CN114598586B - Multi-cloud scene computing power gridding method and system - Google Patents
Multi-cloud scene computing power gridding method and system Download PDFInfo
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Abstract
The invention provides a multi-cloud scene computing power gridding method and a system, comprising the following steps: extracting resource management data through a preset grid division strategy to generate a grid view; matching the operation data of the corresponding virtual machine or container to the grid view, and determining a grid health degree comprehensive evaluation algorithm; determining a health early warning baseline based on the grid health degree comprehensive evaluation algorithm, and obtaining an upper-layer service and grid topology mapping relation based on the health early warning baseline; and adjusting the calculation force between grids according to the mapping relation between the upper-layer service and the grid topology. According to the invention, the resource pool bearing different services in a multi-cloud environment is subjected to non-differentiated data management, so that the application of a unified computing power view angle in a multi-cloud operation mode and an operation mode is promoted.
Description
Technical Field
The invention relates to the technical field of cloud computing, in particular to a method and a system for computing power gridding in a multi-cloud scene.
Background
With the rapid development of cloud computing technology, a cloud data center has replaced a traditional data center to become a mainstream technology of the data center, and on the basis, a virtualization technology and a cloud native technology solve the problem of cross-cloud environment consistency and shorten an application delivery cycle.
Currently, telecommunication operators are generally divided into a public cloud operating externally, a private cloud carrying an IT system internally, a private cloud carrying NFV network elements (e.g. 5 GC), and edge clouds of different purposes according to the usage type of their resource pools. Although the names of the three types of cloud resource pools are different for different operators, the basic differentiation boundaries are the same. With the use boundary of the cloud resource pool of the current telecom operator, when internal operation and maintenance are carried out, although a multi-cloud management platform can be constructed on the upper layer by utilizing information fusion, the multi-cloud management platform is still a unified internal model from various resource pools, and from a maintenance flow, a maintenance perspective to a maintenance basic unit, the unified internal model cannot be formed, and a future computing business mode cannot be supported.
Therefore, a new method for computing power gridding in a multi-cloud scene is needed, which can perform fusion management on a multi-cloud environment bearing various services and improve resource allocation efficiency.
Disclosure of Invention
The invention provides a multi-cloud-scene computing power gridding method and system, which are used for solving the defect that unified management and resource allocation cannot be carried out on various types of cloud data centers in the prior art.
In a first aspect, the present invention provides a method for computing power gridding in a multiple cloud scenario, including:
extracting resource management data through a preset grid division strategy to generate a grid view;
matching the operation data of the corresponding virtual machine or container to the grid view, and determining a grid health degree comprehensive evaluation algorithm;
determining a health early warning baseline based on the grid health degree comprehensive evaluation algorithm, and obtaining an upper-layer service and grid topology mapping relation based on the health early warning baseline;
and adjusting the calculation force between grids according to the mapping relation between the upper-layer service and the grid topology.
According to the multi-cloud-scene computational power gridding method provided by the invention, the resource management data is extracted through a preset grid division strategy to generate a grid view, and the method also comprises the following steps:
and acquiring various cloud environment system data based on a preset docking mode, and determining the resource management data.
According to the multi-cloud-scene computational power gridding method provided by the invention, the resource management data is extracted through a preset grid division strategy to generate a grid view, and the method comprises the following steps:
determining the number of Virtual Central Processing Units (VCPUs) in each grid from virtual machines or containers;
and determining a corresponding number of virtual machines or containers captured by each grid based on a Configuration Management Database (CMDB), wherein the virtual machines or containers of the corresponding number comprise all services borne by the cloud resource pool.
According to the multi-cloud-scene computational power gridding method provided by the invention, the operation data of the corresponding virtual machine or container is matched with the grid view to determine the grid health degree comprehensive evaluation algorithm, and the method comprises the following steps:
matching resource data, performance data, alarm data and energy consumption data of the corresponding virtual machine or container to the grid view;
and outputting the grid health degree comprehensive evaluation algorithm based on the resource data, the performance data, the alarm data and the energy consumption data.
According to the multi-cloud-scene computing power gridding method provided by the invention, the health early warning baseline is determined based on the grid health degree comprehensive evaluation algorithm, and the mapping relation between upper-layer services and grid topology is obtained based on the health early warning baseline, and the method comprises the following steps:
outputting the health early warning baseline according to the grid health degree comprehensive evaluation algorithm;
and based on the health early warning baseline, performing topological mapping on the existing network elements or applications to the grids, and outputting a computational power grid presenting visual angle.
