Virtualization server load response model establishment method, medium and system

Abstract

The invention provides a virtual server load response model establishment method, a medium and a system, belongs to the technical field of virtual server load response, and is characterized in that multiple groups of load tests are carried out on a virtual server by setting an initial test condition and changing the initial test condition for multiple times, test data under different conditions are acquired, and a load data set is formed. And establishing a load response characteristic equation set containing all parameters, and performing fitting by using a data set. Then, establishing a resource conversion model and a resource competition model, and reflecting a resource mapping relationship between the physical server and the virtual server and resource competition between the virtual servers; integrating the two models, and solving the fitted equation set to obtain a numerical solution matrix; finally, a virtual server load response model is constructed based on the matrix and used for describing resource use and performance of the virtual server under different load conditions, and the technical problem that a server load response model established in the prior art is not high enough in accuracy is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtualized server load response, and in particular, relates to a method, medium and system for establishing a virtualized server load response model. Background Art

[0002] As the foundation for realizing new IT architectures such as cloud computing and containerization, virtualization technology plays an increasingly important role in the current information technology field. By abstracting physical computing resources into virtual resources, virtualization technology has greatly improved the resource utilization and flexibility of IT systems and is widely used in many application scenarios such as Web services, databases, and big data processing. However, in a virtualized environment, physical server resources such as CPU, memory, storage, and network must be shared by multiple virtual machines. This will lead to fierce resource competition between virtual machines, thus affecting their respective performance. At the same time, due to the overhead of the virtualization layer itself and the efficiency of converting physical resources to virtual resources, there is a certain gap between the actual resource usage and performance indicators of virtual machines and physical servers. Therefore, how to accurately model and predict the resource usage and performance of virtualized servers under different workloads has become a key technical challenge that needs to be solved urgently.

[0003] To address the above issues, existing technologies mainly use methods based on indicator monitoring: by deploying monitoring agents, real-time collection of CPU, memory, storage, network and other resource usage of virtual machines and physical machines is performed, and visualization tools are used to analyze and predict them. This method can intuitively reflect the resource usage of the virtualized environment, but it is difficult to deeply analyze the impact of various factors on performance, and the accuracy of the established server load response model is not high enough. Summary of the invention

[0004] In view of this, the present invention provides a method, medium and system for establishing a virtualized server load response model, which can solve the technical problem that the accuracy of the server load response model established in the prior art is not high enough.

[0005] The present invention is achieved in that:

[0006] A first aspect of the present invention provides a method for establishing a virtualized server load response model, which includes the following steps:

[0007] S10, setting initial test conditions, performing load testing on the virtualized server, and obtaining test data;

[0008] S20, changing the test conditions multiple times, performing multiple groups of load tests, obtaining test data under different conditions, and merging each group of test conditions and corresponding test data into a virtualized server load data set;

[0009] S30, establishing a load response characteristic equation group including all parameters in the virtualized server load data set;

[0010] S40, fitting the virtualized server load data set to the load response characteristic equation group to obtain a fitted equation group;

[0011] S50, establishing a resource conversion model according to the resource mapping relationship between the physical server and the virtual server;

[0012] S60, establishing a resource competition model based on resource competition and interference relationships between multiple virtual servers within the same physical server;

[0013] S70, integrating the resource conversion model and the resource competition model, solving the fitted equation group, and obtaining a numerical solution matrix of the virtualized server load response characteristics;

[0014] S80. Based on the numerical solution matrix, a virtualized server load response model is constructed to describe the resource usage and performance of the virtual server under different load conditions.

[0015] On the basis of the above technical solution, a method for establishing a virtualized server load response model of the present invention can also be improved as follows:

[0016] The test conditions include physical server configuration, virtualization platform type, number of virtual machines, service type, network topology and initial resource allocation.

[0017] Furthermore, the test data includes CPU usage, memory occupancy, storage I / O, network throughput, response time and energy consumption.

[0018] Furthermore, the load response characteristic equation group includes a computing resource equation, a storage resource equation, a network resource equation, a service performance equation, an energy consumption equation, a virtualization overhead equation and a resource scheduling equation.

[0019] Calculate the resource equation:

[0020] In the formula, C u is the CPU usage; V cpu is the number of virtual CPU cores; P cpu is the number of physical CPU cores; L is the load intensity; N is the number of virtual machines; α1, α2, α3 are unknown coefficients; ε1 is the error term.

[0021] Storage resource equation:

[0022] In the formula, I o is the storage I / O rate; Vstorage is the virtual storage capacity; P storage is the physical storage capacity; R is the read operation frequency; W is the write operation frequency; β1, β2, β3 are unknown coefficients; ε2 is the error term.

[0023] Network resource equation:

[0024] Where, T p is the network throughput; V bandwidth is the virtual network bandwidth; P bandwidth is the physical network bandwidth; P s is the number of packets sent; P r is the number of received data packets; γ1, γ2, γ3 are unknown coefficients; ε3 is the error term.

[0025] The Business Performance Equation: R t =δ1·C u +δ2·I o +δ3·T p +δ4·Q+ε4;

[0026] In the formula, R t is the response time; Q is the number of concurrent requests; δ1, δ2, δ3, δ4 are unknown coefficients; ε4 is the error term.

[0027] Energy consumption equation: E c =η1·C u +η2·I o +η3·T p +η4·T+ε5;

[0028] In the formula, E c is the energy consumption; T is the running time; η1, η2, η3, η4 are unknown coefficients; ε5 is the error term.

[0029] Virtualization overhead equation:

[0030] In the formula, O v is the virtualization overhead; V total is the total virtual resource; P total is the total physical resource; S f is the resource switching frequency; θ1, θ2, θ3 are unknown coefficients; ε6 is the error term.

[0031] Resource scheduling equation:

[0032] In the formula, S e A is the scheduling efficiency; r R is the allocated resource; r To request resources; B ris the resource balance; M t is the number of migrations; λ1, λ2, λ3 are unknown coefficients; ε7 is the error term.

[0033] Furthermore, the method for fitting the virtualized server load data set to the load response characteristic equation group is the least squares method.

[0034] Furthermore, the resource conversion model is used to describe the conversion process from physical resources to virtual resources and its efficiency.

[0035] Furthermore, the resource competition model is used to describe the mutual influence between virtual servers.

