CN114490089A - Cloud computing resource automatic adjusting method and device, computer equipment and storage medium - Google Patents
Cloud computing resource automatic adjusting method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application belongs to the technical field of cloud computing, and relates to a method and a device for automatically adjusting cloud computing resources, computer equipment and a storage medium, wherein the method comprises the steps of setting a cloud host creation rule; acquiring computing resource information of each physical machine, and creating a cloud host according to the acquired computing resource information of each physical machine and a cloud host creation rule; performing statistical analysis according to the created cloud host information; judging whether the computing resources reach a computing resource threshold value, if so, analyzing a physical machine list needing to be started or shut down, adjusting the simulated starting or shutting down to obtain the condition of the adjusted resources, listing the computing resources before and after adjustment, judging whether the resource pool reaches the computing resource threshold value of the shutdown, selecting the physical machine with the minimum resource usage to shut down, and performing comparison and exercise on the computing resources on the physical machine with the minimum resource usage in a hot migration manner according to the distributed state of the computing resources and the actual use condition of the computing resources of the physical machine in combination with historical data to obtain the computing resource threshold value; can save energy and reduce consumption.
Description
Technical Field
The present application relates to the field of cloud computing technologies, and in particular, to a method and an apparatus for automatically adjusting cloud computing resources, a computer device, and a storage medium.
Background
The cloud host is an important component of cloud computing in infrastructure application, is located at the bottom layer of a pyramid of a cloud computing industry chain, and is derived from a cloud computing platform. The platform integrates three core elements of internet application: computing, storage, network, and providing a user with a public internet infrastructure service. The cloud host is a virtualization technology similar to a VPS host, the VPS adopts virtual software, a plurality of parts similar to independent hosts are virtualized on one host by VZ or VM, single-machine multi-user can be realized, each part can be used as an independent operating system, and the management method is the same as that of the host. The cloud host is a part which is similar to a plurality of independent hosts and is virtualized on a group of cluster hosts, and each host in the cluster is provided with a mirror image of the cloud host, so that the safety and stability of the virtual host are greatly improved, and the cloud host cannot access the virtual host unless all the hosts in the cluster have problems.
With the popularization of the cloud concept, the cloud hosts are more and more widely used, the number of the cloud hosts is increased day by day, and the number of the cloud physical machines is increased. Before thousands of servers are managed, when attention is paid to monitoring the performance, downtime and other problems of the servers, the problem of surplus computing resources is often ignored, and therefore the operation cost is increased. In the existing design, the resources of the cloud node are usually managed and controlled manually, such as startup and shutdown adjustment, and huge waste of computing resources is often caused due to untimely adjustment.
Disclosure of Invention
An object of the embodiment of the present application is to provide a method and an apparatus for automatically adjusting cloud computing resources, a computer device, and a storage medium, so as to solve the problem that in the existing design, resources of cloud nodes are usually managed and controlled manually, such as startup and shutdown are adjusted, and huge waste of computing resources is often caused due to untimely adjustment.
In order to solve the above technical problem, an embodiment of the present application provides an automatic cloud computing resource adjustment method, which adopts the following technical scheme and includes the following steps:
setting a cloud host creation rule;
acquiring computing resource information of each physical machine, and creating a cloud host according to the acquired computing resource information of each physical machine and the cloud host creation rule;
according to the created cloud host information, performing statistical analysis to obtain the startup and shutdown thresholds of all physical machine resources of the nodes preset by the node computing resources;
judging whether the computing resources of all physical machines in the resource pool reach a physical machine resource starting or shutdown threshold value, if so, analyzing a physical machine list needing to be started or shut down, adjusting and simulating the starting or shutdown to obtain the condition of the adjusted resources, confirming whether the starting or shutdown is carried out for the cloud platform, listing the computing resource threshold values before and after the adjustment, selecting the physical machine with the minimum resource usage to shut down, and carrying out hot migration on the computing resources to the physical machine with the minimum resource usage, wherein the computing resources refer to the allocated computing resources.
