CN117632369A

CN117632369A - Configuration method and device for virtual machine capacity of application system

Info

Publication number: CN117632369A
Application number: CN202311634934.6A
Authority: CN
Inventors: 王振宇; 李远杰; 张婧; 牛鹤山; 匡红庆
Original assignee: China Construction Bank Corp
Current assignee: China Construction Bank Corp
Priority date: 2023-11-30
Filing date: 2023-11-30
Publication date: 2024-03-01

Abstract

The invention discloses a configuration method and a device for virtual machine capacity of an application system, which relate to the technical field of computer management, and the method comprises the following steps: collecting current CPU data and current memory data of different virtual machines in each deployment unit server in an application system at service peak time points according to the association relation between the types of the deployment unit servers and the service peak time points of the deployment unit servers; taking the virtual machines with the current CPU data and the current memory data exceeding the preset threshold value in the plurality of virtual machines under each deployment unit server as the virtual machines to be adjusted; calculating a virtual machine capacity adjustment strategy corresponding to the virtual machine to be adjusted according to the current CPU data and the current memory data of the virtual machine to be adjusted; and carrying out capacity configuration on the virtual machine to be adjusted according to the capacity adjustment strategy of the virtual machine. The invention is used for realizing the automatic configuration of the capacity of the application system, meeting the personalized requirements of the system, reducing the resource waste and improving the capacity management level of the center.

Description

Configuration method and device for virtual machine capacity of application system

Technical Field

The present invention relates to the field of computer management technologies, and in particular, to a method and an apparatus for configuring a virtual machine capacity of an application system.

Background

This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.

The stable operation of the application system depends on the capacity condition of the system, and the capacity resource is not infinite, so how to reasonably formulate the capacity rule of the application system is important.

The current method generally depends on the experience of an expert, and the rule is not changed for many years after the rule is formulated, so that the method is simple, high in management efficiency and more suitable for a small-scale data center. However, the large data center system has complex multi-service, the scale and service of the application system are continuously changed, and the immobilization rules may have problems as follows: the capacity rules lack individuation, and rationality is to be verified.

The application systems of the data center are various in variety and different in characteristics, and the fixed capacity rule cannot meet the personalized requirements of the system and causes waste of resources. For example, a deployment unit of an application system is responsible for file transmission with a third party company, the concurrency number is low, the requirement on a CPU is not high, but the required memory is high, the currently selectable configuration only has 2C8G and 4C16G,2C8G does not meet the memory requirement, and 4C16G can cause waste of CPU resources. Similar to high memory systems, high concurrency systems also suffer from this problem.

In addition, some schemes are adopted to optimize the formulation of the capacity rule at present, and the resource use condition of different application systems in service peak values is considered, for example, the capacity rule of the system is adjusted according to CPU and memory use rate in actual TPM. However, the method only considers the system personality, does not analyze the resource usage of all application systems, and has no universality.

Disclosure of Invention

The embodiment of the invention provides a configuration method of application system virtual machine capacity, which is used for realizing automatic configuration of the application system capacity, meeting the personalized requirements of the system, reducing the resource waste and improving the central capacity management level, and comprises the following steps:

collecting current CPU data and current memory data of different virtual machines in each deployment unit server in an application system at service peak time points according to the association relation between the types of the deployment unit servers and the service peak time points of the deployment unit servers;

taking the virtual machines with the current CPU data and the current memory data exceeding the preset threshold value in the plurality of virtual machines under each deployment unit server as the virtual machines to be adjusted;

calculating a virtual machine capacity adjustment strategy corresponding to the virtual machine to be adjusted according to the current CPU data and the current memory data of the virtual machine to be adjusted;

And carrying out capacity configuration on the virtual machine to be adjusted according to the capacity adjustment strategy of the virtual machine.

The embodiment of the invention also provides a configuration device of the virtual machine capacity of the application system, which is used for realizing the automatic configuration of the capacity of the application system, meeting the personalized requirements of the system, reducing the resource waste and improving the management level of the capacity of the center, and comprises the following components:

the data acquisition module is used for acquiring current CPU data and current memory data of different virtual machines in each deployment unit server at the service peak time point under the application system according to the type of the deployment unit server and the association relation between the service peak time points of the deployment unit servers;

the to-be-adjusted virtual machine determining module is used for taking a virtual machine with current CPU data and current memory data exceeding a preset threshold value in a plurality of virtual machines under each deployment unit server as the to-be-adjusted virtual machine;

the virtual machine capacity adjustment strategy determining module is used for calculating a virtual machine capacity adjustment strategy corresponding to the virtual machine to be adjusted according to the current CPU data and the current memory data of the virtual machine to be adjusted;

and the capacity configuration module is used for carrying out capacity configuration on the virtual machine to be adjusted according to the capacity adjustment strategy of the virtual machine.

The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the configuration method of the virtual machine capacity of the application system when executing the computer program.

The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the configuration method of the virtual machine capacity of the application system when being executed by a processor.

The embodiment of the invention also provides a computer program product, which comprises a computer program, and the computer program realizes the configuration method of the virtual machine capacity of the application system when being executed by a processor.

According to the embodiment of the invention, according to the association relation between the type of the deployment unit server and the service peak time point of the deployment unit server, the current CPU data and the current memory data of different virtual machines in each deployment unit server under the application system at the service peak time point are collected; taking the virtual machines with the current CPU data and the current memory data exceeding the preset threshold value in the plurality of virtual machines under each deployment unit server as the virtual machines to be adjusted; calculating a virtual machine capacity adjustment strategy corresponding to the virtual machine to be adjusted according to the current CPU data and the current memory data of the virtual machine to be adjusted; according to the virtual machine capacity adjustment strategy, capacity configuration is carried out on the virtual machine to be adjusted, compared with the technical scheme of fixedly setting the virtual machine capacity of the application system in the prior art, the automatic configuration of the capacity of the application system is realized through the collected current CPU data and the current memory data of different virtual machines in the deployment unit server at the service peak time point, the personalized requirements of the system are met, the resource waste is reduced, the utilization rate of the virtual machine resources of the application system is improved, the capacity refinement of the application system is promoted, and the central capacity management level is improved.

Drawings

In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:

fig. 1 is a flow chart of a method for configuring the capacity of a virtual machine of an application system according to an embodiment of the present invention;

FIG. 2 is a specific example diagram of a method for configuring a virtual machine capacity of an application system according to an embodiment of the present invention;

FIG. 3 is a schematic structural diagram of a configuration device for virtual machine capacity of an application system according to an embodiment of the present invention;

FIG. 4 is a specific example diagram of a method for configuring a virtual machine capacity of an application system according to an embodiment of the present invention;

fig. 5 is a schematic diagram of a computer device for configuring a virtual machine capacity of an application system according to an embodiment of the present invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.

The term "and/or" is used herein to describe only one relationship, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.

In the description of the present specification, the terms "comprising," "including," "having," "containing," and the like are open-ended terms, meaning including, but not limited to. Reference to the terms "one embodiment," "a particular embodiment," "some embodiments," "for example," etc., means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. The sequence of steps involved in the embodiments is used to schematically illustrate the practice of the present application, and is not limited thereto and may be appropriately adjusted as desired.

