: Based on weight allocation, guide users to prioritize optimizing the metrics that have the greatest impact on system performance.

[0173] Dynamic performance monitoring: through real-time calculation It helps users track system performance trends and warn of potential performance problems.

[0174] The load forecasting module optimizes the forecasting model using the following loss function:

[0175] ;

[0176] in, This represents the value of the loss function, and N represents the total number of samples in the training data. This represents the actual load value at time point i. This represents the predicted load value at the i-th time point.

[0177] Specifically, the load prediction module is a key module in the integrated hardware and software evaluation system for server clusters. Its core function is to predict future load change trends using historical load data, providing forward-looking support for dynamic resource scheduling and performance optimization. During the training of the prediction model, the module uses mean squared error as the loss function, guiding the parameter optimization of the prediction model by minimizing the error function to improve the accuracy of load prediction.

[0178] The loss function used by the load prediction module is defined as follows:

[0179] ;

[0180] in: This represents the value of the loss function, used to quantify the overall error between the predicted and actual values. N represents the total number of samples in the training data, i.e., the number of sampling points in the historical payload data. This represents the actual load value at time point i, which is the real load data of the server cluster obtained through the data acquisition module. This represents the predicted load value at time point i, which is the prediction result output by the prediction model.

[0181] The core idea of ​​the loss function is to measure the overall performance of the prediction model by calculating the squared error between the predicted value and the actual value and averaging it.

[0182] Error squared term: For each sample point, the square of the error of the i-th point. It measures the difference between the predicted and actual values:

[0183] when = When the squared error is 0, it means the predicted value is completely accurate;

[0184] When the predicted value deviates from the actual value, the squared error increases rapidly with the increase of the deviation, thus applying a higher optimization weight to the predicted points with large deviations.

[0185] Error accumulation term: The squared errors of all sample points are summed to calculate the overall error.

[0186] Sample normalization: Normalize the sum of errors to ensure that the value of the loss function is independent of the number of samples, thereby ensuring the comparability of the model on datasets of different sizes.

[0187] Using this loss function, the load prediction module can quantify the performance of the prediction model on historical data and optimize the model parameters based on this, so that the prediction results are closer to the actual load value.

[0188] In this embodiment, the optimization process of model training is as follows:

[0189] Load prediction modules typically use LSTM (Long Short-Term Memory) networks as the prediction model, and their optimization process incorporates a loss function. The parameter adjustment process is as follows:

[0190] Input data: Input the historical load sequence [L(tk), L(t-k+1), ..., L(t-1)] into the LSTM network. The network outputs the predicted value for the next time step through multi-layer time series modeling. .

[0191] Calculation error: For each sample in the training data, calculate the actual payload value. Compared with the predicted load value The squared error.

[0192] Accumulated error: Sum the squared errors of all samples and calculate the loss function L.

[0193] Backpropagation and parameter update: Based on the gradient of the loss function, the model parameters are updated using optimization algorithms (such as the Adam optimizer or stochastic gradient descent).

[0194] ;

[0195] in: η represents the model's parameters at the k-th iteration; η is the learning rate, used to control the step size of parameter updates. This is the gradient of the loss function with respect to the model parameters.

[0196] Iteration termination condition: When the loss function L converges to a certain threshold (i.e. When ϵ represents the set precision, training stops and the optimized model parameters are output.

[0197] In this embodiment, the optimization effect of the loss function is as follows:

[0198] By using mean squared error as the loss function, the load forecasting module can achieve the following optimization effects:

[0199] Error minimization: by minimizing To ensure the predicted value As close as possible to the actual value This improves the accuracy of model predictions.

[0200] Optimization of large error points: The squared term of the loss function is given higher weight to large error points, so that the model pays more attention to sample points that deviate significantly during the optimization process.

[0201] Enhanced generalization ability: The average calculation method of the loss function imposes global constraints on the model parameters, avoiding overfitting of the model to certain samples and improving the prediction performance on unseen data.

[0202] In actual operation, the load prediction module applies the trained Long Short-Term Memory (LSTM) network to real-time load prediction. The specific steps are as follows:

[0203] Input real-time data: Input the most recent historical load sequence into the model and output the predicted load value for the next time step.

[0204] Rolling forecast: The model uses the current forecast value as subsequent input to achieve rolling forecasts at multiple time steps, forming a trend of future load changes.

[0205] Application of Results: Prediction Results It is provided to the resource optimization and scheduling module to guide the dynamic allocation of hardware resources and software tasks.

[0206] Please see the appendix Figure 4 A method for evaluating the integrated hardware and software of a server cluster includes the following steps:

[0207] Data Acquisition: Collect hardware and software performance data from the server cluster, and clean and normalize the collected data.

[0208] Collaborative evaluation: Construct a performance evaluation model for the collaborative effect of software and hardware, and calculate the collaborative performance of the system based on the model;

[0209] Dynamic load forecasting: Based on historical load data, it uses a long short-term memory network model to predict future load trends;

[0210] Resource optimization scheduling: Dynamically adjust the allocation of hardware resources and software tasks based on the collaborative performance model and load prediction results;

[0211] Comprehensive performance evaluation: Construct a comprehensive performance index system that integrates hardware performance, software performance, and resource utilization for comprehensive scoring;

[0212] Output: Generates a comprehensive performance evaluation report and provides performance optimization suggestions.

