CN115022320B - Cloud resource server determination method, device, equipment and medium - Google Patents
Cloud resource server determination method, device, equipment and medium Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
- H04L67/1004—Server selection for load balancing
- H04L67/1008—Server selection for load balancing based on parameters of servers, e.g. available memory or workload
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Abstract
The method comprises the steps of obtaining service demand processing capacity, system reserved processing capacity, system self-monitoring consumption capacity and system index reserved 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 reserved processing capacity and the system index reserved 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 future development coefficients of the service. Therefore, the method can accurately predict the number of the cloud resource servers to be applied based on different service modes, and comprehensively consider 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 disclosure 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 for cloud resources are in explosive growth. However, although the cloud resource demand is higher, the utilization rate of the existing cloud resource is always lower, so that the problem of cloud resource waste is serious.
Researchers find that the main reason for the problem of low utilization rate of cloud resources is that the problem of excessive application of a cloud resource server exists at the time of application.
Therefore, a method for determining a cloud resource server is needed in the industry to apply for a suitable 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 method for determining a cloud resource server. According to the method, the number of cloud resource servers to be applied can be determined according to various factors, and the utilization rate of cloud resources is improved. The application also provides a corresponding device, equipment, medium and program product of 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 cloud resource servers to be applied according to the target capacity, the internal capacity, the system target operation load and the business future development coefficient.
In some possible implementations, the determining the target capacity required to complete the target service based on the service demand processing capacity and the system self-monitoring consumption capacity includes:
determining a remaining capacity of the system other than the system self-monitoring consumption capacity based on the system self-monitoring consumption capacity;
determining a target capability required for completing a target service based on the remaining capability except the self-monitoring consumption capability of the system and the service demand processing capability.
In some possible implementations, the determining the internal capability required to complete the internal process based on the system reservation processing capability and the system indicator reservation capability includes:
determining a remaining capacity of the system other than the system index reservation capacity based on the system index reservation capacity;
and determining the internal capacity required for completing the internal process based on the residual capacity except the reserved capacity of the system index and the reserved processing capacity of the system.
In some possible implementations, the determining the number of cloud resource servers to be applied according to the target capability, the internal capability, the system target operation load and the future development coefficient of the service includes:
determining the capacity required by completing the target service server according to the target capacity and the internal capacity;
and determining the number of cloud resource servers to be applied according to the capacity required by the fixed-completion target service server, the target operation load of the system and the future development coefficient of the service.
In some possible implementations, the determining the number of cloud resource servers to be applied according to the target capability, the internal capability, the system target operation load and the future development coefficient of the service includes:
determining the redundancy proportion of the server under the condition of the target operating load according to the target operating load of the system;
and determining the number of cloud resource servers to be applied according to the redundancy proportion of the servers under the target operating load condition, the target capacity, the internal capacity and the service future development coefficient.
In some possible implementations, the determining the number of cloud resource servers to be applied according to the target capability, the internal capability, the system target operation load and the future development coefficient of the service includes:
determining the processing capacity of a single server according to the future development coefficient of the service;
and determining the number of cloud resource servers 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 implementations, the service requirement processing capability and the system reservation processing capability are represented by the number of orders processed by the system per minute TPMC value, where the service requirement processing capability is a TPMC value theoretically required for completing a target service, and the system reservation processing capability is a TPMC value consumed by an internal process of the server operating system.
In a second aspect, the present application provides a determining apparatus of a cloud resource server, where the apparatus includes:
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 cloud resource servers to be applied according to the target capacity, the internal capacity, the system target operation load and the business 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;
determining a target capability required for completing a target service based on the remaining capability except the self-monitoring consumption capability of the system and the service demand processing capability.
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;
and determining the internal capacity required for completing the internal process based on the residual capacity except the reserved capacity of the system index and the reserved processing capacity of the system.
In some possible implementations, the second determining module is specifically configured to:
determining the capacity required by completing the target service server according to the target capacity and the internal capacity;
and determining the number of cloud resource servers to be applied according to the capacity required by the fixed-completion target service server, the target operation load of the system and the future development coefficient of the service.
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 operating load according to the target operating load of the system;
and determining the number of cloud resource servers to be applied according to the redundancy proportion of the servers under the target operating 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 future development coefficient of the service;
and determining the number of cloud resource servers 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 implementations, the service requirement processing capability and the system reservation processing capability are represented by the number of orders processed by the system per minute TPMC value, where the service requirement processing capability is a TPMC value theoretically required for completing a target service, and the system reservation processing capability is a TPMC value consumed by an internal process of the 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 device to perform a method of determining a cloud resource server 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 to 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 comprising instructions which, when run on a device, cause the device to perform the method of determining a cloud resource server according to the first aspect or any implementation manner of the first aspect.