The data acquisition, storage, use, processing and the like in the technical scheme meet the relevant regulations of national laws and regulations.

The stable running of an application system is highly dependent on its capacity status, however, the capacity resources are not endless, and therefore, making reasonable application system capacity rules is critical. Current methods typically rely on the experience of an expert and do not change many years after the rules are formulated. The method is simple and efficient to manage, and is more suitable for small-scale data centers. However, the business of large data center systems is complex, and the scale and business of application systems are continually evolving. In this case, the immobilized rule may have problems, for example: capacity rules lack personalization and their rationality remains to be verified.

The application systems of the data center are various in variety and different in characteristics. The use of fixed capacity rules does not meet the personalized needs of these systems and may result in wasted resources. For example, a deployment unit of an application system is responsible for file transmission with a third party company, the concurrency number is low, the requirement on a CPU is not high, but the required memory is high. The current alternative configuration is that only 2C8G and 4C16G,2C8G does not meet the memory requirement, and 4C16G can cause the waste of CPU resources. Similarly, the same problem exists with highly concurrent systems.

In addition, although some schemes are adopted to optimize the formulation of the capacity rule at present, the resource use condition of different application systems at the time of service peak is considered, for example, the capacity rule of the system is adjusted according to the CPU and memory use rate at the time of actual TPM. However, this method only considers individual cases of the system, but does not fully analyze the resource usage of all application systems, and thus does not have universal applicability.

In order to solve the above problems, an embodiment of the present invention provides a method for configuring a virtual machine capacity of an application system, which is used to implement automatic configuration of the capacity of the application system, meet the personalized requirements of the system, reduce resource waste, and improve the central capacity management level, and referring to fig. 1, the method may include:

step 101: collecting current CPU data and current memory data of different virtual machines in each deployment unit server in an application system at service peak time points according to the association relation between the types of the deployment unit servers and the service peak time points of the deployment unit servers;

step 102: taking the virtual machines with the current CPU data and the current memory data exceeding the preset threshold value in the plurality of virtual machines under each deployment unit server as the virtual machines to be adjusted;

Step 103: calculating a virtual machine capacity adjustment strategy corresponding to the virtual machine to be adjusted according to the current CPU data and the current memory data of the virtual machine to be adjusted;

step 104: and carrying out capacity configuration on the virtual machine to be adjusted according to the capacity adjustment strategy of the virtual machine.

In step 101, current CPU data and current memory data of different virtual machines in each deployment unit server in the application system at the service peak time point may be collected according to an association relationship between the type of the deployment unit server and the service peak time point of the deployment unit server. For example, if the deployment unit servers are X86 architecture Linux servers, then at the point of traffic peaks, the current CPU data and current memory data for the different virtual machines in each deployment unit server may be collected. Such data may be obtained through monitoring tools or operating system commands.

In step 102, a virtual machine whose current CPU data and current memory data exceed a preset threshold value among the multiple virtual machines under each deployment unit server is used as a virtual machine to be adjusted. For example, if the preset threshold is that the CPU usage rate exceeds 80% or the memory usage rate exceeds 90%, at the time of the traffic peak, the current CPU data and the current memory data of different virtual machines in each deployment unit server may be compared, and if the CPU usage rate of a certain virtual machine exceeds 80% or the memory usage rate exceeds 90%, the virtual machine is regarded as the virtual machine to be adjusted.

In step 103, according to the current CPU data and the current memory data of the virtual machine to be adjusted, a virtual machine capacity adjustment policy corresponding to the virtual machine to be adjusted is calculated. For example, if the CPU usage rate of the virtual machine to be adjusted is high, it may be considered to increase the number of CPU cores of the virtual machine or adjust the CPU usage priority of the virtual machine; if the memory usage rate of the virtual machine to be adjusted is high, it is considered to increase the memory capacity of the virtual machine or adjust the memory usage priority of the virtual machine.

In step 104, according to the capacity adjustment policy of the virtual machine, capacity configuration is performed on the virtual machine to be adjusted. For example, if the number of CPU cores of the virtual machine to be adjusted needs to be increased according to the calculation result, the number of CPU cores of the virtual machine may be increased by modifying a configuration file of the virtual machine or using a monitoring tool; if the memory capacity of the virtual machine to be adjusted needs to be increased, the memory capacity of the virtual machine can be increased by modifying the configuration file of the virtual machine or using a monitoring tool.

According to the embodiment, the current CPU data and the current memory data of different virtual machines in each deployment unit server under the application system at the service peak time are collected, the virtual machine exceeding the preset threshold is used as the virtual machine to be adjusted, the virtual machine capacity adjustment strategy is calculated according to the current CPU data and the current memory data of the virtual machine to be adjusted, and finally the capacity configuration is carried out on the virtual machine to be adjusted according to the virtual machine capacity adjustment strategy, so that the automatic configuration of the capacity of the application system is realized, the personalized requirements of the system are met, the resource waste is reduced, and the central capacity management level is improved.

In addition, the method can dynamically adjust the configuration parameters of the virtual machine according to the service load conditions of different virtual machines, thereby realizing the optimization of an application system. For example, the configuration parameters such as the number of CPU cores and the memory size of the virtual machine can be dynamically adjusted according to the CPU utilization and the memory utilization of different virtual machines, so as to realize better performance and efficiency.

According to the method, the current CPU data and the current memory data of different virtual machines in each deployment unit server in the application system at the service peak time are collected, and the configuration parameters of the virtual machines are dynamically adjusted according to the data, so that the automatic configuration and optimization of the capacity of the application system are realized, the personalized requirements of the system are met, the resource waste is reduced, and the central capacity management level is improved.

In the implementation, according to the association relationship between the type of the deployment unit server and the service peak time point of the deployment unit server, current CPU data and current memory data of different virtual machines in each deployment unit server under the application system at the service peak time point are collected.

In one embodiment, further comprising:

the association relationship between the type of the deployment unit server and the service peak time point of the deployment unit server is established as follows:

Establishing association between a deployment unit server which is a WEB server and a service peak time point representing that the number of the deployment unit connections reaches the peak time;

establishing association between a deployment unit server of which the type is an online wireless access point and a service peak time point representing that the deployment unit reaches the peak time of the online transaction processing amount per second;

the method comprises the steps that a deployment unit server with the type of batch wireless access points is associated with a service peak time point representing the moment that a batch period of deployment units reaches a machine CPU utilization peak;

establishing an association between a deployment unit server of the type online DB and service peak time points representing the time when a plurality of online wireless access points connected with the deployment unit reach the peak value of online transaction processing amount per second;

and establishing association between a deployment unit server with the type of batch DB and a service peak time point representing the time when a batch task time period reaches the peak of the utilization rate of the CPU of the machine.

In the implementation, according to the collected CPU data and memory data, the CPU utilization rate and the memory utilization rate of different virtual machines in each deployment unit server in the application system at the service peak time point are calculated.

In one embodiment, further comprising:

and respectively calculating the performance scores of different virtual machines in each deployment unit server under the application system according to the CPU utilization rate and the memory utilization rate at the service peak time.

