Virtual machine migration scheduling evaluation method and system based on data center

Abstract

The invention discloses a virtual machine migration scheduling evaluation method and system based on a data center, and relates to the field of electric digital data processing. Calculating a peak-valley complementation degree, and identifying an advantage complementation interval based on the peak-valley complementation degree; calculating a resource utilization efficiency improvement potential value of the migrated target physical server; setting a self-adaptive scoring reference line according to the resource utilization efficiency improvement potential value; comparing and analyzing the matching degree between the resource demand feature of the to-be-migrated virtual machine and the state fingerprint feature of the target physical server; generating a migration integrating degree score in combination with the matching degree and the self-adaptive scoring datum line; and generating an evaluation result of the to-be-evaluated virtual machine migration scheme according to the migration integrating degree score and the continuous stability of the advantage complementation interval. The method and the device are used for improving the accuracy of virtual machine migration scheduling evaluation when a novel business load or hardware equipment appears in a data center.

Description

Technical Field

[0001] The present application belongs to the field of electronic digital data processing, and in particular, relates to a method and system for evaluating the migration scheduling of virtual machines in a data center. Background Art

[0002] With the continuous expansion of cloud computing data centers, virtual machine migration scheduling has become an important means of resource optimization management. However, in practical applications, due to the lack of a systematic evaluation system, a large number of virtual machine migration scheduling decisions are often difficult to accurately judge their rationality and effectiveness, which not only affects the formulation of migration plans, but may also lead to a large deviation between the effect after migration and the expected effect, resulting in resource waste and performance degradation.

[0003] In related technologies, it is possible to construct quantitative indicators that include multiple evaluation dimensions such as resource utilization, load balancing, service quality, and energy efficiency, and establish an evaluation model in combination with machine learning algorithms, thereby achieving a systematic evaluation of virtual machine migration solutions and improving the accuracy of the evaluation.

[0004] However, in the actual operation of data centers, the machine learning models used in existing evaluation methods are trained based on historical data, so their evaluation accuracy depends on the integrity and representativeness of the training data. When new business loads or hardware devices appear in data centers, due to the lack of corresponding historical data support, the evaluation model is difficult to accurately understand and evaluate the migration decision-making effects in these new scenarios, which reduces the accuracy of the evaluation of virtual machine migration scheduling. Summary of the invention

[0005] The present application provides a method and system for evaluating the scheduling of virtual machine migration based on a data center, which is used to improve the accuracy of the evaluation of the scheduling of virtual machine migration when new business loads or hardware equipment appear in the data center.

[0006] In a first aspect, the present application provides a data center virtual machine migration scheduling evaluation method, which obtains resource status data of a source physical server and a target physical server before migration included in a virtual machine migration plan to be evaluated; Based on the resource status data, the status fingerprint feature of each physical server resource is constructed. The status fingerprint feature is generated by arranging the utilization of different resource types in the resource status data in chronological order and extracting key change points; Calculate the peak-valley complementarity between the source physical server and the target physical server in resource status data, and identify the advantageous complementary interval of resource utilization based on the peak-valley complementarity; Calculate the potential value of improving the resource utilization efficiency of the target physical server after migration based on the complementary advantage interval and the resource demand characteristics of the virtual machine to be migrated; Set an adaptive scoring baseline based on the potential value of improving resource utilization efficiency; Compare and analyze the matching degree between the resource demand characteristics of the virtual machine to be migrated and the state fingerprint characteristics of the target physical server; Combine the matching degree and adaptive scoring baseline to generate a migration fit score; An evaluation result of the virtual machine migration solution to be evaluated is generated according to the migration fit score and the continuous stability of the complementary advantage interval.

[0007] By adopting the above technical solution, the resource utilization characteristics of the physical server are characterized by constructing state fingerprint features, and the complementary advantage interval of resource utilization is identified in combination with the peak-valley complementary degree, so that the evaluation scheme can accurately grasp the complementarity of the source physical server and the target physical server in resource utilization. Based on the complementary advantage interval, the potential value for improving resource utilization efficiency is calculated and an adaptive scoring baseline is set, so that the scoring standard can be dynamically adjusted according to the actual effect. By analyzing the matching degree between the virtual machine resource demand characteristics and the state fingerprint characteristics of the target physical server, and combining the continuous stability of the complementary advantage interval, the evaluation results not only reflect the immediate rationality of the migration plan, but also take into account the long-term stability of the plan, thereby improving the scientificity and reliability of the migration plan evaluation.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, calculating the peak-valley complementarity between the source physical server and the target physical server in resource status data, and identifying the advantageous complementary interval of resource utilization based on the peak-valley complementarity, specifically includes: Divide resource status data into multiple time segments according to preset time granularity; Extract the peak and trough of resource utilization data in each time segment to obtain the peak and trough periods of resource utilization of the source and target physical servers; Calculate a first overlapping time ratio between a resource utilization peak period of the source physical server and a resource utilization valley period of the target physical server, and a second overlapping time ratio between a resource utilization valley period of the source physical server and a resource utilization peak period of the target physical server; The peak-valley complementarity is determined based on the first overlapping time ratio and the second overlapping time, and a time interval in which the peak-valley complementarity is greater than a preset threshold is marked as an advantageous complementary interval.

[0009] By adopting the above technical solution, by dividing the resource status data according to the preset time granularity and extracting the peaks and troughs, the periodic characteristics of resource utilization of the source physical server and the target physical server can be characterized. The degree of peak-valley complementarity is quantified by calculating the overlapping time ratio of the peak and trough periods of resource utilization, making the complementarity evaluation more objective and accurate. The method of identifying the advantageous complementary interval based on the overlapping time ratio enables the evaluation system to accurately locate the time window with the optimal resource utilization efficiency. This refined analysis method based on time series enables the evaluation system to accurately identify the resource utilization complementary relationship between physical servers and reduce the performance loss caused by resource competition.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, calculating the resource utilization efficiency improvement potential value of the target physical server after migration according to the complementary advantage interval and the resource demand characteristics of the virtual machine to be migrated specifically includes: Extract resource demand fluctuation patterns of the resource demand characteristics of the virtual machines to be migrated within the complementary advantage range; Calculate the resource usage of the target physical server by the virtual machine to be migrated at different time points based on the resource demand fluctuation law; Calculate the resource utilization of the target physical server at each time point after migration based on the current resource utilization and resource occupancy of the target physical server; Based on the change curve of resource utilization of the target physical server before and after migration, calculate the standard deviation of resource utilization in the same time interval; The proportion of time intervals in which the standard deviation after migration is less than the standard deviation before migration is used as the potential value for improving the resource utilization efficiency of the target physical server.

