CN115022320A - Method, device, equipment and medium for determining cloud resource server - Google Patents
Method, device, equipment and medium for determining cloud resource server Download PDFInfo
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Abstract
The method comprises the steps of obtaining service demand processing capacity, system reservation processing capacity, system self-monitoring consumption capacity and system index reservation capacity, then determining target capacity required by completing target service based on the service demand processing capacity and the system self-monitoring consumption capacity, determining capacity required by completing internal processes based on the system reservation processing capacity and the system index reservation capacity, and finally determining the number of cloud resource servers required to be applied according to the target capacity, the internal capacity, system target operation load and service future development coefficients. Therefore, the method can accurately predict the number of the cloud resource servers to be applied based on different business modes, and comprehensively considers various factors influencing the utilization of the cloud resource servers, so that the utilization rate of the cloud resources can be improved.
Description
Technical Field
The present application relates to the field of computers, and in particular, to a method, an apparatus, a device, a medium, and a product for determining a cloud resource server.
Background
With the continuous expansion of network big data application, the demands of various network systems on cloud resources are increased explosively. However, although the demand of cloud resources is high, the utilization rate of the existing cloud resources is always low, which causes a serious problem of cloud resource waste.
Researchers find that the main reason for the low utilization rate of the cloud resources is that the cloud resource server application is in excess during application.
Therefore, there is a need in the art for a method for determining a cloud resource server to apply for a proper cloud resource server, so as to improve the utilization rate of cloud resources and reduce the waste of cloud resources.
Disclosure of Invention
The application provides a determination method of a cloud resource server. The method can determine the number of the cloud resource servers required to be applied according to various factors, and improve the utilization rate of the cloud resources. The application also provides a device, equipment, a medium and a program product corresponding to the method.
In a first aspect, the present application provides a method for determining a cloud resource server, where the method includes:
acquiring service demand processing capacity, system reservation processing capacity, system self-monitoring consumption capacity and system index reservation capacity;
determining a target capacity required for completing a target service based on the service demand processing capacity and the system self-monitoring consumption capacity, and determining an internal capacity required for completing an internal process based on the system reservation processing capacity and the system index reservation capacity;
and determining the number of the cloud resource servers required to be applied according to the target capacity, the internal capacity, the system target operation load and the service future development coefficient.
In some possible implementations, the determining a target capability required for completing a target service based on the service demand processing capability and the system self-monitoring consumption capability includes:
determining a remaining capacity of the system other than the system self-monitoring consumption capacity based on the system self-monitoring consumption capacity;
and determining the target capacity required for completing the target service based on the residual capacity except the self-monitoring consumption capacity of the system and the service demand processing capacity.
In some possible implementations, the determining, based on the system reservation processing capability and the system index reservation capability, an internal capability required to complete an internal process includes:
determining a remaining capacity of the system other than the system index reservation capacity based on the system index reservation capacity;
determining the internal capacity required for completing the internal process based on the remaining capacity except the system index reservation capacity and the system reservation processing capacity.
In some possible implementation manners, the determining, according to the target capacity, the internal capacity, the system target operation load, and the future service development coefficient, the number of cloud resource servers that need to be applied for includes:
determining the capacity required by the target service server according to the target capacity and the internal capacity;
and determining the number of the cloud resource servers required to be applied according to the capacity required by the target service server, the system target operation load and the service future development coefficient.
In some possible implementation manners, the determining, according to the target capacity, the internal capacity, the system target operation load, and the future service development coefficient, the number of cloud resource servers that need to be applied for includes:
determining the redundancy proportion of the server under the condition of the target operation load according to the target operation load of the system;
and determining the number of the cloud resource servers required to be applied according to the redundancy proportion of the servers under the target operation load condition, the target capacity, the internal capacity and the service future development coefficient.
In some possible implementation manners, the determining, according to the target capacity, the internal capacity, the system target operation load, and the future service development coefficient, the number of cloud resource servers that need to be applied for includes:
determining the processing capacity of a single server according to the service future development coefficient;
and determining the number of the cloud resource servers required to be applied according to the target capacity, the internal capacity, the system target operation load and the processing capacity of the single server.