And comparing the performance scores of the different deployment unit servers, and optimizing the application system according to the comparison result. For example, for a deployment unit server with a lower performance score, optimization measures such as upgrading hardware, adjusting system configuration, adding resources, etc. may be taken to improve its performance score.

By the embodiment, the real-time monitoring and optimization of the application system can be realized, and the overall performance and user experience of the system are improved. Meanwhile, by establishing the association relation between the types of the deployment unit servers and the service peak time points, the performance conditions of the deployment unit servers of different types can be reflected more accurately, and more accurate data support is provided for system optimization.

In the implementation, after current CPU data and current memory data of different virtual machines in each deployment unit server in an application system at a service peak time point are collected according to an association relationship between the type of the deployment unit server and the service peak time point of the deployment unit server, the virtual machine with the current CPU data and the current memory data exceeding a preset threshold value in a plurality of virtual machines in each deployment unit server is used as a virtual machine to be adjusted.

In the implementation, according to the association relationship between the type of the deployment unit server and the service peak time point of the deployment unit server, current CPU data and current memory data of different virtual machines in each deployment unit server under the application system at the service peak time point are collected. And then, taking the virtual machine with the current CPU data and the current memory data exceeding the preset threshold value in the plurality of virtual machines under each deployment unit server as the virtual machine to be adjusted.

For these virtual machines to be tuned, a series of optimization measures may be taken. Firstly, according to the load condition of the virtual machine, the CPU and the memory of the virtual machine can be dynamically adjusted. For example, when the CPU and memory usage of the virtual machine exceeds a preset threshold, the CPU and memory allocation of the virtual machine may be increased. In contrast, when the CPU and memory usage of the virtual machine is lower than the preset threshold, the CPU and memory allocation of the virtual machine may be reduced, so as to avoid wasting resources.

In addition, the application system can be optimized according to the characteristics and service requirements of the application system. For example, for some services that require processing large amounts of data, the data processing flow of the application system may be optimized to reduce CPU and memory usage. For some services that need to process a large number of user requests, the concurrent processing capacity of the application system may be optimized to enhance the performance of the application system.

In addition, the performance of the application system can be monitored and analyzed in real time through a monitoring and analyzing tool. Thus, the method can help to find and solve the problems of the application system in time and optimize the performance of the application system.

In summary, by collecting the current CPU data and the current memory data of the virtual machine and dynamically adjusting the resource allocation of the virtual machine and implementation of the application system optimization measures, the performance and reliability of the application system can be effectively improved, so as to meet the requirements of service peak time points.

In one embodiment, further comprising:

and setting the preset threshold corresponding to different deployment unit servers according to the types of the deployment unit servers and the service types of the application systems.

In one embodiment, further comprising:

and selecting a corresponding optimization strategy to optimize according to the types and the number of the virtual machines to be adjusted so as to solve the problem of insufficient resources of the virtual machines.

Among the optimization strategies include, but are not limited to: and selecting a corresponding virtual machine migration strategy, a resource allocation strategy or a load balancing strategy for optimization according to the types and the quantity of the virtual machines to be adjusted.

For example, if the virtual machine to be tuned is a virtual machine with insufficient CPU resources, a policy may be adopted to migrate part of the computing task to other virtual machines with abundant CPU resources, or to increase the allocation of CPU resources of the virtual machine for optimization.

For example, if the virtual machine to be adjusted is a virtual machine with insufficient memory resources, a policy may be adopted to migrate a portion of memory data to other virtual machines with rich memory resources, or to increase the memory resource allocation of the virtual machine for optimization.

For example, if the virtual machine to be tuned is an excessive number of virtual machines, a policy may be adopted to migrate part of the virtual machines to other deployment unit servers, or to merge part of the virtual machines for optimization.

By the method, the problem of insufficient resources of the virtual machine can be effectively solved, and the performance and stability of the application system are improved.

As an example, different thresholds are set according to different service types and service peak time points, respectively:

1. for a deployment unit server of a WEB server type, setting data with CPU utilization rate exceeding 80% and memory utilization rate exceeding 70% as data exceeding a preset threshold;

2. for a deployment unit server of an online wireless access point type, setting data with CPU usage rate exceeding 60% and memory usage rate exceeding 50% as data exceeding a preset threshold;

3. for a deployment unit server of a batch wireless access point type, setting data with CPU usage rate exceeding 80% and memory usage rate exceeding 70% as data exceeding a preset threshold;

4. For an online DB type deployment unit server, setting data with CPU usage rate exceeding 80% and memory usage rate exceeding 70% as data exceeding a preset threshold;

5. for the deployment unit server of the batch DB type, data in which the CPU usage exceeds 85% and the memory usage exceeds 75% is set as data exceeding a preset threshold.

By the method, the capacity configuration of the virtual machine to be adjusted can be determined more accurately, so that the performance of the application system is optimized.

In the implementation, after the virtual machines with the current CPU data and the current memory data exceeding the preset threshold value in the plurality of virtual machines under each deployment unit server are used as the virtual machines to be adjusted, the capacity adjustment strategy of the virtual machines corresponding to the virtual machines to be adjusted is calculated according to the current CPU data and the current memory data of the virtual machines to be adjusted.

In the implementation, a virtual machine with current CPU data and current memory data exceeding a preset threshold value in a plurality of virtual machines under each deployment unit server is used as a virtual machine to be adjusted, and a virtual machine capacity adjustment strategy corresponding to the virtual machine to be adjusted is calculated according to the current CPU data and the current memory data of the virtual machine to be adjusted.

For each virtual machine to be adjusted, firstly analyzing current CPU data and current memory data of the virtual machine to be adjusted, and judging whether capacity adjustment is needed or not according to a preset threshold value. If the adjustment is needed, the adjustment strategy needed by the virtual machine is calculated according to the current CPU data and the current memory data.

The specific calculation method can comprise the following steps: and comparing the current CPU utilization rate with a preset threshold value to obtain a CPU adjustment strategy, and comparing the current memory utilization rate with the preset threshold value to obtain a memory adjustment strategy. After the adjustment strategy is obtained, the corresponding capacity adjustment can be performed on the virtual machine so as to enable the virtual machine to meet the requirement of a preset threshold.

In performing capacity adjustment, various manners may be adopted, such as increasing or decreasing the CPU core number and the memory size of the virtual machine, or migrating the virtual machine to other servers with better performance. These adjustment strategies can be selected and combined according to actual requirements to achieve the best capacity adjustment effect.

In summary, by analyzing the current CPU data and the current memory data of the virtual machine to be adjusted and calculating the corresponding virtual machine capacity adjustment policy, the virtual machine can be accurately adjusted in capacity, so as to achieve more efficient and flexible cloud computing resource management.

In one embodiment, collecting current CPU data and current memory data of different virtual machines in each deployment unit server under an application system at a service peak time point includes:

and periodically monitoring the running state of the application system, and collecting the current CPU data and the current memory data of different virtual machines in each deployment unit server under the application system at the service peak time point.

In specific implementation, the collected CPU data and memory data may be compared with a preset threshold, so as to determine which virtual machines need to be adjusted. For the virtual machine to be adjusted, the corresponding virtual machine capacity adjustment strategy can be calculated according to the current CPU data and the current memory data.