[0011] By adopting the above technical solution, by analyzing the fluctuation pattern of resource demand of the virtual machine to be migrated within the complementary advantage range and calculating its dynamic resource occupancy of the target physical server, the evaluation system can accurately predict the resource utilization status after migration. Based on the standard deviation change of resource utilization before and after migration, the potential value of improving resource utilization efficiency is quantified, so that the evaluation system can objectively measure the actual improvement effect brought by the migration plan. This analysis method based on resource utilization volatility not only considers the absolute value change of resource utilization, but also pays attention to the improvement of resource utilization stability, so that the evaluation results can more accurately reflect the role of the migration plan in improving the overall operating efficiency of the data center.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, setting an adaptive scoring baseline according to the resource utilization efficiency improvement potential value specifically includes: Calculate the degree of improvement of the target physical server in different resource dimensions based on the potential value of improving resource utilization efficiency; Determine the weight coefficient of each resource dimension based on the current overall load level of the data center; The improvement degree and the weight coefficient are weighted and calculated to obtain an adaptive scoring baseline.

[0013] By adopting the above technical solution, by calculating the improvement degree of the target physical server in different resource dimensions and determining the weight coefficient according to the current overall load level of the data center, the scoring baseline can adapt to the changes in the importance of different resource dimensions. This dynamic weight allocation method that takes into account the overall load level of the data center enables the scoring baseline to be adaptively adjusted according to the actual operating environment, reducing the evaluation deviation that may be caused by fixed scoring standards. The adaptive scoring baseline obtained by weighted calculation of the improvement degree and the weight coefficient can more truly reflect the actual value of the migration solution in the current data center environment, improving the practicality of the evaluation results.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, generating an evaluation result of the virtual machine migration solution to be evaluated according to the migration compatibility score and the continuous stability of the complementary advantage interval specifically includes: Calculate the duration distribution of complementary advantage intervals in historical time series; Evaluate the stability coefficient of complementary advantage interval based on duration distribution; Perform nonlinear combination operation on the migration fit score and the stability coefficient to obtain the operation result; An evaluation result of the virtual machine migration solution to be evaluated is generated according to the calculation result.

[0015] By adopting the above technical solution, the stability coefficient of the complementary advantage interval is evaluated based on the duration distribution, which can quantify the reliability of the resource complementary relationship. The migration fit score and the stability coefficient are nonlinearly combined, and the immediate effect and long-term stability of the migration plan are considered in the evaluation results, which reduces the evaluation bias caused by relying solely on static matching evaluation, making the evaluation results more comprehensive and accurate. By incorporating dynamic timing features into the evaluation system, the scientificity and credibility of the virtual machine migration plan evaluation are improved.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after generating an evaluation result of the virtual machine migration solution to be evaluated according to the migration compatibility score and the continuous stability of the complementary advantage interval, the method further includes: Extract the load mutation characteristics of each virtual machine on the target physical server; Construct a load mutation propagation chain based on load mutation characteristics; Simulate various burst load scenarios based on load mutation propagation chain; Calculate the evaluation scores of the virtual machine migration solutions to be evaluated under different burst load scenarios, and extract the stability index of the evaluation scores; The evaluation result is corrected according to the stability index to obtain a corrected evaluation result.

[0017] By adopting the above technical solution, by extracting the load mutation characteristics of each virtual machine on the target physical server and constructing a load mutation propagation chain, the correlation and influence of load fluctuations between virtual machines can be revealed. Based on the load mutation propagation chain, a variety of burst load scenarios are simulated, and the adaptability of virtual machine migration solutions under different load conditions can be predicted. By calculating the evaluation scores under different burst load scenarios and extracting the stability index of the score, the resistance of virtual machine migration solutions to load fluctuations can be quantitatively evaluated. The evaluation results are corrected according to the stability index, so that the evaluation results fully consider the impact of burst loads on the migration plan, and the accuracy and reliability of the evaluation results are improved. It can effectively identify migration plans that still have good effects under load fluctuations, reduce the impact of burst loads on data center performance, and improve the stable operation capability of the data center.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, calculating the evaluation score of the virtual machine migration solution to be evaluated under different burst load scenarios specifically includes: For each burst load scenario, calculate the fluctuation range of each resource dimension in the target physical server; Based on the fluctuation range, the probability density function of resource utilization is generated by using the extreme value distribution fitting method; According to the probability density function, the risk probability of resource utilization exceeding the preset safety threshold after migration is calculated; The risk probability and migration fit score are weighted and combined to obtain the evaluation score.

[0019] By adopting the above technical solution, by calculating the fluctuation range of each resource dimension in the target physical server and using the extreme value distribution fitting method to generate the probability density function of resource utilization, the fluctuation law of resource utilization can be accurately characterized. Based on the probability density function, the risk probability of resource utilization exceeding the preset safety threshold after migration is calculated, which can quantitatively evaluate the resource overlimit risk that may be caused by virtual machine migration. The evaluation score is obtained by weighted combination of the risk probability and the migration fit score, and the migration benefits and potential risks are balanced in the evaluation results. Modeling and analyzing resource utilization under sudden load scenarios through probabilistic statistical methods provides a more objective and reliable evaluation basis, which helps to select a migration solution that can both improve resource utilization efficiency and ensure safe and stable operation of the system.

