CN116566844A - Data management and control method based on multi-cloud fusion and multi-cloud fusion management platform - Google Patents

Data management and control method based on multi-cloud fusion and multi-cloud fusion management platform Download PDF

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Publication number
CN116566844A
CN116566844A CN202310819597.1A CN202310819597A CN116566844A CN 116566844 A CN116566844 A CN 116566844A CN 202310819597 A CN202310819597 A CN 202310819597A CN 116566844 A CN116566844 A CN 116566844A
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module
management
cloud platform
resource
cloud
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CN116566844B (en
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邓正秋
吕绍和
邓頔
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Hunan Malanshan Video Advanced Technology Research Institute Co ltd
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Hunan Malanshan Video Advanced Technology Research Institute Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/147—Network analysis or design for predicting network behaviour
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H04L43/0876—Network utilisation, e.g. volume of load or congestion level
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

The invention discloses a data management and control method based on multi-cloud fusion and a multi-cloud fusion management platform, wherein the method comprises the following steps: forming an internal resource module corresponding to the cloud platform; constructing a common resource module based on a multi-cloud fusion public architecture outside each cloud platform; establishing a control table for controlling the internal resource module and the public resource module; constructing a mapping relation between the instruction and the first management table or the second management table in the fusion management layer; acquiring a real-time instruction sent by a user application layer to a command processing layer, and identifying the real-time instruction through the command processing layer; and according to the identification result of the real-time instruction, the fusion management layer realizes the resource module call of the multi-cloud fusion through the mapping relation so as to generate response data according to the real-time instruction processing and send the response data to the user application layer. The technical scheme provided by the invention is beneficial to solving the defect of cross-platform cloud resource management and control of users.

Description

Data management and control method based on multi-cloud fusion and multi-cloud fusion management platform
Technical Field
The invention relates to the technical field of multi-cloud fusion, in particular to a data management and control method based on multi-cloud fusion and a multi-cloud fusion management platform.
Background
Cloud computing platforms, also referred to as cloud platforms, refer to services that provide computing, networking, and storage capabilities based on hardware resources and software resources. Cloud computing platforms can be divided into 3 classes according to functional emphasis: a storage type cloud platform mainly used for data storage, a computing type cloud platform mainly used for data processing and a comprehensive cloud computing platform taking both computing and data storage processing into consideration. Cloud computing platforms may also be classified into various types according to use.
Because the various cloud platforms have respective advantages and functions, in the service system, a hybrid of multiple cloud resources is generally generated to support the service system, so that management of the multiple cloud platforms occurs. Different cloud platforms have great differences in development, operation and maintenance technology and management experience, and resources cannot be dynamically shared among the cloud platforms. In the traditional technical scheme, when managing multiple cloud platforms, a manager needs to enter different cloud platforms, different management strategies exist for various cloud platforms, when a user performs cloud resource management and control in a cross-platform mode, the management strategies of the multiple cloud platforms need to be adapted, the user needs to process in a cross-platform mode when performing multi-cloud management and control, the operation is complex, and the user experience is poor.
Disclosure of Invention
The invention mainly aims to provide a data management and control method based on multi-cloud fusion and a multi-cloud fusion management platform, and aims to solve the defects that when a user carries out cloud resource management and control across platforms, the user needs to adapt to management strategies of multiple cloud platforms, so that the user needs to respectively process across platforms when carrying out multi-cloud management and control, the operation is complex, and the user experience is poor.
In order to achieve the above objective, in the data management and control method based on the multi-cloud fusion, the multi-cloud fusion management platform comprises a user application layer, a command processing layer, a fusion management layer and a cloud platform layer, wherein the cloud platform layer comprises a plurality of cloud platforms; the method comprises the following steps:
the fusion management layer acquires resource information of a cloud platform accessed in the cloud platform layer;
according to the resource information of the cloud platforms, the first computing module, the first storage module, the first network module and the first management module of each cloud platform are constructed into an internal resource module of the corresponding cloud platform;
constructing a common resource module based on a multi-cloud fusion public architecture outside each cloud platform based on a second computing module, a second storage module, a second network module and a second management module of each cloud platform according to the resource information of the cloud platform, wherein the common resource module comprises a virtual computing module, a virtual storage module, a virtual network module and a virtual management module;
Establishing a management and control table for managing and controlling the internal resource modules and the public resource modules, wherein the management and control table comprises a first management and control table for calling the public resource modules through the internal resource modules of all cloud platforms and a second management and control table for calling the internal resource modules of all cloud platforms through the public resource modules;
constructing a mapping relation between the instruction and the first management table or the second management table in the fusion management layer;
acquiring a real-time instruction sent by a user application layer to a command processing layer, and identifying the real-time instruction through the command processing layer;
and according to the identification result of the real-time instruction, the fusion management layer realizes the resource module call of the multi-cloud fusion through the mapping relation so as to generate response data according to the real-time instruction processing and send the response data to the user application layer.
Preferably, the step of constructing the first computing module, the first storage module, the first network module and the first management module of each cloud platform into the internal resource module of the corresponding cloud platform according to the resource information of the cloud platform includes:
acquiring a historical instruction received by each cloud platform, and marking the instruction type of each cloud platform as a proprietary instruction and a universal instruction according to the historical instruction received by each cloud platform;
Acquiring first workloads respectively corresponding to all modules in the cloud platform when each cloud platform processes all exclusive instructions in a test period;
acquiring second workloads respectively corresponding to all modules in the cloud platform when all the universal instructions are processed by each cloud platform in a test period;
according to the time distribution condition of the special instructions in the historical instructions and the first workload corresponding to each special instruction, a first computing module is segmented from the computing modules of each cloud platform, a first storage module is segmented from the storage modules of each cloud platform, a first network module is segmented from the network modules of each cloud platform, and the first management module is segmented from the management modules of each cloud platform to form an internal resource module of the corresponding cloud platform.
Preferably, the step of constructing a common resource module based on a multi-cloud fusion common architecture outside each cloud platform based on the second computing module, the second storage module, the second network module and the second management module of each cloud platform according to the resource information of the cloud platform includes:
according to the time distribution condition of the general instructions in the historical instructions and the second workload corresponding to each general instruction, calculating the time distribution of the computing resource sum of the general instructions for processing all cloud platforms in the test period, the time distribution of the storage resource sum, the time distribution of the network resource sum and the time distribution of the management resource sum;
And according to the distribution of the computing resource sum along with time, the distribution of the storage resource sum along with time, the distribution of the network resource sum along with time and the distribution of the management resource sum along with time, the second computing module is cut from the computing modules of each cloud platform, the second storage module is cut from the storage modules of each cloud platform, the second network module is cut from the network modules of each cloud platform, the second management module is cut from the management modules of each cloud platform, and the public resource module based on the multi-cloud fusion public architecture outside each cloud platform is constructed.
