TWM583564U - Cloud resource management system - Google Patents

Abstract

The present disclosure provides a cloud resource management system, which includes a network interface and a processor. The processor is configured to identify an instruction, which indicates a cloud resource demand; obtain, through the network interface, a plurality of cloud resource demand items associated with the instruction from cloud environments; compute a plurality of resource combinations according to parameters of the cloud resource demand items related to the instruction; select a first resource combination from the plurality of resource combinations; transmit, through the network interface, a first request to a first cloud environment, so as to configure a first cloud resource in the first cloud environment; and transmit, through the network interface, a second request to a second cloud environment, so as to configure a second cloud resource in the second cloud environment.

Description

Cloud resource management system

The present disclosure relates to a management system, and more particularly to a cloud resource management system.

Generally speaking, when an enterprise or service provider needs to provide enterprise services or business through the Internet, it often needs to spend the cost of building hardware resources to provide services on hardware resources. With the development of cloud computing technology, service providers can use cloud resources to replace past hardware construction, so that service providers no longer need to spend too high hardware costs.

On the other hand, in the traditional practice, the service provider not only needs to spend hardware costs, but also occupies a certain amount of costs in terms of maintenance of hardware resources. Therefore, switching to providing services on cloud resources has become one of the choices of many service providers.

Benefiting from the diversified development of cloud services, cloud services are quite diverse. However, for users, it is often difficult to choose a cloud service to use between multiple cloud environments. In view of this, how to generate cloud service strategies among multiple cloud resources to maximize benefits is Technical problems that need to be solved urgently.

According to an embodiment of the present disclosure, a cloud resource management system is disclosed, adapted to interface a plurality of cloud environments, each of which includes a plurality of cloud resources. The cloud resource management system includes a network interface and a processor. The network interface is used to provide an interface between the cloud resource management system and the plurality of cloud resource interfaces. The processor is coupled to a network interface, where the processor is used to: identify an instruction, wherein the instruction defines a cloud resource requirement; and obtain a plurality of cloud resource requirement items associated with the instruction from the cloud environments via the network interface, each of the cloud The resource requirement item includes a parameter value, where the parameter value includes at least one of a resource type information, a resource specification information, a resource quantity information, a resource time information, a resource price information, a charging method information, and a resource status information. Calculate a plurality of resource combinations according to the instruction for the parameter values of the cloud resource demand items, where each resource combination includes at least one of the cloud resources; a first resource combination is selected from the resource combinations, the first The resource combination includes a first cloud resource and a second cloud resource. The first resource combination satisfies the commanded cloud resource allocation requirements. Among them, the first cloud resource belongs to one of the cloud environments. The first cloud environment and the second cloud resource Belongs to one of these cloud environments, the second cloud environment; sends to the first cloud environment through the network interface A first request to a first configuration of the first cloud in cloud environment End resource; and sending a second request to the second cloud environment via a network interface to configure the second cloud resource in the second cloud environment.

In order to make the above technical features and advantages of this creation more comprehensible, embodiments are described below in detail with the accompanying drawings as follows.

In order to make the above and other objects, features, advantages, and embodiments of the present disclosure more comprehensible, the description of the attached symbols is as follows:

100‧‧‧ Cloud Resource Management System

110‧‧‧Interface

120‧‧‧ processor

130‧‧‧user interface

140‧‧‧Database

150a ~ 150n‧‧‧cloud environment

200‧‧‧ Cloud Resource Management Method

300‧‧‧ Cloud Resource Management Method

400‧‧‧ Cloud resource management method

500‧‧‧ cloud resource management device

510‧‧‧Identification command module

520‧‧‧ Cloud Resource Demand Project Acquisition Module

530‧‧‧Resource combination calculation and selection module

540‧‧‧ Machine Learning Model

550‧‧‧Request to send module

S210 ~ S260‧‧‧step

S305 ~ S335‧‧‧step

S410 ~ S440‧‧‧step

The following detailed description, when read in conjunction with the accompanying drawings, will facilitate a better understanding of the aspects of this disclosure. It should be noted that, according to the practical requirements of the description, the features in the drawings are not necessarily drawn to scale. In fact, the size of each feature may be arbitrarily increased or decreased for clarity of discussion.

FIG. 1 is a functional block diagram of a cloud resource management system according to some embodiments of the present disclosure.

FIG. 2 is a flowchart of steps in a cloud resource management method according to some embodiments of the present disclosure.

FIG. 3 is a flowchart of steps in a cloud resource management method according to some embodiments of the present disclosure.

FIG. 4 is a flowchart of steps in a cloud resource management method according to some embodiments of the present disclosure.

FIG. 5 is a functional block diagram of a cloud resource management device according to some embodiments of the present disclosure.