[0020] In the second aspect, an embodiment of the present application provides a data center virtual machine migration scheduling and evaluation system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a system, causes the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on a system, the system executes the method described in any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The present application provides a method for evaluating the migration scheduling of virtual machines based on a data center. It describes the resource utilization characteristics of physical servers by constructing state fingerprint features, and identifies the complementary advantage interval of resource utilization in combination with the degree of peak-valley complementarity, so that the evaluation scheme can accurately grasp the complementarity of the source physical server and the target physical server in resource utilization. The potential value for improving resource utilization efficiency is calculated based on the complementary advantage interval and an adaptive scoring baseline is set, so that the scoring criteria can be dynamically adjusted according to the actual effect. By analyzing the matching degree between the virtual machine resource demand characteristics and the target physical server state fingerprint characteristics, and combining the continuous stability of the complementary advantage interval, the evaluation results not only reflect the immediate rationality of the migration plan, but also take into account the long-term stability of the plan, thereby improving the scientificity and reliability of the migration plan evaluation.

[0024] 2. The present application provides a method for evaluating the migration scheduling of virtual machines in a data center. By analyzing the fluctuation patterns of resource demand of the virtual machines to be migrated within the complementary advantage interval and calculating their dynamic resource occupancy of the target physical server, the evaluation system can accurately predict the resource utilization status after the migration. The potential value for improving resource utilization efficiency is quantified based on the standard deviation change of resource utilization before and after migration, so that the evaluation system can objectively measure the actual improvement effect brought about by the migration plan. This analysis method based on resource utilization volatility not only takes into account the absolute value changes of resource utilization, but also pays attention to the improvement of resource utilization stability, so that the evaluation results can more accurately reflect the role of the migration plan in improving the overall operating efficiency of the data center.

[0025] 3. The present application provides a method for evaluating the scheduling of virtual machine migration based on a data center. By extracting the load mutation characteristics of each virtual machine on the target physical server and constructing a load mutation propagation chain, the correlation and influence of load fluctuations between virtual machines can be revealed. Based on the load mutation propagation chain, a variety of burst load scenarios are simulated, and the adaptability of virtual machine migration solutions under different load conditions can be predicted. By calculating the evaluation scores under different burst load scenarios and extracting the stability index of the score, the ability of virtual machine migration solutions to resist load fluctuations can be quantitatively evaluated. The evaluation results are corrected according to the stability index, so that the evaluation results fully consider the impact of burst loads on the migration plan, and improve the accuracy and reliability of the evaluation results. It can effectively identify migration plans that still have good effects under load fluctuations, reduce the impact of burst loads on data center performance, and improve the stable operation capability of the data center. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a method for evaluating migration scheduling of virtual machines in a data center according to an embodiment of the present application.

[0027] Figure 2 It is a flow chart of an optimization method for an evaluation method taking into account the impact of burst load in an embodiment of the present application.

[0028] Figure 3 It is a schematic diagram of the structure of a physical device based on a data center virtual machine migration scheduling evaluation system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0030] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0031] The following uses an embodiment and combines Figure 1, a data center virtual machine migration scheduling evaluation method based on an embodiment of the present application is described: See also Figure 1 , which is a flow chart of a data center virtual machine migration scheduling evaluation method in an embodiment of the present application.

[0032] S101, obtaining resource status data of a source physical server and a target physical server before migration included in the virtual machine migration solution to be evaluated; In this step, the system needs to obtain the resource status data of the source physical server and the target physical server involved in the virtual machine migration solution before the migration is executed. These resource status data may include but are not limited to the usage of various resources such as CPU utilization, memory utilization, disk I / O utilization, network bandwidth utilization, etc. The system can collect various resource indicators of the physical server in real time through the resource monitoring component, and store the collected data in a time series manner. In addition to real-time collection, the system can also obtain the resource status data of the physical server in a specific time period from the historical monitoring database.

[0033] Specifically, the system can regularly report resource usage through resource monitoring deployed on physical servers, and the monitoring period can be set to seconds or minutes. For historical monitoring data, the system can set data retention policies and use tools such as time series databases to manage and query monitoring data. When obtaining resource status data, the system can flexibly select the time granularity and data volume of the data based on the time span of the evaluation.

[0034] S102, constructing a status fingerprint feature of each physical server resource based on the resource status data; The system constructs the status fingerprint features of each physical server resource based on the resource status data. The status fingerprint features are generated by arranging the utilization of different resource types in the resource status data in chronological order and extracting key change points.

[0035] In this step, the system needs to construct the state fingerprint feature of the physical server resources based on the acquired resource state data. The state fingerprint feature is an abstract representation of the physical server resource usage status. The purpose is to extract the key change points of resource utilization and arrange them in chronological order to form a compact and representative feature vector. The system can construct state fingerprint features for different types of resources, such as the state fingerprint feature of the CPU, the state fingerprint feature of the memory, etc.

[0036] In specific implementation, the system can process resource status data in a sliding window manner, with each window corresponding to a time segment. In each time segment, the system analyzes the resource utilization data and extracts key change points, such as the peak and valley positions of utilization, or the inflection point positions of utilization changes. The extracted key points are arranged in chronological order to form the status fingerprint features of the resource. Considering the volatility of resource utilization, the system can also smooth the original sampled data to eliminate noise interference on a short time scale.

[0037] S103, calculating the peak-valley complementarity degree of the source physical server and the target physical server in the resource status data, and identifying the advantageous complementary interval of resource utilization based on the peak-valley complementarity degree; The system calculates the peak-valley complementarity of the source physical server and the target physical server in the resource status data, and identifies the advantageous complementary interval of resource utilization based on the peak-valley complementarity, specifically including: dividing the resource status data into multiple time segments according to a preset time granularity; extracting the peaks and valleys of the resource utilization data in each time segment to obtain the resource utilization peak period and resource utilization trough period of the source physical server and the target physical server; calculating the first overlapping time ratio of the resource utilization peak period of the source physical server and the resource utilization trough period of the target physical server, and the second overlapping time ratio of the resource utilization trough period of the source physical server and the resource utilization peak period of the target physical server; determining the peak-valley complementarity based on the first overlapping time ratio and the second overlapping time, and marking the time interval in which the peak-valley complementarity is greater than a preset threshold as an advantageous complementary interval.

[0038] In this step, the system needs to analyze the peak-valley complementarity of the resource status data of the source physical server and the target physical server, and identify the advantageous complementary intervals of resource utilization. The peak-valley complementarity reflects the overlap between the peak resource utilization period of the source physical server and the trough resource utilization period of the target physical server, as well as the overlap between the trough resource utilization period of the source physical server and the peak resource utilization period of the target physical server. The higher the complementarity, the higher the target physical server has enough idle resources to accommodate the migrated virtual machines when the resource utilization rate of the source physical server is high, and vice versa.

