CN106254166A - A kind of cloud platform resource allocation method based on Disaster Preparation Center and system - Google Patents

Abstract

The invention provides a kind of cloud platform resource allocation method based on Disaster Preparation Center and system, relate to disaster tolerance technology field.Described method includes: gather the load data of the server that each operation system of Disaster Preparation Center's deployment is corresponding in cloud platform；Capturing service service data from production environment；Described load data and service operation data are classified according to the disaster tolerance mode hierarchies preset, obtains the loading condition data of each operation system under different disaster tolerance pattern；According to default tactful allocation list and loading condition data, described cloud platform is carried out resource distribution.Achieve and for different calamity molar formula corresponding different strategy, virtual machine, physical machine are migrated, reach to economize on resources, the effect of energy efficient.

Description

A cloud platform resource configuration method and system based on disaster recovery center

technical field

The present invention relates to the technical field of disaster recovery, in particular to the resource scheduling technology of the disaster recovery center, specifically a cloud platform resource configuration method and system based on the disaster recovery center.

Background technique

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

With the development of business, the disaster recovery center has deployed more and more business systems, including the core production system of active-active, primary and secondary architectures, as well as various disaster recovery of hot standby, warm standby, and cold standby architectures A large number of business systems in the model are used to ensure the safe and stable operation of the production system, and most of the business systems will be gradually migrated to the cloud platform. At present, in the physical machine cluster of the cloud platform, there are many virtual machines running on each physical machine on average. During peak business hours, it is easy for a certain physical machine to have a high load while other physical machines in the same cluster have a low load, that is, physical machine resources The unbalanced utilization rate will affect the efficiency of business operation and cause waste of resources.

At present, Patrol monitoring is generally used for physical machine monitoring, but Patrol monitoring and the cloud platform are independent platforms without a common interface, and it is impossible to combine the load of the physical machine with advanced features such as hot migration of the cloud platform.

Therefore, how to research and develop a new solution to configure cloud platform resources for different disaster recovery modes is a technical problem to be solved urgently in this field.

Contents of the invention

In order to overcome the above-mentioned technical problems existing in the prior art, the present invention provides a cloud platform resource configuration method and system based on the disaster recovery center, by obtaining the load data of the servers corresponding to the various business systems deployed by the disaster recovery center on the cloud platform, And obtain business operation data from the production environment, introduce the preset disaster recovery mode level to classify, and combine the preset policy configuration table to configure resources, and realize different strategies corresponding to different disaster recovery modes for virtual machines, physical Machines are migrated to achieve the effect of saving resources and energy consumption.

In order to achieve the above object, the present invention provides a cloud platform resource configuration method based on a disaster recovery center, the method comprising: collecting the load data of servers corresponding to each business system deployed by the disaster recovery center on the cloud platform; collecting data from the production environment Business operation data; classify the load data and business operation data according to the preset disaster recovery mode level to obtain the load status data of each business system in different disaster recovery modes; according to the preset policy configuration table and load status data The cloud platform configures resources.

In a preferred embodiment of the present invention, a script is used to collect load data of a server, and the server includes a virtual machine and a physical machine.

In a preferred embodiment of the present invention, the load data includes CPU usage, memory usage, and storage capacity usage, and the business operation data includes a daily average value of transaction volume and a daily peak value of transaction volume.

In a preferred embodiment of the present invention, the disaster recovery mode includes a dual-active disaster recovery mode, a master-slave disaster recovery mode, a warm-standby disaster recovery mode, and a cold-standby disaster recovery mode.

In a preferred embodiment of the present invention, when the business system is in a dual-active disaster recovery mode or a primary-slave disaster recovery mode, configuring resources on the cloud platform according to the policy configuration table and load data includes: Collect prediction information in the environment; determine the optimization strategy of the business system according to the prediction information and the policy configuration table; configure the resources of the business system according to the optimization strategy and load data.

In a preferred embodiment of the present invention, when the business system is in warm standby disaster recovery mode or cold standby disaster recovery mode, configuring resources on the cloud platform according to the policy configuration table and load data includes determining the An optimization strategy of the business system; performing resource allocation on the business system according to the optimization strategy and load data.