Please refer to FIG. 1, which illustrates a functional block diagram of a cloud resource management system 100 according to some embodiments of the present disclosure. The cloud resource management system 100 is adapted to interface with a plurality of cloud environments 150a, 150b, ..., 150n. In some embodiments, the cloud resource management system 100 is used for computing and allocating computing resources required for cloud resources, so as to calculate the most suitable computing task resource among the cloud resources respectively belonging to the plurality of cloud environments, and / or complete the cloud The minimum cost required for resource requirements, and / or meeting one or more preferences, etc. For example, when a user wants to establish an e-commerce system, the cloud resource management system 100 will consider a plurality of cloud environments 150a-150n to determine cloud resources that meet the operational requirements of the e-commerce system among these cloud environments. In some embodiments, the plurality of cloud environments may refer to a multi-cloud environment, for example, a plurality of cloud environments provided by different cloud service providers. In some embodiments, the plurality of cloud environments may refer to a hybrid cloud environment. For example, the plurality of cloud environments may include at least one environment deployed by the cloud and one environment deployed locally. In this sense, cross-cloud can also refer to deployment of resources across geographies and clouds. In some embodiments, the plurality of cloud environments may include a multi-cloud environment and a hybrid cloud environment. In some embodiments, the plurality of cloud environments include cloud environments each including different cloud resources. For example, different cloud environments include different operation instructions, operation methods, virtual or physical resource types, and so on. For example, if you perform the same task in different cloud environments, you need to perform operations specific to individual cloud environments.

As shown in FIG. 1, the cloud resource management system 100 includes a network interface 110 and a processor 120. The network interface 110 is connected to the cloud An interface between the resource management system 100 and the cloud environments 150a-150n to transmit and / or receive instructions. In order to make the description concise, the following describes the related operations with three cloud environments 150a-150c in a specific example case. It should be noted that the cloud resource management system 100 of the present disclosure is not limited to operating in three cloud environments, and any number of cloud environments are within the scope of the present disclosure. As mentioned above, the plurality of cloud environments may include multi-cloud, hybrid cloud, ground deployment, or any of the above.

The plurality of cloud environments such as the cloud environments 150a to 150c each include a plurality of cloud resources. Cloud resources may include, but are not limited to, virtual and / or physical resources. For example, virtual resources may include virtualization resources, software-defined resources, and the like. For example, the virtual resources may include one or more virtual processors or virtual machines. For another example, the virtual resources may include resources defined based on different physical resources. For example, the virtual resources may include one or more virtual private clouds based on different physical servers. Another example is that virtual resources may include resources across geographic locations. For another example, the virtual resources may include cloud resources across multiple cloud environments. In some embodiments, the cloud resources in the plurality of cloud environments, such as cloud environments 150a-150c, are resources that include all the same / equivalent specifications or the same / equivalent specifications. Equivalent specifications can include specifications that have the same performance, the same level, or can meet specific needs. The equivalent specification may refer to a performance comparison between a plurality of specifications of a single type, or a performance comparison between a plurality of specification assemblies including a plurality of types. For example, a first specification may include four virtual processors and a 16GB memory corresponding to each virtual processor, and a second specification equivalent to the first specification may include two virtual processors and corresponding virtual processors. A 32GB note Memory. In some embodiments, the four virtual processors of the first specification may be equivalent to two virtual processors of the second specification, and / or the four memories of the first specification may be equivalent to two of the second specifications of the second processor. Memory, and / or the first specification is equivalent to the second specification. In some embodiments, the equivalent of the first specification and the second specification means that the performance of the first specification and the second specification are the same, or have the same level, or both can meet the requirements of a certain cloud service. For example, the equivalent first specification and the second specification may include the same, or the same level, or both can meet the computing capabilities of specific cloud service requirements. For another example, one cloud storage resource in one cloud environment may be equivalent to multiple cloud storage resources in the other two cloud environments. In some embodiments, cloud resources can be described by various types of information. For example, the information describing cloud resources may include resource type information, resource specification information, resource quantity information, resource time information, resource price information, charging method information, resource status information, and so on. Resource type information can include information describing different cloud resources such as virtual machines, containers, databases, loaders, and gateways. Resource specification information may include information describing resource specifications, resource versions, resource models, resource levels, and so on. The resource specifications may include information related to the processor, memory, storage space, IOPS, throughput, bandwidth, and so on. In some embodiments, the relevant information of the operating system or its version can be used as resource type information or resource specification information. The operating system may include Windows, Linux, or other cloud-based operating systems. The resource quantity information may include information describing available resource quantity, maximum available resource quantity, minimum available resource quantity, non-fixed quantity, and on-demand quantity. Resource time information may include a description of when the resource was started, when it was suspended Time, resource release time, resource restart time, resource availability period, resource life cycle, resource online / offline time or period, resource bidding time and other information. Resource price information can include on-demand prices, fixed or flexible prices, spare or reserved resource prices, data transfer prices, bid prices, and more. The charging method information may include information such as prepayment, non-prepayment, and installment payment. Resource status information can include information about resource use or availability status, resource use or availability conditions, and more. In some embodiments, the content of the foregoing types of information may be different from the foregoing classification according to the classification of each cloud environment.

As shown in FIG. 1, the cloud resource management system 100 includes a network interface 110 and a processor 120. The network interface 110 serves as an interface directly or indirectly between the cloud resource management system 100 and the cloud environments 150a to 150n to transmit and / or receive instructions, data, and the like. For example, the processor 120 may make an application program interface (API) call to the cloud environments 150a-150n through the network interface 110.