Further, the step of setting the cloud host creation rule specifically includes:
setting each physical machine to have an allocation switch state, wherein the allocation state is on and indicates that the physical machine can create the cloud host, and the allocation state is off and indicates that the physical machine cannot create the cloud host;
and preferentially allocating the relatively idle physical machine as the cloud host.
Further, the step of acquiring the computing resource information of each physical machine specifically includes:
and acquiring the cpu, the memory, the allocated cpu, the allocated memory information of each physical machine, and the cpu and the memory use condition information of the physical machine.
Further, the step of obtaining the power on/off threshold values of all physical computer resources of the node preset by the node computing resources through statistical analysis according to the created cloud host information specifically includes:
counting the memory utilization rate and the memory utilization rate of the physical machine according to the created cloud host information;
obtaining a computing resource threshold value of a node computing resource for starting or closing a physical machine, wherein the threshold value comprises a shutdown rule, the shutdown rule comprises that when the number of the physical machines in a resource pool is more than or equal to 10, machines with the number of 50% are shut down at most, and the shutdown rule is set on the premise that the resources meet the index that the memory utilization rate is less than 60% after shutdown,
if the number of the physical machines in the resource pool is less than 10 and more than 3, at least 3 machines are reserved, and the power-off premise is set, namely the resources meet the index that the memory utilization rate is less than 60% after the power-off.
Further, the steps of determining whether the computing resources of all physical machines in the resource pool reach a physical machine resource startup or shutdown threshold, if so, analyzing a physical machine list needing to be started or shut down, adjusting and simulating startup or shutdown to obtain the condition of the adjusted resources, determining whether to start or shut down the physical machines, listing the computing resource thresholds before and after adjustment, selecting the physical machine with the minimum resource usage to shut down the physical machine, and performing hot migration on the computing resources to the physical machine with the minimum resource usage specifically include:
counting the computing resource conditions of all physical machines in the resource pool in real time by taking the resource pool as a unit, and comparing the computing resource conditions with a computing resource threshold value to obtain a conclusion whether the machine needs to be started or shut down;
if the computer needs to be shut down, analyzing a physical machine list needing to be shut down, and listing the use conditions of the computing resources before and after adjustment.
Further, the created cloud host information includes:
history creating cpu resource information, history creating memory information, history cpu computing resource actual use information, history memory computing resource actual use information, cpu information allocated to the physical machine, and memory information allocated to the physical machine.
Further, the step of hot-migrating the computing resource to the physical machine with the least resource usage comprises:
and migrating the computing resources to a relatively idle physical machine, and triggering a shutdown command after migration is finished.
In order to solve the above technical problem, an embodiment of the present application further provides an automatic cloud computing resource adjusting device, which adopts the following technical scheme, including:
the setting module is used for setting a cloud host creation rule;
the creating module is used for acquiring the computing resource information of each physical machine and creating a cloud host according to the acquired computing resource information of each physical machine and the cloud host creating rule;
the statistical module is used for carrying out statistical analysis according to the created cloud host information to obtain the startup and shutdown threshold values of all physical machine resources of the nodes preset by the node computing resources;
and the adjusting module is used for judging whether the computing resources reach the computing resource threshold value, if so, analyzing a physical machine list needing to be started or shut down, adjusting the simulated starting or shutting down to obtain the condition of the adjusted resources, listing the computing resource threshold values before and after adjustment, judging that the resource pool reaches the computing resource threshold value of shutdown, selecting the physical machine with the minimum resource usage to shut down, and performing hot migration on the computing resources to the physical machine with the minimum resource usage.
In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:
the cloud computing resource automatic adjusting method comprises a memory and a processor, wherein computer readable instructions are stored in the memory, and the processor executes the computer readable instructions to realize the steps of the cloud computing resource automatic adjusting method.
In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:
the computer readable storage medium stores computer readable instructions, and when executed by the processor, the computer readable instructions implement the steps of the cloud computing resource automatic adjustment method described above.
Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:
comparing and practicing according to the distributed state of the computing resources and the actual use condition of the computing resources of the physical machine and by combining historical data to obtain a computing resource threshold value, and automatically adjusting the physical machine, including turning on and off the physical machine when the computing resources reach the computing resource threshold value; the cost can be reduced by automatic adjustment without manual calculation intervention, and the excessively idle physical machine can be automatically closed, so that the effects of energy conservation and consumption reduction are achieved.
Drawings
In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
FIG. 1 is an exemplary system architecture diagram to which the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a cloud computing resource autotuning method of the present application;
FIG. 3 is a flow diagram of another embodiment of a cloud computing resource autotuning method of the present application;
fig. 4 is a schematic structural diagram of an embodiment of the cloud computing resource automatic adjustment apparatus according to the present application;
FIG. 5 is a schematic block diagram of one embodiment of a computer device of the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein may be combined with other embodiments.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, the system architecture 100 may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the first terminal device 101, the second terminal device 102, the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like, may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103.
The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, motion Picture Experts compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, motion Picture Experts compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server that provides various services, such as a background server that provides support for pages displayed on the first terminal apparatus 101, the second terminal apparatus 102, and the third terminal apparatus 103.
It should be noted that, the cloud computing resource automatic adjustment method provided in the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the cloud computing resource automatic adjustment apparatus is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The physical machine, also called an independent server, runs faster, has higher load and is more expensive than a common computer. The server provides calculation or application service for other clients (such as various websites, mobile phone APP, PC, smart phone, ATM and other terminals, even train system and other large-scale equipment) in the network. The server has high-speed CPU computing capability, long-time reliable operation, strong I/O external data throughput capability and better expansibility. Generally, a server has the capability of responding to a service request, supporting a service, and guaranteeing the service according to the service provided by the server. The server is used as an electronic device, and the internal structure of the server is quite complex, but the internal structure of the server is not much different from that of a common computer. But the cpu, hard disk, memory, system, etc. of the physical machine are all independent.
The cloud host (also called VPS, which is an upgraded version of VPS) is an important component of cloud computing in infrastructure application, is located at the bottom of a pyramid of a cloud computing industry chain, and a product is derived from a cloud computing platform. The cloud computing platform integrates three core elements of internet application: computing, storage, network, and providing a user with a public internet infrastructure service.
The VPS adopts virtual software, and a plurality of parts similar to independent hosts are virtualized on one physical host by VZ or VM, so that single machine and multiple users can be realized, each part can be used as an independent operating system, and the management method is the same as that of the host. The cloud host is a part which is similar to a plurality of independent hosts and is virtualized on a group of cluster hosts, and each host in the cluster is provided with a mirror image of the cloud host, so that the safety and stability of the virtual host are greatly improved, and the cloud host cannot access the virtual host unless all the hosts in the cluster have problems.
The cloud host is an IT infrastructure capacity renting service integrating computing, storage and network resources, and is a server renting service capable of providing on-demand use and on-demand payment capacity based on a cloud computing mode. The client can deploy the required server environment through the self-service platform of the web interface.
The difference between the physical machine and the cloud host is as follows: the physical machine is an independent server, and each part of the physical machine is independent, such as a CPU, a hard disk, a memory bank and the like; the cloud host is an upgraded version of VPS, one or more physical machines are integrated, a plurality of different systems are separated through a virtualization technology and used for users to use, the cloud host and the virtual machine share hardware and systems of one or more physical machines, and after the cloud host and the virtual machine are separated, the systems are independent and do not interfere with each other.
With continued reference to fig. 2, a flow diagram of one embodiment of a method of cloud computing resource autotuning in accordance with the present application is shown. The automatic cloud computing resource adjusting method comprises the following steps:
step S201, setting a cloud host creation rule.