In a preferred embodiment of the present invention, configuring the resources of the business system according to the optimization strategy and load data includes obtaining setting information from the optimization strategy;

When the resource configuration of the business system does not meet the set information, read the virtual machine corresponding to the physical machine from the preset database according to the name of the physical machine corresponding to the business system; from the load data Obtain the CPU usage ratio and the memory usage ratio of the virtual machine; determine the coefficient of the virtual machine according to the CPU usage ratio and the memory usage ratio; select the virtual machine according to the virtual machine coefficient and the optimization strategy The virtual machine to be migrated; selecting the physical machine to be migrated according to the virtual machine to be migrated; migrating the physical machine to be migrated and the virtual machine to be migrated, so that the service system after migration meets the set information.

One of the purposes of the present invention is to provide a cloud platform resource configuration system based on a disaster recovery center, the system includes a load data collection device for collecting the servers corresponding to each business system deployed by the disaster recovery center on the cloud platform The load data; the operation data acquisition device is used to collect business operation data from the production environment; the data classification device is used to classify the load data and business operation data according to the preset disaster recovery mode level to obtain different disaster recovery The load status data of each business system in the mode; the resource configuration device is used to configure the resources of the cloud platform according to the preset policy configuration table and the load status data.

In a preferred embodiment of the present invention, when the business system is in warm standby disaster recovery mode or cold standby disaster recovery mode, the resource configuration device includes a first optimization policy determination module, which is used to determine the business system according to the policy configuration table an optimization strategy; a resource configuration module configured to configure resources for the business system according to the optimization strategy and load data.

In a preferred embodiment of the present invention, when the business system is in the active-active disaster recovery mode or the primary-slave disaster recovery mode, the resource configuration device further includes a prediction information collection module, which is used to collect prediction information from the production environment information;

The second optimization strategy determination module is configured to determine the optimization strategy of the business system according to the prediction information and the strategy configuration table.

In a preferred embodiment of the present invention, the resource configuration module includes an acquisition unit, configured to acquire setting information from the optimization strategy;

A reading unit, configured to read the virtual machine corresponding to the physical machine from a preset database according to the name of the physical machine corresponding to the business system when the resource configuration of the business system does not meet the set information; The usage acquisition unit is configured to obtain the CPU usage ratio and the memory usage ratio of the virtual machine from the load condition data; the coefficient determination unit is configured to determine the CPU usage ratio and the memory usage ratio according to the CPU usage ratio and the memory usage ratio. The coefficient of the virtual machine; the first determination unit is used to select the virtual machine to be migrated according to the coefficient of the virtual machine and the optimization strategy; the second determination unit is used to select the physical machine to be migrated according to the virtual machine to be migrated and a migration unit, configured to migrate the physical machine to be migrated and the virtual machine to be migrated, so that the service system after migration meets the setting information.

The beneficial effect of the present invention is that it provides a cloud platform resource configuration method and system based on the disaster recovery center, by obtaining the load data of the servers corresponding to each business system deployed in the disaster recovery center on the cloud platform, and obtaining the load data from the production environment The business operation data is classified by introducing the preset disaster recovery mode level, and the resource configuration is combined with the preset policy configuration table, which realizes the migration of virtual machines and physical machines according to different policies corresponding to different disaster recovery modes, and achieves saving resources and save energy.

In order to make the above and other objects, features and advantages of the present invention more comprehensible, preferred embodiments will be described in detail below together with the accompanying drawings.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. Those skilled in the art can also obtain other drawings based on these drawings without creative work.

Fig. 1 is a flow chart of a cloud platform resource configuration method based on a disaster recovery center provided by an embodiment of the present invention;

FIG. 2 is a flowchart of Embodiment 1 of step S104 in FIG. 1;

FIG. 3 is a flowchart of the second embodiment of step S104 in FIG. 1;

Fig. 4 is the flowchart of step S303 in Fig. 2;

Fig. 5 is a structural block diagram of a cloud platform resource configuration system based on a disaster recovery center provided by an embodiment of the present invention;

6 is a structural block diagram of Embodiment 1 of a resource configuration device in a cloud platform resource configuration system based on a disaster recovery center provided by an embodiment of the present invention;

7 is a structural block diagram of Embodiment 2 of a resource configuration device in a cloud platform resource configuration system based on a disaster recovery center provided by an embodiment of the present invention;

FIG. 8 is a structural block diagram of a resource configuration module in a cloud platform resource configuration system based on a disaster recovery center provided by an embodiment of the present invention.

detailed description

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, device, method or computer program product. Therefore, the present disclosure may be embodied in the form of complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

The principle and spirit of the present invention will be explained in detail below with reference to several representative embodiments of the present invention.