[0036] Resource conversion model: V r =φ(P r ,H t ,V o )=κ1·P r ·(1-H t )-κ2·V o +ε8;

[0037] Where V r is a virtual resource; r is a physical resource; t is the virtualization layer overhead ratio; V o is the resource virtualization loss; κ1, κ2 are conversion coefficients; ε8 is the error term.

[0038] Resource competition model: C f =ψ(R d ,I l ,P c )=μ1·R d +μ2·I l +μ3·P c +ε9;

[0039] In the formula, C f is the competitive factor; R d I is the resource demand; l is the interference level; P c is the priority coefficient; μ1, μ2, μ3 are weight coefficients; ε9 is the error term.

[0040] Numerical solution matrix: The numerical solution matrix M is specifically expressed as follows:

[0041]

[0042] Where n is the number of test data sets; each row represents the load response characteristics of a set of test data. This matrix is ​​used to describe the performance and resource usage of virtualized servers under different load conditions.

[0043] Virtualized server load response model:

[0044] P m =f(L,R,E,C)=ω1·L+ω2·R+ω3·E+ω4·C+ε 10 ;

[0045] Where P m is the performance index; L is the load vector; R is the resource vector; E is the environment vector; C is the configuration vector; ω1, ω2, ω3, ω4 are weight coefficients; ε 10 is the error term.

[0046] Parameter acquisition method:

[0047] Load vector L: Use the load generator to simulate workloads of different types and intensities and record the usage of CPU, memory, storage, and network.

[0048] Resource vector R: collects resource usage data of virtual machines and physical machines in real time through monitoring tools.

[0049] Environment vector E: includes information such as virtualization platform type and network topology, which is obtained through configuration files or system APIs.

[0050] Configuration vector C: includes the number of virtual machines, resource allocation policy, etc., and is obtained through the virtualization management platform interface.

[0051] Steps for determining weight coefficients ω1, ω2, ω3, ω4:

[0052] Step 1: Collect a large amount of experimental data, including performance indicators under different load, resource, environment, and configuration conditions.

[0053] Step 2: Use the multivariate linear regression analysis method to calculate the influence of each vector on the performance index based on the collected data.

[0054] Step 3: Solve the regression equation by the least squares method to obtain the optimal estimate of the weight coefficient.

[0055] Step 4: Use the cross-validation method to evaluate the accuracy and generalization ability of the model and adjust the weight coefficients if necessary.

[0056] Furthermore, the resource conversion model and the resource competition model are integrated to solve the fitted equation group, specifically: the resource conversion model and the resource competition model are embedded in the fitted load response characteristic equation group for solving.

[0057] Specifically, the step S10 includes the following sub-steps:

[0058] Step 101: Determine the specification parameters of the hardware resources of the physical server, such as CPU, memory, storage and network, as initial condition input.

[0059] Step 102: Select the type of virtualization platform to be used, such as VMware, Hyper-V, or KVM, and record this information.

[0060] Step 103: Determine the number of virtual machines to be initially deployed, usually starting with 1 and gradually increasing to a reasonable range to cover different load intensities.

[0061] Step 104: Determine the business type, such as Web service, database service, or big data application, which determines the characteristics of the workload.

[0062] Step 105: Describe the network topology of the virtualized environment, including the connection method between the virtual machines and the physical machines.

[0063] Step 106: Set an initial resource allocation strategy, such as fixed allocation, dynamic allocation, or priority allocation.

[0064] Step 107: Use a specialized load testing tool to apply workloads of various types and intensities to the virtualized server, and continuously record indicators such as CPU usage, memory usage, storage I / O, network throughput, response time, and energy consumption.

[0065] Specifically, the step S20 includes the following sub-steps:

[0066] Step 201: Keep the physical server configuration unchanged and gradually adjust parameters such as virtualization platform type, number of virtual machines, business type, and initial resource allocation strategy.

[0067] Step 202: According to the test requirements, the range of load intensity is appropriately expanded or reduced to cover different workload conditions.

[0068] Step 203: For each set of test conditions, the load test is repeatedly performed to collect indicator data such as CPU usage, memory usage, storage I / O, network throughput, response time, and energy consumption.

[0069] Step 204: merge each set of test conditions and corresponding test data into a virtualized server load data set, and record the specific parameter configuration of each data set.

[0070] Specifically, the step S30 includes the following sub-steps:

[0071] Step 301: Analyze the relationship between the key performance indicators of the virtualized server and various resource indicators based on the collected test data.

[0072] Step 302: For indicators such as CPU usage, memory usage, storage I / O, network throughput, response time and energy consumption, respectively establish a computing resource equation, a storage resource equation, a network resource equation, a business performance equation, an energy consumption equation and a virtualization overhead equation.

[0073] Step 303: In these equations, influencing factors such as the number of virtual CPU cores, the number of physical CPU cores, load intensity, the number of virtual machines, read and write operation frequency, the number of concurrent requests, and the running time are introduced.

[0074] Step 304: Use a multiple linear regression analysis method to estimate the unknown coefficients in each equation.

[0075] Step 305: In the resource scheduling equation, factors such as allocated resources, requested resources, resource balance, and migration times are considered to describe the resource scheduling efficiency of the virtualized server.

[0076] Specifically, the step S40 includes the following sub-steps:

[0077] Step 401: Use the least square method to perform parameter fitting on the above equation group, and use the various indicator data obtained from the test data set as independent variables and dependent variables.

[0078] Step 402: Solve the undetermined coefficients in each equation through an iterative optimization algorithm so that the equation group can best fit the entire load data set.

[0079] Step 403: Perform statistical tests on the fitting results, including testing indicators such as goodness of fit, significance level, and residual distribution, to ensure the predictive ability of the equation system.

[0080] Step 404: If necessary, the equation form may be further adjusted or other influencing factors may be introduced to improve the fitting effect.

[0081] Specifically, the step S50 includes the following sub-steps:

[0082] Step 501: Analyze how physical resources such as CPU, memory, storage, and network bandwidth are converted into virtual resources for use by virtual machines.

[0083] Step 502: Consider the resource consumption factor of the virtualization layer and the resource loss caused by the limitation of the virtualization technology itself.

[0084] Step 503: Establish resource conversion model V r =φ(P r ,H t ,V o ), where V r is a virtual resource, P r is the physical resource, Ht is the virtualization layer overhead ratio, V o This is the loss of resource virtualization.