[0039] In order to calculate the degree of peak-valley complementarity, the system first divides the resource status data into multiple time segments according to the preset time granularity (such as 1 hour, 30 minutes, etc.). For each time segment, the system performs peak and trough analysis on the resource utilization data of the source physical server and the target physical server respectively, and identifies the peak and trough periods of resource utilization by setting utilization thresholds or using statistical methods (such as Kmeans clustering). Then, the system calculates the overlap ratio of the peak period of the source physical server and the trough period of the target physical server in time, as well as the overlap ratio of the trough period of the source physical server and the peak period of the target physical server. The larger the overlap ratio, the higher the degree of resource complementarity between the two physical servers in the time segment.

[0040] Based on the calculated overlap ratio, the system can set a complementarity threshold and mark the continuous time segments where the peak-valley complementarity exceeds the threshold as the resource utilization advantage complementarity interval. These advantage complementarity intervals represent the time periods when the resource complementarity between the source physical server and the target physical server is the strongest, and are the key objects for evaluating the performance of virtual machine migration.

[0041] In actual applications, there may be a situation where the peak and valley periods of resources of the source physical server and the target physical server are misaligned, resulting in the complementary interval being refined into multiple discontinuous time periods. In order to avoid excessive fragmentation of the complementary interval, the system can set a minimum complementary interval length threshold. For independent complementary intervals that are less than the threshold, they can be merged with adjacent non-complementary intervals to obtain a more stable and operational complementary interval division result. In addition, the system can also fine-tune the boundaries of the complementary intervals based on experience by experts through manual intervention to enhance the rationality of the complementary interval division.

[0042] S104, calculating the potential value of improving resource utilization efficiency of the target physical server after migration according to the complementary advantage interval and the resource demand characteristics of the virtual machine to be migrated; The system calculates the potential value for improving the resource utilization efficiency of the target physical server after migration based on the complementary advantage interval and the resource demand characteristics of the virtual machines to be migrated, specifically including: extracting the resource demand fluctuation law of the resource demand characteristics of the virtual machines to be migrated within the complementary advantage interval; calculating the resource occupancy of the target physical server by the virtual machines to be migrated at different time points based on the resource demand fluctuation law; calculating the resource utilization of the target physical server at each time point after migration based on the current resource utilization rate and resource occupancy of the target physical server; calculating the standard deviation of the resource utilization rate in the same time interval based on the change curve of the resource utilization rate of the target physical server before and after migration; and taking the proportion of the time interval in which the standard deviation after migration is less than the standard deviation before migration as the potential value for improving the resource utilization efficiency of the target physical server.

[0043] In this step, the system needs to predict the potential value of improving resource utilization efficiency that may be brought about by migrating the virtual machine to the target physical server based on the identified complementary advantage interval and the resource demand characteristics of the virtual machine to be migrated. The potential value of improving resource utilization efficiency reflects the improvement space in balancing resource load and reducing resource waste by migrating virtual machines across servers within the complementary advantage interval.

[0044] First, the system needs to extract the fluctuation pattern of resource demand of the virtual machine to be migrated within the complementary advantage range, including CPU demand, memory demand, disk I / O demand, network bandwidth demand, etc. These resource demand data can be obtained by analyzing the historical monitoring data of the virtual machine, or by inferring the prediction model of the virtual machine business.

[0045] Then, the system matches the fluctuation pattern of the virtual machine's resource demand with the current resource utilization of the target physical server, and calculates the additional occupation of the target physical server's resources after the virtual machine migration at different time points. The additional occupation can be directly superimposed on the original resource utilization curve of the target physical server to obtain the expected resource utilization level of the target physical server at various time points after the migration.

[0046] In order to quantify the potential for improving resource utilization efficiency, the system can compare the fluctuations of the resource utilization curve of the target physical server before and after migration, and use statistical indicators such as standard deviation to measure the balance of resource utilization. If the resource utilization curve after migration tends to be stable as a whole and the standard deviation decreases, it means that virtual machine migration helps to alleviate the resource usage peak of the target physical server and optimizes resource utilization efficiency. The system can use the proportion of the time interval in which the standard deviation after migration is less than the standard deviation before migration in the entire complementary advantage interval as a measure of the potential for improvement. The higher the proportion, the greater the potential for improvement.

[0047] S105. Setting an adaptive scoring baseline according to the potential value for improving resource utilization efficiency; The system sets an adaptive scoring baseline based on the potential value for improving resource utilization efficiency, specifically including: calculating the degree of improvement of the target physical server in different resource dimensions based on the potential value for improving resource utilization efficiency; determining the weight coefficient of each resource dimension based on the current overall load level of the data center; and performing weighted calculation of the improvement degree and the weight coefficient to obtain an adaptive scoring baseline.

[0048] After calculating the target physical server's resource utilization efficiency improvement potential, the system needs to set an adaptive scoring baseline as a reference for evaluating the pros and cons of virtual machine cross-server migration solutions. Unlike fixed scoring thresholds, the adaptive scoring baseline can dynamically adjust the evaluation criteria for the degree of improvement in resource utilization efficiency according to changes in the overall load level of the data center, reflecting stronger scenario adaptability.

[0049] In specific implementation, the system first converts the improvement potential value obtained in the previous step into the improvement percentage of each resource dimension, including the CPU utilization improvement percentage, memory utilization improvement percentage, disk I / O utilization improvement percentage, network bandwidth utilization improvement percentage, etc. These improvement percentages represent the optimization effect of virtual machine migration in different resource dimensions.

[0050] Next, the system needs to determine the weight coefficients of each resource dimension to quantify the importance of different resources in the comprehensive scoring. An intuitive way to set the weights is to refer to the supply and demand of various resources in the data center. For resource-constrained data centers, the weight coefficients of the corresponding resource dimensions can be appropriately increased. For example, if the current CPU resources are very tight, the system can give a higher weight to the percentage improvement of CPU utilization to highlight its influence in the overall score.