According to the multi-cloud-scene computing power gridding method provided by the invention, the adjustment of the computing power between grids according to the mapping relation between the upper-layer service and the grid topology comprises the following steps:
based on a management perspective, determining service topology ranking, calculation power overview ranking and health degree ranking of all calculation power grids, and determining adjustment options of services or calculation power among the grids;
and adjusting the calculation power among the grids according to the adjustment options based on the service topology ranking, the calculation power overview ranking and the health degree ranking.
The multi-cloud-scene computing power gridding method provided by the invention further comprises the following steps:
and managing and controlling all layers of the through computational power grid based on the pull-through data model.
In a second aspect, the present invention further provides a multi-cloud scenario computational power gridding system, including:
the generating module is used for extracting resource management data through a preset grid division strategy to generate a grid view;
the matching module is used for matching the operation data of the corresponding virtual machine or container to the grid view and determining a grid health degree comprehensive evaluation algorithm;
the determining module is used for determining a health early warning baseline based on the grid health degree comprehensive evaluation algorithm and obtaining an upper-layer service and grid topology mapping relation based on the health early warning baseline;
and the adjusting module is used for adjusting the computing power between grids according to the mapping relation between the upper-layer service and the grid topology.
In a third aspect, the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of any one of the foregoing methods for computing power gridding in a multi-cloud scenario when executing the program.
In a fourth aspect, the present invention further provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method for computational power meshing in a cloudy scene as described in any one of the above.
In a fifth aspect, the present invention further provides a computer program product, which includes a computer program, and when being executed by a processor, the computer program implements the steps of any one of the methods for computational power gridding of a multi-cloud scenario.
According to the method and the system for gridding computing power in the cloud scene, provided by the invention, the application of a unified computing power view angle in a multi-cloud operation mode and an operation mode is promoted by carrying out non-differentiated data management on resource pools bearing different services in a multi-cloud environment.
Drawings
In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic flow chart of a computational power gridding method for a cloud scene according to the present invention;
FIG. 2 is an implementation schematic diagram of a computational power gridding method for a multi-cloud scene provided by the invention;
FIG. 3 is a schematic structural diagram of a multi-cloud scenario computational power gridding system provided by the present invention;
fig. 4 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow diagram of a computational power gridding method for a cloudy scene, as shown in fig. 1, including:
step S1, extracting resource management data through a preset grid division strategy to generate a grid view;
s2, matching operation data of a corresponding virtual machine or container to the grid view, and determining a grid health degree comprehensive evaluation algorithm;
s3, determining a health early warning baseline based on the grid health degree comprehensive evaluation algorithm, and obtaining an upper-layer service and grid topology mapping relation based on the health early warning baseline;
and S4, adjusting the calculation force between grids according to the mapping relation between the upper-layer service and the grid topology.
Specifically, virtual machine data or container data in the resource management data are extracted through a preset grid division strategy, grid division is carried out, and a grid view is generated; after generating a grid view through a strategy, matching operation data of a corresponding virtual machine or container in a grid to determine a grid health degree comprehensive evaluation algorithm; based on the health degree of the third aspect, a health early warning baseline is determined according to a grid health degree comprehensive evaluation algorithm, and upper-layer services can be mapped to the grid and the topology between grid constituent units; and finally, based on the management view, adjusting the calculation force between grids according to the mapping relation between the upper-layer service and the grid topology.
As shown in fig. 2, the computing power meshing implementation principle in the multi-cloud scenario provided by the present invention includes multiple resource pools divided according to regions, including a network universal resource pool, a large region resource pool, a provincial region resource pool, and a local resource pool, where each resource pool includes multiple indexes, such as an alarm x, an energy consumption y, a resource z, and a service N. The cloud Management comprises a dynamic ring, a CMDB (Configuration Management Database), data sharing and the like, and the computational grid presenting view angle comprising grid strategy, grid division, grid health degree, baseline Management and mapping Management is obtained through the collected resource data, performance data, alarm data and dynamic ring data, and the grid Management also comprises grid Management from the service and grid association view angle, and comprises service topology, service health degree and migration Management respectively.
The invention realizes non-differential data fusion, monitoring and health evaluation on the resource pool bearing different services in a multi-cloud environment, and effectively promotes the application of a unified computing power view angle in a multi-cloud operation mode and a multi-cloud operation mode.
Based on the above embodiment, the method further includes, before step S1:
and acquiring various cloud environment system data based on a preset docking mode, and determining the resource management data.