[0085] Step 504: Determine conversion coefficients κ1 and κ2 to describe the conversion process from physical resources to virtual resources and its efficiency.

[0086] Specifically, the step S60 includes the following sub-steps:

[0087] Step 601: Analyze the interference effect caused by resource demand competition between virtual machines.

[0088] Step 602: Introduce factors such as resource demand, interference level, and priority coefficient to describe resource competition between virtual machines.

[0089] Step 603: Establish resource competition model C f =ψ(R d ,I l ,P c ), where C f is the competitive factor, R d is the resource demand, I l is the interference level, P c is the priority coefficient.

[0090] Step 604: Determine weight coefficients μ1, μ2, and μ3 to quantitatively describe the mutual influence between virtual machines.

[0091] Specifically, the step S70 includes the following sub-steps:

[0092] Step 701: embed the resource conversion model and the resource competition model into each load response characteristic equation.

[0093] Step 702: Use a numerical solution method such as the Newton-Raphson method or an iterative method to jointly solve the system of equations.

[0094] Step 703: During the solution process, the coupling relationship and constraints between the equations need to be considered.

[0095] Step 704: Finally, a numerical solution matrix M including indicators such as CPU usage, storage I / O, network throughput, response time, energy consumption, virtualization overhead and resource scheduling efficiency is obtained.

[0096] Specifically, the step S80 includes the following sub-steps:

[0097] Step 801: Analyze the relationship between various indicator data recorded in the numerical solution matrix M and the load characteristics.

[0098] Step 802: Establish a virtualized server load response model P m =f(l,R,E,C), where P m is the performance indicator, L is the load vector, R is the resource vector, E is the environment vector, and C is the configuration vector.

[0099] Step 803: Use a multivariate linear regression analysis method to determine the influence of each vector on the performance index, that is, the weight coefficients ω1, ω2, v3 and v4.

[0100] Step 804: Solve the regression equation by the least square method to obtain the optimal estimate of the weight coefficient.

[0101] Step 805: Use a cross-validation method to evaluate the accuracy and generalization ability of the model, and adjust the weight coefficient if necessary.

[0102] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned method for establishing a virtualized server load response model.

[0103] A third aspect of the present invention provides a virtualized server load response model establishment system, which includes the above-mentioned computer-readable storage medium.

[0104] Compared with the prior art, the method, medium and system for establishing a virtualized server load response model provided by the present invention have the following beneficial effects:

[0105] 1. Comprehensiveness: The present invention establishes a set of equations including key performance indicators such as CPU utilization, memory usage, storage I / O, network throughput, response time, energy consumption, virtualization overhead and resource scheduling efficiency, which can comprehensively describe the resource consumption and performance of virtualized servers under different load conditions.

[0106] 2. Accuracy: The inventive method determines the quantitative relationship between various performance indicators and influencing factors through fitting and optimization of a large amount of measured data, and can accurately predict the load response characteristics of the virtualized server.

[0107] 3. Explainability: The invented method is based on the resource conversion model and the resource competition model, and deeply analyzes the mapping relationship from physical resources to virtual resources in the virtualization environment, as well as the inherent mechanism of resource competition between virtual machines, and has strong explainability.

[0108] 4. Adaptability: The invented method only needs to collect a small amount of configuration information and performance data of the virtualization environment to build a targeted load response model. It has strong adaptability and is easy to apply to actual virtualization system management and optimization.

[0109] 5. Practicality: The constructed load response model can provide strong support for dynamic resource scheduling, performance prediction and bottleneck diagnosis of virtualization systems, and has broad application prospects in new IT architectures such as cloud computing and containerization.

[0110] In general, the method for establishing a virtualized server load response model proposed in the present invention can effectively make up for the shortcomings of the existing technology and solve the technical problem that the server load response model established by the existing technology is not accurate enough. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] Figure 1 A flow chart of the method provided by the present invention;

[0112] Figure 2 A resource usage comparison chart for the Examples section;

[0113] Figure 3 It is a performance load relationship diagram of the embodiment part;

[0114] Figure 4 A heat map of resource allocation for the example section;

[0115] Figure 5 It is a time series diagram of performance indicators in the embodiment section;

[0116] Figure 6 This is a resource competition network diagram for the implementation example section. DETAILED DESCRIPTION

[0117] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0118] like Figure 1 FIG. 1 is a flow chart of a method for establishing a virtualized server load response model provided by the present invention. The method comprises the following steps:

[0119] S10, setting initial test conditions, performing load testing on the virtualized server, and obtaining test data, wherein the test conditions include physical server configuration, virtualization platform type, number of virtual machines, service type, network topology, and initial resource allocation; the test data includes CPU usage, memory usage, storage I / O, network throughput, response time, and energy consumption;

[0120] S20, changing the test conditions multiple times, performing multiple groups of load tests, obtaining test data under different conditions, and merging each group of test conditions and corresponding test data into a virtualized server load data set;

[0121] S30, establishing a load response characteristic equation group containing all parameters in the virtualized server load data set, including a computing resource equation, a storage resource equation, a network resource equation, a service performance equation, an energy consumption equation, a virtualization overhead equation and a resource scheduling equation;

[0122] S40, fitting the virtualized server load data set to the load response characteristic equation group to obtain a fitted equation group;

[0123] S50, establishing a resource conversion model based on the resource mapping relationship between the physical server and the virtual server, which is used to describe the conversion process from physical resources to virtual resources and its efficiency;

[0124] S60, based on the resource competition and interference relationship between multiple virtual servers in the same physical server, establish a resource competition model to describe the mutual influence between the virtual servers;

[0125] S70, integrating the resource conversion model and the resource competition model, solving the fitted equation group, and obtaining a numerical solution matrix of the virtualized server load response characteristics;

[0126] S80. Based on the numerical solution matrix, a virtualized server load response model is constructed to describe the resource usage and performance of the virtual server under different load conditions.

[0127] The specific implementation methods of the above steps are described in detail below:

[0128] The step S10 includes the following sub-steps:

[0129] Step 101: Determine the specification parameters of the hardware resources of the physical server, such as CPU, memory, storage and network, as the initial condition input. These parameters are denoted as P cpu , P memory , P storage and P bandwidth The hardware configuration of the physical server will directly affect the availability of virtualized resources and is the basis for establishing a resource conversion model.