In this embodiment, the step of setting the cloud host creation rule further includes:
setting each physical machine to have an allocation switch state, wherein the allocation state is on and indicates that the physical machine can create the cloud host, and the allocation state is off and indicates that the physical machine cannot create the cloud host;
and preferentially allocating the relatively idle physical machine as the cloud host. The relatively free physical machine may refer to a relatively free physical machine in the same resource pool, or may be a relatively free physical machine that selects a different resource pool. By judging the physical machines in the same resource pool, the resource allocation of the physical machines is less, namely the physical machines are relatively idle by comparing the allocation conditions of the cup and the memory, and the relatively idle physical machines can be determined in such a way.
In this embodiment, an electronic device (for example, the server/terminal device shown in fig. 1) on which the cloud computing resource automatic adjustment method operates may receive the cloud computing resource automatic adjustment request through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G/5G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.
Step S202, computing resource information of each physical machine is obtained, and a cloud host is created according to the obtained computing resource information of each physical machine and the cloud host creation rule.
In this embodiment, the step of acquiring the computing resource information of each physical machine specifically includes:
and acquiring the cpu, the memory, the allocated cpu and the allocated memory information of each physical machine, and the cpu and the memory use condition information of the physical machine.
The created cloud host information includes: history creation cpu resource information, history creation memory information, history cpu calculation resource actual usage information, history memory calculation resource actual usage information, cpu information allocated to the physical machine, and memory information allocated to the physical machine.
Step S203, according to the created cloud host information, performing statistical analysis to obtain the startup and shutdown threshold values of all physical machine resources of the nodes preset by the node computing resources.
In this embodiment, the step of obtaining, according to the created cloud host information and through statistical analysis, a computing resource threshold value for a node computing resource to turn on or turn off a physical machine specifically includes:
counting the memory utilization rate and the memory utilization rate of the physical machine according to the created cloud host information;
setting a shutdown rule, wherein the shutdown rule comprises that when the number of physical machines in the resource pool is more than or equal to 10, machines with the number of 50% are shut down at most, and the shutdown rule is set on the premise that resources meet the index that both the memory utilization rate and the memory utilization rate are less than 60% after shutdown,
if the number of the physical machines in the resource pool is less than 10 and more than 3 machines, at least 3 machines are reserved, and the power-off premise is set, namely the resources meet the index that both the memory utilization rate and the memory utilization rate are less than 60% after the power-off.
Of course, the shutdown rules may also be set according to the particular circumstances in which the data center is computing power. For example, a stricter shutdown rule may be set, where, for example, when the number of the resource pool physical machines is greater than or equal to 10, machines with the number of 60% are shut down at most, and the shutdown rule is set on the premise that resources after shutdown meet an index that the memory usage rate is less than 70%, and the number of the resource pool physical machines is less than 10 and greater than 3, at least 3 machines are reserved, and the shutdown rule is set on the premise that resources after shutdown meet an index that the memory usage rate is less than 70%; or more loose shutdown rules can be set, if the number of the resource pool physical machines is more than or equal to 10, the machines with the number of 40% are shut down at most, the shutdown is set on the premise that the resources meet the index that the memory utilization rate is less than 50% after shutdown, the number of the resource pool physical machines is less than 10 and more than 3, at least 3 machines are reserved, and the shutdown is set on the premise that the resources meet the index that the memory utilization rate is less than 50% after shutdown.
Step S204, judging whether the computing resource reaches the computing resource threshold value, if so, analyzing the physical machine list needing to be shut down, listing the computing resource threshold values before and after adjustment, and performing thermal migration on the computing resource.
In this embodiment, the step of determining whether the computing resource reaches the computing resource threshold, if so, analyzing the physical machine list that needs to be shut down, and listing the computing resource threshold before and after adjustment further includes:
counting the computing resource conditions of all physical machines in the resource pool in real time by taking the resource pool as a unit, and comparing the computing resource conditions with a computing resource threshold value to obtain a conclusion whether the machine needs to be started or shut down;
if yes, analyzing a physical machine list needing to be shut down, and listing steps of calculating resource thresholds before and after adjustment.