In some possible implementation manners, the service requirement processing capacity and the system reservation processing capacity are represented by a TPMC value of the number of orders processed by the system per minute, the service requirement processing capacity is a TPMC value theoretically required for completing a target service, and the system reservation processing capacity is a TPMC value consumed by an internal process of a server operating system.
In a second aspect, the present application provides an apparatus for determining a cloud resource server, the apparatus including:
the acquisition module is used for acquiring the service demand processing capacity, the system reservation processing capacity, the system self-monitoring consumption capacity and the system index reservation capacity;
a first determining module, configured to determine a target capability required for completing a target service based on the service demand processing capability and the system self-monitoring consumption capability, and determine an internal capability required for completing an internal process based on the system reservation processing capability and the system index reservation capability;
and the second determining module is used for determining the number of the cloud resource servers required to be applied according to the target capacity, the internal capacity, the system target operation load and the service future development coefficient.
In some possible implementations, the first determining module is specifically configured to:
determining a remaining capacity of the system other than the system self-monitoring consumption capacity based on the system self-monitoring consumption capacity;
and determining the target capacity required by the target service based on the residual capacity except the self-monitoring consumption capacity of the system and the service demand processing capacity.
In some possible implementations, the first determining module is specifically configured to:
determining a remaining capacity of the system other than the system index reservation capacity based on the system index reservation capacity;
determining the internal capacity required for completing the internal process based on the remaining capacity except the system index reservation capacity and the system reservation processing capacity.
In some possible implementations, the second determining module is specifically configured to:
determining the capacity required by the target service server according to the target capacity and the internal capacity;
and determining the number of the cloud resource servers required to be applied according to the capacity required by the target service server, the system target operation load and the service future development coefficient.
In some possible implementations, the second determining module is specifically configured to:
determining the redundancy proportion of the server under the condition of the target operation load according to the target operation load of the system;
and determining the number of the cloud resource servers required to be applied according to the redundancy proportion of the servers under the target operation load condition, the target capacity, the internal capacity and the service future development coefficient.
In some possible implementations, the second determining module is specifically configured to:
determining the processing capacity of a single server according to the service future development coefficient;
and determining the number of the cloud resource servers required to be applied according to the target capacity, the internal capacity, the system target operation load and the processing capacity of the single server.
In some possible implementation manners, the service requirement processing capacity and the system reservation processing capacity are represented by TPMC values of the number of orders processed by the system per minute, the service requirement processing capacity is a TPMC value theoretically required for completing a target service, and the system reservation processing capacity is a TPMC value consumed by an internal process of a server operating system.
In a third aspect, the present application provides an apparatus comprising a processor and a memory. The processor and the memory communicate with each other. The processor is configured to execute instructions stored in the memory to cause the apparatus to perform a method of cloud resource server determination as in the first aspect or any implementation of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and the instructions instruct a device to perform the method for determining a cloud resource server according to the first aspect or any implementation manner of the first aspect.
In a fifth aspect, the present application provides a computer program product containing instructions that, when run on a device, cause the device to perform the method for determining a cloud resource server according to the first aspect or any implementation manner of the first aspect.
The present application can further combine to provide more implementations on the basis of the implementations provided by the above aspects.
According to the technical scheme, the embodiment of the application has the following advantages:
the embodiment of the application provides a method for determining cloud resource servers, which comprises the steps of obtaining service demand processing capacity, system reservation processing capacity, system self-monitoring consumption capacity and system index reservation capacity, then determining target capacity required by completing target service based on the service demand processing capacity and the system self-monitoring consumption capacity, determining capacity required by completing internal processes based on the system reservation processing capacity and the system index reservation capacity, and finally determining the number of cloud resource servers required to be applied according to the target capacity, the internal capacity, system target operation load and service future development coefficients. Therefore, the method can accurately predict the number of the cloud resource servers to be applied based on different business modes, and comprehensively considers various factors influencing the utilization of the cloud resource servers, so that the utilization rate of the cloud resources can be improved.
Drawings
In order to more clearly illustrate the technical method of the embodiments of the present application, the drawings needed to be used in the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without inventive labor.