[0130] Step 102: Select the type of virtualization platform to be used, such as VMware, Hyper-V, or KVM, and record this information. Different virtualization platforms have different resource management and scheduling strategies, which will affect the performance of the virtual machine, so they need to be taken into account in the model. type Indicates the virtualization platform type.

[0131] Step 103: Determine the number of virtual machines N to be initially deployed, usually starting with 1 and gradually increasing to a reasonable range to cover different load intensities. The number of virtual machines is a key factor affecting resource competition and needs to be reflected in the model.

[0132] Step 104: Determine business type B type , such as Web services, database services, or big data applications, which determines the characteristics of the workload. Different business types will have different demands for server resources, which need to be distinguished in the model.

[0133] Step 105: Describe the network topology N of the virtualized environment topo , including the connection between virtual machines and physical machines. Network topology affects resource competition and interference between virtual machines and is an important factor in resource competition modeling.

[0134] Step 106: Set the initial resource allocation strategy R init , such as fixed allocation, dynamic allocation or priority allocation, etc. The resource allocation strategy directly determines the amount of resources that the virtual machine can obtain and needs to be used as an input parameter of the model.

[0135] Step 107: Use a dedicated load testing tool to apply workloads L of various types and intensities to the virtualized server and continuously record the CPU usage C. u 、Memory usage M u 、Storage I / O Rate I o , network throughput T p , response time R t and energy consumption E c These test data will constitute the virtualized server load data set.

[0136] Among them, the load intensity L can be expressed as:

[0137] L={L1,L2,…,L n};

[0138] Among them, L i Represents the load intensity of the i-th type.

[0139] Step 108: Set the initial condition parameter P cpu , P memory , P storage , P bandwidth 、V type , N, B type 、N topo , R init And test data C u 、M u ,I o 、Tp , R t 、E c The data are combined into an initial dataset D0, which describes the performance characteristics of the virtualized server under initial conditions.

[0140] Through the above steps, the load test data of the virtualized server under the initial conditions are obtained. These data reflect the load response characteristics of the virtualized server under specific configuration and environment.

[0141] Next, in step S20, it is necessary to change the initial conditions multiple times, perform multiple groups of load tests, and obtain test data under different conditions. Specifically, it includes the following sub-steps:

[0142] Step 201, for i=1, 2, ..., m, where m is the number of test groups:

[0143] (1) Keep the physical server configuration unchanged and gradually adjust the virtualization platform type Number of virtual machines N (i) , Business Type and initial resource allocation strategy And other parameters.

[0144] (2) According to the test requirements, adjust the load intensity range appropriately

[0145] (3) For each set of test conditions Repeat the load test k times and collect CPU usage Memory usage Storage I / O rate Network throughput Response time and energy consumption And other indicator data, where j = 1, 2,…, k.

[0146] Step 202: Combine each set of test conditions and corresponding test data into a virtualized server load data set

[0148] By adjusting the initial conditions several times and performing load tests, we obtain a virtualized server load data set D = {D0, D1, D2, …, D m}. These data will provide a basis for subsequent model building.

[0149] In step S30, it is necessary to establish a load response characteristic equation group including all parameters in the virtualized server load data set. Specifically, it includes the following sub-steps:

[0150] Step 301: Analyze the relationship between the key performance indicators of the virtualized server and various resource indicators based on the collected test data.

[0151] Step 302: CPU usage C u 、Memory usage M u 、Storage I / O Rate I o , network throughput T p , response time R t and energy consumption E c The following computing resource equation, storage resource equation, network resource equation, service performance equation, energy consumption equation and virtualization cost equation are established respectively:

[0152] Calculate the resource equation:

[0153]

[0154] Among them, V cpu is the number of virtual CPU cores, P cpu is the number of physical CPU cores, L is the load intensity, N is the number of virtual machines, α1, α2, α3 are unknown coefficients, and ε1 is the error term. This equation describes the relationship between CPU utilization and the number of virtual CPU cores, load intensity, and number of virtual machines.

[0155] Storage resource equation:

[0156]

[0157] Among them, V storage is the virtual storage capacity, P storage is the physical storage capacity, R is the read operation frequency, W is the write operation frequency, β1, β2, β3 are unknown coefficients, and ε2 is the error term. This equation describes the relationship between the storage I / O rate and the virtual storage capacity and the read and write operation frequency.

[0158] Network resource equation:

[0159]

[0160] Among them, V bandwidth is the virtual network bandwidth, P bandwidth is the physical network bandwidth, P s is the number of packets sent, P r is the number of received packets, γ1, γ2, γ3 are unknown coefficients, and ε3 is the error term. This equation describes the relationship between network throughput and virtual network bandwidth, and the number of packets sent and received.

[0161] Business Performance Equation:

[0162] R t=δ1·C u +δ2·I o +δ3·T p +δ4·Q+ε4;

[0163] Among them, Q is the number of concurrent requests, δ1, δ2, δ3, δ4 are unknown coefficients, and ε4 is the error term. This equation describes the relationship between response time and CPU utilization, storage I / O rate, network throughput, and the number of concurrent requests.

[0164] Energy consumption equation:

[0165] E c =η1·C u +η2·I o +η3·T p +η4·T+ε5;

[0166] Among them, T is the running time, η1, η2, η3, η4 are unknown coefficients, and ε5 is the error term. This equation describes the relationship between energy consumption and CPU utilization, storage I / O rate, network throughput, and running time.

[0167] Virtualization overhead equation:

[0168]

[0169] Among them, V total is the total virtual resource, P total is the total physical resource, S f is the resource switching frequency, θ1, θ2, θ3 are unknown coefficients, and ε6 is the error term. This equation describes the relationship between virtualization overhead and the number of virtual machines, virtual resource utilization, and resource switching frequency.

[0170] Resource scheduling equation:

[0171]

[0172] Among them, A r is the allocated resource, R r To request resources, B r is the resource balance, M t is the number of migrations, λ1,λ2,λ3 are unknown coefficients, and ε7 is the error term. This equation describes the relationship between resource scheduling efficiency and allocated resources, resource balance, and the number of migrations.

[0173] By establishing these equations, we can fully describe the resource consumption and performance of virtualized servers under different load conditions. These equations will provide a basis for subsequent model solutions.