In another embodiment, the current CPU data and the current memory data of different virtual machines in each deployment unit server under the application system at the service peak time point can be collected by periodically monitoring the running state of the application system. These data may be used to evaluate the use of the virtual machine and determine if adjustments are needed.

In the implementation process, more detailed and specific steps can be formulated in combination with specific application scenes and requirements. For example, the specific implementation of the virtual machine capacity adjustment policy, the timing of adjustment, the degree of adjustment, and the like may be further determined. Meanwhile, factors in the aspects of stability, availability, expandability and the like of an application system also need to be considered, so that the implementation scheme can meet the actual requirements.

In the implementation, after calculating a capacity adjustment strategy of the virtual machine corresponding to the virtual machine to be adjusted according to the current CPU data and the current memory data of the virtual machine to be adjusted, capacity configuration is carried out on the virtual machine to be adjusted according to the capacity adjustment strategy of the virtual machine.

After calculating the capacity adjustment strategy of the virtual machine, the system configures the capacity of the virtual machine to be adjusted according to the strategy. This process may be accomplished by automated tools or may be performed manually.

If an automation tool is used, the system automatically calculates configuration parameters of the virtual machine according to the strategy, including the requirements of resources such as a CPU and a memory, and then automatically configures the virtual machine. The mode can greatly reduce manual intervention and improve the stability and reliability of the system.

If manual mode is used, the system provides an interface for the manager to manually set the configuration parameters of the virtual machine according to the strategy. This approach allows for more flexibility in meeting different needs, but requires a certain expertise and experience of the administrator during operation.

In either way, it is necessary to ensure that the configuration of the virtual machine is consistent with the policy to ensure the stability and reliability of the system. At the same time, the system also needs to provide monitoring and alarm functions in order to discover and solve any possible problems in time.

In one embodiment, calculating a virtual machine capacity adjustment policy corresponding to a virtual machine to be adjusted according to current CPU data and current memory data of the virtual machine to be adjusted includes:

when the current CPU data is larger than a second preset threshold value and/or the current memory data is larger than a fourth preset threshold value, performing capacity expansion processing on the virtual machine capacity corresponding to the virtual machine to be adjusted;

when the current CPU data is smaller than a first preset threshold value and/or the current memory data is smaller than a third preset threshold value, carrying out capacity reduction processing on the virtual machine capacity corresponding to the virtual machine to be adjusted;

performing capacity unchanged processing on the capacity of the virtual machine corresponding to the virtual machine to be adjusted when the current CPU data is smaller than or equal to a second preset threshold value and larger than or equal to a first preset threshold value and/or when the current memory data is smaller than or equal to a fourth preset threshold value and larger than or equal to a third preset threshold value; the second preset threshold value is larger than the first preset threshold value; the fourth preset threshold value is larger than a third preset threshold value.

1. And when the current CPU data is larger than a second preset threshold value and/or the current memory data is larger than a fourth preset threshold value, performing capacity expansion processing on the virtual machine capacity corresponding to the virtual machine to be adjusted. In this case, the number of CPU cores, memory capacity, or hard disk storage space of the virtual machine may be increased to improve the processing capacity and storage capacity of the virtual machine.

2. And when the current CPU data is smaller than a first preset threshold value and/or the current memory data is smaller than a third preset threshold value, performing capacity reduction processing on the virtual machine capacity corresponding to the virtual machine to be adjusted. In this case, the number of CPU cores, memory capacity, or hard disk storage space of the virtual machine may be reduced to reduce the processing power and storage capacity of the virtual machine.

3. And performing capacity unchanged processing on the capacity of the virtual machine corresponding to the virtual machine to be adjusted when the current CPU data is smaller than or equal to a second preset threshold value and larger than or equal to a first preset threshold value and/or when the current memory data is smaller than or equal to a fourth preset threshold value and larger than or equal to a third preset threshold value. In this case, the number of CPU cores, the memory capacity, or the hard disk storage space of the virtual machine may be kept unchanged to avoid unnecessary influence on the performance of the virtual machine.

The second preset threshold value is larger than the first preset threshold value and/or the fourth preset threshold value is larger than the third preset threshold value, so that capacity expansion processing is ensured to be carried out under the condition that certain performance requirements are met, and capacity expansion processing is avoided under the condition that capacity expansion is not needed.

In addition, the strategy can be adjusted and optimized according to actual conditions, for example, future load conditions can be predicted according to historical data, dynamic resource allocation can be performed according to actual application requirements, and the like. By the method, a more intelligent and flexible virtual machine capacity adjustment strategy can be realized, and the utilization efficiency and the service quality of cloud computing resources are improved.

In the process of implementing the capacity adjustment of the virtual machine, the strategy can be further refined and optimized according to actual requirements. For example, in the case where the CPU usage rate continues to be higher than the second preset threshold, it may be considered to increase the number of CPUs of the virtual machine to be adjusted or to increase the CPU frequency thereof, so as to increase the system processing capability. Similarly, when the memory usage rate is continuously higher than the fourth preset threshold, the memory capacity of the virtual machine to be adjusted may be increased, so as to ensure the normal operation of the virtual machine and meet the higher memory requirement.

In the opposite case, when the CPU usage is continuously below the first preset threshold or the memory usage is continuously below the third preset threshold, reducing the resource occupation of the virtual machine to be adjusted may be considered. The method can reduce unnecessary resource waste and provide space for other virtual machines requiring more resources.

Finally, when the CPU utilization is between the first preset threshold and the second preset threshold or the memory utilization is between the third preset threshold and the fourth preset threshold, the current resource configuration may be selected to be kept unchanged. Thus, performance fluctuation and system overhead caused by frequent capacity adjustment can be avoided.

It should be noted that the preset threshold should be set reasonably according to the actual service requirement and the system load condition. Meanwhile, for different application scenes and business requirements, finer and flexible capacity adjustment strategies are required to be formulated according to specific situations.

In the above embodiment, the resource utilization rate is smaller than the first threshold value, the resource utilization rate is larger than the second threshold value, the number of virtual machines with the resource utilization rate between the first threshold value and the second threshold value is acquired according to the production data, and considering the actual operation and maintenance working condition, the embodiment of the invention only adjusts the capacity rule and does not involve modifying the number of virtual machines.

In the above embodiment, when the resource usage rate is lower than the threshold value, the system triggers capacity adjustment to increase the number of virtual machines to meet the performance requirement of the application program. In contrast, when the resource usage exceeds the second threshold, the system triggers capacity adjustment to reduce the number of virtual machines to avoid excessive consumption of resources. And between the first threshold value and the second threshold value, capacity adjustment of the system is determined according to the condition of production data acquisition, and capacity rules are only adjusted according to the actual operation and maintenance working condition, and the number of virtual machines is not modified.

The method for dynamically adjusting the number of the virtual machines not only can ensure the performance requirements of the application program, but also can effectively control the resource consumption and improve the utilization efficiency of the resources. Meanwhile, the operation and maintenance workload can be reduced and the operation and maintenance efficiency can be improved by only adjusting the capacity rule without modifying the number of virtual machines.

