CN112395089A - Cloud heterogeneous computing method and device - Google Patents
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Abstract
The invention relates to the field of heterogeneous computing, in particular to a cloud heterogeneous computing method and cloud heterogeneous computing equipment. Wherein, the method comprises the following steps: the method comprises the steps that local equipment obtains task data of current business application and user data related to the current business application; the local equipment determines a calculation task to be executed according to the task data and the user data; the local equipment divides the calculation task to be executed into a local calculation task and a cloud calculation task according to local hardware information and network resource information; the local device executes the local computing task based on heterogeneous operation and sends the cloud computing task to a cloud processing device so that the cloud processing device executes the cloud computing task based on heterogeneous operation; and the local equipment acquires an execution result of the cloud computing task sent by the cloud processing equipment. By the scheme of the embodiment of the invention, the system resource utilization rate of the local equipment can be improved, and the cost of the local equipment can be reduced.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the field of heterogeneous computing, in particular to a cloud heterogeneous computing method and cloud heterogeneous computing equipment
[ background of the invention ]
Heterogeneous computing is an efficient parallel and distributed computing approach that can coordinate the use of machines with varying performance and structure to meet different computing needs, and enable code or code segments to be executed in a manner that achieves maximum overall performance. Heterogeneous computing can be performed by a single independent computer that can support both simd and mimd modes, or by a group of independent computers interconnected by a high-speed network.
In the current heterogeneous computing scheme, common heterogeneous implementations include: CPU + GPU, CPU + FPGA, CPU + ASIC, etc. These common heterogeneous implementations all accomplish heterogeneous computations at the local device. However, with the explosive development of the internet and the popularization of informatization, many fields such as machine learning, deep learning, artificial intelligence, industrial simulation, etc. have been developed in recent years. These several areas place extremely high demands on computational performance, far exceeding the computational performance thresholds of conventional CPU processors. Therefore, problems such as low parallelism, insufficient bandwidth, high latency, etc. occur. In the prior art, a common method for solving the above problems is as follows: using a better performing processor or using multiple processors for calculations. However, the above method has disadvantages, such as high requirements for hardware devices, which increases the cost, or increases the power consumption, reduces the processor lifetime, and so on.
[ summary of the invention ]
The embodiment of the invention provides a cloud heterogeneous computing method and device, which can reduce the performance requirement on local equipment and further reduce the cost of the local equipment.
In a first aspect, an embodiment of the present invention provides a cloud heterogeneous computing method, including:
the method comprises the steps that local equipment obtains task data of current business application and user data related to the current business application;
the local equipment determines a calculation task to be executed according to the task data and the user data;
the local equipment divides the calculation task to be executed into a local calculation task and a cloud calculation task according to local hardware information and network resource information;
the local device executes the local computing task based on heterogeneous operation and sends the cloud computing task to a cloud processing device so that the cloud processing device executes the cloud computing task based on heterogeneous operation;
and the local equipment acquires an execution result of the cloud computing task sent by the cloud processing equipment.
In the scheme, the local device and the cloud processing device respectively undertake part of heterogeneous operation, so that the calculation processing load of the local device can be reduced, the utilization rate of system resources is optimized, and the performance requirement on the local device is reduced.
In one possible implementation manner, the acquiring, by the local device, the user data associated with the current service application includes:
the local device acquires user data associated with the current business application from the cloud processing device, wherein the cloud processing device is provided with a tensor database, and various types of data associated with the current business application are stored in the tensor database.
In one possible implementation manner, the user data includes: and the user executes the habit data of the current business application.
In one possible implementation manner, the to-be-executed computing task includes a plurality of subtasks; the method further comprises the following steps: the local device determining priorities of the plurality of subtasks;
the local device divides the computation task to be executed into a local computation task and a cloud computation task according to local hardware information and network resource information, and the method comprises the following steps:
and the local equipment divides the subtasks into local computing tasks and cloud computing tasks according to local hardware information, network resource information and the priorities of the subtasks.
In one possible implementation manner, the local hardware information includes:
local device heterogeneous computing capability information; local device maximum load handling capacity information;
the network resource information includes: network communication quality information; and the cloud end processes the resource occupation information of the equipment.