[0174] Step 303: In the above equation, introduce influencing factors such as the number of virtual CPU cores Vcpu 、Number of physical CPU cores P cpu , load intensity L, number of virtual machines N, read and write operation frequencies R and W, number of concurrent requests Q, running time T, etc. These factors describe the hardware configuration, workload characteristics, and business requirements of the virtualized server.

[0175] Step 304: Use multiple linear regression analysis to estimate the unknown coefficients α1, α2, α3, β1, β2, β3, γ1, γ2, γ3, δ1, δ2, δ3, δ4, η1, η2, η3, η4, θ1, θ2, θ3, λ1, λ2, λ3 in each equation. These coefficients reflect the influence of each factor on the corresponding indicator.

[0176] Step 305: The above equations constitute a complete virtualized server load response characteristic model. The model covers key indicators such as CPU usage, memory usage, storage I / O rate, network throughput, response time, energy consumption, virtualization overhead, and resource scheduling efficiency, and can fully describe the performance of the virtualized server under different load conditions.

[0177] In step S40, it is necessary to fit the virtualized server load data set D to the aforementioned load response characteristic equation group to obtain a fitted equation group. Specifically, the following sub-steps are included:

[0178] Step 401: Use the least square method to perform parameter fitting on the above equations. This requires using various indicator data obtained from the data set D as independent variables and dependent variables.

[0179] For example, for the computational resource equation The following fitting function can be constructed:

[0180]

[0181] Where i = 1, 2, ..., m represents the test group, j = 1, 2, ..., k represents the number of repeated tests. The optimal estimated values ​​of α1, α2 and α3 can be solved.

[0182] Step 402: Solve the undetermined coefficients in each equation by an iterative optimization algorithm, such as the Newton-Raphson method or the gradient descent method, so that the equation group can best fit the entire load data set D.

[0183] Step 403: Perform statistical tests on the fitting results, including testing the fit, significance level, residual distribution and other indicators to ensure the predictive ability of the equation system. If the fitting effect is not ideal, you can consider adjusting the form of the equation or introducing other influencing factors.

[0184] Step 404: After parameter fitting, a set of equations that can accurately describe the load response characteristics of the virtualized server is obtained. These equations will provide a basis for subsequent resource conversion and resource competition modeling.

[0185] In step S50, it is necessary to establish a resource conversion model based on the resource mapping relationship between the physical server and the virtual server. Specifically, it includes the following sub-steps:

[0186] Step 501: Analyze how physical resources such as CPU, memory, storage and network bandwidth are converted into virtual resources for use by virtual machines. The following resource conversion model can be established:

[0187] V r =φ(P r ,H t ,V o )=κ1·P r ·(1-H t )-κ2·V o +ε8;

[0188] Among them, V r is a virtual resource, P r For physical resources, H t is the virtualization layer overhead ratio, V o is the resource virtualization loss, κ1 and κ2 are conversion coefficients, and ε8 is the error term.

[0189] Step 502: Consider the resource consumption factor H of the virtualization layer such as the hypervisor or container runtime. t ,This part of overhead will reduce the actual amount of resources available for virtual machines. At the same time, the resource virtualization loss factor V o ,Describes the resource loss caused by the limitations of virtualization technology itself.

[0190] Step 503: Establish resource conversion model V r =φ(P r ,H t ,V o ), can describe the conversion process from physical resources to virtual resources and its efficiency. This model will provide a basis for subsequent resource competition modeling.

[0191] Step 504: Determine conversion coefficients κ1 and κ2 to quantitatively describe resource loss during the virtualization process. This requires parameter estimation based on specific virtualization platform characteristics and test data.

[0192] In step S60, it is necessary to establish a resource competition model based on the resource competition and interference relationship between multiple virtual servers in the same physical server. Specifically, it includes the following sub-steps:

[0193] Step 601: Analyze the interference effect caused by resource demand competition between virtual machines, which may cause the performance of the virtual machines to degrade.

[0194] Step 602: Introduce resource demand R d , Interference Level I l and the priority coefficient P c Factors such as resource competition between virtual machines are used to describe the resource competition between virtual machines. The following resource competition model can be established:

[0195] C f =ψ(R d ,I l ,P c )=μ1·R d +μ2·I l +μ3·P c +ε9;

[0196] Among them, C f is the competition factor, μ1, μ2, μ3 are weight coefficients, and ε9 is the error term.

[0197] Step 603: Establish resource competition model C f =ψ(R d ,I l ,P c ), which can quantitatively describe the mutual influence between virtual machines. This model will provide support for the subsequent load response characteristics solution.

[0198] Step 604: Determine weight coefficients μ1, μ2, μ3 to quantify the contribution of resource demand, interference level and priority to resource competition. This requires parameter estimation based on test data and related domain knowledge.

[0199] In step S70, it is necessary to integrate the resource conversion model and the resource competition model, solve the above-mentioned fitted equations, and obtain the numerical solution matrix of the virtualized server load response characteristics. Specifically, it includes the following sub-steps:

[0200] Step 701: Convert resource to model V r =φ(P r ,H t ,V o ) and resource competition model C f =ψ(R d ,I l ,P c ) is embedded into each load response characteristic equation.

[0201] For example, for the computational resource equation V cpu Replace with φ(Pcpu ,H t ,V o ), to reflect the impact of virtualization on CPU resources. At the same time, C f As an adjustment coefficient, to describe the resource competition between virtual machines. The modified equation form is:

[0202]

[0203] Step 702: Use a numerical solution method such as the Newton-Raphson method or the iteration method to jointly solve the modified set of equations. This requires considering the coupling relationship and constraints between the equations to ensure the convergence and stability of the solution.

[0204] Step 703: During the solution process, the impact of the resource conversion model and resource competition model on each load response indicator needs to be considered. For example, the CPU utilization rate is affected by the number of virtual CPU cores and resource competition, the storage I / O rate is affected by the virtual storage capacity and resource competition, and so on.

[0205] Step 704: Finally, a CPU usage C is obtained. u 、Storage I / O Rate I o , network throughput T p , response time R t , Energy consumption E c , Virtualization Overhead v and resource scheduling efficiency S e The numerical solution matrix M of indicators such as etc. This matrix completely describes the performance and resource usage of the virtualized server under different load conditions.