In addition, the embodiment of the invention also provides an automatic capacity adjustment method. The method can automatically judge whether capacity adjustment is needed or not by monitoring the resource use condition of the system. If necessary, the system automatically adjusts the capacity rules to increase or decrease the number of virtual machines. The automatic capacity adjustment method can adjust the resources more timely, and avoid the situations of resource waste or application program performance degradation.

Performing capacity expansion processing on the virtual machine capacity corresponding to the virtual machine to be adjusted, wherein the capacity expansion processing comprises the following steps:

expanding the capacity of the virtual machine corresponding to the virtual machine to be adjusted to a first capacity; the first capacity is calculated as follows:

first capacity=a× (1+B)

Wherein A is the value of the current CPU data or the current memory data; b is a second preset threshold in percent format.

Performing capacity reduction processing on the virtual machine capacity corresponding to the virtual machine to be adjusted, wherein the capacity reduction processing comprises the following steps:

the capacity of the virtual machine corresponding to the virtual machine to be adjusted is contracted to be a second capacity; the second capacity is calculated as follows:

second capacity=a×b

In the above, the adjustment of the capacity of the virtual machine is divided into two cases, expansion and contraction. For the expansion process, the first capacity calculated by the formula takes into account the value of the current CPU data or current memory data and the percentage of the second preset threshold. For the capacity reduction processing, the second capacity calculated by the formula only considers the value of the current CPU data or the current memory data and the second preset threshold value.

The processing mode can realize flexible adjustment of the capacity of the virtual machine. When the capacity of the virtual machine needs to be increased, a calculation formula of the first capacity can be used, and new capacity can be calculated according to the current use condition of the CPU or the memory and a preset threshold value. And when the capacity of the virtual machine needs to be reduced, a calculation formula of the second capacity can be used, which only considers the current use condition of the CPU or the memory to calculate the new capacity.

The capacity adjustment mode based on the current CPU data or the memory data can effectively avoid the problems of resource waste caused by overlarge capacity adjustment or insufficient resources caused by overlarge capacity adjustment while ensuring the normal operation of the service. Meanwhile, through setting of a preset threshold value, automatic early warning and automatic adjustment of the capacity of the virtual machine can be realized, so that the automatic management degree of the virtualized environment is further improved.

As a specific example, the capacity adjustment strategy is as follows:

(1) When the CPU/memory utilization rate exceeds the threshold value II, the capacity is expanded, and the expanded CPU and/or memory configuration is equal to the current CPU/memory configuration multiplied by (1+threshold value II), and the capacity is rounded upwards.

(2) And when the CPU/memory utilization rate is lower than a threshold value, carrying out capacity reduction, wherein the CPU and/or memory configuration after capacity reduction is equal to the current CPU/memory configuration multiplied by a threshold value II, and rounding up.

(3) When the CPU/memory usage is between threshold one and threshold two, the capacity remains unchanged.

In the example, it is assumed that there is a cloud server, and its CPU and memory are configured as follows:

* Initial CPU configuration: 2 cores

* Initial memory configuration: 4GB

* Threshold one: CPU utilization rate is less than 50%, memory utilization rate is less than 70%

* Threshold two: CPU utilization rate is more than 70%, memory utilization rate is more than 90%

Now, according to the provided capacity adjustment strategy, one simulates:

1. when the CPU utilization exceeds 70%, the capacity expansion is performed. The expanded CPU configuration is equal to the current CPU configuration multiplied by (1+70/100), i.e., 2× (1+0.7) =4.4 cores, and rounded up, i.e., 5 cores.

2. And when the memory utilization rate exceeds 90%, expanding the capacity. The expanded memory configuration is equal to the current memory configuration multiplied by (1+90/100), i.e., 4× (1+0.9) =7.6 GB, and rounded up, i.e., 8GB.

3. And when the CPU utilization rate and the memory utilization rate are lower than the respective threshold values, performing capacity shrinking. The scaled CPU configuration is equal to the current CPU configuration multiplied by 0.7, i.e., 2×0.7=1.4 cores, and rounded up, i.e., 2 cores. The scaled memory configuration is equal to the current memory configuration multiplied by 0.9, i.e., 4×0.9=3.6 GB, and rounded up, i.e., 4GB.

4. When the CPU utilization is between 50% and 70%, the capacity remains unchanged, being 2 cores. When the memory utilization rate is between 70% and 90%, the capacity is kept unchanged and is 4GB.

Through the simulation, the resource utilization rate of the cloud server can be effectively improved through the combined use of the threshold setting and the capacity adjustment strategy, and meanwhile, resource waste is avoided. In practical application, the threshold and capacity adjustment strategy can be adjusted according to different service requirements and practical situations.

The specific algorithm of capacity rule adjustment (taking CPU as an example, memory calculation is similar to that of the CPU), if the current CPU utilization rate is not between the first threshold value and the second threshold value, capacity expansion or capacity reduction is carried out according to the CPU utilization rate, if the capacity utilization after one-time adjustment is reasonable, the capacity rule is not adjusted any more, and otherwise, the capacity expansion or capacity reduction adjustment is continued.

The pseudocode of a specific algorithm for capacity rule adjustment is as follows:

while (| threshold one < current CPU usage < threshold two):

if (current CPU usage < threshold one):

post-capacity CPU configuration = current CPU/memory configuration (threshold two), and rounding up

if (current CPU usage > threshold two):

capacity-expanded CPU/memory configuration = current CPU/memory configuration (1 + threshold two), and rounding up

The first threshold and the second threshold are preset CPU utilization rate thresholds, and are used for judging whether the current CPU utilization rate needs to be adjusted or not. And if the current CPU utilization rate is smaller than the first threshold value, performing the capacity shrinking operation, multiplying the current CPU configuration by the second threshold value and rounding up to obtain the CPU configuration after capacity shrinking. If the current CPU utilization rate is greater than the second threshold value, performing capacity expansion operation, dividing the current CPU configuration by the memory configuration, multiplying the current CPU configuration by the (1+the second threshold value), and rounding up to obtain the expanded CPU/memory configuration.

In this way, the specific algorithm for capacity rule adjustment can be dynamically adjusted according to the current CPU usage to maintain high availability and high performance operation of the system. Meanwhile, the algorithm also considers the influence of memory configuration, and can be flexibly adjusted according to actual conditions.

It should be noted that the above pseudo code only provides a specific algorithm for capacity rule adjustment, and other factors, such as stability, reliability, load balancing, etc., of the system need to be considered in practical application. Meanwhile, the algorithm needs to be adjusted and optimized according to actual conditions so as to adapt to different systems and application scenes.

In addition, the specific algorithm of capacity rule adjustment needs to be flexibly adjusted according to actual conditions. For example, when the capacity usage after one adjustment is still unreasonable, the capacity expansion or contraction adjustment may be continued until a reasonable capacity usage is reached. Meanwhile, the influence of factors such as different server models, network bandwidths and the like on the system performance is also required to be considered so as to formulate a finer capacity rule adjustment strategy.