In one possible implementation manner, the method further includes:
the cloud processing equipment acquires operation information of a user when executing a current business application;
and displaying the operation information on the cloud processing equipment.
In one possible implementation manner, the sending, by the local device, the cloud computing task to a cloud processing device, and obtaining an execution result of the cloud computing task, where the execution result is sent by the cloud processing device, includes:
the local device sends the cloud computing task to a cloud processing device based on a 5G communication technology, and obtains an execution result of the cloud computing task sent by the cloud processing device.
In a second aspect, an embodiment of the present invention provides a cloud heterogeneous computing device, including:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for task data of current business application and user data related to the current business application;
the processing module is used for determining a to-be-executed computing task according to the task data and the user data and dividing the to-be-executed computing task into a local computing task and a cloud computing task according to local hardware information and network resource information;
the processing module is further configured to execute the local computing task;
the sending module is used for sending the cloud computing task to a cloud processing device;
the acquisition module is further configured to acquire an execution result of the cloud computing task, which is sent by the cloud processing device;
the sending module can send the cloud computing task to a cloud processing device based on a 5G communication technology;
the obtaining module may obtain, based on a 5G communication technology, an execution result of the cloud computing task sent by the cloud processing device.
In a third aspect, an embodiment of the present invention provides a cloud heterogeneous computing device, including:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, the processor calling the program instructions to be able to perform the method provided by the first aspect.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method provided in the first aspect.
It should be understood that the second to fourth aspects of the embodiment of the present invention are consistent with the technical solution of the first aspect of the embodiment of the present invention, and the beneficial effects obtained by the aspects and the corresponding possible implementation manners are similar, and are not described again.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a cloud heterogeneous computing method according to an embodiment of the present invention;
fig. 2 is a structural diagram of a cloud heterogeneous computing device according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
[ detailed description ] embodiments
For better understanding of the technical solutions in the present specification, the following detailed description of the embodiments of the present invention is provided with reference to the accompanying drawings.
It should be understood that the described embodiments are only a few embodiments of the present specification, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given in the present description without any inventive step, shall fall within the scope of protection of the present invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the specification. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
Current heterogeneous computing is typically done based on processing components of the local device, which may have relatively high hardware requirements and thus may also result in high power consumption of the hardware. In order to solve the above problem, the embodiment of the present invention combines cloud heterogeneous computing with local heterogeneous computing, so as to reduce a hardware requirement on a local device in a heterogeneous computing scenario.
Fig. 1 is a flowchart of a cloud heterogeneous computing method according to an embodiment of the present invention, and as shown in fig. 1, the cloud heterogeneous computing method may include:
In some embodiments, the local device may obtain user data from the cloud processing device. Specifically, a tensor database may be set in the cloud processing device, and various types of data associated with the current service application are stored in the tensor database. The local device may obtain user data associated with the current business application from various types of data in the tensor database.
In some embodiments, the user data may include habit data of a user executing a current business application. The habit data is used to obtain a calculation result that better conforms to the user's habit. The habit data may be habit data of the user when executing the current business application, or habit data of a group similar to the user when executing the current business application. The calculation result can be closer to the expected result of the user by acquiring the habit data.
And step 102, the local equipment determines a calculation task to be executed according to the task data and the user data.
Wherein the user data is used to determine the achievement of the current task. And determining habit data of the user data according to the characteristics of the user data so as to determine how the current task is specifically achieved. In some embodiments, the current task may be a navigation task. Determining that a band performs a computational task may be, for example: the method comprises the steps of firstly obtaining age data of a user, and then searching driving habit data of a user group close to the age data of the user from a tensor database of the cloud processing equipment. And the cloud processing equipment sends the searched driving habit data to the local equipment. The local device determines how to plan a route for the current navigation task based on the driving habit data.
In some embodiments, historical driving data of the user may be searched from a tensor database of the cloud processing device, and a calculation task to be executed may be determined according to characteristics of the historical driving data of the user. For example, the current task is a navigation task, and after the user determines the destination, the local device acquires historical driving data of the user from a tensor database of the cloud processing device. From the historical driving data, driving habit data of the user is determined, for example, the user likes to walk a highway, or the user likes to avoid a toll booth, etc. And providing a plurality of navigation routes which accord with the driving habits of the user for the user according to the driving habit data.