Among the currently used cloud platform management tools, the distribution of each virtual machine can be monitored, and the monitoring function of physical machine resource usage is temporarily absent. Physical machine monitoring uses Patrol commercial product monitoring, but Patrol monitoring and cloud resource management platforms are independent of each other. Platform, without a common interface, cannot combine the load of the physical machine with advanced features such as live migration of the cloud platform.

At present, the virtual machine migration and other functions of cloud platform management tools are aimed at the high-availability migration of physical machine failures. This function is the most basic high-availability feature of the pervasive cloud computing platform. It is necessary to realize the automation suitable for the business characteristics of the disaster recovery center For resource optimization, functions such as data acquisition, data analysis, process control, optimization, and decision-making must be introduced to the cloud platform.

Aiming at the above technical problems, the present invention proposes a cloud platform resource configuration method and system based on a disaster recovery center.

Fig. 1 is the concrete flowchart of a kind of cloud platform resource allocation method based on disaster recovery center that the present invention proposes, please refer to Fig. 1, described method comprises:

S101: Collect load data of servers corresponding to various business systems deployed in the disaster recovery center on the cloud platform.

In a specific embodiment, the load data of the server may be collected through a script or the like. The server includes a virtual machine and a physical machine, and the load data includes CPU usage, memory usage, and storage capacity usage.

S102: Collect business operation data from the production environment. In the present invention, the mentioned production environment refers to the IT system environment used to process actual financial transaction information in a typical financial system. In a specific implementation manner, the business operation data includes a daily average value of transaction volume and a daily peak value of transaction volume. That is, step S102 opens up the transaction monitoring interface between the disaster recovery center and the production center

S103: Classify the load data and business operation data according to the preset disaster recovery mode level to obtain load data of each business system in different disaster recovery modes. In a specific implementation manner, the preset disaster recovery mode levels include active-active disaster recovery mode, primary and secondary disaster recovery mode, warm-standby disaster recovery mode, and cold-standby disaster recovery mode. The disaster recovery mode level is configurable, and the load data and business operation data are classified to form load data with the business system as the dimension for decision-making in subsequent steps.

S104: Perform resource allocation on the cloud platform according to a preset policy configuration table and load condition data.

Fig. 2 is a flow chart of the first embodiment of step S104, please refer to Fig. 2, in the first embodiment, when the business system is in warm backup disaster recovery mode or cold backup disaster recovery mode, step S104 includes:

S201: Determine the optimization strategy of the business system according to the strategy configuration table;

S202: Perform resource allocation on the business system according to the optimization strategy and load data.

In Embodiment 1, the policy configuration table is shown in Table 1, which shows when the business system is in the warm standby disaster recovery mode or the cold standby disaster recovery mode, corresponding optimizations under the three scenarios of weekdays, weekends and holidays Strategy. Taking the warm standby disaster recovery mode as an example, the optimization strategy corresponding to the working day is "the average value of the historical peak value, and the peak value of the previous week is used as a reference", that is, in this case, the average value of the historical peak value of the previous week is used as a reference.

Table 1

FIG. 3 is a flow chart of the second implementation of step S104. Please refer to FIG. 3. In the second implementation, when the business system is in a dual-active disaster recovery mode or a master-slave disaster recovery mode, step S104 includes:

S301: Collect forecast information from the production environment. The forecast information here comes from the forecast platform of the production environment. Usually, the input is a specified future time period, and the output is the predicted business volume peak value in this time period. In this The significance of obtaining forecast information in the two modes is that it can not only guarantee the processing capacity of the historical peak traffic volume, but also guarantee the processing capacity of the peak traffic volume predicted by the forecasting platform.

S302: Determine the optimization strategy of the business system according to the strategy configuration table;

S303: Perform resource allocation on the business system according to the optimization strategy and load data.