Preferably, the step of establishing a management table for managing the internal resource module and the common resource module includes:
establishing a tree-shaped management control table, and establishing a fusion management node at a first level of the tree-shaped management control table;
creating a common resource module node which is positioned at the lower layer of the fusion management node and used for managing the common resource module, and creating an internal resource module node which is positioned at the lower layer of the fusion management node and used for managing the internal resource module of each cloud platform respectively at the second level of the tree management table;
creating virtual computing module nodes, virtual storage module nodes, virtual network module nodes and virtual management module nodes which are respectively positioned at the lower layers of the public resource module nodes at a third level of the tree management and control table, and respectively creating corresponding first computing module nodes, first storage module nodes, first network module nodes and first management module nodes at the lower layers of each internal resource module node;
Creating a computing unit node representing the attribution of different cloud platforms for a virtual computing module node of a third level, a storage unit node representing the attribution of different cloud platforms for a virtual storage module node, a network unit node representing the attribution of different cloud platforms for a virtual network module node and a network unit node representing the attribution of different cloud platforms for a virtual management module node in a fourth level of the tree management and control table;
establishing a lower node of each internal resource module node in a third level according to attribution of the cloud platform, and indexing the corresponding unit node in a fourth level to form a first management and control table for calling a public resource module through the internal resource module of each cloud platform;
and establishing an index relation from each unit node in the fourth level to a lower node corresponding to the internal resource module node in the third level according to the attribution of the cloud platform so as to form a second management and control table for calling the internal resource modules of each cloud platform by the public resource module.
Preferably, the step of constructing the mapping relationship between the instruction and the first management table or the second management table in the fusion management layer includes:
acquiring exclusive instruction libraries of all cloud platforms, acquiring hierarchy nodes related to original resource modules of all cloud platforms in each hierarchy node of a tree-shaped management and control table, and establishing a first mapping relation table of the exclusive instructions and the hierarchy nodes in the tree-shaped management and control table according to the exclusive instructions of all cloud platforms and the hierarchy nodes related to the original resource modules of all cloud platforms;
The method comprises the steps of obtaining a general instruction library, presetting unit nodes called in a tree-shaped management and control table when each general instruction is processed, obtaining a cloud platform to which each called unit node in the tree-shaped management and control table belongs, and establishing a second mapping relation table of the general instruction, the called unit node and the hierarchical node of an internal resource module of the called unit node belonging to the cloud platform, wherein the cloud platform belongs to the hierarchical node of the internal resource module corresponding to the cloud platform.
Preferably, the step of enabling the fusion management layer to implement resource module call of multi-cloud fusion according to the identification result of the real-time instruction to generate response data according to the real-time instruction processing and send the response data to the user application layer includes:
the command processing layer sends the identification result of the real-time command to the fusion management layer, and the fusion management layer judges whether the type of the real-time command belongs to a proprietary command or a general command;
the fusion management layer forwards the real-time instruction belonging to the exclusive instruction to a first management module node of the internal resource module node corresponding to the cloud platform, and the first management module node internally transmits the real-time instruction at a lower node of the corresponding internal resource module node;
Judging whether an internal resource module node receiving the real-time instruction has the capability of executing the instruction after the instruction is internally transmitted;
if yes, the internal resource module node receiving the real-time instruction processes the generated response data, and the response data is sent to a user application layer after being stored and managed by the virtual management module node; if not, forwarding the real-time instruction from the corresponding internal resource module node to a unit node associated with the internal resource module node for receiving the real-time instruction, so as to process and generate response data, and sending the response data to a user application layer after storage management is carried out by the virtual management module node;
the fusion management layer forwards the real-time instruction belonging to the general instruction to a virtual management module node of the public resource module node, the virtual management module node calls a unit node in the public resource module node to process the generated response data, and the response data is backed up to an internal resource module node associated with the unit node which actually processes the general instruction and then is sent to the user application layer.
Preferably, the method further comprises:
detecting the waiting sequence change of the general instruction in the public resource module and the workload change of the public resource module;
Predicting a peak period and a workload peak of the workload of the public resource module according to the waiting sequence change and the workload change;
according to the peak time period, the work load peak value and the waiting sequence condition in the peak time period, an elastic capacity expansion instruction is sent to the fusion management layer, so that the temporary elastic capacity expansion of the public resource module is realized by utilizing the internal resource module of the cloud platform;
and the fusion management layer establishes an elastic capacity expansion management and control table according to the elastic capacity expansion instruction, and in the elastic capacity expansion management and control table, third-level nodes established inside the second-level internal resource module nodes of the tree-shaped management and control table are mapped into unit nodes of a fourth level newly added in the tree-shaped management table according to the module function so as to realize the resource capacity expansion of the public resource module.
Preferably, the capacity of the elastic expansion is determined in the following way:
predicting a first period when the workload of the virtual computing module exceeds a first preset load according to a workload-time curve of the virtual computing module corresponding to the general instruction;
predicting a second period when the workload of the virtual storage module exceeds a second preset load according to a workload-time curve of the virtual storage module corresponding to the general instruction;
Predicting a third period when the workload of the virtual network module exceeds a third preset load according to the workload-time curve of the virtual network module corresponding to the general instruction;
predicting a fourth period when the workload of the virtual management module exceeds a fourth preset load according to the workload-time curve of the virtual management module corresponding to the general instruction;
obtaining a union set of the first time period, the second time period, the third time period and the fourth time period to obtain an elastic capacity expansion time period of the public resource module;
according to a work load-time curve of the virtual computing module, predicting a computing load maximum value in a first period, and computing to obtain elastic capacity expansion data of the virtual computing module;
according to the work load-time curve of the virtual storage module, predicting the maximum value of the storage load in the second period, and calculating to obtain the elastic capacity expansion data of the virtual storage module;
according to the work load-time curve of the virtual network module, determining the maximum value of the network load in a third period, and calculating to obtain the elastic capacity expansion data of the virtual network module;
and predicting the maximum value of the management load in the fourth period according to the working load-time curve of the virtual management module, and calculating to obtain the elastic capacity expansion data of the virtual management module.