[0206] Finally, in step S80, it is necessary to construct a virtualized server load response model based on the above numerical solution matrix M. Specifically, it includes the following sub-steps:

[0207] Step 801: Analyze the relationship between the various indicator data recorded in the numerical solution matrix M and the load characteristics. The following virtualized server load response model can be established:

[0208] P m =f(L,R,E,C)=ω1·L+ω2·R+ω3·E+ω4·C+ε 10 ;

[0209] Among them, P m is the performance index, L is the load vector, R is the resource vector, E is the environment vector, C is the configuration vector, ω1, ω2, ω3, ω4 are weight coefficients, ε 10 is the error term.

[0210] Step 802: Load vector L includes CPU, memory, storage and network usage, which can be obtained by simulating workloads of different types and intensities through a load generator. Resource vector R reflects resource usage data of virtual machines and physical machines, which can be collected in real time through monitoring tools. Environment vector E includes information such as virtualization platform type and network topology, which can be obtained from configuration files or system APIs. Configuration vector C includes the number of virtual machines, resource allocation strategy, etc., which can be obtained through the virtualization management platform interface.

[0211] Step 803: Use the multivariate linear regression analysis method to determine the influence of each vector on the performance index, that is, the weight coefficients ω1, ω2, ω3, ω4. This requires the collection of a large amount of experimental data, including performance indicators under different loads, resources, environments, and configuration conditions.

[0212] Step 804: Solve the regression equation using the least square method to obtain the optimal estimated value of the weight coefficient. These weight coefficients reflect the relative importance of load, resources, environment and configuration to the performance of the virtualized server.

[0213] Step 805: Use the cross-validation method to evaluate the accuracy and generalization ability of the model, and adjust the weight coefficient if necessary. This helps to ensure that the constructed virtualized server load response model has high prediction accuracy and practical value.

[0214] In summary, the method for establishing a virtualized server load response model proposed in the present invention makes full use of mathematical modeling and numerical analysis techniques, including key modules such as linear regression, resource conversion, and resource competition. By acquiring data through multiple groups of load tests, a load response characteristic equation group covering various performance indicators is established, and resource conversion and competition mechanisms are integrated to finally construct a comprehensive model that can predict the performance of virtualized servers.

[0215] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned method for establishing a virtualized server load response model.

[0216] A third aspect of the present invention provides a virtualized server load response model establishment system, which includes the above-mentioned computer-readable storage medium.

[0217] Specifically, the principle of the present invention is:

[0218] 1. Load testing and data collection: First, set the initial conditions of the virtualized server, including physical server configuration, virtualization platform type, number of virtual machines, business type, network topology, and initial resource allocation strategy. Then, for different initial conditions, conduct multiple groups of load tests, record indicators such as CPU usage, memory usage, storage I / O, network throughput, response time, and energy consumption, and build a virtualized server load data set.

[0219] 2. Establishment of load response characteristic equations: Based on the collected load data set, the computing resource equation, storage resource equation, network resource equation, business performance equation, energy consumption equation and virtualization overhead equation are established respectively. These equations describe the quantitative relationship between various performance indicators and key influencing factors such as the number of virtual CPU cores, the number of physical CPU cores, load intensity, number of virtual machines, read and write operation frequency, and number of concurrent requests. At the same time, a resource scheduling equation is also constructed to reflect the resource allocation efficiency of the virtualized server.

[0220] 3. Resource conversion and competition modeling: For the conversion process of physical resources to virtual resources, a resource conversion model is established, taking into account factors such as virtualization layer overhead and resource loss. At the same time, based on the resource demand competition and interference relationship between virtual machines, a resource competition model is established to describe the mutual influence between virtual machine performance.

[0221] 4. Solving the load response characteristics: Integrate the resource conversion model and resource competition model into the aforementioned load response characteristic equation group, and use the numerical solution method to obtain a complete numerical solution matrix, which describes the numerical characteristics of indicators such as CPU utilization, storage I / O, network throughput, response time, energy consumption, virtualization overhead and resource scheduling efficiency of the virtualized server under different load conditions.

[0222] 5. Load response model construction: Finally, based on the numerical solution matrix, a virtualized server load response model is established, which can accurately predict the server performance under different load, resource, environment and configuration conditions. The model determines the weight coefficients of various influencing factors through multivariate linear regression analysis, reflecting their relative contribution to server performance.

[0223] The core principle of the above technical solution is:

[0224] 1) Make full use of a large amount of measured data to establish a set of equations covering various key performance indicators of virtualized servers, providing a basis for subsequent modeling.

[0225] 2) It integrates the two major mechanisms of resource conversion and resource competition, deeply describes the mapping relationship from physical resources to virtual resources in a virtualized environment, as well as the mutual influence between virtual machines, and enhances the interpretability of the model.

[0226] 3) Using numerical solution technology, we can efficiently obtain the numerical matrix of performance characteristics under different load conditions, providing supporting data for the construction of the final load response model.

[0227] 4) The weight coefficients of various influencing factors were determined by multivariate linear regression analysis, which demonstrated that the method of the present invention has good adaptability and predictive ability.

[0228] In summary, the virtualization server load response model establishment method proposed in the present invention fully combines mathematical modeling and numerical analysis techniques to theoretically construct a comprehensive, accurate and explainable virtualization performance analysis framework and model.

[0229] In order to better understand and implement the present invention, an embodiment of a specific application scenario of the present invention is provided below: an Internet enterprise is deploying a new virtualized server cluster to support its Web services, databases, big data analysis and other businesses, and urgently needs to understand the load response characteristics of the server in the virtualized environment in order to perform reasonable resource allocation and dynamic scheduling. The enterprise decides to adopt the virtualized server load response model establishment method proposed in the present invention to conduct a comprehensive performance analysis and prediction of the virtualized environment.

[0230] First, the enterprise determined the initial virtualization server configuration information. The physical server uses an Intel Xeon Gold 6240R 24-core processor with a main frequency of 2.4 GHz, equipped with 256 GB memory and a 12 TB SAS hard disk. VMware vSphere 7.0 is used as the virtualization platform, and 20 virtual machines are initially deployed. Among these virtual machines, 10 run Web services, 5 run MySQL databases, and 5 run Hadoop big data analysis clusters. The network topology uses a 10 Gigabit Ethernet structure, and each virtual machine is allocated 4 vCPUs, 16 GB memory, and 500 GB virtual storage space. VMware's DRS dynamic load balancing strategy is used for resource allocation.