First, the present invention collects data of various systems in a cloud environment, such as a cloud management platform of a three-party manufacturer, a CMDB, a dynamic ring system, or other types of management systems, in a docking manner such as an adaptation manner, where the collected data includes resource data, performance data, alarm data, dynamic ring data, and the like in different cloud resource pools, as shown in fig. 2.
It should be noted that the CMDB is a logical database, and contains information of the full life cycle of the configuration items and relationships between the configuration items, including physical relationships, real-time communication relationships, non-real-time communication relationships, and dependency relationships. The CMDB stores and manages various configuration information of equipment in the IT architecture of the enterprise, is closely connected with all service support and service delivery processes, supports the operation of the processes, exerts the value of the configuration information and simultaneously depends on the related processes to ensure the accuracy of data.
Before the cloud computing power grid division, the method provides detailed data basis for the grid division by collecting various system data in different cloud resource pools.
Based on any of the above embodiments, the method step S1 includes:
determining the number of Virtual Central Processing Units (VCPUs) in each grid from virtual machines or containers;
and determining a corresponding number of virtual machines or containers captured by each grid based on a Configuration Management Database (CMDB), wherein the virtual machines or containers of the corresponding number comprise all services borne by the cloud resource pool.
Specifically, virtual machine data or container data in the resource management data is extracted through a preset grid division strategy to perform grid division, where the division strategy includes but is not limited to:
the basic number of VCPUs in the grid is set, and the VCPUs are read from virtual machine or container resource data and are cells of the grid, and can also be regarded as a policy framework generated automatically or manually by the computational grid.
Further, each grid captures a corresponding number of virtual machines or containers by itself, if the service types carried by the cloud resource pool are various, the virtual machines or containers captured by each grid must cover each service type, if an IT system cloud is carried, and an NFV network element cloud is also carried, the corresponding virtual machines or containers must be captured from the two cloud environments when the grid is divided, and usually, one grid includes 50 VCPUs or other types of computational basic units; and finally generating a grid view.
The invention can obtain a grid view which contains more comprehensive resources based on the collected system data and the preset grid division strategy, and is convenient for subsequent grid unified management.
Based on any of the above embodiments, step S2 of the method includes:
matching resource data, performance data, alarm data and energy consumption data of the corresponding virtual machine or container to the grid view;
and outputting the grid health degree comprehensive evaluation algorithm based on the resource data, the performance data, the alarm data and the energy consumption data.
Specifically, after a grid view is generated through a preset grid division strategy in the foregoing embodiment, resource data, performance data, alarm data, and energy consumption data of a corresponding virtual machine or container are matched into a grid, where the energy consumption data takes power consumption of a physical machine in a dynamic ring system as a benchmark, power consumption of each virtual machine or container in a certain physical machine is obtained through an averaging algorithm, and a grid health degree comprehensive evaluation method (usually adopting a factor analysis method) and a scoring and scoring display method thereof are further determined, and may generally be represented as: and the model can be displayed as an N-dimensional model by clicking.
A health degree evaluation method is used for scoring the health degree of each grid, scoring dimensions comprise failure times, performance utilization degree, power consumption and the like, and the dimensions can be flexibly adjusted.
The grid health degree is comprehensively scored, the grid health degree is definitely quantized by a specific numerical value, and the grid health is managed by visual operation, so that the grid health management method has intuitiveness and high efficiency.
Based on any of the above embodiments, step S3 of the method includes:
outputting the health early warning baseline according to the grid health degree comprehensive evaluation algorithm;
and based on the health early warning baseline, performing topological mapping on the existing network elements or applications to the grids, and outputting a computational power grid presenting visual angle.
Optionally, the present invention sets health baseline early warning management based on the health degree of the third aspect, and may perform topology mapping on the upper-layer service, including the existing network elements or applications, to the grid and the grid constituent units, so as to form a service-computational grid-infrastructure perspective.
The invention is convenient for resource root finding and fault topology discovery by setting the computing power grid health baseline early warning management.
Based on any of the above embodiments, the method step S4 includes:
based on a management perspective, determining service topology ranking, calculation power overview ranking and health degree ranking of all calculation power grids, and determining adjustment options of services or calculation power among the grids;
and adjusting the calculation power among the grids according to the adjustment options based on the service topology ranking, the calculation power overview ranking and the health degree ranking.
Specifically, as shown in fig. 2, the present invention also presents, through a management perspective, a service topology, an algorithm overview, a health ranking, etc., of all algorithm grids, and a migration adjustment option of a service or an algorithm between grids.