[0051] After obtaining the improvement percentage and weight coefficient of each resource dimension, the system calculates a comprehensive improvement score by weighted average and uses it as the adaptive scoring baseline for virtual machine migration. When the resource utilization efficiency improvement potential value of a virtual machine migration plan is higher than the scoring baseline, the migration plan can be determined to be desirable. The level of the scoring baseline reflects the system's expectation of the improvement in virtual machine migration performance, and its value will be dynamically adjusted as the data center load status and business needs change.

[0052] S106, comparing and analyzing the matching degree between the resource demand characteristics of the virtual machine to be migrated and the state fingerprint characteristics of the target physical server; In the process of evaluating the virtual machine migration plan, the system needs to deeply analyze the resource matching between the virtual machine to be migrated and the target physical server. By comparing the resource demand characteristics of the virtual machine to be migrated with the status fingerprint characteristics of the target physical server, the system can determine the degree of fit between the two in different resource dimensions, providing a more fine-grained reference for migration decisions.

[0053] First, the system needs to extract the resource demand characteristics of the virtual machine to be migrated, including CPU demand intensity and its changing trend, memory demand and its changing trend, disk I / O demand frequency and its changing trend, network bandwidth demand fluctuation law, etc. These characteristics can be obtained by statistically analyzing the historical monitoring data of the virtual machine, and the obtained demand characteristics are usually presented in the form of time series.

[0054] Secondly, the system needs to read the state fingerprint characteristics of the target physical server in the corresponding time period and align them with the resource demand characteristics of the virtual machine. Since the resource state fingerprint extracts the key feature points of resource utilization changes, it can accurately reflect the resource usage rules of the physical server.

[0055] After aligning the virtual machine demand features and the physical server fingerprint features, the system can use a similarity measurement algorithm to calculate the matching degree between the two in different resource dimensions. Common similarity measurement algorithms include Euclidean distance, Manhattan distance, cosine similarity, etc. The system can calculate the similarity of the CPU dimension, memory dimension, disk I / O dimension, and network bandwidth dimension respectively to obtain a set of dimension matching scores. The higher the dimension matching score, the closer the resource demand pattern of the virtual machine is to the resource usage pattern of the physical server, and the better the resource matching degree.

[0056] When calculating the matching degree, the system can also consider the correlation between the virtual machine resource demand and the physical server resource status at different time scales. By introducing the time decay factor, the system can give a smaller weight to historical data that is far from the current moment, and a larger weight to the most recent data, thereby more accurately describing the dynamic matching relationship between virtual machines and physical servers.

[0057] S107, generating a migration fit score by combining the matching degree and the adaptive scoring baseline; After calculating the resource matching degree between the virtual machine to be migrated and the target physical server, the system needs to further combine the adaptive scoring baseline obtained in the previous step to generate a comprehensive migration fit score. The migration fit score reflects the degree of fit between the virtual machine and the physical server in terms of resource utilization mode, as well as the overall resource optimization effect that the virtual machine migration may bring, and is a key indicator for evaluating the feasibility of the migration plan.

[0058] In specific implementation, the system first needs to perform weighted aggregation on the scores of each dimension of resource matching to obtain a unified matching metric. When determining the weight coefficient, the system can refer to the importance ranking of different resource dimensions and the current level of tension of various resources in the data center. Generally, the matching degree of CPU and memory resources will be given a relatively high weight, while the matching degree weight of disk I / O and network bandwidth resources will be relatively low.

[0059] The system then compares the aggregated resource matching metric with the adaptive scoring baseline and generates a migration fit score through certain mathematical operations. Common calculation methods include weighted summation, product summation, etc. For example, you can subtract the scoring baseline from the resource matching metric to get a relative fit score, and then multiply it by a scaling factor so that the final score falls within the range of 0 to 100 points.

[0060] In the process of synthesizing the migration fit score, the system can also embed some additional bonus and deduction items to comprehensively consider the various factors affecting virtual machine migration. For example, if the virtual machine and the physical server have strong complementarity in the peak and valley of CPU resources and memory resources, the system can give certain bonus points; conversely, if the migration of the virtual machine will lead to increased resource competition for other virtual machines, the system can impose certain deduction penalties.

[0061] In addition to the above basic scoring synthesis methods, the system can also use some more complex machine learning models to automatically learn the optimal combination of resource matching and scoring baselines by training historical migration case data. This method can fully explore the nonlinear correlation between various evaluation indicators and improve the accuracy and generalization of migration fit scoring.

[0062] S108. Generate an evaluation result of the virtual machine migration solution to be evaluated according to the migration compatibility score and the continuous stability of the complementary advantage interval.

[0063] The system generates an evaluation result of the virtual machine migration plan to be evaluated based on the migration fit score and the continuous stability of the complementary advantage interval, specifically including: calculating the duration distribution of the complementary advantage interval in the historical time series; evaluating the stability coefficient of the complementary advantage interval based on the duration distribution; performing a nonlinear combination operation on the migration fit score and the stability coefficient to obtain the operation result; and generating the evaluation result of the virtual machine migration plan to be evaluated based on the operation result.

[0064] After generating the migration fit score, the system also needs to evaluate the continuous stability of the identified complementary advantage interval to ensure that the migration plan formulated according to the resource complementarity law can remain effective over a period of time. A continuous and stable complementary advantage interval means that the resource peak state between the source physical server and the target physical server can be maintained for a long time. After the virtual machine is migrated, it can continue to benefit from the resource complementarity between the two and improve the overall resource utilization.

[0065] In order to measure the stability of the complementary interval, the system first needs to look back at the historical monitoring data to examine the frequency and duration of the complementary interval in the past period of time. The system can traverse data statistical windows of different time scales, such as days, weeks, months, etc., calculate the start and end time points of the complementary interval in each window period, and record its duration. After obtaining the duration distribution of the complementary interval, the system can identify stable complementary intervals based on empirical thresholds or clustering algorithms. For example, a complementary interval that lasts for more than one day can usually be considered a stable interval.