In some embodiments, the computing task to be performed may include a plurality of subtasks. Therefore, the local device may also determine the priority of multiple subtasks. Dividing the computational task to be performed into a plurality of sub-tasks may be understood as dividing the computational task into several specific computational steps. The priority of some important steps is divided to be higher, so that the computing efficiency in the process of executing computing tasks can be effectively improved.
And 103, dividing the calculation task to be executed into a local calculation task and a cloud calculation task by the local equipment according to the local hardware information and the network resource information. The local hardware information may specifically be local device heterogeneous computing capability information and local device maximum load processing capability information. The network resource information may specifically be network communication quality information and cloud processing device resource occupation information. By acquiring the local hardware information of the local equipment, more reasonable tasks can be divided for the local equipment, so that the execution of local calculation tasks is faster, and the hardware utilization rate of the local equipment is improved. The network resource information is acquired to provide assistance for the execution of the computing task to be executed, and when the current network information is better, more tasks can be divided into cloud computing tasks and delivered to the cloud processing equipment for execution. The local computing task of the local device is relatively reduced, so that the computing load of the local device is increased, and the computing efficiency of the computing task to be executed is improved.
In some embodiments, dividing the local computing tasks and the cloud computing tasks may be: the local device divides a part of tasks into local tasks from the computing tasks to be executed according to the heterogeneous computing capability of the local device and the processing capability of the local device. The task amount of the local tasks is close to the maximum processing amount of the local equipment, and the rest tasks are divided into cloud computing tasks. For example, 80% of the tasks of the maximum processing amount may be divided into local tasks, and the rest of the tasks may be divided into cloud computing tasks, which are then sent to the cloud processing device for computing processing.
In some embodiments, the tasks to be executed may also be divided according to an algorithm. For example, if the local device has higher computational efficiency for the simd algorithm, more tasks of the simd algorithm may be divided into local computation tasks. Or the cloud processing device has higher calculation efficiency with the mim algorithm, more tasks of the mim algorithm can be divided into cloud calculation tasks.
In some embodiments, the partitioning may also be based on network resources. For example, when the current network communication quality is good, more tasks can be divided into cloud computing tasks. Or the occupation of the processing resources of the cloud end equipment is low, and when the cloud end processing equipment is idle, more tasks can be divided into cloud end computing tasks.
And step 104, the local device executes the local computing task based on the heterogeneous operation and sends the cloud computing task to the cloud processing device. In some embodiments, when the cloud computing task is sent to the cloud processing device, a wireless communication technology may be used for data transmission. In one specific example, the 5G communication technology may be adopted to send the cloud computing task to the cloud processing device. The 5G communication technology has the characteristics of high bandwidth, high transmission rate, low delay and the like. Therefore, the data transmission by adopting the 5G communication technology can improve the transmission efficiency of tasks.
And 105, executing the cloud computing task by the cloud processing equipment based on the heterogeneous operation and sending an execution result to the local equipment. In some embodiments, when the cloud processing device sends the execution result of the cloud computing task to the local device, a wireless communication technology may be used for data transmission. In one specific example, 5G communication technology may be employed to send the execution result of the cloud computing task to the local device.
And step 106, the local device acquires an execution result of the cloud computing task sent by the cloud processing device. The local device summarizes the execution result of the cloud computing task and the execution result of the local task, and presents the computation result of the computing task to be executed to a user. For example, when the current business application is a navigation application, the corresponding computing task to be performed is an alternative route to the destination. When all the computing tasks to be executed are completed, several alternative routes meeting the requirements of the user are presented to the user for selection.
In some embodiments, the cloud processing device may further obtain and display operation information of the user when executing the current business application. The operation information may be operation information for the user to manually change the navigation route, positioning information of the user in the current navigation route, or current driving condition information of the user.
In some embodiments, the operation information may also be the current driving state information of the user, such as whether the user is tired of driving or has both hands away from the steering wheel, etc. Specifically, the cloud processing device may obtain current driving state information of the user through the local device. The local device can obtain specific driving state information by setting a camera or in other ways.
By acquiring the operation information of the user when the current business application is executed, the tensor database of the cloud processing equipment can be increased, and the abundance of the user data is increased, so that the tensor database is more perfect. Furthermore, the cloud processing equipment can also observe the current driving state of the user, so that the traffic safety of the user is ensured.