Based on any of the above embodiments, the method further comprises:
and managing and controlling all layers of the through computational power grid based on the pull-through data model.
Optionally, in addition to the aforementioned management methods, other management methods and means may be implemented by using a pull-through data model throughout the upper and lower layers of the computational power network.
The invention can integrate and manage the multi-cloud environment bearing various services through the calculation power grid management.
The cloud scene computational power gridding system provided by the invention is described below, and the cloud scene computational power gridding system described below and the cloud scene computational power gridding method described above can be referred to correspondingly.
Fig. 3 is a schematic structural diagram of a computing power gridding system for a cloudy scene, as shown in fig. 3, including: a generating module 31, a matching module 32, a determining module 33 and an adjusting module 34, wherein:
the generating module 31 is configured to extract resource management data through a preset grid partitioning policy, and generate a grid view; the matching module 32 is configured to match the operation data of the corresponding virtual machine or container to the grid view, and determine a grid health degree comprehensive evaluation algorithm; the determining module 33 is configured to determine a health early warning baseline based on the grid health degree comprehensive evaluation algorithm, and obtain an upper-layer service and grid topology mapping relationship based on the health early warning baseline; the adjusting module 34 is configured to adjust the computation power between the grids according to the mapping relationship between the upper layer service and the grid topology.
The invention realizes non-differential data fusion, monitoring and health evaluation on the resource pool bearing different services in a multi-cloud environment, and effectively promotes the application of a unified computing power view angle in a multi-cloud operation mode and a multi-cloud operation mode.
Based on the above embodiment, the system further includes an obtaining module 35, where the obtaining module 35 is configured to:
and acquiring various cloud environment system data based on a preset docking mode, and determining the resource management data.
Before the cloud computing power grid division, detailed data basis is provided for the grid division by collecting various system data in different cloud resource pools.
Based on any of the above embodiments, the generating module 31 is specifically configured to:
determining the number of Virtual Central Processing Units (VCPUs) in each grid from virtual machines or containers;
and determining a corresponding number of virtual machines or containers captured by each grid based on a Configuration Management Database (CMDB), wherein the corresponding number of virtual machines or containers comprises all services borne by the cloud resource pool.
The invention can obtain a grid view containing more comprehensive resources based on the collected system data and the preset grid division strategy, thereby facilitating the subsequent grid unified management.
Based on any of the above embodiments, the matching module 32 is configured to:
matching resource data, performance data, alarm data and energy consumption data of the corresponding virtual machine or container to the grid view;
and outputting the grid health degree comprehensive evaluation algorithm based on the resource data, the performance data, the alarm data and the energy consumption data.
The grid health degree is comprehensively scored, the grid health degree is definitely quantized by a specific numerical value, and the grid health is managed by visual operation, so that the grid health management method has intuitiveness and high efficiency.
Based on any of the above embodiments, the determining module 33 is specifically configured to:
outputting the health early warning baseline according to the grid health degree comprehensive evaluation algorithm;
and based on the health early warning baseline, carrying out topological mapping on the existing network elements or applications to the grids, and outputting a computational power grid presenting visual angle.
The invention is convenient for resource root finding and fault topology discovery by setting the computing power grid health baseline early warning management.
Based on any of the above embodiments, the adjusting module 34 is specifically configured to:
based on a management perspective, determining service topology ranking, calculation overview ranking and health degree ranking of all calculation grids, and determining adjustment options of services or calculation among the grids;
and adjusting the computing power among grids according to the adjustment options based on the service topology ranking, the computing power overview ranking and the health degree ranking.
Based on any embodiment, the system further includes a management module 36, where the management module 36 is configured to:
and managing and controlling all layers of the through computational power grid based on the pull-through data model.
The invention can integrate and manage the multi-cloud environment bearing various services through the calculation power grid management.
Fig. 4 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 4: a processor (processor) 410, a communication Interface 420, a memory (memory) 430 and a communication bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 are communicated with each other via the communication bus 440. The processor 410 may invoke logic instructions in the memory 430 to perform a multi-cloud scenario computational gridding method, the method comprising: extracting resource management data through a preset grid division strategy to generate a grid view; matching the operation data of the corresponding virtual machine or container to the grid view, and determining a grid health degree comprehensive evaluation algorithm; determining a health early warning baseline based on the grid health degree comprehensive evaluation algorithm, and obtaining an upper-layer service and grid topology mapping relation based on the health early warning baseline; and adjusting the calculation force between grids according to the mapping relation between the upper-layer service and the grid topology.