[0213] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A server cluster integrated hardware and software evaluation system, characterized in that, include: The data acquisition module is used to collect hardware performance data and software performance data from the server cluster, and to clean and normalize the collected data. The collaborative evaluation module is used to construct a performance evaluation model of hardware and software collaboration based on the hardware performance data and software performance data, and to calculate the collaborative performance of the system. The performance evaluation model includes: a hardware performance function generated based on the hardware performance data, a software performance function generated based on the software performance data, and a collaborative function used to quantify the nonlinear interaction relationship between the hardware performance function and the software performance function. The load prediction module is used to predict the future load change trend of the server cluster based on the collected historical load data and output the load prediction results. The resource optimization and scheduling module is used to dynamically adjust the hardware resource allocation and software task scheduling scheme of the server cluster based on the performance evaluation model of the hardware and software synergy and the load prediction results. The dynamic adjustment is carried out with the optimization goal of maximizing the synergy performance under the preset total resource constraints. The comprehensive evaluation index module is used to construct a comprehensive performance score and output the performance evaluation results based on hardware latency, software response time, and resource utilization. The visualization module is used to display hardware performance, software performance, load prediction results, and the performance evaluation results in real time.

2. The server cluster integrated hardware and software evaluation system according to claim 1, characterized in that, The data collected by the data acquisition module includes hardware performance data and software performance data. The hardware performance data includes CPU utilization, GPU utilization, memory usage, and storage device read / write speed. The software performance data includes task response time, task queuing time, and throughput.

3. The server cluster integrated hardware and software evaluation system according to claim 1, characterized in that, The collaborative evaluation module includes: The collaborative model construction unit is used to construct a mathematical model as a performance evaluation model of the hardware-software collaboration based on the hardware performance function H(x) and the software performance function S(x); The model parameter optimization unit is used to optimize the parameters of the performance evaluation model based on historical data, including the weight parameters α, β, γ of the hardware and software interaction function. The collaborative performance calculation unit is used to input real-time performance data and calculate the collaborative performance value P(x).

4. The server cluster integrated hardware and software evaluation system according to claim 3, characterized in that, The collaborative performance value of the collaborative performance calculation unit is calculated using the following formula: P(x)=H(x)·S(x)+∫f(H(x),S(x))dx Where f(H(x),S(x)) represents the hardware-software synergy function, ∫f(H(x),S(x))dx represents the cumulative effect of the synergy, and x represents the input parameter.

5. The server cluster integrated hardware and software evaluation system according to claim 4, characterized in that, The resource optimization and scheduling module includes: The objective function construction unit is used to construct the objective function for resource allocation optimization, so as to maximize collaborative performance under the constraint of total resource C; The optimization algorithm unit is used to iteratively optimize the resource allocation scheme based on the gradient descent method and update the hardware resource allocation amount R. h (t) and software task allocation R s (t).

6. The server cluster integrated hardware and software evaluation system according to claim 5, characterized in that, The optimization formula for the objective function in the objective function construction unit is: Constraints: R h (t)+R s (t)=C in, To optimize the objective, C represents the total amount of resources.

7. The server cluster integrated hardware and software evaluation system according to claim 1, characterized in that, The comprehensive evaluation index module calculates the comprehensive performance score using the following formula: Among them, Q i w represents the normalized score of the i-th performance metric. i Let represent the weight of the i-th performance indicator, and m represent the total number of performance indicators.

8. The server cluster integrated hardware and software evaluation system according to claim 1, characterized in that, The load prediction module optimizes the prediction model using the following loss function: in, L represents the value of the loss function, N represents the total number of training data samples, and L represents the value of the loss function. i This represents the actual load value at time point i. This represents the predicted load value at the i-th time point.

9. A method for evaluating the integrated hardware and software of a server cluster, characterized in that, Includes the following steps: Data Acquisition: Collect hardware and software performance data from the server cluster, and clean and normalize the collected data. Collaborative evaluation: Construct a performance evaluation model for the collaborative effect of hardware and software, and calculate the collaborative performance of the system based on the model. The performance evaluation model includes: a hardware performance function generated based on the hardware performance data, a software performance function generated based on the software performance data, and a collaborative effect function for quantifying the nonlinear interaction relationship between the hardware performance function and the software performance function. Dynamic load forecasting: Based on historical load data, it uses a long short-term memory network model to predict the future load trend of the server cluster and outputs the load forecast results. Resource optimization scheduling: Based on the performance evaluation model and load prediction results of the hardware and software synergy, the allocation of hardware resources and software tasks is dynamically adjusted, wherein the dynamic adjustment is carried out with the optimization objective of maximizing the synergy performance under the preset total resource constraints. Comprehensive performance evaluation: Construct a comprehensive performance index system that integrates hardware performance, software performance, and resource utilization for comprehensive scoring; Output: Generates a comprehensive performance evaluation report and provides performance optimization suggestions.

Images