Preferably, the elastic expansion data of the virtual computing module is calculated in the following manner:
wherein ,elastic capacity expansion data for virtual computing module, < ->To calculate the load maximum, y 1 To calculate the load peak ratio value, and is constant, 0 < y 1 ＜1；A 1 For the first calculation of the capacity expansion data, A 2 For the second calculation of the expansion data, A 1 and A2 Constants respectively greater than 0, A 2 ＞A 1 ；/>The computing resource size used as the first computing module for the ith cloud platform,is the ithThe cloud platform is used as the computing resource size of the second computing module, i is more than 1 and less than or equal to n, and n is the number of the cloud platforms;
the elastic expansion data of the virtual storage module is calculated in the following mode:
wherein ,for the elastic capacity-expanding data of the virtual memory module, < >>To store the load maximum value, y 2 To store the load peak ratio value and is constant, 0 < y 2 ＜1；B 1 For first storing the expanded data, B 2 For the second storage of the capacity-expanded data, B 1 and B2 Constants greater than 0, B 2 ＞B 1 ；/>Storage resource size for the ith cloud platform as the first storage module, +.>The storage resource size used as the second storage module for the ith cloud platform;
the elastic capacity expansion data of the virtual network module is calculated by the following mode:
wherein ,for the elastic capacity-expanding data of the virtual network module, < > >For the maximum network load, y 3 Is a network load peak ratio value and is constant, 0 < y 3 ＜1；C 1 Expanding data for a first network, C 2 Expanding data for the second network, C 1 and C2 Constants greater than 0, C 2 ＞C 1 ；/>Network resource size for the ith cloud platform as the first network module, +.>The network resource size used as the second network module for the ith cloud platform;
the elastic expansion data of the virtual management module is calculated in the following mode:
wherein ,elastic capacity-expansion data for virtual management module, < ->To manage the load maximum, y 4 To manage the peak ratio of load, and is constant, 0 < y 4 ＜1；D 1 For the first management of capacity-expanded data, D 2 For the second management of the expanded data, D 1 and D2 Respectively a constant greater than 0, D 2 ＞D 1 ；/>The management resource size used as the first management module for the ith cloud platform,and the management resource size serving as the second management module for the ith cloud platform.
In addition, in order to achieve the above objective, the present invention further proposes a multi-cloud fusion management platform for executing the method of any one of the above aspects; the multi-cloud fusion management platform comprises a user application layer, a command processing layer, a fusion management layer and a cloud platform layer, wherein the cloud platform layer comprises a plurality of cloud platforms.
According to the technical scheme, the multi-cloud fusion management platform comprises an internal resource module and a public resource module, wherein the internal resource module is reserved in each cloud platform connected to the multi-cloud fusion management platform, and can be used for processing proprietary instructions which are sent by a user application layer and cannot be identified by the public resource module, so that the multi-cloud fusion management platform reserves the function of independently processing the proprietary instructions by each cloud platform. And the public resource module is a public resource which is divided from each cloud platform and is used for realizing a unified cloud computing function on the multi-cloud fusion management platform. When the user application layer sends out a general instruction which can be processed by the public resource module, the unified processing function is completed by calling the public resource module. Therefore, the multi-cloud fusion management platform realizes the function of fusion management of each cloud platform. Furthermore, in the invention, if the internal resource module is insufficient in processing the dedicated instruction, the internal resource module of the cloud platform can call the public resource module (specifically, call the resource module of the cloud platform to which the public resource module originally belongs) to process the dedicated instruction through the first management and control table, so that the function of the multi-cloud fusion management platform in processing the dedicated instruction is not affected. Furthermore, after the common resource module formed by fusion completes the processing of the general instruction, the second management and control table can be called, and the processing result is stored not only in the common resource module, but also in the actual resource module of a cloud platform actually executing the data processing in the common resource module, so that the data backup is realized.
Therefore, the cloud resource management and control is not needed to be carried out across the platforms, the real-time instruction of the user is only received through the user application layer, the command processing layer is used for judging whether the real-time instruction belongs to the proprietary instruction or the universal instruction, the fusion management layer is used for sending the proprietary instruction to the internal resource module of the corresponding cloud platform for processing, and the universal instruction is fused into the public resource module for processing, so that the cloud platforms can realize the special service and the public service.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an embodiment of a data management and control method based on multi-cloud fusion according to the present invention;
fig. 2 is a functional block diagram of the cloud fusion management platform according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The description as it relates to "first", "second", etc. in the present invention is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
In the present invention, unless specifically stated and limited otherwise, the terms "connected," "affixed," and the like are to be construed broadly, and for example, "affixed" may be a fixed connection, a removable connection, or an integral body; can be mechanically or electrically connected; either directly or indirectly, through intermediaries, or both, may be in communication with each other or in interaction with each other, unless expressly defined otherwise. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In addition, the technical solutions of the embodiments of the present invention may be combined with each other, but it is necessary to be based on the fact that those skilled in the art can implement the technical solutions, and when the technical solutions are contradictory or cannot be implemented, the combination of the technical solutions should be considered as not existing, and not falling within the scope of protection claimed by the present invention.
Referring to fig. 1 to fig. 2, in a first embodiment of a data management and control method based on multi-cloud fusion according to the present invention, a multi-cloud fusion management platform includes a user application layer, a command processing layer, a fusion management layer, and a cloud platform layer, where the cloud platform layer includes a plurality of cloud platforms; the method comprises the following steps:
step S10, acquiring resource information of a cloud platform accessed in a cloud platform layer by a fusion management layer;
step S20, according to the resource information of the cloud platforms, constructing a first computing module, a first storage module, a first network module and a first management module of each cloud platform into an internal resource module of the corresponding cloud platform;
step S30, constructing a common resource module based on a multi-cloud fusion public architecture outside each cloud platform based on a second computing module, a second storage module, a second network module and a second management module of each cloud platform according to the resource information of the cloud platform, wherein the common resource module comprises a virtual computing module, a virtual storage module, a virtual network module and a virtual management module;
Step S40, a management and control table for managing and controlling the internal resource modules and the public resource modules is established, wherein the management and control table comprises a first management and control table for calling the public resource modules through the internal resource modules of all cloud platforms and a second management and control table for calling the internal resource modules of all cloud platforms through the public resource modules;
s50, constructing a mapping relation between the instruction and the first management table or the second management table in the fusion management layer;
step S60, acquiring a real-time instruction sent by a user application layer to a command processing layer, and identifying the real-time instruction through the command processing layer;
and step S70, according to the identification result of the real-time instruction, the fusion management layer realizes the resource module call of the multi-cloud fusion through the mapping relation so as to generate response data according to the real-time instruction processing and send the response data to the user application layer.