[0231] Based on the above initial conditions, the enterprise began to load test and collect data on the virtualized environment.

[0232] 1. Load testing and data collection

[0233] For three different business types: Web services, databases, and big data analysis, the company has designed corresponding load testing solutions:

[0234] (1) Web service load test: Use the Apache Bench tool to simulate concurrent user requests. The test sets the number of concurrent requests to increase from 10 to 100, increasing by 10 each time. Record indicators such as CPU usage, memory usage, network throughput, and response time.

[0235] (2) Database load test: Use the sysbench tool to perform stress testing on the MySQL database, including select, insert, update, and delete operations. The test database table size increases from 100MB to 1GB, increasing by 100MB each time. Record indicators such as CPU usage, memory usage, disk IOPS, and response time.

[0236] (3) Big data analysis load test: Use the official test program to test the MapReduce job on the Hadoop cluster. The test data size increases from 100GB to 1TB, with an increase of 100GB each time. Record indicators such as CPU usage, memory usage, disk throughput, and job execution time.

[0237] By adjusting the load intensity and data scale multiple times, the enterprise collected 30 sets of virtualized server performance test data under different conditions, forming the initial load data set D0. Table 1 below lists some of the data samples:

[0238] Table 1 Data sample table

[0239]

[0240]

[0241] These test data cover the performance of different business types and load intensities in a virtualized environment, laying the foundation for the subsequent establishment of a load response model.

[0242] 2. Establishment of load response characteristic equation

[0243] Based on the collected load test data set D0, the enterprise began to establish a set of equations for the load response characteristics of virtualized servers.

[0244] (1) Calculation resource equation:

[0245]

[0246] Among them, C u is the CPU usage, V cpu is the number of virtual CPU cores, P cpu is the number of physical CPU cores, L is the number of concurrent connections or data size, N is the number of virtual machines, α1, α2, α3 are unknown coefficients, and ε1 is the error term. Through multiple linear regression analysis of the test data, we get α1 = 0.6, α2 = 0.015, α3 = 0.02.

[0247] (2) Storage resource equation:

[0248]

[0249] Among them, I o is storage IOPS, V storage is the virtual storage capacity, P storage is the physical storage capacity, R is the read operation frequency, W is the write operation frequency, β1, β2, β3 are unknown coefficients, and ε2 is the error term. Regression analysis yields β1=0.8, β2=100, β3=150.

[0250] (3) Network resource equation:

[0251]

[0252] Among them, T p is the network throughput, V bandwidth is the virtual network bandwidth, P bandwidth is the physical network bandwidth, P s is the number of packets sent, P r is the number of received data packets, γ1, γ2, γ3 are unknown coefficients, and ε3 is the error term. Regression analysis yields γ1 = 0.9, γ2 = 0.002, and γ3 = 0.003.

[0253] (4) Business performance equation:

[0254] R t =δ1·C u +δ2·I o +δ3·T p +δ4·Q+ε4;

[0255] Among them, R t is the response time or job execution time, Q is the number of concurrent requests or data size, δ1, δ2, δ3, δ4 are unknown coefficients, and ε4 is the error term. Regression analysis shows that δ1 = 2.5, δ2 = 0.0005, δ3 = 0.001, and δ4 = 0.01.

[0256] (5) Energy consumption equation:

[0257] E c =η1·C u +η2·I o +η3·T p +η4·T+ε5;

[0258] Among them, E c is the energy consumption, T is the running time, η1, η2, η3, η4 are unknown coefficients, and ε5 is the error term. Regression analysis shows that η1 = 30, η2 = 0.05, η3 = 0.1, η4 = 10.

[0259] (6) Virtualization overhead equation:

[0260]

[0261] Among them, O v is the virtualization overhead, V total is the total virtual resource, P total is the total physical resource, S f is the resource switching frequency, θ1, θ2, θ3 are unknown coefficients, and ε6 is the error term. Regression analysis yields θ1=2, θ2=5, θ3=20.

[0262] (7) Resource scheduling equation:

[0263]

[0264] Among them, S e is the scheduling efficiency, A r is the allocated resource, R r To request resources, B r is the resource balance, M t is the number of migrations, λ1, λ2, λ3 are unknown coefficients, and ε7 is the error term. Regression analysis yields λ1=0.8, λ2=0.15, λ3=0.05.

[0265] Through the above steps, the enterprise has established a complete set of virtualization server load response characteristic equations, covering key indicators such as CPU, memory, storage, network, business performance, energy consumption and resource scheduling, laying the foundation for the subsequent establishment of resource conversion models and resource competition models.

[0266] 3. Resource conversion and resource competition modeling

[0267] (1) Resource Conversion Model

[0268] Based on the configuration information of the aforementioned physical servers and virtual machines, the enterprise established the following resource conversion model:

[0269] V r =φ(P r ,H t ,V o )=κ1·P r ·(1-H t )-κ2·V o +ε8;

[0270] Among them, V r is a virtual resource, P r For physical resources, H t is the virtualization layer overhead ratio, V o is the resource virtualization loss, κ1=0.9, κ2=0.1, and ε8 is the error term.

[0271] Through the analysis of measured data, the enterprise determined the virtualization layer overhead ratio of VMware vSphere 7.0 t About 10%, resource virtualization loss V o Therefore, the conversion efficiency of physical resources to virtual resources is about 81%.

[0272] (2) Resource Competition Model

[0273] In response to the resource competition between virtual machines, the enterprise has established the following resource competition model:

[0274] C f =ψ(R d ,I l ,P c )=μ1·R d +μ2·I l +μ3·P c +ε9;

[0275] Among them, C f is the competitive factor, R d is the resource demand, I l is the interference level, P c is the priority coefficient, μ1=0.6, μ2=0.3, μ3=0.1, ε9 is the error term.

[0276] After analysis, the enterprise found that the resource demand of the Web service virtual machine was higher, followed by the database virtual machine, and the big data analysis virtual machine was the lowest. At the same time, the interference between the Web service and database virtual machines was greater, while the interference between the big data analysis virtual machine and other virtual machines was smaller. In addition, the enterprise adopted the DRS dynamic load balancing strategy, and the priority coefficients between virtual machines were relatively balanced.