The specific algorithm of capacity rule adjustment is an important link in system operation and maintenance, and flexible adjustment and optimization are required according to actual conditions so as to ensure the stability and high-performance operation of the system. And dynamically adjusting a preset threshold value and the target virtual machine capacity according to the service requirement and the system performance of the application system so as to ensure that the performance and the service requirement of the application system are met.

In one embodiment, the method further comprises:

and redeploying the configured virtual machine into the application system, and monitoring the running state of the virtual machine to ensure the stability and performance of the application system.

In an embodiment, the method further comprises the steps of:

firstly, the virtual machine is redeployed into the application system according to preset configuration parameters. This process includes installing and configuring the virtual machine operating system, applications, and all necessary drivers to ensure that the virtual machine is able to function properly.

And then, monitoring the running state of the redeployed virtual machine in real time through a monitoring tool. These monitoring tools may include system performance monitors, application monitors, network monitors, etc. for monitoring the CPU utilization, memory usage, disk space usage, application running status, network connectivity, etc. of the system.

During the monitoring process, if any abnormality or error is found, the monitoring system immediately gives an alarm and processes the alarm through a preset emergency plan so as to minimize the influence on the application system. In addition, the method can dynamically adjust the virtual machine according to the requirement so as to adapt to different requirements of an application system.

Through the steps, the method can ensure the stability and performance of the application system, and simultaneously improve the flexibility and expandability of the system.

In one embodiment, the method further comprises:

according to the running state and business requirement of the application system, the application system is optimized and regulated regularly to further improve the performance and efficiency of the application system; and recording and analyzing the running data and the configuration information of the application system, and providing data support for future system optimization and adjustment.

In another embodiment, the method further comprises: according to user feedback and system logs of the application system, potential problems of the application system are checked and repaired regularly to ensure stability and reliability of the application system; and the hardware and software environment of the application system is monitored and maintained, so that the normal operation and stability of the application system are ensured.

In another embodiment, the method further comprises: according to the service requirement and performance index of the application system, the algorithm and data of the application system are optimized and adjusted regularly so as to improve the processing capacity and efficiency of the application system; and evaluating and protecting the safety and privacy of the application system, and ensuring the data safety and user privacy of the application system.

In another embodiment, the method further comprises: according to user feedback and system logs of the application system, the interface and interaction of the application system are optimized and improved regularly, so that the satisfaction degree and the use experience of the user on the application system are improved; and evaluating and optimizing the user experience of the application system so as to improve the satisfaction degree and the use experience of the user on the application system.

In one embodiment, the method further comprises:

in configuring the capacity of the virtual machine, the optimal virtual machine configuration scheme is determined by considering different characteristics and service requirements of the virtual machine and the overall performance and stability of the application system.

In an embodiment, when configuring virtual machine capacity, it is necessary to consider different characteristics and service requirements of the virtual machine, and overall performance and stability of the application system, to determine an optimal virtual machine configuration scheme. Specifically, the following aspects need to be considered:

1. computing resource requirements: and determining resources such as CPU, memory, storage and the like required by the virtual machine according to the computing requirements of the application system. When the capacity of the virtual machine is configured, the proportion of computing resources required by different virtual machines needs to be considered, so that each virtual machine can stably run when running, and the service requirement can be met.

2. Network resource requirements: network communication and data transmission between virtual machines require certain network resources. In configuring the capacity of the virtual machines, indexes such as network bandwidth and network delay required by an application system need to be considered so as to ensure that data transmission and communication between the virtual machines can be performed stably and reliably.

3. Storage resource requirements: the storage requirements of the virtual machine include hard disk space, I/O performance, etc. When the capacity of the virtual machine is configured, storage resources and performance requirements required by the application system need to be considered so as to ensure that the storage of the virtual machine can meet the running requirements of the application system.

4. Virtualization property requirements: virtualization has many characteristics, such as isolation, sharing, flexibility, etc. When configuring the capacity of the virtual machine, the virtualization characteristics required by the application system need to be considered, so as to ensure that the characteristics of the virtual machine can meet the requirements of the application system.

5. Safety and stability requirements: the security of the virtual machine includes data encryption, access control, and the like. When the capacity of the virtual machine is configured, the safety and stability requirements of the application system need to be considered, so that the safety and stability of the virtual machine can be ensured to meet the requirements of the application system.

In summary, in configuring the virtual machine capacity, the above aspects need to be considered in combination to determine the optimal virtual machine configuration scheme. The optimal virtual machine configuration scheme needs to meet the performance and stability requirements of the application system, and can fully utilize various characteristics and resource advantages of the virtual machine to meet the service requirements and improve the overall performance.

In one embodiment, the method further comprises:

when determining the target virtual machine capacity, considering different characteristics and service requirements of the virtual machine and the overall performance and stability of an application system to determine the optimal target virtual machine capacity;

when the preset threshold value and the target virtual machine capacity are dynamically adjusted, the optimal adjustment scheme is determined by considering different characteristics and business requirements of the virtual machine and the overall performance and stability of an application system;

in periodically optimizing and adjusting the application system, the optimal optimization and adjustment scheme is determined by taking into consideration the different characteristics and business requirements of the virtual machine and the overall performance and stability of the application system.

In addition, the method further comprises the following steps:

when determining the target virtual machine capacity, taking different characteristics and service requirements of the virtual machine as consideration factors, and combining the overall performance and stability of an application system, and determining the optimal target virtual machine capacity by analyzing historical data and using a performance test tool;

When the preset threshold value and the target virtual machine capacity are dynamically adjusted, the preset threshold value and the target virtual machine capacity are automatically adjusted according to different characteristics and business requirements of the virtual machine and the overall performance and stability of the application system, so that the stable operation of the application system is ensured;

when the application system is optimized and regulated regularly, the optimization and regulation work is carried out regularly according to different characteristics and business requirements of the virtual machine and the overall performance and stability of the application system so as to improve the performance and stability of the application system to the greatest extent.

A specific example is given below to illustrate a specific application of the method of the invention. In this embodiment, as shown in fig. 2, production data will be acquired, analyzed, and new rules recommended, promoted by the following steps.

1. Obtaining production data

In the step, CPU and memory data of different deployment unit servers of the application system are collected. These data will be used to analyze and optimize the performance of the system. The acquisition time is the business peak period, which is to ensure that the acquired data can truly reflect the performance of the system under the actual load.

1. For a WEB server, the service peak time point is the time of the peak number of connection of the deployment unit. At this point in time, both the CPU and memory usage of the server will peak.

2. For an online AP, the traffic spike point is the time of the deployment unit TPM spike. The TPM represents the number of transactions per minute and when the TPM reaches a peak, it indicates that the processing power of the AP has reached a limit.

3. For batch AP, the traffic peak time point is the time at which the CPU utilization of each machine is maximum for the deployment unit batch period. The CPU utilization may reach a maximum value when processing tasks in batches.

4. For the online DB, the traffic peak time is the TPM peak time of each online AP connected thereto. The DB server needs to interact with multiple APs, and when the TPM of an AP peaks, the load of the DB increases accordingly.

5. For the batch DB, the traffic peak time point is the time at which each batch AP connected thereto has the maximum CPU utilization during the batch task period. Similarly to the batch AP, when the CPU usage of the batch AP reaches the maximum value, the load of the batch DB increases accordingly.