According to the technical scheme, the multi-cloud fusion management platform comprises an internal resource module and a public resource module, wherein the internal resource module is reserved in each cloud platform connected to the multi-cloud fusion management platform, and can be used for processing proprietary instructions which are sent by a user application layer and cannot be identified by the public resource module, so that the multi-cloud fusion management platform reserves the function of independently processing the proprietary instructions by each cloud platform. And the public resource module is a public resource which is divided from each cloud platform and is used for realizing a unified cloud computing function on the multi-cloud fusion management platform. When the user application layer sends out a general instruction which can be processed by the public resource module, the unified processing function is completed by calling the public resource module. Therefore, the multi-cloud fusion management platform realizes the function of fusion management of each cloud platform. Furthermore, in the invention, if the internal resource module is insufficient in processing the dedicated instruction, the internal resource module of the cloud platform can call the public resource module (specifically, call the resource module of the cloud platform to which the public resource module originally belongs) to process the dedicated instruction through the first management and control table, so that the function of the multi-cloud fusion management platform in processing the dedicated instruction is not affected. Furthermore, after the common resource module formed by fusion completes the processing of the general instruction, the second management and control table can be called, and the processing result is stored not only in the common resource module, but also in the actual resource module of a cloud platform actually executing the data processing in the common resource module, so that the data backup is realized.
Therefore, the cloud resource management and control is not needed to be carried out across the platforms, the real-time instruction of the user is only received through the user application layer, the command processing layer is used for judging whether the real-time instruction belongs to the proprietary instruction or the universal instruction, the fusion management layer is used for sending the proprietary instruction to the internal resource module of the corresponding cloud platform for processing, and the universal instruction is fused into the public resource module for processing, so that the cloud platforms can realize the special service and the public service.
The cloud platform layer comprises at least two cloud platforms. Each cloud platform may be a same-function cloud platform or a different-function cloud platform.
In the present invention, the resource modules of each cloud platform are divided into a computing module, a storage module, a network module and a management module, and of course, the division of the resource modules may also be in other manners, which are not limited herein.
The proportion of the internal resource module of each cloud platform serving as the internal resource module and the proportion of the public resource module serving as the public resource module can be divided according to the set proportion of the user, and the internal resource is insufficient to call the resources originally belonging to the cloud platform in the public resource module for auxiliary processing because the internal resource module is adopted when each cloud platform processes the exclusive instruction, so that the proportion of the internal resource module and the proportion of the public resource module do not influence the processing result. However, when the internal resource module and the external resource module are used in intermodulation, mapping is required to be performed through the first management table and the second management table, which leads to the extension of the processing time of the cloud fusion management platform for real-time instructions. Therefore, before the dedicated instruction processing peak period or the processing peak period of the multi-cloud fusion management platform comes, the proportion of the internal resource modules of each cloud platform serving as the internal resource modules can be dynamically increased, so that the processing time of the dedicated instruction is reduced. In contrast, before the processing peak period or the processing peak period of the general instruction of the multi-cloud fusion management platform comes, the proportion of the public resource modules of the cloud platforms serving as the public resource modules can be dynamically increased, so that the processing time of the general instruction is reduced. The internal resource modules and the common resource modules in the present invention can be dynamically adjusted, and the first and second management tables can be adjusted accordingly.
In a second embodiment of the present invention, based on the first embodiment of the present invention, the step S20 includes:
step S21, acquiring a history instruction received by each cloud platform, and marking the instruction type of each cloud platform as a proprietary instruction and a universal instruction according to the history instruction received by each cloud platform;
step S22, acquiring first workloads respectively corresponding to all modules in the cloud platform when each cloud platform processes all exclusive instructions in a test period;
step S23, obtaining second workloads corresponding to each module in the cloud platform when each cloud platform processes each general instruction in a test period;
step S24, according to the time distribution condition of the proprietary instruction in the historical instruction and the first workload corresponding to each proprietary instruction, the first computing module is segmented from the computing modules of each cloud platform, the first storage module is segmented from the storage modules of each cloud platform, the first network module is segmented from the network modules of each cloud platform, and the first management module is segmented from the management modules of each cloud platform to form the internal resource module of the corresponding cloud platform.
Specifically, in step S22, according to the type of the proprietary instruction in the historical instruction, a type of proprietary instruction is issued to the cloud platform at a time in the test period, and the first workload in each module inside the cloud platform is monitored when the type of proprietary instruction is processed, so that by adopting the same method, different proprietary instruction types can be processed sequentially through the cloud platform, and the first workload in each module inside the cloud platform when each type of proprietary instruction is processed is obtained.
Similarly, in step S23, according to the type of the general instruction in the history instruction, one type of general instruction is issued to the cloud platform at a time in the test period, and the second workload in each module inside the cloud platform is monitored when the type of general instruction is processed, so that by adopting the same method, different general instruction types can be processed sequentially through the cloud platform, and the second workload in each module inside the cloud platform when each type of specific instruction is processed can be obtained.
In step S24, since the first workload occupied by each module of the cloud platform when each proprietary instruction runs is determined, internal resources required by the cloud platform for processing the proprietary instruction in different time intervals can be obtained according to the time-dependent distribution experience of the proprietary instruction, so that the first computing module, the first storage module, the first network module and the first management module are segmented from each cloud platform to form the internal resource module of each cloud platform.
In a third embodiment of the present invention, based on the second embodiment of the present invention, the step S30 includes:
step S31, calculating the distribution of the computing resource sum of the general instructions for processing all cloud platforms along with time, the distribution of the storage resource sum along with time, the distribution of the network resource sum along with time and the distribution of the management resource sum along with time in a test period according to the time distribution condition of the general instructions in the historical instructions and the second workload corresponding to each general instruction;
And S32, according to the distribution of computing resource sum along with time, the distribution of storage resource sum along with time, the distribution of network resource sum along with time and the distribution of management resource sum along with time, splitting a second computing module from the computing modules of each cloud platform, splitting the second storage module from the storage modules of each cloud platform, splitting the second network module from the network modules of each cloud platform, and splitting the second management module from the management modules of each cloud platform, constructing a public resource module based on a multi-cloud fusion public architecture outside each cloud platform.
Further, through step S23, the second workload occupied by each module of the cloud platform when each general instruction runs is determined, and resources required by the cloud platform for processing the general instructions in different time intervals can be obtained according to the time-dependent distribution experience of the general instructions, so that the second computing module, the second storage module, the second network module and the second management module are segmented from each cloud platform to form a public resource module.
In a specific embodiment, after each module of each cloud platform completes the segmentation of the internal resources and the public resources, the remaining resources can be used as maneuvering resources, and the maneuvering resources are used for random allocation to increase the internal resources or increase the public resources. In another embodiment, after each module of each cloud platform completes the internal resource splitting, other resources may be used as common resources. In the method, when dedicated instructions are processed, the internal resources of each cloud platform can be processed by calling the public resources originally belonging to the cloud platform through the first management and control table, so that the size of the internal resource module can be actually expanded through the first management and control table, each cloud platform can preferentially complete the segmentation of the public resources, and then the residual resources are used as the internal resources.