Fig. 1 is a schematic flowchart of a method for determining a cloud resource server according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of a cloud resource application evaluation process provided in an embodiment of the present application;
fig. 3 is a schematic diagram of a CPU control provided in an embodiment of the present application;
fig. 4 is a schematic diagram of a memory control according to an embodiment of the present disclosure;
fig. 5 is a schematic diagram of a cloud resource utilization short-board classification statistic provided in an embodiment of the present application;
fig. 6 is a development schematic diagram of a method for determining a cloud resource server according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a device for determining a cloud resource server according to an embodiment of the present application.
Detailed Description
The scheme in the embodiments provided in the present application will be described below with reference to the drawings in the present application.
The terms "first", "second" in the embodiments of the present application are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or as implying any indication of the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.
It should be noted that the login based on multiple applications provided by the present invention can be used in the network security field or the financial field. The above is merely an example, and the application field of the login based on the plurality of applications provided by the present invention is not limited.
With the continuous expansion of network big data application, the demands of various network systems on cloud resources are increased explosively. However, although the demand of cloud resources is high, the utilization rate of the existing cloud resources is always low, which causes a serious problem of cloud resource waste.
Researchers find that the main reason for the low utilization rate of the cloud resources is that the cloud resource server application is in excess during application.
Therefore, there is a need in the art for a method for determining a cloud resource server to apply for a proper cloud resource server, so as to improve the utilization rate of cloud resources and reduce the waste of cloud resources.
In view of this, the present application provides a method for determining a cloud resource server. The method is applied to the electronic equipment. The electronic device is a device having a data processing capability, and may be, for example, a server, or a terminal device such as a desktop computer, a notebook computer, or a smart phone.
Specifically, the electronic device obtains service demand processing capacity, system reservation processing capacity, system self-monitoring consumption capacity and system index reservation capacity, then determines target capacity required for completing target service based on the service demand processing capacity and the system self-monitoring consumption capacity, determines capacity required for completing internal process based on the system reservation processing capacity and the system index reservation capacity, and finally determines the number of cloud resource servers required to be applied according to the target capacity, the internal capacity, system target operation load and service future development coefficient. Therefore, the method can accurately predict the number of the cloud resource servers to be applied based on different business modes, and comprehensively considers various factors influencing the utilization of the cloud resource servers, so that the utilization rate of the cloud resources can be improved.
In order to facilitate understanding of the technical solution of the present application, a method for determining a cloud resource server provided by the present application is described below with reference to fig. 1.
Referring to a flowchart of a method for determining a cloud resource server shown in fig. 1, specific steps of the method are as follows.
S102: the electronic equipment acquires service requirement processing capacity, system reservation processing capacity, system self-monitoring consumption capacity and system index reservation capacity.
Wherein the processing capacity can be expressed by the number of orders processed by the system per minute (TPMC value). TPMC values are widely used both domestically and abroad to measure the transaction processing capabilities of computer systems.
Specifically, the traffic demand handling capacity L i The system reserved processing capacity S may be a TPMC value consumed by an internal process of the server operating system, in order to complete a TPMC value theoretically required by the target service. System self-monitoring consumption capability alpha i The system index reservation capability β may be a ratio of a TPMC value consumed by a service system self-monitoring program to a reserved redundant processing capability.
S104: the electronic equipment determines the target capacity required by the target service based on the service demand processing capacity and the system self-monitoring consumption capacity.
The target capacity required for the target service may represent the application load, i.e. the transaction capacity required for different services.
The electronic device may determine the remaining capacity of the system other than the system self-monitoring consumption capacity based on the system self-monitoring consumption capacity, and then determine the target capacity required to complete the target service based on the remaining capacity other than the system self-monitoring consumption capacity and the service demand processing capacity. That is, the electronic device can self-monitor the consumption capability α based on the system i Determining the remaining capacity (1-alpha) of the system in addition to the self-monitoring consumption capacity of the system i ) Then based on the remaining capacity (1-alpha) in addition to the self-monitoring consumption capacity of the system i ) And business requirement handling capacity L i Determining a target capability L required to complete a target service i /(1-α i ) Thereby acquiring an application load.