In some embodiments, the processor 120 of the cloud resource management system 100 is configured to request one or more of the cloud resources in the cloud environments 150a to 150n according to a cloud resource demand, so as to execute a request that can meet the cloud resource demand. task. For example, one or more cloud resources in the cloud environments 150a-150n are configured by the cloud resource management system 100 according to a user's needs. In some embodiments, a cloud resource requirement is generated by the cloud resource management system 100 automatically, in response to an event, or periodically, causing the cloud resource management system 100 to perform the task of cloud resource allocation. For example, cloud resource management system 100 The processor 120 may perform various tasks disclosed in this document without directly receiving instructions from the user.

For easy explanation of how to request the allocation and operation of cloud resource allocation, please refer to Figure 1 and Figure 2 together. FIG. 2 is a flowchart of steps in a cloud resource management method 200 according to some embodiments of the present disclosure. The cloud resource management system 100 executes the steps of the cloud resource management method 200 to deploy the most suitable cloud resources.

In step S210, an instruction is identified through the processor 120, where the instruction defines a cloud resource requirement.

In some embodiments, the processor 120 of the cloud resource management system 100 may receive an instruction from a user, or receive an instruction that is automatically generated, generated in response to an event, or generated periodically. The instruction may include information on a cloud resource requirement. For example, cloud resource requirements may include starting a specific number of virtual machines (and / or other cloud resources) at a certain time. The time point can be defined as absolute time (such as 10 am on June 5, 2019), relative time (such as 15 hours later), and the time point triggered by a specific event (such as virtual machine or processor usage) Up to 80%), at least one of a certain period (such as June 1, 2019 to September 30, 2019, or every Tuesday at 10 o'clock in the evening). Cloud resource requirements may include requirements that are not limited to specific cloud environments or specific cloud resources. For example, cloud resource requirements only define specific computing power or specific time to start cloud resources, but not limited to which cloud environment or cloud resource or amount of resources to meet the demand. And the generation of instructions automatically, in response to a specific event, or periodically can also be generated in various ways as defined above for time points. As another example, when a user wants to create When the e-commerce system has the execution capability but does not want to purchase hardware equipment by itself, it can send instructions to the cloud resource management system through a method such as a network communication protocol, user interface 130, or API that can communicate with the cloud resource management system 100 100. In some embodiments, after the processor 120 reads the instructions, it can perform processing such as translation or conversion on the instructions, interpret cloud resource requirements or retrieve cloud resource requirements to perform subsequent cloud resource deployment. For example, the processor 120 may generate one or more subsequent instructions after processing the instructions, and transmit the one or more subsequent instructions to one or more cloud environments, respectively.

In step S220, a plurality of cloud resource demand items related to the commanded cloud resource demand are obtained from a plurality of cloud environments such as cloud environments 150a-150c via the network interface 110.

In some embodiments, the cloud resource requirement item includes a cloud resource specification. Cloud resource demand items can be resource types, resource specifications, resource quantities, resource time, resource prices, charging methods, etc. Cloud resource requirement items can include information such as resource type information, resource specification information, resource quantity information, resource time information, resource price information, charging method information, resource status information, and so on. The cloud resource requirement project may include parameter values, and the parameter value is used to indicate the required resource amount and / or computing power of the cloud resource requirement project. In some embodiments, the parameter value may correspond to the information contained in the aforementioned cloud resource requirement item. In some embodiments, the parameter value may include at least one of resource type information, resource specification information, resource quantity information, resource time information, resource price information, charging method information, and resource status information. In some embodiments, the processor 120 sends a request to a plurality of cloud environments via the network interface 110 to obtain a plurality of cloud resource demand items associated with the commanded cloud resource demand. The processor 120 may send a request for returning a cloud resource requirement item to a plurality of cloud environments after interpreting or retrieving the demand content contained in the instruction. The cloud resource management system 100 may receive and store cloud resource demand items fed back from a plurality of cloud environments in a storage 150 for subsequent operations. In some embodiments, the processor 120 may analyze the content in the instruction, and may check whether the cloud resource requirement items fed back by the plurality of cloud environments can fully reflect the content of the instruction. For example, if the requested content of the instruction includes starting a specific cloud resource at a specific time, the processor 120 may check whether the cloud resource demand items fed back from the plurality of cloud environments include information of the specific time and the specific cloud resource. In some embodiments, if the cloud resource requirement item fed back by the specific cloud environment does not include the information of the specific time and the specific cloud resource, the processor 120 may remove the specific cloud environment from a plurality of cloud environment options, or This particular cloud environment is no longer considered in this task. In some embodiments, the cloud resource management system 100 may receive a plurality of cloud resource demand items of the cloud environment periodically or in an event triggered manner. For example, the cloud resource demand items that can be based on this task can be the most recently received or the latest cloud resource demand items.