In a specific embodiment, when some internal resource modules of cloud platforms are segmented more, resources for the public resource part are fewer, so that overall time distribution conditions of all cloud platforms for processing general instructions need to be counted from historical instructions, and computing resource total sum, storage resource total sum, network resource total sum and management resource total sum of all cloud platforms for processing general instructions are distributed with time respectively by the computing multi-cloud fusion management platform, so that whether the segmented public resource modules can exceed the computing resource total sum, the storage resource total sum, the network resource total sum and the management resource total sum is checked, and if not enough, compensation can be performed from all cloud platforms according to insufficient proportion to re-segment the first computing module, the first storage module, the first network module and the first management module of each cloud platform, so that available resources of the public resource modules are improved.
In a fourth embodiment of the present invention, based on the second embodiment of the present invention, the step S40 includes:
step S41, establishing a tree-shaped management and control table, and establishing a fusion management node at a first level of the tree-shaped management and control table;
step S42, creating a public resource module node which is positioned at the lower layer of the fusion management node and used for managing the public resource module, and creating an internal resource module node which is positioned at the lower layer of the fusion management node and used for managing the internal resource modules of all cloud platforms respectively at the second level of the tree management and control table;
Step S43, creating virtual computing module nodes, virtual storage module nodes, virtual network module nodes and virtual management module nodes which are respectively positioned at the lower layers of the public resource module nodes at the third level of the tree management and control table, and respectively creating corresponding first computing module nodes, first storage module nodes, first network module nodes and first management module nodes at the lower layers of each internal resource module node;
step S44, at the fourth level of the tree management and control table, creating a computing unit node representing the attribution of different cloud platforms for the virtual computing module node of the third level, creating a storage unit node representing the attribution of different cloud platforms for the virtual storage module node, creating a network unit node representing the attribution of different cloud platforms for the virtual network module node, and creating a network unit node representing the attribution of different cloud platforms for the virtual management module node, respectively creating unit nodes representing the attribution of different cloud platforms so as to represent the attribution cloud platforms of each module in the public resource module through the unit nodes;
step S45, establishing a lower node of each internal resource module node in a third level and an index relation of corresponding unit nodes in a fourth level according to attribution of the cloud platform so as to form a first management and control table for calling a public resource module through the internal resource module of each cloud platform;
Step S46, establishing an index relation from each unit node in the fourth level to a lower node corresponding to the internal resource module node in the third level according to attribution of the cloud platform so as to form a second management and control table for calling the internal resource modules of each cloud platform by the public resource module.
In step S45, the first computing module node of the internal resource module node of each cloud platform in the third hierarchy may be associated with a computing unit node belonging to the same cloud platform in the fourth hierarchy, the first storage module node of the internal resource module node of each cloud platform may be associated with a storage unit node belonging to the same cloud platform in the fourth hierarchy, the first network module node of the internal resource module node of each cloud platform may be associated with a network unit node belonging to the same cloud platform in the fourth hierarchy, and the first management module node of the internal resource module node of each cloud platform may be associated with a management unit node belonging to the same cloud platform in the fourth hierarchy.
In step S46, the computing unit node to which each cloud platform belongs in the fourth hierarchy can be associated with the first computing module node in the internal resource module node of each cloud platform in the third hierarchy, the storage unit node to which each cloud platform belongs can be associated with the first storage module node in the internal resource module node of each cloud platform in the third hierarchy, the network unit node to which each cloud platform belongs can be associated with the first network module node in the internal resource module node of each cloud platform in the third hierarchy, and the management unit node to which each cloud platform belongs can be associated with the first management module node in the internal resource module node of each cloud platform in the third hierarchy.
In a fifth embodiment of the present invention, based on the fourth embodiment of the present invention, the step S50 includes:
step S51, a proprietary instruction library of each cloud platform is obtained, a hierarchical node related to an original resource module of each cloud platform in each hierarchical node of a tree-shaped management and control table is obtained, and a first mapping relation table of the proprietary instruction and the hierarchical node of the tree-shaped management and control table is established according to the proprietary instruction of each cloud platform and the hierarchical node related to the original resource module of each cloud platform;
step S52, a general instruction library is obtained, unit nodes called in the tree-shaped management and control table are preset when each general instruction is processed, a cloud platform to which each called unit node in the tree-shaped management and control table belongs and a hierarchical node of an internal resource module corresponding to the cloud platform are obtained, and a second mapping relation table of the general instruction, the called unit node and the hierarchical node of the internal resource module of the called unit node belonging to the cloud platform is established.
Specifically, after the command processing layer obtains the real-time command, the real-time command is identified according to the proprietary command library and the universal command library, so as to determine whether the real-time command belongs to the proprietary command corresponding to a certain cloud platform or the universal command. And sending the identification result to the fusion management layer.
And the fusion management layer queries the first mapping relation table or the second mapping relation table according to the type of the identification result of the real-time instruction.
If the real-time instruction is a proprietary instruction corresponding to a cloud platform, the fusion management layer queries a first mapping relation table, marks the queried corresponding cloud platform as a target cloud platform, forwards the real-time instruction to an internal resource module node for managing an internal resource module of the target cloud platform, manages and controls a first calculation module node, a first storage module node, a first network module node and a first management module node at the lower layer by the internal resource module node of the internal resource module of the target cloud platform, finishes the processing of the real-time instruction, stores a processing result in the first storage module, and transmits the processing result to a virtual storage module of a public resource module, and the storage modules of different cloud platforms in the virtual storage module back up the processing result.
If the internal resource module nodes of the internal resource module of the target cloud platform are insufficient to complete the processing of the exclusive instruction, the index relation is queried according to the first mapping relation table so as to call the resources which belong to the target cloud platform in the public resource module to complete the processing of the exclusive instruction.
If the real-time instruction is a general instruction, the fusion management layer inquires a second mapping relation table, forwards the real-time instruction to a public resource module node, and completes the processing of the real-time instruction by a virtual computing module node, a virtual storage module node, a virtual network module node and a virtual management module node of the public resource module node, and stores the processing result in the virtual storage module. And acquiring the cloud platform actually corresponding to the resource for completing the processing of the universal instruction, and sending the processing result to a first storage module of the actually corresponding cloud platform for storage through a node for completing the resource for completing the processing of the universal instruction according to a second mapping relation table so that the instruction actually processed by each cloud platform forms a result backup.