2. Analysis of production data

In step, data collected during peak traffic hours will be analyzed. By observing the CPU and memory use conditions of different deployment units, the resource allocation schemes corresponding to different service types and service characteristics can be summarized. For example, for a deployment unit with CPU, memory usage greater than 50%, it may be necessary to add configuration of the CPU or memory; for deployment units with CPU usage between 25% and 50%, it may be necessary to adjust the configuration of the CPU or memory; for deployment units with CPU utilization less than 25% and memory utilization less than 50%, it may be desirable to reduce the configuration of the CPU or memory.

3. Recommendation rules

According to the data analysis result, the configuration can be adjusted, for example, various capacity rules such as 2C10G,2C12G,6C10G and the like are increased, and the service characteristics of the application system are more adapted. These rules are recommended based on the results of the data analysis, which can effectively improve the performance and response speed of the system.

4. New rule popularization

And applying the new capacity configuration rule to production, observing the running condition of the system, and then re-analyzing the production data according to the service development condition at fixed intervals, and updating the rule. This ensures that the system remains in an optimal operating state at all times.

5. Practical examples

An example is given to illustrate the practical effect of the present invention. For example, in the current production environment, there are 1448 deployment units on-line to deploy unit 4C16G virtual machines. After data analysis, the utilization rate of 20% of virtual machine CPU is less than 25%, and the utilization rate of memory is less than 50%. This means that these virtual machines do not fully utilize their resources. If the configuration is performed according to the new capacity rule 2C10G, the capacity threshold requirement can be met as well, and meanwhile, more than C of the CPU3000 and more than 1 ten thousand G of memory can be saved. Thus, not only the performance of the system can be improved, but also the cost can be reduced.

6. Effect assessment and optimization

After implementing the new capacity rule, the running state of the system needs to be continuously monitored, and the effect of the new rule is evaluated. And evaluating the improvement effect of the new rule on the system performance by observing indexes such as CPU and memory utilization rate, transaction processing capacity, system response time and the like of the system.

If new rules are found to be not apparent to the improvement of system performance, or there is a bottleneck, the rules may be further optimized, such as strategies to adjust resource configuration, or more advanced resource scheduling algorithms may be introduced.

7. Summary

By the method, automatic acquisition and analysis of production data can be realized, new capacity rules are recommended and promoted according to analysis results, the performance and response speed of the system are improved, and meanwhile, the cost is reduced. The method can be widely applied to various application systems, and helps enterprises to realize efficient resource allocation and optimization.

In addition, as shown in fig. 4, the method for planning capacity rule provided in this embodiment may be implemented as follows:

Step 105: judging whether the virtual machine to be adjusted reaches an optimal running state or not according to the current CPU data and the current memory data of the virtual machine to be adjusted;

step 106: if the virtual machine to be adjusted does not reach the optimal running state, returning to the step 103, and continuously calculating a virtual machine capacity adjustment strategy corresponding to the virtual machine to be adjusted;

step 107: and if the virtual machine to be adjusted reaches the optimal running state, carrying out capacity configuration on the virtual machine to be adjusted, and ending the steps.

The embodiment provides a new method for planning capacity rules, breaks through the working mode established by expert experience, analyzes the resource use condition of an application system in a production environment from actual, and pertinently adjusts the capacity rules to meet the personalized requirements of the application system. The method realizes the automatic generation of the optimal configuration of the capacity rule of the application system, and is compared with the previously fixed capacity rule. The method is more flexible, meets the actual demands, realizes reasonable utilization of resources, promotes the refinement of the capacity of an application system, improves the management level of the capacity of the center, and realizes the boosting double-carbon target.

The embodiment also provides a method for monitoring the use condition of the application system resources, which comprises the following steps:

1. and (3) carrying out real-time monitoring on the resource use condition of the application system, wherein the resource use condition comprises indexes such as CPU use rate, memory occupancy rate, disk space use rate, network bandwidth and the like, and various combined resource use conditions.

2. And analyzing the monitored data to identify peak periods and valley periods of resource use and association relations among various resources.

3. And according to the analysis result, a corresponding capacity rule adjustment strategy is formulated, such as increasing the number of servers in the peak period of resource use, optimizing program codes to improve the resource utilization rate, adjusting the storage space size and the like.

4. And applying the adjusted capacity rule to an application system, carrying out real-time monitoring and data analysis, and carrying out fine adjustment according to the actual running condition so as to achieve the optimal capacity configuration.

By the method, the capacity of the application system can be dynamically adjusted and optimized, the resource utilization rate and the system performance are improved, the energy consumption and the carbon emission are reduced, and the application system is promoted to develop towards the green low-carbon direction. Meanwhile, the method of the embodiment can be applied to other similar fields, such as cloud computing, big data centers and the like, and has wide application prospects.

Of course, it is to be understood that other variations of the above detailed procedures are also possible, and all related variations should fall within the protection scope of the present invention.

The embodiment of the invention also provides a device for configuring the capacity of the virtual machine of the application system, which is expressed in the following embodiment. Because the principle of the device for solving the problem is similar to that of the configuration method of the application system virtual machine capacity, the implementation of the device can refer to the implementation of the configuration method of the application system virtual machine capacity, and the repetition is omitted.

The embodiment of the invention also provides a configuration device of the virtual machine capacity of the application system, which is used for realizing the automatic configuration of the capacity of the application system, meeting the personalized requirements of the system, reducing the resource waste and improving the management level of the capacity of the center, and as shown in fig. 3, the device comprises:

the data acquisition module 301 is configured to acquire current CPU data and current memory data of different virtual machines in each deployment unit server at a service peak time point under the application system according to an association relationship between a type of the deployment unit server and the service peak time point of the deployment unit server;

the to-be-adjusted virtual machine determining module 302 is configured to use, as a to-be-adjusted virtual machine, a virtual machine whose current CPU data and current memory data exceed a preset threshold value among the plurality of virtual machines under each deployment unit server;

The virtual machine capacity adjustment policy determining module 303 is configured to calculate a virtual machine capacity adjustment policy corresponding to the virtual machine to be adjusted according to current CPU data and current memory data of the virtual machine to be adjusted;

and the capacity configuration module 304 is configured to perform capacity configuration on the virtual machine to be adjusted according to the capacity adjustment policy of the virtual machine.

In one embodiment, further comprising:

the association relation establishment module is used for:

In one embodiment, further comprising:

In one embodiment, performing capacity expansion processing on the virtual machine capacity corresponding to the virtual machine to be adjusted includes:

first capacity=a× (1+B)

In one embodiment, the capacity reduction processing for the virtual machine capacity corresponding to the virtual machine to be adjusted includes:

Second capacity=a×b

In one embodiment, the apparatus further comprises:

the timing module is used for periodically sending capacity configuration instructions to the data acquisition module, the virtual machine to be adjusted determining module, the virtual machine capacity adjustment strategy determining module and the capacity configuration module.