In some embodiments, the parameter values of the resource type information and the resource quantity information in the cloud resource item obtained by the processor 120 are, for example, information of 4 processors, 16GB of memory, 32GB of storage resources, 10GB of input and output. Cloud resource items obtained by the processor 120 The current resource price information parameter values are information such as enabling a processor to cost $ 0.4328 per hour.

In step S230, the processor 120 calculates a plurality of resource combinations for the parameter values of the target cloud resource demand item according to the instruction.

For example, the parameter value of the cloud resource requirement item obtained by the processor 120 in the foregoing step S220 includes 12 processors and 128GB of memory. The processor 120 may determine a plurality of cloud resources, such as a plurality of cloud resources in a cloud environment 150a to 150c Multiple resource combinations that meet or meet the value of this parameter.

Please refer to the following Tables 1 to 3 for the cloud resource embodiments of the cloud environment 150a ~ 150c, respectively.

As exemplified in Tables 1 to 3 above, cloud resource demand items can include processors, memory, storage resources, resource prices, at least a plurality of demand items in the operating system, and different parameter values (or a combination of parameter values) according to the demand items. There are different configurations. In some embodiments, the processor 120 may calculate a plurality of resource combinations of the cloud resource list of Tables 1 to 3 according to parameter values of the cloud resource demand items. In some embodiments, the cloud resource management system 100 may normalize the parameter values of the cloud resource demand items that are fed back. For example, different cloud environments may have different definitions or settings for the same demand item, resulting in parameter values specific to a specific cloud environment, and the cloud resource management system 100 may apply such cloud-based environments Differences in features are eliminated to obtain normalized parameter values based on the same criteria.

In some embodiments, if there is a cloud computing requirement to start 12 processors and 128GB of memory at 12 noon, the processor 120 obtains the relevant specifications corresponding to the cloud environment 150a ~ 150c to generate a plurality of Resource combinations, such as all possible resource combinations in the targeted cloud environment. Please refer to Table 4 below, which is an example of a plurality of resource combinations.

When 12 processors and 128GB of memory are required, the processor 120 calculates four combinations, but this creation is not limited to this. The first combination includes the third configuration of the cloud environment 150a, which requires a total of 16 units, that is, the third configuration of the 16 groups of cloud environments 150a (hereinafter referred to as the first resource combination). First The second combination includes the fifth configuration of the cloud environment 150a, which requires a total of 8 units, that is, the fifth configuration of the eight groups of cloud environments 150a (hereinafter referred to as the second resource combination). The third combination includes the third configuration of the cloud environment 150a, which requires a total of 4 units (that is, the third configuration of four groups of cloud environments 150a), and the second configuration of the cloud environment 150b, which requires a total of 4 units (that is, the first group of 4 groups of cloud environments 150b) Two configurations), and the fifth configuration of the cloud environment 150c, requires a total of 1 unit (that is, the fifth configuration of a group of cloud environments 150c) (hereinafter referred to as the third resource combination). The fourth combination includes the fourth configuration of cloud environment 150b, which requires a total of 1 unit (that is, the fourth configuration of 4 groups of cloud environments 150b), and the fourth configuration of cloud environment 150c, which requires a total of 2 units (that is, 2 groups of cloud environments 150c Fourth configuration) (hereinafter referred to as the fourth resource combination). In some embodiments, each resource combination listed in Table 4 is a resource combination including an equivalent specification. For example, these resource combinations are equivalent to each other, that is, each resource combination can produce the same performance or meet the cloud computing requirements. For equivalent embodiments, refer to other relevant parts of this document.

In step S240, the processor 120 selects a resource combination from the resource combinations, and the selected resource combination can meet the commanded cloud resource allocation requirements.

For example, the processor 120 calculates the first resource combination to the fourth resource combination as shown in Table 4 above according to the parameter values of the cloud resource demand items.

In some embodiments, the processor 120 executes a numerical algorithm, a machine learning algorithm, or other algorithms to calculate a plurality of resource combinations. For example, the processor 120 uses Linear Programming or Non-Linear Programming. Programming), to calculate the combined demand value according to the parameter value of the resource combination. Alternatively, the processor 120 uses machine learning regression analysis, such as minimum root mean square, random forest regression factor (Regress Forest Regressor), linear regression (Linear Regression), support vector machine (Support Vector Regression (SVR), etc.) to calculate different resources combination.

After the processor 120 obtains the first resource combination, the second resource combination, the third resource combination, and the fourth resource combination, the processor 120 selects one of them as the selected resource combination to meet the commanded cloud resource allocation requirements. In some embodiments, when there may be multiple resource combinations that can meet the cloud resource configuration requirements, the processor 120 may further determine whether other conditions assist it in selecting one of the resource combinations. For example, the processor 120 may select the lowest total price among a plurality of resource combinations that satisfy the cloud resource allocation requirements according to a selection rule; or, the processor 120 may select an average performance parameter among the plurality of resource combinations that meet the cloud resource allocation requirements. The highest value. In some embodiments, the selection rule may be set or adjusted according to a cloud resource management policy. For example, the cloud resource management policy can reflect user preferences. The user can set the current cloud resource management policy to be the least cost-oriented. Therefore, under this policy, the processor 120 can select the lowest-priced resource combination according to the corresponding rules; or, the cloud The resource management policy can be set to maximize the number of virtual machines and to minimize the performance of the processor itself, so that the processor 120 prefers to select a low-level processor resource combination.