In Embodiment 2, the policy configuration table is shown in Table 2, which shows that when the business system is in the active-active disaster recovery mode or the primary and secondary disaster recovery mode, the corresponding optimization strategy. Taking the active-active disaster recovery model as an example, the optimization strategy corresponding to the working day is "the sum of the historical peak values of the two centers, and the peak value of the previous month as a parameter", that is, in this case, the historical peak value of the previous month of the two centers is used as The sum of the peaks is for reference, and the dual centers here refer to the Beijing center and the Shanghai center.

Table 2

FIG. 4 is a flow chart of steps S202 and S303. Please refer to FIG. 4. Resource allocation for the business system according to the optimization strategy and load data includes:

S401: Obtain setting information from the optimization strategy. Taking the active-active disaster recovery model as an example, step S303 determines that the optimization strategy corresponding to the working day is "the sum of the historical peak values of the two centers, and the peak value of the previous month is used as a parameter", that is, in this case, the previous peak value of the two centers is used as a parameter. The sum of the historical peaks of the month is used as a reference. For example, if the historical peak value of the Shanghai center in the previous month is 1500tps (that is, 1500 transactions per second), and the historical peak value of the Beijing center in the previous month is 1000tps, then the optimization strategy corresponding to the working day in the active-active disaster recovery mode is determined The output setting information is 2500tps.

S402: When the resource configuration of the business system does not meet the set information, read the virtual machine corresponding to the physical machine from the preset database according to the name of the physical machine corresponding to the business system, if the set information is met, If the information is specified, no resource adjustments are required. The resource configuration mentioned here refers to the computing power of the CPU and memory of the virtual machine.

S403: Obtain the CPU usage ratio and memory usage ratio of the virtual machine from the load status data;

S404: Determine the coefficient of the virtual machine according to dividing the CPU usage rate and the memory usage rate. In a specific implementation manner, divide the CPU usage rate of the virtual machine by the memory usage rate to calculate the coefficient. The reason why this coefficient is adopted in the present invention is that the high CPU usage directly affects the load of the physical machine, and the memory size will affect the migration time. A large coefficient value indicates high usage or low memory usage, and the migration will have a significant impact on the load of the physical machine.

S405: Select a virtual machine to be migrated according to the coefficient of the virtual machine and the optimization strategy;

S406: Select a physical machine to be migrated according to the virtual machine to be migrated.

S407: Migrate the physical machine to be migrated and the virtual machine to be migrated, so that the resource configuration of the service system after migration satisfies the setting information.

In a specific implementation, after selecting a virtual machine to be migrated, a physical machine suitable for migration may be selected, and then it is calculated whether the resource configuration after migration meets the set information, and if the condition is not met, the selection is performed again. If the conditions are met, the migration operation is performed.

The above is a cloud platform resource configuration method based on the disaster recovery center provided by the present invention, which obtains the load data of the servers corresponding to the various business systems deployed in the disaster recovery center on the cloud platform, and obtains business operation data from the production environment, and introduces Classify the preset disaster recovery mode levels and configure resources in combination with the preset policy configuration table to automatically trigger different policies for different disaster recovery modes to migrate virtual machines, such as migrating virtual machines at least Number of physical machines and shut down other physical machines to save resources and energy consumption.

It should be noted that, although operations of the methods of the present invention are depicted in the drawings in a particular order, this does not require or imply that the operations must be performed in that particular order, or that all illustrated operations must be performed to achieve the desired results. . Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step for execution, and/or one step may be decomposed into multiple steps for execution.

After introducing the method in the exemplary embodiment of the present invention, next, the cloud platform resource configuration system in the exemplary embodiment of the present invention will be introduced with reference to FIG. 5 . For the implementation of the system, reference may be made to the implementation of the above-mentioned method, and repeated descriptions will not be repeated. The terms "module" and "unit" used below may be software and/or hardware that realize predetermined functions. Although the modules described in the following embodiments are preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.

Fig. 5 is a structural block diagram of a cloud platform resource configuration system based on a disaster recovery center provided by an embodiment of the present invention, please refer to Fig. 5, the system includes:

The load data collection device 101 is configured to collect load data of servers corresponding to various business systems deployed in the disaster recovery center on the cloud platform.

In a specific embodiment, the load data of the server may be collected through a script or the like. The server includes a virtual machine and a physical machine, and the load data includes CPU usage, memory usage, and storage capacity usage.