[0066] On the basis of identifying the stable complementary interval, the system can further calculate a quantitative stability coefficient, which represents the confidence level that the complementary interval can be maintained continuously. A common calculation method is to use the idea of ​​exponential decay to assign different weights to complementary intervals of different durations. The longer the duration, the higher the weight. Then the weighted duration is normalized to obtain the final stability coefficient. The coefficient usually ranges from 0 to 1. The closer it is to 1, the stronger the stability of the complementary interval.

[0067] After obtaining the complementary advantage interval stability coefficient, the system can combine it with the previous migration fit score and use a certain mathematical model to generate the final evaluation result of the virtual machine migration solution. Considering that there may be nonlinear interactions between the two indicators of stability and fit, the system can use machine learning models such as polynomial regression and support vector machines to fit the mapping relationship between the two and the evaluation results, rather than simple linear weighting. By training historical migration cases and expert scoring data, the system can automatically learn the optimal combination strategy of stability coefficient and fit score, so that the evaluation results are closer to the actual migration effect.

[0068] The evaluation result can be a comprehensive score of 100 points, representing the overall quality of the migration plan to be evaluated, or a multi-level evaluation level, such as "strongly recommended", "recommended", "considerable" and "not recommended". In addition to presenting the final evaluation results, the system can also generate an evaluation report, detailing the key indicators and calculation process of the migration plan in terms of resource matching, complementary interval stability, etc., and giving targeted optimization suggestions for existing deficiencies, helping decision makers to more comprehensively examine the feasibility and benefits of the migration plan.

[0069] In the above embodiment, the resource utilization characteristics of the physical server are characterized by constructing state fingerprint features, and the complementary advantage interval of resource utilization is identified in combination with the peak-valley complementarity degree, so that the evaluation scheme can accurately grasp the complementarity of the source physical server and the target physical server in resource utilization. The potential value for improving resource utilization efficiency is calculated based on the complementary advantage interval and an adaptive scoring baseline is set, so that the scoring criteria can be dynamically adjusted according to the actual effect. By analyzing the matching degree between the virtual machine resource demand characteristics and the target physical server state fingerprint characteristics, and combining the continuous stability of the complementary advantage interval, the evaluation results not only reflect the immediate rationality of the migration plan, but also take into account the long-term stability of the plan, thereby improving the scientificity and reliability of the migration plan evaluation.

[0070] The above describes the basic process of a data center virtual machine migration scheduling evaluation method in an embodiment of the application. This evaluation method evaluates the rationality of the migration plan by analyzing the resource status of the physical server and the resource demand characteristics of the virtual machine. However, in actual application scenarios, the load of the data center usually exhibits dynamic fluctuations, and burst loads may have a significant impact on the evaluation results of the migration plan. Therefore, in order to further improve the accuracy and reliability of the evaluation results, the embodiment of the present application also provides an evaluation method optimization method that takes into account the impact of burst loads. The following is combined with Figure 2 , describes an optimization method for an evaluation method considering the impact of burst load in the embodiment of the present application: please refer to Figure 2 , which is a flow chart of an optimization method for an evaluation method taking into account the impact of burst load in an embodiment of the present application.

[0071] S201, extracting load mutation characteristics of each virtual machine on the target physical server; In this step, the system needs to analyze the load changes of each virtual machine running on the target physical server and identify the load mutation characteristics. Load mutation characteristics describe the situation where the resource usage of the virtual machine changes dramatically in a short period of time, such as a sharp increase or decrease in CPU utilization, a sharp increase or decrease in memory usage, etc. Extracting load mutation characteristics can help the system predict possible sudden load events and evaluate their impact on the virtual machine migration plan.

[0072] In specific implementation, the system can collect resource monitoring indicator data of virtual machines within a certain time range, such as CPU utilization, memory usage, disk I / O rate, network traffic, etc., to build multiple time series. Then, the system uses an anomaly detection algorithm to analyze each time series and identify the mutation points of the monitoring indicators. Common anomaly detection algorithms include statistical process control, wavelet analysis, ARIMA model, etc. The system can select a suitable algorithm based on the characteristics of the data and real-time requirements. The detected mutation points can be characterized by a variety of indicators, such as mutation amplitude, mutation duration, resource usage level before and after the mutation, etc., to form a load mutation feature vector.

[0073] S202, constructing a load mutation propagation chain according to the load mutation characteristics; After extracting the load mutation characteristics of each virtual machine, the system needs to further analyze the association between virtual machines and build a load mutation propagation chain. The load mutation propagation chain describes how the load mutation of a virtual machine affects other virtual machines and reflects the transmission and diffusion process of the burst load between virtual machines. Building a load mutation propagation chain helps predict cascading failures and evaluate the impact range of burst loads.

[0074] In specific implementation, the system first needs to identify the dependencies between virtual machines, that is, how the operating status of one virtual machine affects other virtual machines. This dependency can be inferred by analyzing the business logic and network communication patterns carried by the virtual machines. For example, if two virtual machines often interact with data or provide services for the same business together, then there is a certain dependency between them. When identifying dependencies, the system can use association rule mining, causal inference and other technologies in machine learning to automatically learn the association patterns between virtual machines from massive operation and maintenance logs and network communication records.

[0075] Based on the understanding of the virtual machine dependency, the system can use graph theory to construct a load mutation propagation chain. Each virtual machine is regarded as a node in the graph, the dependency is regarded as a directed edge, and the load mutation characteristics are regarded as the attributes of the node. If the load mutation of node A affects node B, then a directed edge is connected between the two. And so on, a directed graph is finally formed, which reflects the propagation path of the load mutation in the entire physical server.

[0076] S203, simulating various burst load scenarios based on load mutation propagation chain; With the load mutation propagation chain, the system can simulate a variety of sudden load scenarios and predict the risks that virtual machine migration solutions may face under different abnormal load conditions. By simulating various extreme conditions, the system can comprehensively evaluate the robustness of the migration solution, identify weak links, and provide reference for optimization decisions.

[0077] In specific implementation, the system can adopt a graph-based Monte Carlo simulation method. One or more nodes are randomly selected from the load mutation propagation chain as the source of the burst load, the initial load intensity is set, and then the burst load is propagated in the graph according to the probability rule based on the dependency between the nodes. During the propagation process, the system needs to synchronously update the load intensity attributes of each node until the load intensity is lower than the set threshold or propagates to the boundary of the graph. By repeating the random simulation many times, the system can obtain a set of representative burst load scenarios.