In some embodiments, cloud resource requirements include target requirements Evaluation, such as resource costs. The processor 120 calculates a combination demand value according to the parameter value of each resource combination, so as to select one of the plurality of resource combinations. The combined demand value is, for example, a resource cost. For example, the resource cost of the first resource combination is $ 23.8512, the resource cost of the second resource combination is $ 43.7592, the resource cost of the third resource combination is $ 12.4407, and the resource cost of the fourth resource combination is $ 8.338. In some embodiments, the cloud resource requirements may include restrictions on specific parameter values, such as a resource price, and the processor 120 may first exclude non-compliant resource configurations according to the restrictions. For example, if the user requests to exclude configurations with a price higher than $ 4 per hour, the processor 120 may remove all configurations of $ 4 per hour before calculating the resource combination. For another example, if only the latest version of the resources is to be used, the processor 120 may first remove all the configurations of the old version before calculating the resource combination. According to this, in some cases, the calculation amount can be greatly reduced.

The processor 120 selects a resource combination according to the target demand value and the combined demand value of each resource combination, for example, compares the target demand value and the combined demand value of each resource combination to select one of the resource combinations.

In some embodiments, the processor 120 selects a resource combination that is smaller than the target demand value in the combined demand value as the selected resource combination. For example, if the target demand value is a resource cost of $ 30.0, the processor 120 selects a resource combination with a resource cost less than $ 30.0, such as a third resource combination with a resource cost of $ 12.4407.

In other embodiments, the processor 120 selects a resource combination according to the smallest of the combined demand values of the resource combination. For example, the processor 120 may select a fourth resource combination with the lowest resource cost.

In step S250, a first request is sent to the first cloud environment via the network interface 110 to configure the first cloud resource in the first cloud environment, where the first cloud resource belongs to the first cloud environment in the cloud environments.

For example, when the processor 120 selects the fourth resource combination in Table 4, as described in the foregoing step S240, the fourth resource combination includes a fourth configuration of one unit of cloud resources of the cloud environment 150b. Therefore, a request is sent to the cloud environment 150b via the network interface 110, so that one unit of 16 processors, one unit of 32GB memory, and a CentOS operating system are configured in the cloud environment 150b. The cloud environment 150b can be configured accordingly according to the request.

In step S260, a second request is sent to the second cloud environment via the network interface 110 to configure a second cloud resource in the second cloud environment, where the second cloud resource belongs to the second cloud environment in the cloud environments.

For example, when the processor 120 selects a fourth resource combination, as described in step S240, the fourth resource combination further includes a fourth configuration of two units of cloud resources of the cloud environment 150c. Therefore, a request is sent to the cloud environment 150c via the network interface 110, so that two units of eight processors, two units of 30GB memory, and two units of 16GB storage resources are configured in the cloud environment 150c. The cloud environment 150c can be configured accordingly according to the request. According to this, the cloud resource management system 100 can allocate requirements to the cloud environment 150b and cloud environment 150c according to the instructions disclosed in this document, so that users can still use the cloud environment without having to worry about the selection and configuration details of the cloud environment. Complete the requirements.

In some embodiments, the processor 120 may identify a weight value associated with at least one of the cloud resource demand items as a basis for calculating a resource combination. In some embodiments, when the instructions associated with the read in step S210 are obtained from a plurality of cloud environments such as cloud environments 150a to 150c via the network interface 110, the processor 120 identifies the weight value associated with the cloud resource requirement item according to the instruction . Then, the processor 120 calculates a resource combination according to the weight value of the cloud resource demand item and the parameter value of the cloud resource demand item. It is worth mentioning that the weight value of the cloud resource demand item can be an optional parameter, and this weight value is used to increase or decrease the probability that the cloud resource item is selected. For example, if the operating system (or its release version) in Table 2 is set with a weight value, for example, the weight value of Red Hat is 0.8 and the weight value of CentOS is 0.4. If the command also includes the weight value of the operating system If required, the processor 120 will prioritize cloud resources with a high weight value. For example, Red Hat's operating system will be preferred over CentOS.

In some embodiments, the weight value of the cloud resource demand item may be increased according to the selected number of times. For example, the fourth configuration of cloud resources in cloud environment 150b and the fourth configuration of cloud resources in cloud environment 150c are selected. Therefore, after this resource configuration, the fourth configuration of cloud resources in cloud environment 150b and cloud environment The selection or configuration record of the fourth configuration of the 150c cloud resource will be used as historical data, and the weight value of the cloud resource is updated according to this historical data, for example, the weight value of such configurations is increased. In this way, cloud resource management The system 100 can learn the resource configuration every time, and avoid the change of each resource combination or the total resource cost to be too large, so as to meet the user's usage habits. For example, the cloud resource management system 100 optionally includes a database 140 for storing a model including weight setting rules to update the weight value by training the model with historical data. In some embodiments, the database 140 may also be used to store historical data and / or weight values.