S106: the electronic device determines internal capabilities required to complete the internal processes based on the system reservation processing capability and the system index reservation capability.
The internal capabilities required to complete the internal processes may represent system reservations, i.e. transaction processing capabilities required to process the internal processes of the system.
The electronic device may determine a remaining capability of the system other than the system index reservation capability based on the system index reservation capability, and then determine an internal capability required to complete the internal process based on the remaining capability other than the system index reservation capability and the system reservation processing capability. Namely, the electronic device may determine the remaining capacity (1- β) of the system other than the system index reservation capacity based on the system index reservation capacity β, and then determine the internal capacity S/(1- β) required to complete the internal process based on the remaining capacity (1- β) other than the system index reservation capacity and the system reservation processing capacity S, thereby obtaining the system reservation.
It should be noted that the execution sequence of S104 and S106 is not limited in this embodiment, and the electronic device may first determine the target capability required for completing the target service based on the service demand processing capability and the system self-monitoring consumption capability, and then determine the internal capability required for completing the internal process based on the system reservation processing capability and the system index reservation capability. The electronic device may also determine the internal capability required to complete the internal process based on the system reservation processing capability and the system index reservation capability, and then determine the target capability required to complete the target service based on the service demand processing capability and the system self-monitoring consumption capability. The electronic device may also execute S104 and S106 simultaneously, that is, the electronic device determines the target capability required for completing the target service based on the service demand processing capability and the system self-monitoring consumption capability, and determines the internal capability required for completing the internal process based on the system reservation processing capability and the system index reservation capability.
S108: and the electronic equipment determines the number of the cloud resource servers required to be applied according to the target capacity, the internal capacity, the system target operation load and the service future development coefficient.
The system target operation load represents an operation requirement, i.e., a target load of the system design, and may be 60% in a general case, and may be represented by γ.
The business future development factor represents a development trend, i.e., a transaction processing capability required by the business in the future, which may increase or decrease according to the development of the planned business. In particular, the business future growth factor may be in θ i Where i denotes different traffic types.
The electronic device can be based on the target capability L i /(1-α i ) Internal capacity S/(1-beta), system target operation load gamma and service future development coefficient theta i Determining a required applicationThe number of cloud resource servers. Specifically, the electronic device may be based on the target capability L i /(1-α i ) Internal capacity S/(1-beta), determining the capacity L required to complete the target service server i /(1-α i ) + S/(1-. beta.). Capability L required for completing target service server i /(1-α i ) + S/(1- β) represents a TPMC value required to complete the whole service server. And the electronic equipment determines the redundancy ratio 1/gamma of the server under the condition of the target operation load according to the target operation load gamma of the system. The redundancy ratio 1/γ of the server under the target operation load condition represents the redundancy ratio of the TPMC value required by the entire server under the target operation load condition. The electronic equipment develops the coefficient theta according to the business future i The processing capacity of the single server, i.e. the TPMC value of the single server, is determined.
Further, the electronic device may vary the target capability L by the target capability L according to the calculated value and the published value (optimum value) with an empirical value of 1+ Δ i /(1-α i ) Internal capacity S/(1-beta), system target operation load gamma and service future development coefficient theta i Determining the number N of the cloud resource servers required to be applied, as shown in a formula:
aiming at the problem of low resource utilization rate, the industry proposes target values of the resource utilization rate, such as the average utilization rate of a CPU (Central processing Unit) being more than 35% and less than 80%, and the average utilization rate of a memory being more than 50% and less than 90%. Under the evaluation criterion, there are many cases in the related art in which the resource utilization rate does not satisfy the criterion, as shown in table 1.
TABLE 1 Primary System cloud resource scenarios
Table 1 provides a main system cloud resource condition for determining the number of application servers without using the present solution, where the CPU utilization rates of the system a, the system B, the system F, and the system H do not satisfy the above-mentioned standard of more than 35%, and the memory occupancy rates of the system B, the system C, the system D, the system G, and the system H do not satisfy the above-mentioned standard of more than 50%. Based on the determination method of the cloud resource servers provided in the present solution, the number of cloud resource servers that need to be applied for can be determined again, as shown in table 2.