That is, the real-time statistical analysis is performed, the computing resource conditions of all the physical machines in the resource pool are counted in real time, and the analysis is performed only if the computing resources of all the physical machines in the resource pool reach a threshold value, that is, the analysis is required to be started or shut down only if the computing resources reach the threshold value.
In specific implementation, the step of selecting the physical machine with the minimum resource usage to shut down and hot-migrating the computing resource to the physical machine with the minimum resource usage may further include the steps of: and migrating the computing resources to a relatively idle physical machine, and triggering a shutdown command after migration is finished. The integrity of the data is ensured by performing the migration of the computing resources first and then shutting down.
A resource pool is a logical abstraction for flexibly managing resources. The resource pools may be grouped into hierarchies for partitioning the available CPU and memory resources according to the hierarchy. The hot migration refers to the migration of computing resources to other resource-rich terminals for running.
A computing resource pool refers to a collection of computer rooms computing physical machines. Collectively referred to herein as computing resources.
According to the method, comparison and drilling are carried out by combining historical data according to the distributed state of the computing resources and the actual use condition of the computing resources of the physical machine, so as to obtain a computing resource threshold value, and when the computing resources reach the computing resource threshold value, the physical machine is automatically adjusted, such as on and off; the cost can be reduced by automatic adjustment without manual calculation intervention, and the excessively idle physical machine can be automatically closed, so that the effects of energy conservation and consumption reduction are achieved.
The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The 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 memory storage devices.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer readable instructions, which can be stored in a computer readable storage medium, and when executed, the processes of the embodiments of the methods described above can be included. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
Fig. 3 is a flowchart of another embodiment of a cloud computing resource auto-tuning method according to the present application. As shown in fig. 3, a method for automatically adjusting cloud computing resources includes the steps of:
s301, establishing a computing resource analysis system.
In particular implementation, establishing the computing resource analysis system may include the docking of the cloud platform monitoring platform to collect the following information: collecting host information, and collecting host information memory and cpu of each physical machine; and collecting the allocated memory information, the allocated virtual CPU information and the like of each physical machine.
In some optional implementations, the step S301 of establishing the computing resource analysis system may further include the steps of:
s3011, the computing resource analysis system collects computing resource information of the physical machine.
S3012, the computing resource analysis system collects computing resources distributed by the cloud server.
S302, adjusting computing resources.
The comparison exercise can be performed by combining historical data according to the distributed state of the computing resources and the actual use condition of the computing resources of the physical machine, and the computing resources are adjusted.
And S3021, calculating the utilization ratio of the computing resources of each physical machine.
And S3022, listing comparison data of the computing resource threshold before and after adjustment.
The computing resource analysis system collects computing resource information of the physical machine. The computing resource analysis system collects computing resources allocated by the cloud server. And acquiring the utilization rate of the allocated memory of the computing resource, and judging whether the threshold value of the computing resource is met during shutdown and startup.
S303, judging whether the threshold value of the power-off and power-on computing resources is met.
The method comprises the steps of counting the memory utilization rate of physical machines according to created cloud host information, setting a shutdown rule, wherein the shutdown rule comprises shutting down 50% of machines at most when the number of the physical machines in a resource pool is more than or equal to 10, and at least 3 machines are reserved if the number of the physical machines in the resource pool is less than 10 and more than 3 when the resources meet the index that the memory utilization rate is less than 60% after shutdown, and the shutdown rule is set if the resources meet the index that the memory utilization rate is less than 60% after shutdown.
Step S303 performs the following operations in accordance with the shutdown computing resource threshold:
and S304, listing the physical machines which can be shut down.
S306, closing the physical machine distribution.
And S308, judging whether a cloud host runs, if so, executing S310, and if not, executing S311.
And S310, carrying out thermal migration on the cloud host.
S311, closing the physical machine.
Step S303 performs the following operations in accordance with the boot computing resource threshold:
and S305, listing the physical machines capable of being started.
And S307, starting up.
And S309, setting the physical machine state as an allocable state.
With further reference to fig. 4, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an automatic cloud computing resource adjusting apparatus, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.