The operation data collection device 102 is used to collect business operation data from the production environment. In a specific implementation manner, the business operation data includes a daily average value of transaction volume and a daily peak value of transaction volume. That is, the operation data acquisition device 102 opens up the transaction monitoring interface between the disaster recovery center and the production center.

The data classification device 103 is configured to classify the load data and business operation data according to the preset disaster recovery mode level, so as to obtain the load data of each business system in different disaster recovery modes. In a specific implementation manner, the preset disaster recovery mode levels include active-active disaster recovery mode, primary and secondary disaster recovery mode, warm-standby disaster recovery mode, and cold-standby disaster recovery mode. The disaster recovery mode level is configurable, and the load data and business operation data are classified to form load data with the business system as the dimension for decision-making in subsequent steps.

The resource configuration device 104 is configured to configure resources on the cloud platform according to a preset policy configuration table and load condition data.

Fig. 6 is a structural block diagram of the first embodiment of the resource configuration device 104 in the cloud platform resource configuration system based on the disaster recovery center provided by the embodiment of the present invention. Please refer to Fig. 6. In the first embodiment, when the business system When it is a warm standby disaster recovery mode or a cold standby disaster recovery mode, the resource allocation device 104 includes:

The first optimization strategy determination module 201 is used to determine the optimization strategy of the business system according to the strategy configuration table;

The resource configuration module 202 is configured to configure resources of the business system according to the optimization strategy and load data.

In Embodiment 1, the policy configuration table is shown in Table 1, which shows when the business system is in the warm standby disaster recovery mode or the cold standby disaster recovery mode, corresponding optimizations under the three scenarios of weekdays, weekends and holidays Strategy. Taking the warm standby disaster recovery mode as an example, the optimization strategy corresponding to the working day is "the average value of the historical peak value, and the peak value of the previous week is used as a reference", that is, in this case, the average value of the historical peak value of the previous week is used as a reference.

Fig. 7 is a structural block diagram of the second embodiment of the resource configuration device in the cloud platform resource configuration system based on the disaster recovery center provided by the embodiment of the present invention. Please refer to Fig. 7. In the second embodiment, when the business system is In the dual-active disaster recovery mode or the primary-slave disaster recovery mode, the resource configuration device 104 also includes:

The prediction information collection module 203 is configured to collect prediction information from the production environment.

In Embodiment 2, the policy configuration table is shown in Table 2, which shows that when the business system is in the active-active disaster recovery mode or the primary and secondary disaster recovery mode, the corresponding optimization strategy. Taking the active-active disaster recovery model as an example, the optimization strategy corresponding to the working day is "the sum of the historical peak values of the two centers, and the peak value of the previous month as a parameter", that is, in this case, the historical peak value of the previous month of the two centers is used as The sum of the peaks is for reference, and the dual centers here refer to the Beijing center and the Shanghai center.

Fig. 8 is a structural block diagram of a resource configuration module 202 in a cloud platform resource configuration system based on a disaster recovery center provided by an embodiment of the present invention. Referring to Fig. 8, the resource configuration module 202 includes:

The obtaining unit 301 is configured to obtain setting information from the optimization strategy. Taking the active-active disaster recovery model as an example, step S303 determines that the optimization strategy corresponding to the working day is "the sum of the historical peak values of the two centers, and the peak value of the previous month is used as a parameter", that is, in this case, the previous peak value of the two centers is used as a parameter. The sum of the historical peaks of the month is used as a reference. For example, if the historical peak value of the Shanghai center in the previous month is 1500tps, and the historical peak value of the Beijing center is 1000tps in the previous month, then the setting information determined by the optimization strategy corresponding to the working day in the active-active disaster recovery mode is 2500tps.

The reading unit 302 is configured to read the virtual machine corresponding to the physical machine from a preset database according to the name of the physical machine corresponding to the business system when the resource configuration of the business system does not meet the set information , if the setting information is satisfied, there is no need to adjust the resources.