In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, where the computer program product includes a computer program, the computer program may be stored on a non-transitory computer readable storage medium, and when the computer program is executed by a processor, a computer can execute the method for multi-cloud-scenario computational power meshing provided by the above methods, where the method includes: extracting resource management data through a preset grid division strategy to generate a grid view; matching the operation data of the corresponding virtual machine or container to the grid view, and determining a grid health degree comprehensive evaluation algorithm; determining a health early warning baseline based on the grid health degree comprehensive evaluation algorithm, and obtaining an upper-layer service and grid topology mapping relation based on the health early warning baseline; and adjusting the calculation force between grids according to the mapping relation between the upper-layer service and the grid topology.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, the computer program, when executed by a processor, implementing a method for computing power gridding in a multi-cloud scenario provided by the above methods, the method including: extracting resource management data through a preset grid division strategy to generate a grid view; matching the operation data of the corresponding virtual machine or container to the grid view, and determining a grid health degree comprehensive evaluation algorithm; determining a health early warning baseline based on the grid health degree comprehensive evaluation algorithm, and obtaining an upper-layer service and grid topology mapping relation based on the health early warning baseline; and adjusting the calculation force between grids according to the mapping relation between the upper-layer service and the grid topology.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A multi-cloud scene computing power gridding method is characterized by comprising the following steps:
extracting resource management data through a preset grid division strategy to generate a grid view;
matching the operation data of the corresponding virtual machine or container to the grid view, and determining a grid health degree comprehensive evaluation algorithm;
determining a health early warning baseline based on the grid health degree comprehensive evaluation algorithm, and obtaining an upper-layer service and grid topology mapping relation based on the health early warning baseline;
and adjusting the calculation force between grids according to the mapping relation between the upper-layer service and the grid topology.
2. The method according to claim 1, wherein the extracting resource management data through a preset grid division strategy to generate a grid view further comprises:
and acquiring various cloud environment system data based on a preset docking mode, and determining the resource management data.
3. The method according to claim 1 or 2, wherein the extracting resource management data through a preset grid division strategy to generate a grid view comprises:
determining the number of Virtual Central Processing Units (VCPUs) in each grid from virtual machines or containers;
and determining a corresponding number of virtual machines or containers captured by each grid based on a Configuration Management Database (CMDB), wherein the corresponding number of virtual machines or containers comprises all services borne by the cloud resource pool.
4. The method for computational power gridding according to claim 1, wherein the step of matching the operation data of the corresponding virtual machine or container to the grid view to determine a grid health degree comprehensive evaluation algorithm comprises:
matching resource data, performance data, alarm data and energy consumption data of the corresponding virtual machine or container to the grid view;
and outputting the grid health degree comprehensive evaluation algorithm based on the resource data, the performance data, the alarm data and the energy consumption data.
5. The multi-cloud-scene computing power gridding method according to claim 1, wherein the step of determining a health early warning baseline based on the grid health degree comprehensive evaluation algorithm and obtaining an upper-layer service and grid topology mapping relation based on the health early warning baseline comprises the steps of:
outputting the health early warning baseline according to the grid health degree comprehensive evaluation algorithm;
and based on the health early warning baseline, performing topological mapping on the existing network elements or applications to the grids, and outputting a computational power grid presenting visual angle.
6. The method according to claim 1, wherein the adjusting the computation power between grids according to the mapping relationship between the upper-layer service and the grid topology comprises:
based on a management perspective, determining service topology ranking, calculation overview ranking and health degree ranking of all calculation grids, and determining adjustment options of services or calculation among the grids;
and adjusting the calculation power among the grids according to the adjustment options based on the service topology ranking, the calculation power overview ranking and the health degree ranking.
7. The multi-cloud scenario computational power gridding method according to claim 1, further comprising:
and managing and controlling all layers of the through computational power grid based on the pull-through data model.
8. A multi-cloud scenario computational power gridding system, comprising:
the generating module is used for extracting resource management data through a preset grid division strategy to generate a grid view;
the matching module is used for matching the operation data of the corresponding virtual machine or container to the grid view and determining a grid health degree comprehensive evaluation algorithm;
the determining module is used for determining a health early warning baseline based on the grid health degree comprehensive evaluation algorithm and obtaining an upper-layer service and grid topology mapping relation based on the health early warning baseline;
and the adjusting module is used for adjusting the computing power between grids according to the mapping relation between the upper-layer service and the grid topology.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the program.
10. A non-transitory computer readable storage medium, having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the steps of the method of any one of claims 1 to 7 for computational power meshing of a multi-cloud scenario.
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