Further combinations of the present application may be made to provide further implementations based on the implementations provided in the above aspects.
From the above technical solutions, the embodiments of the present application have 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 for completing target service based on the service demand processing capacity and the system self-monitoring consumption capacity, determining capacity required for completing internal processes based on the system reservation processing capacity and the system index reservation capacity, and finally 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. Therefore, the method can accurately predict the number of the cloud resource servers to be applied based on different service modes, and comprehensively consider 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 that are required to be used in the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort to those of ordinary skill in the art.
Fig. 1 is a flow chart of a method for determining a cloud resource server according to an embodiment of the present application;
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 control diagram of a CPU according to an embodiment of the present disclosure;
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 classification statistics of a cloud resource utilization short board according to 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 determining device of a cloud resource server according to an embodiment of the present application.
Detailed Description
The embodiments of 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 implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature.
It should be noted that, the login based on multiple applications provided by the present invention may be used in the network security field or the financial field. The above is merely an example, and is not intended to limit the application field of login based on multiple applications provided by the present invention.
With the continuous expansion of network big data application, the demands of various network systems for cloud resources are in explosive growth. However, although the cloud resource demand is higher, the utilization rate of the existing cloud resource is always lower, so that the problem of cloud resource waste is serious.
Researchers find that the main reason for the problem of low utilization rate of cloud resources is that the problem of excessive application of a cloud resource server exists at the time of application.
Therefore, a method for determining a cloud resource server is needed in the industry to apply for a suitable 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 refers to a device with data processing capability, for example, may be a server, or a terminal device such as a desktop, a notebook, or a smart phone.
Specifically, the electronic device acquires a service demand processing capability, a system reserved processing capability, a system self-monitoring consumption capability and a system index reserved capability, then determines a target capability required for completing a target service based on the service demand processing capability and the system self-monitoring consumption capability, determines a capability required for completing an internal process based on the system reserved processing capability and the system index reserved capability, and finally determines the number of cloud resource servers required to be applied according to the target capability, the internal capability, a system target operation load and a service future development coefficient. Therefore, the method can accurately predict the number of the cloud resource servers to be applied based on different service modes, and comprehensively consider 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 in the present application is described below with reference to fig. 1.
Referring to the flowchart of the method for determining the cloud resource server shown in fig. 1, specific steps of the method are as follows.
S102: the electronic equipment acquires service demand 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 at home and abroad to measure the transactional capabilities of computer systems.
Specifically, business demand processing capability L i The processing power S may be reserved for TPMC values theoretically required to complete the target service, and the TPMC values consumed by internal processes of the server operating system. Self-monitoring consumption capability alpha of system i The system index reservation capability beta may be the duty cycle for reserving redundant processing capability for the TPMC value duty cycle consumed by the service system self-monitoring program.
S104: the electronic device determines a target capability required to complete the target service based on the service demand processing capability and the system self-monitoring consumption capability.
The target capabilities required by the target service may represent the application load, i.e. the transaction capabilities required by the different services.
Electronic equipmentThe remaining capacity of the system in addition to the system self-monitoring consumption capacity may be determined based on the system self-monitoring consumption capacity, and then the target capacity required to complete the target service may be determined based on the remaining capacity in addition to the system self-monitoring consumption capacity and the service demand processing capacity. I.e. the electronic device can monitor the consumption capability alpha based on the system itself i Determining remaining capacity (1-alpha) of a system other than self-monitoring consumption capacity of the system i ) And then based on the remaining capacity (1-alpha) other than the self-monitoring consumption capacity of the system i ) And business demand processing capability 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 process based on the system reservation processing capabilities and the system index reservation capabilities.
The internal capabilities required to complete an internal process may represent system reservations, i.e., transaction processing capabilities required to process the internal process of the system.
The electronic device may determine a remaining capacity of the system other than the system index reservation capacity based on the system index reservation capacity, and then determine an internal capacity required to complete the internal process based on the remaining capacity other than the system index reservation capacity and the system reservation processing capacity. That is, 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, in this embodiment, the execution sequence of S104 and S106 is not limited, and the electronic device may determine, first, the target capability required for completing the target service based on the service requirement processing capability and the system self-monitoring consumption capability, and then determine, based on the system reserved processing capability and the system index reserved capability, the internal capability required for completing the internal process. The electronic device may also determine, based on the reserved processing capability of the system and the reserved capability of the system index, an internal capability required for completing the internal process, and then determine, based on the processing capability of the service requirement and the self-monitoring consumption capability of the system, a target capability required for completing the target service. The electronic device may also perform S104 and S106 simultaneously, that is, the electronic device determines, based on the service demand processing capability and the system self-monitoring consumption capability, the target capability required for completing the target service, and determines, based on the system reservation processing capability and the system index reservation capability, the internal capability required for completing the internal process.