[0078] In order to improve the authenticity of the simulation, the system can refer to the data of historical emergencies and learn the intensity distribution and propagation rules of load mutations from actual cases. Using data mining techniques such as clustering and association analysis, the system can discover typical load patterns of different types of emergencies to guide the setting of simulation parameters. In addition, the system can also introduce time factors to consider the dynamic change trend of node load. One feasible idea is to add the time dimension to the graph model, construct a spatiotemporal propagation chain, and simulate the evolution of burst load in time and space.

[0079] S204, calculating the evaluation score of the virtual machine migration solution to be evaluated under different burst load scenarios, and extracting the stability index of the evaluation score; The system calculates the evaluation scores of the virtual machine migration scheme to be evaluated under different burst load scenarios, including: for each burst load scenario, calculating the fluctuation range of each resource dimension in the target physical server; based on the fluctuation range, using the extreme value distribution fitting method to generate the probability density function of resource utilization; according to the probability density function, calculating the risk probability that the resource utilization exceeds the preset safety threshold after migration; weighted combination of the risk probability and the migration fit score to obtain the evaluation score. And extract the stability index of the evaluation score.

[0080] After simulating multiple burst load scenarios, the system needs to evaluate the performance of the virtual machine migration solution under each scenario and calculate the corresponding evaluation score. The evaluation score reflects the ability of the migration solution to cope with burst loads. The higher the score, the stronger the adaptability and reliability of the solution. At the same time, the system also analyzes the fluctuation of the evaluation score under different scenarios and extracts the stability index of the score to determine the overall risk level of the migration solution.

[0081] In specific implementation, the system can adopt the following processing flow: First, for each burst load scenario, the system must calculate the utilization fluctuation range of each resource dimension on the target physical server. This requires superimposing the resource utilization baseline value before and after the virtual machine migration on the basis of the simulation results of the load mutation propagation chain. The utilization fluctuation range can be expressed in the form of confidence interval, maximum and minimum values, etc.

[0082] Then, the system can use the GPD (generalized Pareto distribution) model in extreme value distribution theory to fit the utilization distribution of each resource dimension and generate a probability density function of utilization. The GPD model can well characterize the tail distribution characteristics of extreme events and is suitable for describing the impact of sudden loads. Through distribution fitting, the system can obtain the probability of extreme utilization exceeding the safety threshold.

[0083] Next, the system can convert the probability of extreme utilization into risk metrics, such as risk probability and risk value. Risk probability indicates the likelihood that resource utilization will exceed the threshold under a given burst load scenario. Risk value further considers the business losses that may result from utilization exceeding the threshold. The system can pre-define a risk-loss mapping table to estimate the potential loss value based on different utilization levels.

[0084] Finally, the system weights the risk indicators and the migration fit scores obtained in the basic evaluation to form a comprehensive evaluation score. The weight setting can be determined based on the system's risk preference and service level agreement (SLA) requirements. Systems with high risk preferences can give risk indicators greater weights to avoid business interruptions caused by emergencies; while systems with low risk preferences can emphasize migration fit to pursue improved resource utilization. By summarizing the evaluation scores under various burst load scenarios, the system can obtain a multi-dimensional score distribution.

[0085] After obtaining the evaluation score distribution, the system also needs to extract some stability indicators to reflect the reliability of the migration plan in a dynamic environment. Common stability indicators include the mean score, variance, coefficient of variation, etc. The mean represents the overall performance level of the plan, while the variance and coefficient of variation characterize the degree of dispersion of the score. The smaller the dispersion, the stronger the adaptability of the plan to environmental changes. The system can calculate the stability indicators for multiple migration plans to be evaluated, and select the best plan by comparing the stability of different plans.

[0086] S205. Correct the evaluation result according to the stability index to obtain a corrected evaluation result.

[0087] After obtaining the comprehensive evaluation score and stability index of the migration plan, the system needs to perform the final step of correction to balance the credibility of the score and risk control requirements. The purpose of the correction is to adjust the evaluation results so that they can better reflect the expected performance of the plan in the actual dynamic environment and avoid overly optimistic or pessimistic estimates.

[0088] In specific implementation, the system can design a correction function, which takes the evaluation score and stability index as input to generate the corrected evaluation result. The correction function can be linear or nonlinear, and the parameters can be set according to historical experience or expert knowledge. A simple linear correction function is to add the evaluation score and the stability index together, and the weight reflects the importance the system attaches to stability. The larger the weight of the stability index, the more robust and conservative the corrected evaluation result will be.

[0089] In addition to linear weighting, the system can also use some more complex nonlinear correction functions, such as exponential functions, logarithmic functions, etc. Nonlinear functions can introduce some thresholds or threshold mechanisms to achieve segmented correction. For example, when the stability index is below a certain threshold, the correction amplitude is small, and when the stability index exceeds this threshold, the correction amplitude increases rapidly. This segmented strategy can amplify the impact of the evaluation score when the stability is good, and quickly reduce the weight of the evaluation score when the stability is poor.

[0090] In actual applications, there may be a weak correlation between the evaluation score and the stability index, and a simple weighted combination may not accurately reflect the true value of the solution. To address this problem, the system can use some machine learning methods to automatically optimize the structure and parameters of the correction function by training historical data. For example, a multivariate regression model can be used to fit the mapping relationship between the evaluation index and the actual effect to obtain a data-driven correction function. For another example, a reinforcement learning algorithm can be used to allow the system to autonomously try different correction strategies in a simulated environment and learn the best correction method through a reward and punishment mechanism.

[0091] After obtaining the corrected evaluation results, the system can output a final migration plan selection recommendation for decision makers' reference. The recommendation content may include the recommended migration time window, target physical server, risk points that need special attention, etc. For plans with lower corrected evaluation results, the system can also provide some possible optimization measures, such as adjusting the resource configuration of the virtual machine, increasing redundant capacity, adjusting the load balancing strategy, etc., to help users further improve the plan.