TABLE 2 number of servers redetermined
This hypothesis may be verified based on the number of servers redetermined in table 2.
H0 shows that the new model evaluation results are the same as the original method, and H1 shows that the new model evaluation results are different from the original method. Significance level α ═ 0.05. Based on the calculated t value, t is calculated to be-1.984, and then the H1 hypothesis is determined to be accepted and the H0 hypothesis is rejected by checking a t value table, so that the new model evaluation result is considered to have a significant difference from the original evaluation method, namely, the resource utilization rate can be effectively improved.
Furthermore, the method provided by the scheme can be subjected to pilot evaluation through a model. Illustratively, two of analytical server-model 1 and high-end application server-model 7A may be selected.
The peak value of holidays of PS domain XDR data and CS domain MC port XDR data reaches about 30TB, the evaluation predicts that a processing bottleneck (average per minute signaling word acquisition data: about 21083333) of about 10TB exists at the later stage, and the gap of processing capacity is about: 2945 ten thousand TPMCs, according to different data processing requirements, 20 analytical server- models 1 and 5 high-end application server-models 7A are required to be applied. The specific evaluation process is shown in fig. 2.
After the off-line analysis and the streaming processing as shown in fig. 2, random sampling and a control chart inspection measure can be adopted to respectively count the CPU occupancy rate and the memory occupancy rate after the cloud resources of the comprehensive performance analysis system are on-line. The CPU occupancy control chart sample data and control chart parameters are shown in table 3.
TABLE 3 CPU occupancy map Table sample
In which, as shown in figure 3,
in the control chart, the Center Line (CL) is:
the Upper Control Limit (UCL) is:
the Lower Control Limit (LCL) is:
in the R control chart, Centerline (CL) is:
the Upper Control Limit (UCL) is:
the Lower Control Limit (LCL) is:
since the CPU occupancy map does not cross the control boundary and no alignment defect occurs, it can be considered that the effective CPU occupancy index satisfies the above-mentioned preset condition, i.e., between 35% and 80%.
The memory occupancy control map sample data and control map parameters may be as shown in table 4.
TABLE 4 sample memory occupancy control map
In which, as shown in figure 4,
in the control chart, the Center Line (CL) is:
the Upper Control Limit (UCL) is:
the Lower Control Limit (LCL) is:
in the R control map, the Centerline (CL) is:
the Upper Control Limit (UCL) is:
the Lower Control Limit (LCL) is:
since the memory occupancy does not exceed the control limit and the arrangement defect does not occur, it can be considered that the effective memory occupancy index satisfies the above-mentioned preset condition, i.e., between 50% and 90%.
Further, after the number of the cloud resource servers is determined according to the method for determining the cloud resource servers provided by the present solution, the types of reasons for low network cloud resource utilization rate are classified and summarized after the activity is implemented, as shown in table 5.
Table 5 statistical table for cloud resource utilization rate short board by resource classification
| Utilization stub resource types | Amount of resources | In proportion of | Cumulative percentage of occupation |
| Centralized storage | 283 | 60.99％ | 60.99％ |
| CPU | 89 | 19.18％ | 80.17％ |
| Others | 53 | 11.42％ | 91.59％ |
| Memory device | 39 | 8.41％ | 100.00％ |
| Total up to | 464 | 100.00％ | 100.00％ |
As shown in fig. 5, the arrangement diagram correspondingly drawn according to table 5 shows that the low calling party syndrome of cloud resource utilization after activity, namely "low CPU occupancy" and "low memory occupancy", is no longer the main reason, so that the scheme can effectively improve the utilization rate of resources.
The method can be applied to a PaaS cloud platform, in the development process, as shown in FIG. 6, the CPU and memory use conditions of a rack server and a virtual machine are collected through a collection layer, then the CPU and memory use conditions are led into a database of a processing layer for processing, finally a statistical form, an analysis view and alarm presentation are displayed on a display layer, and concentrated faults are displayed under some conditions.