[0277] 4. Numerical solution matrix construction

[0278] The resource conversion model and resource competition model are integrated into the aforementioned load response characteristic equation group. The enterprise uses the Newton-Raphson iteration method to numerically solve the equation group and finally obtains a 10×7 numerical solution matrix M, as shown in Table 2 below:

[0279] Table 2 Numerical solution matrix

[0280]

[0281]

[0282] The numerical solution matrix completely describes the performance of the virtualized environment under different load conditions, and provides basic data for the subsequent establishment of a load response model.

[0283] 5. Load response model construction

[0284] Finally, the enterprise uses the aforementioned numerical solution matrix M to establish the following virtualized server load response model:

[0285] P m =f(L,R,E,C)=ω1·L+ω2·R+ω3·E+ω4·C+ε 10 ;

[0286] Among them, P m is the performance index, L is the load vector, R is the resource vector, E is the environment vector, C is the configuration vector, ω1=0.5, ω2=0.3, ω3=0.15, ω4=0.05, ε 10 is the error term.

[0287] The load vector L includes the number of concurrent requests, data size, etc., the resource vector R includes CPU utilization, storage IOPS, network throughput, etc., the environment vector E includes virtualization platform type, network topology, etc., and the configuration vector C includes the number of virtual machines, resource allocation strategy, etc.

[0288] Through multiple linear regression analysis, the enterprise determined the above weight coefficients, which reflect the relative influence of different factors on the performance of virtualized servers. Among them, load intensity is the most important influencing factor, followed by resource usage, and environmental and configuration factors are relatively small.

[0289] In order to verify the accuracy of the load response model, the enterprise selected 10% of the test data as an independent validation set. The results showed that the model's prediction root mean square error was only 8%, which was able to better meet the performance prediction requirements of the virtualized environment. The specific test charts are as follows: Figure 2-Figure 6 shown.

[0290] Figure 2 : Resource usage comparison of different workloads. This is a grouped bar chart that shows the comparison of CPU usage, memory usage, storage IOPS, and network throughput for three workloads: Web service, database, and big data analysis.

[0291] Figure 3 : 3D relationship between load, concurrency and performance. This is a three-dimensional surface graph that shows the relationship between load percentage, concurrency and performance indicators. The color gradient reflects the change in performance.

[0292] Figure 4 : Heat map of virtual machine resource allocation. This heat map shows the allocation of CPU, memory, storage, and network resources by different types of virtual machines (Web, Database, Hadoop). The darker the color, the more resources are allocated.

[0293] Figure 5 : Performance indicator time series, this is a dual-axis time series chart, the upper part shows the change of CPU usage over time, and the lower part shows the change of response time over time. This helps to analyze the time pattern and correlation of performance indicators.

[0294] Figure 6 : Virtual machine resource competition network diagram, which is a network diagram that shows the resource competition relationship between different virtual machines. The node size indicates the resource demand of the virtual machine, the color indicates the type of virtual machine, and the thickness of the edge indicates the intensity of competition.

[0295] Based on the constructed virtualized server load response model, enterprises can conduct comprehensive performance analysis and optimization of the virtualized cluster:

[0296] 1) For different business types, predict their resource consumption and performance under high load conditions to provide a basis for dynamic resource scheduling.

[0297] 2) Analyze the degree of resource competition between virtual machines, identify potential performance bottlenecks, and adjust resource allocation strategies to improve overall efficiency.

[0298] 3) Evaluate the impact of virtualization platform upgrades or physical resource expansions on performance, and provide support for IT infrastructure optimization decisions.

[0299] 4) Adjust resource allocation in advance based on business peak forecasts to ensure the achievement of service quality goals.

[0300] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for establishing a virtualized server load response model, characterized in that: The following steps are involved: S10, setting initial test conditions, performing load testing on the virtualized server, and obtaining test data; S20, changing the test conditions multiple times, performing multiple groups of load tests, obtaining test data under different conditions, and merging each group of test conditions and corresponding test data into a virtualized server load data set; S30, establishing a load response characteristic equation group including all parameters in the virtualized server load data set; S40, fitting the virtualized server load data set to the load response characteristic equation group to obtain a fitted equation group; S50, establishing a resource conversion model according to the resource mapping relationship between the physical server and the virtual server; S60, establishing a resource competition model based on resource competition and interference relationships between multiple virtual servers within the same physical server; S70, integrating the resource conversion model and the resource competition model, solving the fitted equation group, and obtaining a numerical solution matrix of the virtualized server load response characteristics; S80. Based on the numerical solution matrix, a virtualized server load response model is constructed to describe the resource usage and performance of the virtual server under different load conditions.

2. A method for establishing a virtualized server load response model according to claim 1, characterized in that: The test conditions include physical server configuration, virtualization platform type, number of virtual machines, business type, network topology and initial resource allocation.

3. A method for establishing a virtualized server load response model according to claim 2, characterized in that: The test data includes CPU usage, memory usage, storage I / O, network throughput, response time and energy consumption.

4. A method for establishing a virtualized server load response model according to claim 3, characterized in that: The load response characteristic equation group includes a computing resource equation, a storage resource equation, a network resource equation, a service performance equation, an energy consumption equation, a virtualization overhead equation and a resource scheduling equation.

5. A method for establishing a virtualized server load response model according to claim 4, characterized in that: The method for fitting the virtualized server load data set to the load response characteristic equation group is the least squares method.

6. A method for establishing a virtualized server load response model according to claim 5, characterized in that: The resource conversion model is used to describe the conversion process from physical resources to virtual resources and its efficiency.

7. A method for establishing a virtualized server load response model according to claim 6, characterized in that: The resource competition model is used to describe the mutual influence between virtual servers.

8. The method for establishing a virtualized server load response model according to claim 7, characterized in that: The resource conversion model and the resource competition model are integrated to solve the fitted equation group, specifically: the resource conversion model and the resource competition model are embedded in the fitted load response characteristic equation group for solving.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program instructions, and when the program instructions are executed in a computer, the method for establishing a virtualized server load response model according to any one of claims 1 to 8 is used to execute the method.

10. A virtualized server load response model establishment system, characterized in that: A computer-readable storage medium comprising the computer-readable storage medium of claim 9.

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