According to the fourth to fifth embodiments of the present invention, in a sixth embodiment of the present invention, the step S70 includes:
step S71, the command processing layer sends the identification result of the real-time command to the fusion management layer, and the fusion management layer judges whether the type of the real-time command belongs to a proprietary command or a general command;
step S72, the fusion management layer forwards the real-time instruction belonging to the exclusive instruction to a first management module node of the internal resource module node corresponding to the cloud platform, and the first management module node performs real-time instruction internal transmission at a lower node of the corresponding internal resource module node;
Step S73, judging whether an internal resource module node receiving the real-time instruction has the capability of executing the instruction after the instruction is internally sent;
if yes, go to step S74: the internal resource module node receiving the real-time instruction processes the generated response data, and sends the response data to a user application layer after storage management is carried out on the response data through the virtual management module node;
if not, go to step S75: forwarding the real-time instruction from the corresponding internal resource module node to a unit node associated with the internal resource module node receiving the real-time instruction, processing and generating response data, and sending the response data to a user application layer after storage management is carried out on the response data through the virtual management module node;
and step S76, the fusion management layer forwards the real-time instruction belonging to the general instruction to a virtual management module node of the public resource module node, the virtual management module node calls a unit node in the public resource module node to process the generated response data, and the response data is backed up to an internal resource module node associated with the unit node which actually processes the general instruction and then is sent to the user application layer.
Based on the fourth embodiment of the present invention, in a seventh embodiment of the present invention, the method further includes:
Step S80, detecting the waiting sequence change of the general instruction in the public resource module and the workload change of the public resource module;
step S90, predicting the peak period and the work load peak value of the work load of the public resource module according to the waiting sequence change and the work load change;
step S100, according to the peak time period, the work load peak value and the situation of waiting for a sequence in the peak time period, an elastic capacity expansion instruction is sent to the fusion management layer so as to realize temporary elastic capacity expansion of the public resource module by utilizing an internal resource module of a cloud platform;
step S110, the fusion management layer establishes an elastic capacity expansion management and control table according to the elastic capacity expansion instruction, and in the elastic capacity expansion management and control table, third-level nodes established in the second-level internal resource module nodes of the tree management and control table are mapped into unit nodes of a fourth-level newly added in the tree management table according to the module function so as to realize the resource capacity expansion of the public resource module.
Specifically, predicting the peak period and the peak workload of the utility module may specifically be performed in the following manner:
acquiring a mapping relation table allowing acceleration between a set sequence interval and a waiting sequence interval; the sequence interval refers to a quantity interval in which the number of instructions waiting for execution in a waiting sequence of the public resource module is located;
Detecting an actual sequence interval corresponding to the waiting sequence;
detecting a current load value and an actual acceleration corresponding to the change of the working load;
when the actual acceleration rate exceeds the allowable acceleration rate corresponding to the actual sequence interval, calculating a period reaching a first set load value as a peak period according to the current load value, the actual acceleration value and the change condition of the waiting sequence, and taking the calculated maximum value of the work load as a work load peak value.
The specific calculation mode is as follows:
and->When (I)>；
wherein ,for the number of waiting process instructions contained in the waiting sequence, +.>Waiting for the sequence interval g; p (P) x For the current load value of the workload of the xth virtual function module in the common resource module, x represents one of the virtual computing module, the virtual storage module, the virtual network module and the virtual management module,/-A>Setting a load value for a first set of an xth virtual function module in the public resource module; v x Actual acceleration of the workload of the xth virtual function module in the common resource module;allowing the workload corresponding to the g waiting sequence interval of the x virtual function module in the public resource module to increase speed;
t x1 a duration from a predicted peak period start time to a current time for an xth virtual function module in the utility resource module;
；
wherein ,tx2 A duration of a predicted peak period for an xth virtual function module in the utility resource module;a second set load value for an xth virtual function module in the utility module,
；q x for the detected workload average processing rate for the xth virtual function module in the common resource module, and (2)>Is the newly added workload after the current moment of the xth virtual function module in the public resource module.
Based on the seventh embodiment of the present invention, in an eighth embodiment of the present invention, the method further includes:
the capacity of the elastic expansion is determined in the following way:
step 1101, predicting a first period when the workload of the virtual computing module exceeds a first preset load according to a workload-time curve of the virtual computing module corresponding to the general instruction;
step S1102, predicting a second period when the workload of the virtual storage module exceeds a second preset load according to a workload-time curve of the virtual storage module corresponding to the general instruction;
step S1103, predicting a third period when the workload of the virtual network module exceeds a third preset load according to the workload-time curve of the virtual network module corresponding to the general command;
step S1104, according to the workload-time curve of the virtual management module corresponding to the general instruction, predicting a fourth period when the workload of the virtual management module exceeds a fourth preset load;
Step S1105, the first time period, the second time period, the third time period and the fourth time period are obtained in a union mode, and the elastic capacity expansion time period of the public resource module is obtained;
step S1106, according to the workload-time curve of the virtual computing module, predicting the computing load maximum value in the first period, and computing to obtain the elastic capacity expansion data of the virtual computing module;
step S1107, predicting the maximum value of the storage load in the second period according to the workload-time curve of the virtual storage module, and calculating to obtain the elastic capacity expansion data of the virtual storage module;
step S1108, according to the work load-time curve of the virtual network module, determining the maximum value of the network load in the third period, and calculating to obtain the elastic capacity expansion data of the virtual network module;
step S1109, according to the workload-time curve of the virtual management module, predicting the maximum value of the management load in the fourth period, and calculating to obtain the elastic capacity expansion data of the virtual management module.
The elastic expansion data of the virtual computing module are computed in the following mode:
wherein ,elastic capacity expansion data for virtual computing module, < ->To calculate the load maximum, y 1 To calculate the load peak ratio value, and is constant, 0 < y 1 ＜1；A 1 For the first calculation of the capacity expansion data, A 2 For the second calculation of the expansion data, A 1 and A2 Constants respectively greater than 0, A 2 ＞A 1 ；/>Computing resource size for the ith cloud platform as the first computing module, +.>The size of the computing resource serving as the second computing module for the ith cloud platform is greater than 1 and less than or equal to n, wherein n is the number of the cloud platforms;
the elastic expansion data of the virtual storage module is calculated in the following mode:
wherein ,for the elastic capacity-expanding data of the virtual memory module, < >>To store the load maximum value, y 2 To store the load peak ratio value and is constant, 0 < y 2 ＜1；B 1 For first storing the expanded data, B 2 For the second storage of the capacity-expanded data, B 1 and B2 Constants greater than 0, B 2 ＞B 1 ；/>The storage resource size used as the first storage module for the ith cloud platform,the storage resource size used as the second storage module for the ith cloud platform;
the elastic capacity expansion data of the virtual network module is calculated by the following mode:
wherein ,for the elastic capacity-expanding data of the virtual network module, < >>For the maximum network load, y 3 Is a network load peak ratio value and is constant, 0 < y 3 ＜1；C 1 Expanding data for a first network, C 2 Expanding data for the second network, C 1 and C2 Constants greater than 0, C 2 ＞C 1 ；/>Network resource size for the ith cloud platform as the first network module, +. >The network resource size used as the second network module for the ith cloud platform;
the elastic expansion data of the virtual management module is calculated in the following mode:
wherein ,elastic capacity-expansion data for virtual management module, < ->To manage the load maximum, y 4 To manage the peak ratio of load, and is constant, 0 < y 4 ＜1；D 1 For the first management of capacity-expanded data, D 2 For the second management of the expanded data, D 1 and D2 Respectively a constant greater than 0, D 2 ＞D 1 ；/>The management resource size used as the first management module for the ith cloud platform,and the management resource size serving as the second management module for the ith cloud platform.