Based on the description above, the present application improves this determination method for a cloud resource server. The electronic equipment acquires service demand processing capacity, system reservation processing capacity, system self-monitoring consumption capacity and system index reservation capacity, then determines target capacity required by completing target service based on the service demand processing capacity and the system self-monitoring consumption capacity, determines capacity required by completing internal process based on the system reservation processing capacity and the system index reservation capacity, and finally determines the number of cloud resource servers required to be applied according to the target capacity, the internal capacity, system target operation load and service future development coefficient. Therefore, the method can accurately predict the number of the cloud resource servers to be applied based on different business modes, and comprehensively considers various factors influencing the utilization of the cloud resource servers, so that the utilization rate of the cloud resources can be improved.
Corresponding to the above method embodiment, the present application further provides a device for determining a cloud resource server, and referring to fig. 7, the device 700 includes: an acquisition module 702, a first determination module 704, and a second determination module 706.
The acquisition module is used for acquiring the service demand processing capacity, the system reservation processing capacity, the system self-monitoring consumption capacity and the system index reservation capacity;
a first determining module, configured to determine a target capability required for completing a target service based on the service demand processing capability and the system self-monitoring consumption capability, and determine an internal capability required for completing an internal process based on the system reservation processing capability and the system index reservation capability;
and the second determining module is used for determining the number of the cloud resource servers required to be applied according to the target capacity, the internal capacity, the system target operation load and the service future development coefficient.
In some possible implementations, the first determining module is specifically configured to:
determining a remaining capacity of the system other than the system self-monitoring consumption capacity based on the system self-monitoring consumption capacity;
and determining the target capacity required by the target service based on the residual capacity except the self-monitoring consumption capacity of the system and the service demand processing capacity.
In some possible implementations, the first determining module is specifically configured to:
determining a remaining capacity of the system other than the system indicator reservation capacity based on the system indicator reservation capacity;
determining the internal capacity required for completing the internal process based on the remaining capacity except the system index reservation capacity and the system reservation processing capacity.
In some possible implementations, the second determining module is specifically configured to:
determining the capacity required by the target service server according to the target capacity and the internal capacity;
and determining the number of the cloud resource servers required to be applied according to the capacity required by the target service server, the system target operation load and the service future development coefficient.
In some possible implementations, the second determining module is specifically configured to:
determining the redundancy proportion of the server under the condition of the target operation load according to the target operation load of the system;
and determining the number of the cloud resource servers required to be applied according to the redundancy proportion of the servers under the target operation load condition, the target capacity, the internal capacity and the service future development coefficient.
In some possible implementations, the second determining module is specifically configured to:
determining the processing capacity of a single server according to the service future development coefficient;
and determining the number of the cloud resource servers required to be applied according to the target capacity, the internal capacity, the system target operation load and the processing capacity of the single server.
In some possible implementation manners, the service requirement processing capacity and the system reservation processing capacity are represented by a TPMC value of the number of orders processed by the system per minute, the service requirement processing capacity is a TPMC value theoretically required for completing a target service, and the system reservation processing capacity is a TPMC value consumed by an internal process of a server operating system.
The application provides a device for realizing a login method based on a plurality of applications. The apparatus includes a processor and a memory. The processor and the memory communicate with each other. The processor is configured to execute instructions stored in the memory to cause the apparatus to perform the method for determining a cloud resource server described above.
The present application provides a computer-readable storage medium having stored therein instructions, which, when run on a device, cause the device to execute the above-mentioned method for determining a cloud resource server.
The present application provides a computer program product containing instructions which, when run on a device, cause the device to perform the above-described method of determining a cloud resource server.