As shown in fig. 4, the cloud computing resource automatic adjusting apparatus 400 according to this embodiment includes: a setup module 401, a creation module 402, a statistics module 403, and an adjustment module 404. Wherein:
a setting module 401, configured to set a cloud host creation rule;
a creating module 402, configured to obtain computing resource information of each physical machine, and create a cloud host according to the obtained computing resource information of each physical machine and the cloud host creating rule;
a statistical module 403, configured to perform statistical analysis according to the created cloud host information to obtain power on/off thresholds of all physical computer resources of the node preset by the node computing resource;
and the adjusting module 404 is configured to determine whether the computing resource reaches a computing resource threshold, and if so, analyze a physical machine list that needs to be turned on or turned off, and list the computing resource threshold before and after adjustment, so as to perform thermal migration on the computing resource.
According to the method, comparison and drilling are carried out by combining historical data according to the distributed state of the computing resources and the actual use condition of the computing resources of the physical machine, so as to obtain a computing resource threshold value, and when the computing resources reach the computing resource threshold value, the physical machine is automatically adjusted, such as on and off; the cost can be reduced by automatic adjustment without manual calculation intervention, and the excessively idle physical machine can be automatically closed, so that the effects of energy conservation and consumption reduction are achieved.
In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 5, fig. 5 is a block diagram of a basic structure of a computer device according to the present embodiment.
The computer device 6 comprises a memory 61, a processor 62, a network interface 63 communicatively connected to each other via a system bus. It is noted that only the computer device 6 having the component memory 61, the processor 62 and the network interface 63 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 61 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 61 may be an internal storage unit of the computer device 6, such as a hard disk or a memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 6. Of course, the memory 61 may also comprise both an internal storage unit of the computer device 6 and an external storage device thereof. In this embodiment, the memory 61 is generally used for storing an operating system installed in the computer device 6 and various types of application software, such as computer readable instructions of a cloud computing resource automatic adjustment method. Further, the memory 61 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 62 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 62 is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is configured to execute computer readable instructions stored in the memory 61 or process data, for example, execute computer readable instructions of the cloud computing resource automatic adjustment method.
The network interface 63 may comprise a wireless network interface or a wired network interface, and the network interface 63 is typically used for establishing a communication connection between the computer device 6 and other electronic devices.
According to the method, comparison and drilling are carried out by combining historical data according to the distributed state of the computing resources and the actual use condition of the computing resources of the physical machine, so as to obtain a computing resource threshold value, and when the computing resources reach the computing resource threshold value, the physical machine is automatically adjusted, such as on and off; the cost can be reduced by automatic adjustment without manual calculation intervention, and the excessively idle physical machine can be automatically closed, so that the effects of energy conservation and consumption reduction are achieved.
The present application further provides another embodiment, which is to provide a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the cloud computing resource auto-adjustment method as described above.
According to the method, comparison and drilling are carried out by combining historical data according to the distributed state of the computing resources and the actual use condition of the computing resources of the physical machine, so that a computing resource adjusting computing resource threshold value is obtained, and when the computing resources reach the computing resource threshold value, the physical machine is automatically adjusted, such as on/off and the like; the cost can be reduced by automatic adjustment without manual calculation intervention, and the excessively idle physical machine can be automatically closed, so that the effects of energy conservation and consumption reduction are achieved.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.
Claims (10)
1. A cloud computing resource automatic regulation method is characterized by comprising the following steps:
setting a cloud host creation rule;
acquiring computing resource information of each physical machine, and creating a cloud host according to the acquired computing resource information of each physical machine and the cloud host creation rule;
according to the created cloud host information, performing statistical analysis to obtain the startup and shutdown thresholds of all physical machine resources of the nodes preset by the node computing resources;
judging whether the computing resources of all physical machines in the resource pool reach a physical machine resource starting or shutdown threshold value, if so, analyzing a physical machine list needing to be started or shut down, adjusting and simulating the starting or shutdown to obtain the condition of the adjusted resources, confirming whether the cloud platform is started or shut down, listing the computing resource threshold values before and after adjustment, selecting the physical machine with the minimum resource usage to shut down, and thermophoretically transferring the computing resources to the physical machine with the minimum resource usage, wherein the computing resources refer to the allocated computing resources.