Please refer to FIG. 1 again, the cloud resource management system 100 includes a user interface 130. The user interface 130 is coupled to the processor 120. The user interface 130 provides, for example, a user graphical interface or a command line interface, so that the processor 120 obtains a weight value of a cloud resource requirement item through the user interface 130. In this way, the user can set the weight value of the cloud resource requirement item through the user interface 130.

As mentioned in the cloud resource management policy mentioned above, users can also set other preferences based on actual needs. For example, the preference condition can be the operating system (or version) of the desired cloud resource, the upper limit / lower limit, the available period, the physical location of the cloud resource (such as the country or region or other geographic location, such as the location of the computer room), etc. . For example, users can restrict or exclude cloud resources from a certain geographic location. The preference condition can also be set by a weight value. For example, a higher weight value indicates that the cloud resource is a resource that the user wants to use. In some embodiments, the preference condition may further distinguish the restriction condition, so as to increase the operation degree of the calculation. For example, the preferences may include the number of virtual machines, the upper limit of the number of virtual machines, the preferred cloud environment, etc., and the restrictions may include the operating system and version, hardware (such as a server or server) Server), the old and new and available periods, the location of the cloud service room, etc. In some embodiments, a preference condition may be used as a "soft" condition, and a restriction condition may be used as a "hard" condition. For example, a soft condition may be assigned a lower weight value, while a hard condition may be assigned a higher weight value.

In other embodiments, the processor 120 executes an ARIMA (Autoregressive Integrated Moving Average Model) to generate an automatic regression tree model to generate past cloud resource usage records and predicted cloud resource requirements. Correlation to estimate future cloud resource requirements. The processor 120 may also execute a Recurrent Neural Network (RNN) and use historical data to predict cloud resource requirements for each cycle. In this way, the cloud resource management system 100 not only calculates the required resource combinations based on the cloud resource requirements, but also can use historical data to predict the resource combinations that meet the cloud resource requirements in a future period. For example, the e-commerce traffic is quite regular. For example, the traffic at one hour at noon and 7 to 12 pm on weekdays is the highest, and the traffic is lowest from 3 to 6 am. Seven days a week, traffic is higher on weekends than on weekdays. According to this, the processor 120 can collect the traffic changes over a period of time for historically used cloud resources as historical data (such as Log), and the historical data can include the usage status of the cloud-based cloud processor (such as usage percentage), and the memory usage status. Data, IOPS and other data, you can get the historical curve of the system performance of the cloud resources currently used. The processor 120 may use this historical curve or the like as a model training value for machine learning.

For example, the cloud resource packages in Tables 1 to 3 above Includes cloud resource specifications. On the other hand, the resource combination calculated by the processor 120 includes an equivalent specification. Therefore, the processor 120 calculates the equivalent specifications according to the cloud resource specifications, and calculates the cloud resources that meet or meet the requirements of the cloud resources among all the cloud resources equivalent to the cloud resource specifications. For example, those whose execution capabilities of the cloud resources involved in this document meet the requirements of cloud resources can be used in cloud computing, not limited to the exact same hardware and / or virtual resources, such as the same model and type Processors of the same grade, or the same generation of computing power, but manufactured or branded by different manufacturers, can fall into the category of equivalent specifications.

FIG. 3 is a flowchart of steps in a cloud resource management method 300 according to some embodiments of the present disclosure. In step S305, the cloud resource management system 100 obtains a desired total cost and startup time of a cloud resource demand. For example, the desired total cost may include an absolute cost value, or a cost interval. The desired total cost may include a cost threshold as a parameter for the cloud resource management system 100 to subsequently calculate and / or select a resource combination. As mentioned above, the startup time may be an absolute time, a periodic time, or the like.

In step S310, the cloud resource management system 100 obtains one or more preference conditions. The one or more preference conditions can be obtained from the user or the user's system via the user interface 130 or the API. In some embodiments, the preference conditions may have been stored in the self-cloud resource management system 100. For examples of preferred conditions, reference may be made to the relevant sections above in this document. For example, preferences can include payment terms such as prepayment, no prepayment, Installment payments, etc. For another example, the preference conditions may include the geographical location of cloud resources, the number of cloud resources, and the price of cloud resources. Moreover, the preference conditions can be obtained by using the cloud-based cloud management policy as described above.

In step S315, the cloud resource management system 100 obtains a plurality of cloud resource lists provided by a plurality of cloud environments. The plurality of cloud environments may be multiple cloud environments that are incompatible in terms of communication methods. For example, each cloud environment has its own API. The cloud resource list can include different configurations provided by the cloud resource demand project as described above (and please refer to Tables 1 to 3).

In step S320, the cloud resource management system 100 compares the plurality of cloud resource lists with the preference conditions, removes the configurations that do not meet the preference conditions from the plurality of cloud resource lists, and generates a configuration for calculation. For example, when the preference condition restricts the cloud operating system to the Linux operating system, the configuration using the non-Linux operating system in the cloud resource list will be eliminated, and the remaining configuration can be used for subsequent calculations.