[0092] In the above embodiment, by extracting the load mutation characteristics of each virtual machine on the target physical server and constructing a load mutation propagation chain, the correlation and influence mode of load fluctuations between virtual machines can be revealed. Based on the load mutation propagation chain, a variety of burst load scenarios are simulated, and the adaptability of the virtual machine migration solution under different load conditions can be predicted. By calculating the evaluation scores under different burst load scenarios and extracting the stability index of the score, the resistance of the virtual machine migration solution to load fluctuations can be quantitatively evaluated. The evaluation results are corrected according to the stability index, so that the evaluation results fully consider the impact of the burst load on the migration solution, and the accuracy and reliability of the evaluation results are improved. It can effectively identify migration solutions that still have good effects under load fluctuations, reduce the impact of burst loads on the performance of the data center, and improve the stable operation capability of the data center.

[0093] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the structure of a physical device based on a data center virtual machine migration scheduling and evaluation system provided in an embodiment of the present application.

[0094] It should be noted that Figure 3 The structure of the system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0095] like Figure 3As shown, the system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through a bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0096] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a liquid crystal display (LCD) and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0097] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0098] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.

[0099] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0100] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiment; or may exist independently without being assembled into the system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiment.

[0101] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0102] As used in the above embodiments, the term "when..." may be interpreted as "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted as "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0103] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0104] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A data center virtual machine migration scheduling evaluation method, characterized in that: include: Obtain resource status data of a source physical server and a target physical server before migration included in the virtual machine migration solution to be evaluated; Constructing a status fingerprint feature of each physical server resource based on the resource status data, wherein the status fingerprint feature is generated by arranging the utilization rates of different resource types in the resource status data in chronological order and extracting key change points; Calculating the peak-valley complementarity degree between the source physical server and the target physical server on the resource status data, and identifying the advantageous complementary interval of resource utilization based on the peak-valley complementarity degree; Calculating the resource utilization efficiency improvement potential value of the target physical server after migration based on the complementary advantage interval and the resource demand characteristics of the virtual machine to be migrated; Setting an adaptive scoring baseline according to the resource utilization efficiency improvement potential value; Comparative analysis of the matching degree between the resource demand characteristics of the virtual machine to be migrated and the state fingerprint characteristics of the target physical server; generating a migration fit score by combining the matching degree and the adaptive scoring baseline; An evaluation result of the virtual machine migration solution to be evaluated is generated according to the migration compatibility score and the continuous stability of the complementary advantage interval.

2. The method according to claim 1, characterized in that The calculating the peak-valley complementarity degree between the source physical server and the target physical server in the resource status data, and identifying the advantageous complementary interval of resource utilization based on the peak-valley complementarity degree, specifically includes: Dividing the resource status data into multiple time segments according to a preset time granularity; Extracting peaks and valleys of resource utilization data in each time segment to obtain peak periods and valley periods of resource utilization of the source physical server and the target physical server; Calculate a first overlapping time ratio between a resource utilization peak period of the source physical server and a resource utilization valley period of the target physical server, and a second overlapping time ratio between a resource utilization valley period of the source physical server and a resource utilization peak period of the target physical server; The peak-valley complementarity degree is determined based on the first overlapping time ratio and the second overlapping time, and a time interval in which the peak-valley complementarity degree is greater than a preset threshold is marked as an advantageous complementary interval.

3. The method according to claim 1, characterized in that The calculating, according to the complementary advantage interval and the resource demand characteristics of the virtual machine to be migrated, the resource utilization efficiency improvement potential value of the target physical server after the migration specifically includes: Extract resource demand characteristics of the virtual machine to be migrated and the resource demand fluctuation law within the complementary advantage interval; Calculate the resource occupation of the target physical server by the to-be-migrated virtual machine at different time points based on the resource demand fluctuation rule; Calculating the resource utilization of the target physical server at each time point after the migration according to the current resource utilization of the target physical server and the resource occupancy; Based on the change curve of the resource utilization of the target physical server before and after the migration, calculating the standard deviation of the resource utilization within the same time interval; The proportion of time intervals in which the standard deviation after migration is less than the standard deviation before migration is used as the potential value for improving the resource utilization efficiency of the target physical server.

4. The method according to claim 1, characterized in that: The step of setting an adaptive scoring baseline according to the resource utilization efficiency improvement potential value specifically includes: Calculating the improvement degree of the target physical server in different resource dimensions based on the resource utilization efficiency improvement potential value; Determine the weight coefficient of each resource dimension according to the current overall load level of the data center; The improvement degree and the weight coefficient are weighted and calculated to obtain an adaptive scoring baseline.

5. The method according to claim 1, characterized in that Generating the evaluation result of the virtual machine migration scheme to be evaluated according to the migration compatibility score and the continuous stability of the complementary advantage interval specifically includes: Calculate the duration distribution of the complementary advantage interval in the historical time series; Evaluating the stability coefficient of the complementary advantage interval based on the duration distribution; Performing a nonlinear combination operation on the migration fit score and the stability coefficient to obtain an operation result; An evaluation result of the virtual machine migration solution to be evaluated is generated according to the calculation result.

6. The method according to claim 1, characterized in that After the evaluation result of the virtual machine migration solution to be evaluated is generated according to the migration compatibility score and the continuous stability of the complementary advantage interval, the method further includes: Extracting load mutation characteristics of each virtual machine on the target physical server; Constructing a load mutation propagation chain according to the load mutation characteristics; Simulating various burst load scenarios based on the load mutation propagation chain; Calculating the evaluation score of the virtual machine migration solution to be evaluated under different burst load scenarios, and extracting the stability index of the evaluation score; The evaluation result is corrected according to the stability index to obtain a corrected evaluation result.

7. The method according to claim 6, characterized in that The calculating the evaluation score of the virtual machine migration solution to be evaluated under different burst load scenarios specifically includes: For each of the burst load scenarios, calculating the fluctuation range of each resource dimension in the target physical server; Based on the fluctuation range, an extreme value distribution fitting method is used to generate a probability density function of resource utilization; Calculate the risk probability of resource utilization exceeding a preset safety threshold after migration according to the probability density function; The risk probability and the migration compatibility score are weighted and combined to obtain an evaluation score.

8. A data center virtual machine migration scheduling evaluation system, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to execute the method according to any one of claims 1 to 7.

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