In addition, in order to achieve the above objective, the present invention further provides a cloud fusion management platform for executing the method; the multi-cloud fusion management platform comprises a user application layer, a command processing layer, a fusion management layer and a cloud platform layer, wherein the cloud platform layer comprises a plurality of cloud platforms.
The foregoing description of the preferred embodiments of the present invention should not be construed as limiting the scope of the invention, but rather utilizing equivalent structural changes made in the present invention description and drawings or directly/indirectly applied to other related technical fields are included in the scope of the present invention.

Claims (10)

1. The data management and control method based on the multi-cloud fusion is characterized in that a multi-cloud fusion management platform comprises a user application layer, a command processing layer, a fusion management layer and a cloud platform layer, and the cloud platform layer comprises a plurality of cloud platforms; the method comprises the following steps:
the fusion management layer acquires resource information of a cloud platform accessed in the cloud platform layer;
according to the resource information of the cloud platforms, the first computing module, the first storage module, the first network module and the first management module of each cloud platform are constructed into an internal resource module of the corresponding cloud platform;
constructing a common resource module based on a multi-cloud fusion public architecture outside each cloud platform based on a second computing module, a second storage module, a second network module and a second management module of each cloud platform according to the resource information of the cloud platform, wherein the common resource module comprises a virtual computing module, a virtual storage module, a virtual network module and a virtual management module;
establishing a management and control table for managing and controlling the internal resource modules and the public resource modules, wherein the management and control table comprises a first management and control table for calling the public resource modules through the internal resource modules of all cloud platforms and a second management and control table for calling the internal resource modules of all cloud platforms through the public resource modules;
Constructing a mapping relation between the instruction and the first management table or the second management table in the fusion management layer;
acquiring a real-time instruction sent by a user application layer to a command processing layer, and identifying the real-time instruction through the command processing layer;
and according to the identification result of the real-time instruction, the fusion management layer realizes the resource module call of the multi-cloud fusion through the mapping relation so as to generate response data according to the real-time instruction processing and send the response data to the user application layer.
2. The method for managing and controlling data based on multi-cloud fusion according to claim 1, wherein the step of constructing the first computing module, the first storage module, the first network module and the first management module of each cloud platform into the internal resource module of the corresponding cloud platform according to the resource information of the cloud platform comprises:
acquiring a historical instruction received by each cloud platform, and marking the instruction type of each cloud platform as a proprietary instruction and a universal instruction according to the historical instruction received by each cloud platform;
acquiring first workloads respectively corresponding to all modules in the cloud platform when each cloud platform processes all exclusive instructions in a test period;
acquiring second workloads respectively corresponding to all modules in the cloud platform when all the universal instructions are processed by each cloud platform in a test period;
According to the time distribution condition of the special instructions in the historical instructions and the first workload corresponding to each special instruction, a first computing module is segmented from the computing modules of each cloud platform, a first storage module is segmented from the storage modules of each cloud platform, a first network module is segmented from the network modules of each cloud platform, and the first management module is segmented from the management modules of each cloud platform to form an internal resource module of the corresponding cloud platform.
3. The method for managing and controlling data based on multiple cloud fusion according to claim 2, wherein the step of constructing a common resource module based on a multiple cloud fusion public architecture outside each cloud platform based on the second computing module, the second storage module, the second network module and the second management module of each cloud platform according to the resource information of the cloud platform comprises:
according to the time distribution condition of the general instructions in the historical instructions and the second workload corresponding to each general instruction, calculating the time distribution of the computing resource sum of the general instructions for processing all cloud platforms in the test period, the time distribution of the storage resource sum, the time distribution of the network resource sum and the time distribution of the management resource sum;
And according to the distribution of the computing resource sum along with time, the distribution of the storage resource sum along with time, the distribution of the network resource sum along with time and the distribution of the management resource sum along with time, the second computing module is cut from the computing modules of each cloud platform, the second storage module is cut from the storage modules of each cloud platform, the second network module is cut from the network modules of each cloud platform, the second management module is cut from the management modules of each cloud platform, and the public resource module based on the multi-cloud fusion public architecture outside each cloud platform is constructed.
4. The method for managing and controlling data based on the cloud fusion as recited in claim 2, wherein the step of establishing a management and control table for managing and controlling the internal resource modules and the common resource modules includes:
establishing a tree-shaped management control table, and establishing a fusion management node at a first level of the tree-shaped management control table;
creating a common resource module node which is positioned at the lower layer of the fusion management node and used for managing the common resource module, and creating an internal resource module node which is positioned at the lower layer of the fusion management node and used for managing the internal resource module of each cloud platform respectively at the second level of the tree management table;
Creating virtual computing module nodes, virtual storage module nodes, virtual network module nodes and virtual management module nodes which are respectively positioned at the lower layers of the public resource module nodes at a third level of the tree management and control table, and respectively creating corresponding first computing module nodes, first storage module nodes, first network module nodes and first management module nodes at the lower layers of each internal resource module node;
creating a computing unit node representing the attribution of different cloud platforms for a virtual computing module node of a third level, a storage unit node representing the attribution of different cloud platforms for a virtual storage module node, a network unit node representing the attribution of different cloud platforms for a virtual network module node and a network unit node representing the attribution of different cloud platforms for a virtual management module node in a fourth level of the tree management and control table;
establishing a lower node of each internal resource module node in a third level according to attribution of the cloud platform, and indexing the corresponding unit node in a fourth level to form a first management and control table for calling a public resource module through the internal resource module of each cloud platform;
and establishing an index relation from each unit node in the fourth level to a lower node corresponding to the internal resource module node in the third level according to the attribution of the cloud platform so as to form a second management and control table for calling the internal resource modules of each cloud platform by the public resource module.
5. The method for managing and controlling data based on the cloud fusion according to claim 4, wherein the step of constructing the mapping relation between the instruction and the first management and control table or the second management and control table in the fusion management layer comprises:
acquiring exclusive instruction libraries of all cloud platforms, acquiring hierarchy nodes related to original resource modules of all cloud platforms in each hierarchy node of a tree-shaped management and control table, and establishing a first mapping relation table of the exclusive instructions and the hierarchy nodes in the tree-shaped management and control table according to the exclusive instructions of all cloud platforms and the hierarchy nodes related to the original resource modules of all cloud platforms;
the method comprises the steps of obtaining a general instruction library, presetting unit nodes called in a tree-shaped management and control table when each general instruction is processed, obtaining a cloud platform to which each called unit node in the tree-shaped management and control table belongs, and establishing a second mapping relation table of the general instruction, the called unit node and the hierarchical node of an internal resource module of the called unit node belonging to the cloud platform, wherein the cloud platform belongs to the hierarchical node of the internal resource module corresponding to the cloud platform.