In step S325, the cloud resource management system 100 generates a plurality of cloud resource combinations according to the calculation configuration, and calculates the price of each cloud resource combination. The cloud resource management system 100 may perform calculations according to various methods disclosed in this document. In addition, the cloud resource management system 100 may assign a corresponding weight value to at least one cloud resource requirement item in the configuration used for computing. For the description of the weights, reference may also be made to the embodiments previously disclosed herein.

In step S330, the cloud resource management system 100 selects a desired cloud resource from a plurality of cloud resource combinations according to a desired total cost. Source combination. For example, the cloud resource management system 100 compares the prices of multiple cloud resource combinations with a desired total cost, and selects a cloud resource combination whose price is lower than the desired total cost among the plurality of cloud resource combinations. If there are multiple cloud resource combinations whose price is lower than the desired total cost, the lowest price can be selected.

In step S335, the cloud resource management system 100 performs cloud resource deployment on a plurality of cloud environments according to the selected desired cloud resource combination. For example, if the selected desired cloud resource combination includes resources of the A cloud environment and the B cloud environment, the A cloud environment and the B cloud environment are deployed according to the cloud resource combination to meet the content of cloud resource requirements.

FIG. 4 is a flowchart of steps in a cloud resource management method 400 according to some embodiments of the present disclosure. The main difference between the cloud resource management method 400 and the cloud resource management method 300 is that in the cloud resource management method 400, cloud resource requirements do not include cost conditions and preference conditions, and time conditions are historical data that can be used by the cloud resource management system 100. To decide. For example, the cloud resource management system 100 generates a forecast curve of future usage through machine learning based on historical data recorded using cloud services currently (or in the past). As described above, the cloud resource management system 100 can use deep learning recurrent neural networks (RNNs) to predict time series. In this scenario, there will be many similar characteristic time series (processors, IOPS, etc.) in the same time unit. And using different sequences, such as weekly, daily, and hourly historical data as the smallest unit, can predict the future performance demand curve of the system (cloud service resources).

In step S410, the cloud resource management method 400 identifies a task requirement that includes performing a first task. For example, the task requirements may include instructions to continue performing one of the current or one of the tasks, and the current task is already utilizing a specific cloud resource configuration. Alternatively, the task requirements may include a task that has been completed based on the use of a specific cloud resource configuration, and instructs to perform the same task as the completed task at a specific time in the future for the completed task based on past cloud resource configuration usage records.

In step S420, a second task is identified, and historical data of the second task is obtained. As described above, the second task may be a task that is currently being executed or has been completed. As disclosed in this document, historical data may include cloud resource allocation and / or usage records.

In step S430, the historical data is used to predict the cloud resource requirements of the first task. As disclosed in this document, the demand curve of cloud resources can be predicted through the algorithm disclosed in this document.

In step S440, the cloud resource configuration of a plurality of cloud environments is performed according to the predicted cloud resource requirements. As disclosed in this document, after forecasting cloud resource requirements, the requirements corresponding to multiple cloud environments can be transmitted separately to achieve the corresponding cloud resources allocation in the multiple cloud environments.

FIG. 5 is a functional block diagram of a cloud resource management device 500 according to some embodiments of the present disclosure. The cloud resource management device 500 includes an identification instruction module 510, a cloud resource demand item acquisition module 520, a resource combination calculation and selection module 530, and machine learning Model 540 and request sending module 550.

As shown in FIG. 5, the identification instruction module 510 is used to receive an instruction, or an instruction that is automatically generated, generated in response to an event, or generated periodically. The description of receiving or generating an instruction is the same as step S210 in FIG. 2, and will not be repeated here.

The cloud resource demand item acquisition module 520 is used to retrieve cloud resource demand items. In one embodiment, the cloud resource demand item acquisition module 520 obtains information including the required resource amount and / or computing power of the cloud resource demand item according to the parameter values in the instruction identified by the identification instruction module 510. In another embodiment, the cloud resource demand item acquisition module 520 sends a request to a plurality of cloud environments to obtain a plurality of cloud resource demand items associated with the commanded cloud resource demand.

The resource combination calculation and selection module 530 is configured to calculate a plurality of resource combinations according to the parameter values of the cloud resource demand items targeted by the instruction. The description about calculating a plurality of resource combinations is shown in steps S230 to S240 in FIG. 2, and will not be repeated here.

The machine learning model 540 is used to calculate a new resource combination. In some embodiments, the machine learning model 540 may perform numerical algorithms, machine learning algorithms, or other algorithms for different combinations of resources in the past to train different models, so that the trained models can be used to calculate new resources. combination.

The request sending module 550 is configured to send a request to one or more cloud environments associated with the resource combination for the resource combination calculated by the machine learning model 540. The instructions for sending a request to the cloud environment are as shown in Figure 2. Step S250 is not repeated here.