S108: and the electronic equipment determines the number of cloud resource servers to be applied according to the target capacity, the internal capacity, the system target operation load and the business future development coefficient.
The target operating load of the system represents the operating requirement, i.e., the target load of the system design, which may be 60% in general, and may be represented by γ.
The future evolution coefficient of the business represents the evolution trend, i.e. the transaction capability required in the future of the business, which capability may increase or decrease back depending on the evolution of the planned business. Specifically, the future development coefficient of the business can be calculated by theta i And (c) a representation, wherein i represents a different traffic type.
The electronic device can be based on the target capability L i /(1-α i ) Internal capacity S/(1-beta), system target operating load gamma and future business development coefficient theta i And determining the number of cloud resource servers to be applied. Specifically, the electronic device may be based on the target capability L i /(1-α i ) Internal capability S/(1-beta), determining the capability L required to complete the target traffic server i /(1-α i ) +S/(1-. Beta.). Capability L required for completing target business server i /(1-α i ) +S/(1-. Beta.) represents the value of TPMC required to complete a certain traffic server as a whole. And the electronic equipment determines the redundancy proportion 1/gamma of the server under the condition of the target operating load according to the target operating load gamma of the system. The redundancy ratio 1/γ of the server under the target operating load condition represents the redundancy ratio of the TPMC value required for the entire server under the target operating load condition. The electronic equipment develops 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 can determine the target capability L based on the empirical value 1+delta of the deviation of the calculated value from the published value (optimal value) i /(1-α i ) Internal capacity S/(1-beta), system target operating load gamma and future business development coefficient theta i The number N of cloud resource servers needing to be applied is determined, and the number N is as shown in the formula:
aiming at the problem of low resource utilization, the industry proposes that the target value of the resource utilization, such as CPU average utilization less than or equal to 35% and less than or equal to 80%, and memory average utilization less than or equal to 50% and less than or equal to 90%. Under this evaluation criterion, there are many cases where the resource utilization rate does not satisfy the criterion in the related art, as shown in table 1.
TABLE 1 Main System cloud resource Condition
Table 1 provides a case of determining the number of application servers by using the present scheme, wherein the CPU utilization of system A, system B, system F and system H do not meet the above criteria, and the memory occupancy of system B, system C, system D, system G and system H do not meet the above criteria, which are above 50%. Based on the determination method of the cloud resource server provided in the present solution, the number of cloud resource servers to be applied for may be redetermined, as shown in table 2.
Table 2 redetermined number of servers
This assumption may be checked based on the redetermined number of servers in table 2.
H0 represents that the new model evaluation result is the same as the original method, and H1 represents that the new model evaluation result is different from the original method. Significance level α=0.05. Based on the calculated t value, t= -1.984 is obtained through calculation, and then the assumption that H1 is accepted and the assumption that H0 is rejected is determined through checking a t value table, so that the new model evaluation result can be considered to have a significant difference from the original evaluation method, and the resource utilization rate can be effectively improved.
Further, the method proposed by the scheme can be subjected to trial evaluation through a model. Illustratively, both analytical server-model 1, high-end application server-model 7A may be selected.
PS domain XDR data and CS domain MC port XDR data, holiday peak value has reached about 30TB, and it is estimated that there is about 10TB processing bottleneck (average signaling per minute collected data: 21083333) at the later stage, and the processing capacity gap is about: 2945 ten thousand TPMC, according to the data processing requirement, total 20 of the application analysis server-model 1 and 5 of the high-end application server-model 7A. The specific evaluation process is shown in fig. 2.
After offline analysis and stream processing as shown in fig. 2, random sampling and control diagram checking measures can be adopted to respectively count the CPU occupancy rate and the memory occupancy rate after cloud resources of the comprehensive performance analysis system are online. The sample data and parameters of the CPU occupancy control chart are shown in Table 3.