6. The method for managing and controlling data based on the cloud fusion according to claim 4, wherein the step of enabling the fusion management layer to implement resource module call of the cloud fusion through the mapping relationship according to the identification result of the real-time instruction to process and generate response data according to the real-time instruction, and sending the response data to the user application layer includes:
The command processing layer sends the identification result of the real-time command to the fusion management layer, and the fusion management layer judges whether the type of the real-time command belongs to a proprietary command or a general command;
the fusion management layer forwards the real-time instruction belonging to the exclusive instruction to a first management module node of the internal resource module node corresponding to the cloud platform, and the first management module node internally transmits the real-time instruction at a lower node of the corresponding internal resource module node;
judging whether an internal resource module node receiving the real-time instruction has the capability of executing the instruction after the instruction is internally transmitted;
if yes, the internal resource module node receiving the real-time instruction processes the generated response data, and the response data is sent to a user application layer after being stored and managed by the virtual management module node; if not, forwarding the real-time instruction from the corresponding internal resource module node to a unit node associated with the internal resource module node for receiving the real-time instruction, so as to process and generate response data, and sending the response data to a user application layer after storage management is carried out by the virtual management module node;
the fusion management layer forwards the real-time instruction belonging to the general instruction to a virtual management module node of the public resource module node, the virtual management module node calls a unit node in the public resource module node to process the generated response data, and the response data is backed up to an internal resource module node associated with the unit node which actually processes the general instruction and then is sent to the user application layer.
7. The method of data management based on cloudy fusion of claim 4, further comprising:
detecting the waiting sequence change of the general instruction in the public resource module and the workload change of the public resource module;
predicting a peak period and a workload peak of the workload of the public resource module according to the waiting sequence change and the workload change;
according to the peak time period, the work load peak value and the waiting sequence condition in the peak time period, an elastic capacity expansion instruction is sent to the fusion management layer, so that the temporary elastic capacity expansion of the public resource module is realized by utilizing the internal resource module of the cloud platform;
and the fusion management layer establishes an elastic capacity expansion management and control table according to the elastic capacity expansion instruction, and in the elastic capacity expansion management and control table, third-level nodes established inside the second-level internal resource module nodes of the tree-shaped management and control table are mapped into unit nodes of a fourth level newly added in the tree-shaped management table according to the module function so as to realize the resource capacity expansion of the public resource module.
8. The method for managing and controlling data based on the cloud fusion as recited in claim 7, wherein the capacity of the elastic expansion is determined by:
Predicting a first period when the workload of the virtual computing module exceeds a first preset load according to a workload-time curve of the virtual computing module corresponding to the general instruction;
predicting a second period when the workload of the virtual storage module exceeds a second preset load according to a workload-time curve of the virtual storage module corresponding to the general instruction;
predicting a third period when the workload of the virtual network module exceeds a third preset load according to the workload-time curve of the virtual network module corresponding to the general instruction;
predicting a fourth period when the workload of the virtual management module exceeds a fourth preset load according to the workload-time curve of the virtual management module corresponding to the general instruction;
obtaining a union set of the first time period, the second time period, the third time period and the fourth time period to obtain an elastic capacity expansion time period of the public resource module;
according to a work load-time curve of the virtual computing module, predicting a computing load maximum value in a first period, and computing to obtain elastic capacity expansion data of the virtual computing module;
according to the work load-time curve of the virtual storage module, predicting the maximum value of the storage load in the second period, and calculating to obtain the elastic capacity expansion data of the virtual storage module;
According to the work load-time curve of the virtual network module, determining the maximum value of the network load in a third period, and calculating to obtain the elastic capacity expansion data of the virtual network module;
and predicting the maximum value of the management load in the fourth period according to the working load-time curve of the virtual management module, and calculating to obtain the elastic capacity expansion data of the virtual management module.
9. The data management and control method based on the cloud fusion as claimed in claim 8, wherein:
the elastic expansion data of the virtual computing module are computed in the following mode:
；
wherein ,elastic capacity expansion data for virtual computing module, < ->To calculate the load maximum, y 1 To calculate the load peak ratio value, and is constant, 0 < y 1 ＜1；A 1 For the first calculation of the capacity expansion data, A 2 For the second calculation of the expansion data, A 1 and A2 Constants respectively greater than 0, A 2 ＞A 1 ；/>Computing resource size for the ith cloud platform as the first computing module, +.>The computing resource size used as the second computing module for the ith cloud platform,i is more than 1 and less than or equal to n, wherein n is the number of cloud platforms;
the elastic expansion data of the virtual storage module is calculated in the following mode:
；
wherein ,for the elastic capacity-expanding data of the virtual memory module, < >>To store the load maximum value, y 2 To store the load peak ratio value and is constant, 0 < y 2 ＜1；B 1 For first storing the expanded data, B 2 For the second storage of the capacity-expanded data, B 1 and B2 Constants greater than 0, B 2 ＞B 1 ；/>Storage resource size for the ith cloud platform as the first storage module, +.>The storage resource size used as the second storage module for the ith cloud platform;
the elastic capacity expansion data of the virtual network module is calculated by the following mode:
；
wherein ,for the elastic capacity-expanding data of the virtual network module, < >>For the maximum network load, y 3 For peak network load ratioExample value, and is constant, 0 < y 3 ＜1；C 1 Expanding data for a first network, C 2 Expanding data for the second network, C 1 and C2 Constants greater than 0, C 2 ＞C 1 ；/>Network resource size for the ith cloud platform as the first network module, +.>The network resource size used as the second network module for the ith cloud platform;
the elastic expansion data of the virtual management module is calculated in the following mode:
；
wherein ,elastic capacity-expansion data for virtual management module, < ->To manage the load maximum, y 4 To manage the peak ratio of load, and is constant, 0 < y 4 ＜1；D 1 For the first management of capacity-expanded data, D 2 For the second management of the expanded data, D 1 and D2 Respectively a constant greater than 0, D 2 ＞D 1 ；/>Management resource size for the ith cloud platform as the first management module, +.>And the management resource size serving as the second management module for the ith cloud platform.
10. A multi-cloud fusion management platform for performing the method of any of claims 1 to 9, the multi-cloud fusion management platform comprising a user application layer, a command processing layer, a fusion management layer, and a cloud platform layer comprising a plurality of cloud platforms.
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