A usage rate obtaining unit 303, configured to obtain the CPU usage rate and memory usage rate of the virtual machine from the load situation data;

The coefficient determination unit 304 is configured to determine the coefficient of the virtual machine according to the CPU usage and the memory usage. In a specific embodiment, the CPU usage of the virtual machine is divided by the memory usage to calculate the coefficient . The reason why this coefficient is adopted in the present invention is that the high CPU usage directly affects the load of the physical machine, and the memory size will affect the migration time. A large coefficient value indicates high usage or low memory usage, and the migration will have a significant impact on the load of the physical machine.

The first determining unit 305 is configured to select a virtual machine to be migrated according to the coefficient of the virtual machine and the optimization strategy;

The second determining unit 306 is configured to select a physical machine to be migrated according to the virtual machine to be migrated.

The migration unit 307 is configured to migrate the physical machine to be migrated and the virtual machine to be migrated, so that the resource configuration of the service system after migration satisfies the setting information.

In a specific implementation, after selecting a virtual machine to be migrated, a physical machine suitable for migration may be selected, and then it is calculated whether the resource configuration after migration meets the set information, and if the condition is not met, the selection is performed again. If the conditions are met, the migration operation is performed.

In addition, although several unit modules of the disaster recovery center-based cloud platform resource configuration system are mentioned in the above detailed description, this division is not mandatory. Actually, according to the embodiment of the present invention, the features and functions of two or more units described above may be embodied in one unit. Likewise, the features and functions of one unit described above can also be further divided to be embodied by a plurality of units.

The following specific embodiments take the process of cloud platform resource configuration of a disaster recovery center of a warm standby system A on October 1, 2016 as an example to describe in detail a method and system for configuring cloud platform resources based on a disaster recovery center according to the present invention How to achieve load balancing of cloud platforms in different disaster recovery modes.

1. The load data acquisition device shows that system A of the production environment uses 1 virtual machine VM1 in total. The configuration of VM1 is 8C16G, the CPU usage rate is 20%, and the memory usage rate is 20%. The system A of the disaster recovery center uses 1 VM1 in total. , configured as 2C4G.

2. The running data acquisition device shows that the current business volume of system A in the production environment is 50tps, the average peak value of the historical day is 80tps, and the peak value on October 1, 2015 is 100tps.

3. Query the policy configuration table and learn that system A is in warm standby disaster recovery mode, and initialize the policy.

4. The resource configuration device obtains the data of 1 and 2, and combines the policy configuration table to make target decisions. For this example, the decision target is to automatically adjust 2C4G, the current tps is 50tps, to a virtual machine configuration that can meet the processing capacity of 100tps, and Calculate the target virtual machine configuration as ((8C*20%/50tps)*100tps)/70%=4.5C, ((16G*20%/50tps)*100tps)/70%=9.14G, (Note: 70% is the resource tolerance of the disaster recovery environment and is a fixed parameter), so the decision-making goal is to adjust the configuration of VM1 in the disaster recovery center from 2C4G to 5C10G.

5. The resource configuration device sends the decision target 4 and relevant decision information 1, 2 (server load information and business load information of the production environment) to the disaster recovery system.

6. The disaster recovery system determines that resources need to be adjusted according to the decision-making goal.

7. Calculate the ratio of the cpu usage rate of all virtual machines on the physical machine to the memory size used, and arrange them from large to small; and calculate the load of all physical machines, and arrange them from small to large.

8. Select a suitable target physical machine, and perform migration and resource adjustment on VM1.

The above describes the dynamic adjustment process of resources on October 1, 2016. This example describes the automatic expansion of resources when holidays come. According to the design of this system, resources will be automatically shrunk on normal working days to improve the resource utilization rate of the disaster recovery center.

In summary, a cloud platform resource configuration method and system based on a disaster recovery center proposed by the present invention can perform load balancing for the loads of cloud computing platforms in different disaster recovery modes, the scheme is flexible, and it can also be used for different business The system is set independently, so there is no need to worry about applicability. In addition, manual operation steps can be omitted to save workload, and migration information can be stored in logs or saved to the database for easy viewing. From the perspective of the disaster recovery data center, this solution can effectively utilize the computing resources in the cloud platform resource pool, automatically and reasonably allocate resources, and reduce the number of operators on the premise of ensuring the disaster recovery indicators of each business system in the entire disaster recovery center. workload.