In some embodiments, the cloud resource management method 200 may also be implemented as a computer program including a plurality of codes and stored in a non-transitory computer-readable recording medium. After the code is loaded into the processor 120 as shown in FIG. 1, the processor 120 executes the code and operates as steps S210 to S250 in FIG. 2. For example, the processor 120 reads instructions to retrieve cloud resource requirements, and obtains a plurality of cloud resource requirement items associated with the instructions from a plurality of cloud environments via the network interface 110. The processor 120 calculates a plurality of resource combinations according to the parameter values of the cloud resource demand items according to the instructions. The processor 120 selects a resource combination from the resource combinations, and the selected resource combination includes a first cloud resource and a second cloud resource. Send a first request to the first cloud environment via the network interface 110 to configure the first cloud resource in the cloud environment, and send a second request to the second cloud environment via the network interface 110 to configure the second in the cloud environment Cloud resources.

In some embodiments, the cloud resource management method 300 may also be implemented as a computer program including a plurality of codes, and stored in a non-transitory computer-readable recording medium. After the code is loaded into the processor 120 as shown in FIG. 1, the processor 120 executes the code and operates as steps S305 to S335 in FIG. 3.

In some embodiments, the cloud resource management method 400 may also be implemented as a computer program including a plurality of codes and stored in a non-transitory computer-readable recording medium. After the code is loaded into the processor 120 as shown in FIG. 1, the processor 120 executes the code and operates as shown in FIG. 4. S410 ~ S440.

In some embodiments, the non-transitory computer-readable recording medium may be a read-only memory, a flash memory, a floppy disk, a hard disk, an optical disk, a flash drive, a magnetic tape, a database accessible by a network, or a familiar This artist can easily think of non-transitory computer-readable recording media with the same functions.

In summary, this case provides a cloud resource management system and a cloud resource management method. The network interface 110 sends a request to the cloud environment associated with the selected resource combination to configure cloud resources in the associated cloud environment. , You can achieve dynamic configuration of cloud resources. In addition, in this case, according to the current operating conditions of the resource combination, such as business needs, the number of online customers, system performance, and other factors, the status can be determined in real time and the resource combination can be dynamically adjusted to generate the most suitable cloud service resources.

The features of several embodiments are summarized above, so that those skilled in the art can better understand the aspect of this creation. Those skilled in the art should understand that this creation can be easily used as a basis for designing or modifying other processes and structures in order to implement the same purpose and / or achieve the same advantages of the embodiments described herein. Those familiar with this technology should also realize that such equivalent structures do not depart from the spirit and scope of this creation, and can produce various changes, substitutions and alterations without departing from the spirit and scope of this creation.

Claims (8)

A cloud resource management system is adapted to interface a plurality of cloud environments, each of which includes a plurality of cloud resources. The cloud resource management system includes a network interface for providing the cloud resource management system and the plurality of cloud environments. An interface for each cloud resource interface; and a processor coupled to the network interface, wherein the processor is used to: identify a command that defines a cloud resource requirement; obtained from the cloud environments through the network interface A plurality of cloud resource demand items associated with the instruction, each of which includes a parameter value, wherein the parameter value includes a resource type information, a resource specification information, a resource quantity information, a resource time information, a At least one of resource price information, a charging method information, and a resource status information; according to the instruction, a plurality of resource combinations are calculated for the parameter values of the cloud resource demand items, each of which includes the cloud resources At least one of; selecting a first resource combination from the resource combinations, the first resource combination including A first cloud resource and a second cloud resource, and the first resource combination satisfies the instruction of the cloud resource allocation requirement, wherein the first cloud resource belongs to one of the cloud environments and the second cloud resource The resource belongs to one of the second cloud environments; a first request is sent to the first cloud environment via the network interface to configure the first cloud resource in the first cloud environment; and via the network Lu Jie sends a second request to the second cloud environment to configure the second cloud resource in the second cloud environment. The cloud resource management system according to claim 1, wherein the cloud resource requirement includes a cloud resource specification, and the plurality of resource combinations includes an equivalent specification, which is not the same as but meets the cloud resource requirement The cloud resource specification. The cloud resource management system according to claim 1, wherein the cloud resource demand of the instruction includes a target demand value, and the processor is further configured to: calculate a combined demand value based on the parameter value of each resource combination; and The first resource combination is selected according to the target demand value and the combined demand value of each resource combination. The cloud resource management system according to claim 3, wherein the processor is further configured to execute at least one of the following to select the first resource combination: among the combination demand values, select the resources that are smaller than the target demand value. One of the combinations; or the smallest of the combined demand values. The cloud resource management system according to claim 1, wherein the processor is further configured to: identify a weight value associated with at least one of the cloud resource demand items; and according to the weight of at least one of the cloud resource demand items Value and the parameter values of the cloud resource demand items to calculate the resource combinations. The cloud resource management system according to claim 5, wherein the processor is further configured to update the weight value of at least one of the cloud resource demand items according to a historical data. The cloud resource management system according to claim 5, further comprising a user interface, wherein the processor is further configured to obtain the weight value of at least one of the cloud resource demand items through the user interface. The cloud resource management system according to claim 1, wherein the processor is further configured to execute at least one of a numerical algorithm and a machine learning algorithm to calculate the resource combinations.