TABLE 3 CPU occupancy control map sample
Wherein, as shown in figure 3,in the control diagram, the Center Line (CL) is: />The Upper Control Limit (UCL) is:the Lower Control Limit (LCL) is: />In the R control chart, the Center Line (CL) is: />The Upper Control Limit (UCL) is: />The Lower Control Limit (LCL) is: />
Since the CPU occupancy control map does not cross the control boundary nor has an arrangement defect, it can be considered that the effective CPU occupancy index satisfies the above-mentioned preset condition, that is, between 35% and 80%.
The memory occupancy map sample data and map parameters may be as shown in table 4.
TABLE 4 memory occupancy control map samples
Wherein, as shown in figure 4,in the control diagram, middleThe Core Line (CL) is: />The Upper Control Limit (UCL) is:the Lower Control Limit (LCL) is: />In the R control chart, the Center Line (CL) is: />The Upper Control Limit (UCL) is: />The Lower Control Limit (LCL) is: />
Since the memory occupancy rate does not cross the control boundary line and the arrangement defect does not occur, the effective memory occupancy rate index can be considered to satisfy the above-mentioned preset condition, that is, to be between 50% and 90%.
Further, after the number of cloud resource servers is determined according to the determination method of the cloud resource servers provided by the scheme, the reason types of low network cloud resource utilization rate are classified and summarized after the implementation of the activity, as shown in table 5.
Table 5 cloud resource utilization short board per resource class statistics table
| Utilization short board resource type | Number of resources | Proportion of occupied | Cumulative duty cycle |
| Centralized storage of | 283 | 60.99％ | 60.99％ |
| CPU | 89 | 19.18％ | 80.17％ |
| Others | 53 | 11.42％ | 91.59％ |
| Memory | 39 | 8.41％ | 100.00％ |
| Totalizing | 464 | 100.00％ | 100.00％ |
As shown in FIG. 5, the arrangement chart correspondingly drawn according to Table 5 shows that the calling sign that the cloud resource utilization is low after the activity is "the CPU occupancy rate is low" and the memory occupancy rate is low "are not main reasons any more, so that the resource utilization rate can be effectively improved by the scheme.
The method of the scheme can be applied to the PaaS cloud platform, in the development process, as shown in fig. 6, the CPU and memory use conditions of the rack server and the virtual machine are acquired through the acquisition layer and then are imported into the database of the processing layer for processing, and finally, a statistical report, an analysis view and an alarm presentation are displayed at the display layer, and in some cases, concentrated faults are displayed.
Based on the description above, the present application improves this method for determining a cloud resource server. The electronic equipment acquires service demand processing capacity, system reserved processing capacity, system self-monitoring consumption capacity and system index reserved 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 processes based on the system reserved processing capacity and the system index reserved capacity, and finally determines the number of cloud resource servers required to be applied according to the target capacity, the internal capacity, the system target running load and the service future development coefficient. Therefore, the method can accurately predict the number of the cloud resource servers to be applied based on different service modes, and comprehensively consider 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 determining device of a cloud resource server, 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 cloud resource servers to be applied according to the target capacity, the internal capacity, the system target operation load and the business 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;
determining a target capability required for completing a target service based on the remaining capability except the self-monitoring consumption capability of the system and the service demand processing capability.
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;
and determining the internal capacity required for completing the internal process based on the residual capacity except the reserved capacity of the system index and the reserved processing capacity of the system.
In some possible implementations, the second determining module is specifically configured to:
determining the capacity required by completing the target service server according to the target capacity and the internal capacity;
and determining the number of cloud resource servers to be applied according to the capacity required by the fixed-completion target service server, the target operation load of the system and the future development coefficient of the service.
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 operating load according to the target operating load of the system;
and determining the number of cloud resource servers to be applied according to the redundancy proportion of the servers under the target operating 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 future development coefficient of the service;
and determining the number of cloud resource servers 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 implementations, the service requirement processing capability and the system reservation processing capability are represented by the number of orders processed by the system per minute TPMC value, where the service requirement processing capability is a TPMC value theoretically required for completing a target service, and the system reservation processing capability is a TPMC value consumed by an internal process of the 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 the instructions stored in the memory, to cause the device to perform the method of determining the cloud resource server.
The present application provides a computer readable storage medium having instructions stored therein that, when executed on a device, cause the device to perform the method of determining a cloud resource server described above.
The present application provides a computer program product containing instructions that, when run on a device, cause the device to perform the method of determining a cloud resource server described above.
It should be further noted that the above-described apparatus embodiments are merely illustrative, and that the units described as separate units may or may not be physically separate, and that units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the device provided by the application, the connection relation between the modules represents that the modules have communication connection therebetween, and can be specifically implemented as one or more communication buses or signal lines.