2. The method according to claim 1, wherein the step of setting the cloud host creation rule specifically includes:
setting each physical machine to have an allocation switch state, wherein the allocation state is on and indicates that the physical machine can create the cloud host, and the allocation state is off and indicates that the physical machine cannot create the cloud host;
and preferentially allocating the relatively idle physical machine as the cloud host.
3. The method according to claim 1, wherein the step of obtaining the computing resource information of each physical machine specifically includes:
and acquiring the cpu, the memory, the allocated cpu, the allocated memory information of each physical machine, and the cpu and the memory use condition information of the physical machine.
4. The method according to claim 3, wherein the step of obtaining the power on/off threshold values of all physical computer resources of the nodes preset by the node computing resources through statistical analysis according to the created cloud host information specifically comprises:
counting the memory utilization rate of the physical machine according to the created cloud host information;
setting a shutdown rule, wherein the shutdown rule comprises that machines with the number of 50% at most are shut down when the number of physical machines in the resource pool is more than or equal to 10, and the shutdown rule is set on the premise that the resource pool meets the index that the memory utilization rate is less than 60% after shutdown,
if the number of the physical machines in the resource pool is less than 10 and more than 3, at least 3 machines are reserved, and the power-off premise is set, namely the resources meet the index that the memory utilization rate is less than 60% after the power-off.
5. The method according to claim 1, wherein the step of determining whether the computing resources of all physical machines in the resource pool reach a threshold for starting or shutting down the physical machine resources, if yes, analyzing a list of physical machines that need to be started or shut down, adjusting the physical machines that simulate starting or shutting down to obtain a condition of the adjusted resources, determining whether to start or shut down the computing resources for the cloud platform, listing the threshold for the computing resources before and after adjustment, selecting the physical machine with the smallest resource usage to shut down the computing resources, and hot-migrating the computing resources to the physical machine with the smallest resource usage specifically comprises:
counting the computing resource conditions of all physical machines in the resource pool in real time by taking the resource pool as a unit, and comparing the computing resource conditions with a computing resource threshold value to obtain a conclusion whether the machine needs to be started or shut down;
if the computer needs to be shut down, analyzing a physical machine list needing to be shut down, and listing the use conditions of the computing resources before and after adjustment.
6. The method of claim 1, wherein the created cloud host information comprises:
history creation cpu resource information, history creation memory information, history cpu calculation resource actual usage information, history memory calculation resource actual usage information, cpu information allocated to the physical machine, and memory information allocated to the physical machine.
7. The method of any one of claims 1 to 6, wherein the step of hot-migrating the computing resource to a physical machine with minimal resource usage comprises:
and migrating the computing resources to a relatively idle physical machine, and triggering a shutdown command after migration is finished.
8. An automatic cloud computing resource adjustment device, comprising:
the setting module is used for setting a cloud host creation rule;
the creating module is used for acquiring the computing resource information of each physical machine and creating a cloud host according to the acquired computing resource information of each physical machine and the cloud host creating rule;
the statistical module is used for carrying out statistical analysis according to the created cloud host information to obtain the startup and shutdown threshold values of all physical machine resources of the nodes preset by the node computing resources;
and the adjusting module is used for judging whether the computing resources reach the computing resource threshold value, if so, analyzing a physical computer list needing to be started or closed, listing the computing resource conditions before and after adjustment, and performing thermal migration on the computing resources.
9. A computer device comprising a memory having computer readable instructions stored therein and a processor which when executed implements the steps of the cloud computing resource autotuning method of any of claims 1 to 7.
10. A computer readable storage medium having computer readable instructions stored thereon, which when executed by a processor, implement the steps of the cloud computing resource auto-tuning method of any of claims 1 to 7.
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