This application is based on the perspective of the entire disaster recovery data center, focusing on how the cloud IT system of the disaster recovery center can better meet the operating needs of various business systems in the disaster recovery center (such as how to meet the active-active system, warm standby system or cold standby system) Disaster recovery indicators, and to maximize resource utilization), the core is based on the analysis of the production business volume combined with the business disaster recovery indicators in the disaster recovery center, through decision-making analysis and calculation, so that the resource capacity of the cloud disaster recovery center at any time Automatic adjustment enables the entire cloud disaster recovery center to always have the real-time operation capability of the production system or the takeover capability of the disaster recovery system.

For a technical improvement, it can be clearly distinguished whether it is an improvement on hardware (for example, improvement on circuit structures such as diodes, transistors, switches) or an improvement on software (improvement on method flow). However, with the development of technology, the improvement of many current method flows can be regarded as the direct improvement of the hardware circuit structure. Designers almost always get the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that the improvement of a method flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (Programmable Logic Device, PLD) (such as a Field Programmable Gate Array (Field Programmable Gate Array, FPGA)) is such an integrated circuit, and its logic function is determined by programming the device by a user. It is programmed by the designer to "integrate" a digital system on a PLD, instead of asking a chip manufacturer to design and manufacture a dedicated integrated circuit chip 2 . Moreover, nowadays, instead of making integrated circuit chips by hand, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when writing programs, but before compiling The original code of the computer must also be written in a specific programming language, which is called a hardware description language (Hardware Description Language, HDL), and there is not only one kind of HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. Currently, the most commonly used is VHDL ( Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog2. It should also be clear to those skilled in the art that only a little logical programming of the method flow in the above-mentioned hardware description languages and programming into an integrated circuit can easily obtain a hardware circuit for realizing the logic method flow.

The controller may be implemented in any suitable way, for example the controller may take the form of a microprocessor or processor and a computer readable medium storing computer readable program code (such as software or firmware) executable by the (micro)processor , logic gates, switches, Application Specific Integrated Circuit (ASIC), programmable logic controllers, and embedded microcontrollers, examples of controllers include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320, the memory controller can also be implemented as part of the memory's control logic.

Those skilled in the art also know that, in addition to realizing the controller in a purely computer-readable program code mode, it is entirely possible to make the controller use logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded The same function can be realized in the form of a microcontroller or the like. Therefore, such a controller can be regarded as a hardware component, and the devices included in it for realizing various functions can also be regarded as structures within the hardware component. Or even, means for realizing various functions can be regarded as a structure within both a software module realizing a method and a hardware component.

The systems, devices, modules, or units described in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions.

For the convenience of description, when describing the above devices, functions are divided into various units and described separately. Of course, when implementing the present application, the functions of each unit can be implemented in one or more pieces of software and/or hardware.

It can be known from the above description of the implementation manners that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general-purpose hardware platform. Based on this understanding, the essence of the technical solution of this application or the part that contributes to the prior art can be embodied in the form of software products, and the computer software products can be stored in storage media, such as ROM/RAM, disk , optical disc, etc., including several instructions to enable a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present application.

Each embodiment in this specification is described in a progressive manner, the same and similar parts of each embodiment can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, as for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for the related parts, please refer to the part of the description of the method embodiment.

The application can be used in numerous general purpose or special purpose computer system environments or configurations. Examples: personal computers, server computers, handheld or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, including A distributed computing environment for any of the above systems or devices, etc.

This application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including storage devices.

Although this application has been described by way of example, those of ordinary skill in the art know that there are many variations and changes in this application without departing from the spirit of this application, and it is intended that the appended claims cover these variations and changes without departing from the spirit of this application.

Claims (14)