It should be noted that the above-described embodiments of the apparatus are merely schematic, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiments of the apparatus provided in the present application, the connection relationship between the modules indicates that there is a communication connection therebetween, and may be implemented as one or more communication buses or signal lines.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by software plus necessary general-purpose hardware, and certainly can also be implemented by special-purpose hardware including special-purpose integrated circuits, special-purpose CPUs, special-purpose memories, special-purpose components and the like. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions may be various, such as analog circuits, digital circuits, or dedicated circuits. However, for the present application, the implementation of a software program is more preferable. Based on such understanding, the technical solutions of the present application may be substantially embodied in the form of a software product, which is stored in a readable storage medium, such as a floppy disk, a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, an exercise device, or a network device) to execute the method according to the embodiments of the present application.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may 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. The procedures or functions described in accordance with the embodiments of the application are all or partially generated when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, 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, for example, from one website site, computer, training device, or data center to another website site, computer, training device, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can store or a data storage device, such as a training device, a data center, etc., that incorporates one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
Claims (10)
1. A method for determining a cloud resource server, the method comprising:
acquiring service demand processing capacity, system reservation processing capacity, system self-monitoring consumption capacity and system index reservation capacity;
determining a target capacity required for completing a target service based on the service demand processing capacity and the system self-monitoring consumption capacity, and determining an internal capacity required for completing an internal process based on the system reservation processing capacity and the system index reservation capacity;
and determining the number of the cloud resource servers required to be applied according to the target capacity, the internal capacity, the system target operation load and the service future development coefficient.
2. The method of claim 1, wherein determining the target capacity needed to complete the target service based on the service demand handling capacity and the system self-monitoring consumption capacity comprises:
determining a remaining capacity of the system other than the system self-monitoring consumption capacity based on the system self-monitoring consumption capacity;
and determining the target capacity required by the target service based on the residual capacity except the self-monitoring consumption capacity of the system and the service demand processing capacity.
3. The method of claim 1, wherein determining internal capabilities needed to complete internal processes based on the system reservation handling capability and the system index reservation capability comprises:
determining a remaining capacity of the system other than the system indicator reservation capacity based on the system indicator reservation capacity;
determining the internal capacity required for completing the internal process based on the remaining capacity except the system index reservation capacity and the system reservation processing capacity.
4. The method according to any one of claims 2 to 3, wherein the determining the number of cloud resource servers that need to be applied according to the target capacity, the internal capacity, the system target operation load and the business future development coefficient comprises:
determining the capacity required by the target service server according to the target capacity and the internal capacity;
and determining the number of the cloud resource servers required to be applied according to the capacity required by the target service server, the system target operation load and the service future development coefficient.
5. The method according to any one of claims 2 to 3, wherein the determining the number of cloud resource servers that need to be applied according to the target capacity, the internal capacity, the system target operation load and the business future development coefficient comprises:
determining the redundancy proportion of the server under the condition of the target operation load according to the target operation load of the system;
and determining the number of the cloud resource servers required to be applied according to the redundancy proportion of the servers under the target operation load condition, the target capacity, the internal capacity and the service future development coefficient.
6. The method according to any one of claims 2 to 3, wherein the determining the number of cloud resource servers which need to be applied according to the target capacity, the internal capacity, the system target operation load and the service future development coefficient comprises:
determining the processing capacity of a single server according to the service future development coefficient;
and determining the number of the cloud resource servers required to be applied according to the target capacity, the internal capacity, the system target operation load and the processing capacity of the single server.
7. The method of claim 1, wherein the service requirement processing capability and the system reservation processing capability are expressed by a number of orders per minute (TPMC) value processed by the system, the service requirement processing capability is a TPMC value theoretically required for completing the target service, and the system reservation processing capability is a TPMC value consumed by a process inside the server operating system.
8. An apparatus for determining a cloud resource server, the apparatus comprising:
the acquisition module is used for acquiring the service demand processing capacity, the system reservation processing capacity, the system self-monitoring consumption capacity and the system index reservation capacity;
a first determining module, configured to determine a target capability required for completing a target service based on the service demand processing capability and the system self-monitoring consumption capability, and determine an internal capability required for completing an internal process based on the system reservation processing capability and the system index reservation capability;
and the second determining module is used for determining the number of the cloud resource servers required to be applied according to the target capacity, the internal capacity, the system target operation load and the service future development coefficient.
9. An apparatus, comprising a processor and a memory;
the processor is to execute instructions stored in the memory to cause the device to perform the method of any of claims 1 to 7.
10. A computer-readable storage medium comprising instructions that direct a device to perform the method of any of claims 1-7.
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