From the above description of the embodiments, it will be apparent to those skilled in the art that the present application may be implemented by means of software plus necessary general purpose hardware, or of course may be implemented by dedicated hardware including application specific integrated circuits, dedicated CPUs, dedicated memories, dedicated 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 can be varied, such as analog circuits, digital circuits, or dedicated circuits. However, a software program implementation is a preferred embodiment in many cases for the present application. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product 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, etc., including several instructions for causing a computer device (which may be a personal computer, a training device, or a network device, etc.) to perform the method described in the embodiments of the present application.
In the above embodiments, it may be implemented in whole or in part 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. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, training device, or data center to another website, computer, training device, or data center via a wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be stored by a computer or a data storage device such as a training device, a data center, or the like that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy Disk, a hard Disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
Claims (7)
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, wherein the service demand processing capacity is a TPMC value theoretically required for completing target service, the system reservation processing capacity is a TPMC value consumed by an internal process of a server operating system, the system self-monitoring consumption capacity is a TPMC value duty ratio consumed by a service system self-monitoring program, the system index reservation capacity is a duty ratio for reserving redundant processing capacity, a system target running load is a target load of system design, and a service future development coefficient is a transaction processing capacity required in the future of the service;
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;
determining the number of cloud resource servers to be applied according to the target capacity, the internal capacity, the system target operation load and the future development coefficient of the service;
the determining the target capacity required for completing the target service based on the service demand processing capacity and the self-monitoring consumption capacity of the system comprises the following steps:
determining a remaining capacity of the system other than the system self-monitoring consumption capacity based on the system self-monitoring consumption capacity;
determining a target capacity required for completing a target service based on the remaining capacity except the self-monitoring consumption capacity of the system and the service demand processing capacity;
the determining the internal capability required for completing the internal process based on the system reserved processing capability and the system index reserved capability includes:
determining a remaining capacity of the system other than the system index reservation capacity based on the system index reservation capacity;
and determining the internal capacity required for completing the internal process based on the residual capacity except the reserved capacity of the system index and the reserved processing capacity of the system.
2. The method of claim 1, wherein the determining the number of cloud resource servers to apply for based on the target capacity, the internal capacity, a system target operating load, and a business future development factor comprises:
determining the capacity required by completing the target service server according to the target capacity and the internal capacity;
and determining the number of cloud resource servers to be applied according to the capacity required by the completion target service server, the target operation load of the system and the future development coefficient of the service.
3. The method of claim 1, wherein the determining the number of cloud resource servers to apply for based on the target capacity, the internal capacity, a system target operating load, and a business future development factor comprises:
determining the redundancy proportion of the server under the condition of the target operating load according to the target operating load of the system;
and determining the number of cloud resource servers to be applied according to the redundancy proportion of the servers under the target operating load condition, the target capacity, the internal capacity and the service future development coefficient.
4. The method of claim 1, wherein the determining the number of cloud resource servers to apply for based on the target capacity, the internal capacity, a system target operating load, and a business future development factor comprises:
determining the processing capacity of a single server according to the future development coefficient of the service;
and determining the number of cloud resource servers to be applied according to the target capacity, the internal capacity, the system target operation load and the processing capacity of the single server.
5. A determining apparatus of a cloud resource server, the apparatus comprising:
the system comprises an acquisition module, a system reservation processing capacity, a system self-monitoring consumption capacity and a system index reservation capacity, wherein the system reservation processing capacity is a TPMC value theoretically required for completing a target service, the system reservation processing capacity is a TPMC value consumed by an internal process of a server operating system, the system self-monitoring consumption capacity is a TPMC value duty ratio consumed by a service system self-monitoring program, the system index reservation capacity is a duty ratio for reserving redundant processing capacity, a system target running load is a target load of a system design, and a service future development coefficient is a transaction processing capacity required in the future;
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;
the second determining module is used for determining the number of cloud resource servers to be applied according to the target capacity, the internal capacity, the system target operation load and the business future development coefficient;
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;
determining a target capacity required for completing a target service based on the remaining capacity except the self-monitoring consumption capacity of the system and the service demand processing capacity;
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;
and determining the internal capacity required for completing the internal process based on the residual capacity except the reserved capacity of the system index and the reserved processing capacity of the system.
6. An electronic device comprising a processor and a memory;
the processor is configured to execute instructions stored in the memory to cause the apparatus to perform the method of any one of claims 1 to 4.
7. A computer readable storage medium storing instructions which, when executed by a device, implement the method of any one of claims 1 to 4.
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