The timing module can periodically send instructions to the data acquisition module to acquire current CPU data and current memory data of different virtual machines in each deployment unit server under the application system at the service peak time point; the method comprises the steps that an instruction can be sent to a to-be-adjusted virtual machine determining module regularly, and a virtual machine with current CPU data and current memory data exceeding a preset threshold value in a plurality of virtual machines under each deployment unit server is used as the to-be-adjusted virtual machine; the method comprises the steps that an instruction can be sent to a virtual machine capacity adjustment strategy determining module at regular intervals, and a virtual machine capacity adjustment strategy corresponding to a virtual machine to be adjusted is calculated according to current CPU data and current memory data of the virtual machine to be adjusted; and sending an instruction to the capacity configuration module periodically, and configuring the capacity of the virtual machine to be regulated according to the capacity regulation strategy of the virtual machine.

In one embodiment, the timing module may set the capacity allocation instructions to be sent at intervals. For example, it may be set that the capacity allocation instruction is transmitted every one hour.

In one embodiment, the timing module may also adjust the time interval for sending the instruction according to factors such as the service type of the application system, the type of the deployment unit server, and a preset threshold. For example, during peak business hours of the application system, the time interval for sending the instruction can be shortened; and in the traffic trough period, the time interval for sending the instruction can be prolonged.

In addition, the device can dynamically adjust the preset threshold according to the service requirement and the resource utilization rate of the application system. For example, when the service requirement of the application system increases, the preset threshold may be correspondingly increased to increase the capacity of the system; when the resource utilization rate of the application system is higher, the preset threshold value can be correspondingly reduced so as to fully utilize the system resource.

The device can also be integrated with a cloud platform, and unified management and scheduling of the application system can be performed through the cloud platform. Through the cloud platform, capacity management and resource allocation of a plurality of application systems can be realized, and the efficiency and performance of the whole system are further improved.

In summary, the device can automatically perform capacity configuration and management according to the service requirement and the resource utilization rate of the application system, meet the personalized requirement of the system, reduce resource waste and improve the central capacity management level. Meanwhile, the device can be integrated with a cloud platform, unified management and scheduling of a plurality of application systems are achieved, and efficiency and performance of the whole system are further improved.

The embodiment of the invention provides a computer device for realizing all or part of the content in the configuration method of the virtual machine capacity of the application system, which specifically comprises the following contents:

a processor (processor), a memory (memory), a communication interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete communication with each other through the bus; the communication interface is used for realizing information transmission between related devices; the computer device may be a desktop computer, a tablet computer, a mobile terminal, or the like, and the embodiment is not limited thereto. In this embodiment, the computer device may be implemented with reference to an embodiment of the method for implementing the configuration of the application system virtual machine capacity and an embodiment of the device for implementing the configuration of the application system virtual machine capacity, and the contents thereof are incorporated herein, and are not repeated here.

Fig. 5 is a schematic block diagram of a system configuration of a computer device 1000 according to an embodiment of the present application. As shown in fig. 5, the computer device 1000 may include a central processor 1001 and a memory 1002; the memory 1002 is coupled to the central processor 1001. Notably, this fig. 5 is exemplary; other types of structures may also be used in addition to or in place of the structures to implement telecommunications functions or other functions.

In one embodiment, the configuration functions of the application system virtual machine capacity may be integrated into the central processor 1001. The central processor 1001 may be configured to control, among other things, the following:

In another embodiment, the configuration device of the application system virtual machine capacity may be configured separately from the cpu 1001, for example, the configuration device of the application system virtual machine capacity may be configured as a chip connected to the cpu 1001, and the configuration function of the application system virtual machine capacity is implemented by the control of the cpu.

As shown in fig. 5, the computer device 1000 may further include: a communication module 1003, an input unit 1004, an audio processor 1005, a display 1006, a power supply 1007. It is noted that the computer device 1000 need not include all of the components shown in FIG. 5; in addition, the computer device 1000 may further include components not shown in fig. 5, to which reference is made to the prior art.

As shown in fig. 5, the central processor 1001, sometimes also referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, and the central processor 1001 receives input and controls the operation of the various components of the computer device 1000.

The memory 1002 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information about failure may be stored, and a program for executing the information may be stored. And the central processor 1001 can execute the program stored in the memory 1002 to realize information storage or processing, and the like.

The input unit 1004 provides input to the central processor 1001. The input unit 1004 is, for example, a key or a touch input device. The power supply 1007 is used to provide power to the computer device 1000. The display 1006 is used for displaying display objects such as images and characters. The display may be, for example, but not limited to, an LCD display.

The memory 1002 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), SIM card, and the like. But also a memory which holds information even when powered down, can be selectively erased and provided with further data, an example of which is sometimes referred to as EPROM or the like. Memory 1002 may also be some other type of device. Memory 1002 includes a buffer memory 1021 (sometimes referred to as a buffer). The memory 1002 may include an application/function storage 1022, the application/function storage 1022 for storing application programs and function programs or for executing a flow of operations of the computer apparatus 1000 by the central processor 1001.

The memory 1002 may also include a data store 1023, the data store 1023 for storing data such as contacts, digital data, pictures, sounds, and/or any other data used by a computer device. The driver store 1024 of the memory 1002 can include various drivers for the computer device for communication functions and/or for performing other functions of the computer device (e.g., messaging applications, address book applications, etc.).

The communication module 1003 is a transmitter/receiver 1003 that transmits and receives signals via an antenna 1008. A communication module (transmitter/receiver) 1003 is coupled to the central processor 1001 to provide an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal.

Based on different communication technologies, a plurality of communication modules 1003, such as a cellular network module, a bluetooth module, and/or a wireless lan module, etc., may be provided in the same computer device. The communication module (transmitter/receiver) 1003 is also coupled to a speaker 1009 and a microphone 1010 via an audio processor 1005 to provide audio output via the speaker 1009 and to receive audio input from the microphone 1010 to implement usual telecommunications functionality. The audio processor 1005 may include any suitable buffers, decoders, amplifiers and so forth. In addition, an audio processor 1005 is also coupled to the central processor 1001 so that sound can be recorded locally through the microphone 1010 and so that sound stored locally can be played through the speaker 1009.

It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims

1. The configuration method of the application system virtual machine capacity is characterized by comprising the following steps:

2. The method as recited in claim 1, further comprising:

3. The method as recited in claim 1, further comprising:

4. The method of claim 1, wherein collecting current CPU data and current memory data for different virtual machines in each deployment unit server in the application system at a peak point in time of traffic comprises:

5. The method of claim 1, wherein calculating a virtual machine capacity adjustment policy corresponding to the virtual machine to be adjusted based on current CPU data and current memory data of the virtual machine to be adjusted, comprises:

6. The method of claim 5, wherein performing capacity expansion processing on the virtual machine capacity corresponding to the virtual machine to be adjusted comprises:

first capacity=a× (1+B)

7. The method of claim 5, wherein performing the capacity reduction process on the virtual machine capacity corresponding to the virtual machine to be adjusted comprises:

second capacity=a×b

8. A device for configuring a capacity of a virtual machine of an application system, comprising:

9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 7 when executing the computer program.

10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the method of any of claims 1 to 7.

11. A computer program product, characterized in that the computer program product comprises a computer program which, when executed by a processor, implements the method of any of claims 1 to 7.