1. A cloud platform resource allocation method based on disaster recovery center, is characterized in that, described method comprises: Collect the load data of the server corresponding to each business system deployed in the disaster recovery center on the cloud platform; Collect business operation data from the production environment; classify the load data and business operation data according to the preset disaster recovery mode level, and obtain the load status data of each business system in different disaster recovery modes; Resource allocation is performed on the cloud platform according to the preset policy configuration table and load condition data. 2. The method according to claim 1, wherein the load data of the server is collected by using a script, and the server includes a virtual machine and a physical machine. 3. The method according to claim 2, wherein the load data includes CPU usage, memory usage, and storage capacity usage, and the business operation data includes a daily average value of transaction volume and a daily peak value of transaction volume. 4 . The method according to claim 3 , wherein the disaster recovery mode includes active-active disaster recovery mode, master-slave disaster recovery mode, warm-standby disaster recovery mode, and cold-standby disaster recovery mode. 5. The method according to claim 4, wherein when the business system is in a dual-active disaster recovery mode or a master-slave disaster recovery mode, resource allocation is performed on the cloud platform according to a policy configuration table and load data include: collecting forecast information from said production environment; determining the optimization strategy of the business system according to the prediction information and the strategy configuration table; Resource allocation is performed on the business system according to the optimization strategy and load condition data. 6. The method according to claim 4, wherein when the business system is in warm standby disaster recovery mode or cold standby disaster recovery mode, performing resource allocation on the cloud platform according to the policy configuration table and load condition data includes: Determine the optimization strategy of the business system according to the strategy configuration table; Resource allocation is performed on the business system according to the optimization strategy and load condition data. 7. The method according to claim 5 or 6, wherein configuring the resources of the business system according to the optimization strategy and load data includes: Obtaining setting information from the optimization strategy; When the resource configuration of the business system does not meet the set information, read the virtual machine corresponding to the physical machine from a preset database according to the name of the physical machine corresponding to the business system; Obtain the CPU usage ratio and memory usage ratio of the virtual machine from the load situation data; Determining the coefficient of the virtual machine according to the CPU usage ratio and the memory usage ratio; selecting a virtual machine to be migrated according to the coefficient of the virtual machine and the optimization strategy; Select the physical machine to be migrated according to the virtual machine to be migrated; The physical machine to be migrated and the virtual machine to be migrated are migrated so that the resource configuration of the service system after migration meets the setting information. 8. A cloud platform resource configuration system based on a disaster recovery center, characterized in that the system includes: The load data collection device is used to collect the load data of the servers corresponding to the various business systems deployed in the disaster recovery center on the cloud platform; Operational data collection device, used to collect business operation data from the production environment; The data classification device is used to classify the load data and business operation data according to the preset disaster recovery mode level, and obtain the load status data of each business system in different disaster recovery modes; The resource configuration device is configured to configure resources on the cloud platform according to a preset policy configuration table and load condition data. 9 . The system according to claim 8 , wherein the load data collection device uses a script to collect load data of servers, and the servers include virtual machines and physical machines. 10 . The system according to claim 9 , wherein the load data includes CPU usage, memory usage, and storage capacity usage, and the business operation data includes a daily average value of transaction volume and a daily peak value of transaction volume. 11 . The system according to claim 10 , wherein the disaster recovery mode includes active-active disaster recovery mode, master-slave disaster recovery mode, warm-standby disaster recovery mode, and cold-standby disaster recovery mode. 12. The system according to claim 11, wherein when the business system is in warm standby disaster recovery mode or cold standby disaster recovery mode, the resource configuration device comprises: The first optimization strategy determination module is used to determine the optimization strategy of the business system according to the strategy configuration table; A resource configuration module, configured to configure resources of the business system according to the optimization strategy and load data. 13. The system according to claim 12, wherein when the business system is in a dual-active disaster recovery mode or a master-slave disaster recovery mode, the resource configuration device further comprises: A prediction information collection module, configured to collect prediction information from the production environment; The second optimization strategy determination module is configured to determine the optimization strategy of the business system according to the prediction information and the strategy configuration table. 14. The system according to claim 12 or 13, wherein the resource configuration module comprises: an acquisition unit, configured to acquire setting information from the optimization strategy; A reading unit, configured to read the virtual machine corresponding to the physical machine from a preset database according to the name of the physical machine corresponding to the business system when the resource configuration of the business system does not meet the set information; a utilization rate acquisition unit, configured to obtain the CPU utilization rate and memory utilization rate of the virtual machine from the load condition data; a coefficient determining unit, configured to determine the coefficient of the virtual machine according to the CPU usage ratio and the memory usage ratio; A first determining unit, configured to select a virtual machine to be migrated according to the coefficient of the virtual machine and the optimization strategy; The second determination unit is used to select the physical machine to be migrated according to the virtual machine to be migrated; The migration unit is configured to migrate the physical machine to be migrated and the virtual machine to be migrated, so that the service system after migration satisfies the setting information.