Corresponding to the cloud heterogeneous computing method, an embodiment of the present invention provides a cloud heterogeneous computing system, and as shown in fig. 2, the cloud heterogeneous computing system may include a cloud processing device and a local device. The local device may include: an acquisition module 21, a processing module 22 and a sending module 23.
An obtaining module 21, configured to obtain task data of a current service application and user data associated with the current service application;
the processing module 22 is configured to determine a to-be-executed computing task according to the task data and the user data, and divide the to-be-executed computing task into a local computing task and a cloud computing task according to the local hardware information and the network resource information;
optionally, the processing module 22 is further configured to execute the local computing task;
the sending module 23 is configured to send the cloud computing task to the cloud processing device;
optionally, the obtaining module 21 is further configured to obtain an execution result of the cloud computing task sent by the cloud processing device;
optionally, the sending module 23 may send the cloud computing task to the cloud processing device based on a 5G communication technology;
optionally, the obtaining module 21 may obtain, based on a 5G communication technology, an execution result of the cloud computing task sent by a cloud processing device.
The cloud heterogeneous computing system provided in the embodiment shown in fig. 2 may be used to execute the technical solution of the method embodiment shown in fig. 1 in this specification, and the implementation principle and the technical effect may further refer to the related description in the method embodiment.
FIG. 3 is a schematic block diagram of an embodiment of an electronic device according to the present disclosure, which may include at least one processor, as shown in FIG. 3; and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the cloud heterogeneous computing method provided by the embodiment shown in fig. 1 in this specification.
FIG. 3 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present specification. The electronic device shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present specification.
As shown in fig. 3, the electronic device is in the form of a general purpose computing device. Components of the electronic device may include, but are not limited to: one or more processors 310, a memory 330, and a communication bus 340 that couples various system components (including the memory 330 and the processing unit 310).
Electronic devices typically include a variety of computer system readable media. Such media may be any available media that is accessible by the electronic device and includes both volatile and nonvolatile media, removable and non-removable media.
A program/utility having a set (at least one) of program modules, including but not limited to an operating system, one or more application programs, other program modules, and program data, may be stored in memory 330, each of which examples or some combination may include an implementation of a network environment. The program modules generally perform the functions and/or methodologies of the embodiments described herein.
The processor 310 executes programs stored in the memory 330 to execute various functional applications and data processing, for example, to implement the cloud heterogeneous computing method provided by the embodiment shown in fig. 1 in this specification.
Embodiments of the present description provide a non-transitory computer-readable storage medium storing computer instructions, which cause a computer to execute the cloud heterogeneous computing method provided in the embodiment shown in fig. 1 of the present description.
The non-transitory computer readable storage medium described above may take any combination of one or more computer readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), a flash Memory, an optical fiber, a portable compact disc Read Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present description may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of Network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
In the description of the specification, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present specification, "a plurality" means at least two, e.g., two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present description in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present description.
The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
It should be noted that the terminal referred to in the embodiments of the present disclosure may include, but is not limited to, a Personal Computer (Personal Computer; hereinafter, referred to as PC), a Personal Digital Assistant (Personal Digital Assistant; hereinafter, referred to as PDA), a wireless handheld device, a Tablet Computer (Tablet Computer), a mobile phone, an MP3 player, an MP4 player, and the like.
In the several embodiments provided in this specification, it should be understood that the disclosed system, apparatus, and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions in actual implementation, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present description may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a Processor (Processor) to execute some steps of the methods described in the embodiments of the present disclosure. 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.
The above description is only a preferred embodiment of the present disclosure, and should not be taken as limiting the present disclosure, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.
Claims (10)
1. A cloud heterogeneous computing method is characterized by comprising the following steps:
the method comprises the steps that local equipment obtains task data of current business application and user data related to the current business application;
the local equipment determines a calculation task to be executed according to the task data and the user data;
the local equipment divides the calculation task to be executed into a local calculation task and a cloud calculation task according to local hardware information and network resource information;
the local device executes the local computing task based on heterogeneous operation and sends the cloud computing task to a cloud processing device so that the cloud processing device executes the cloud computing task based on heterogeneous operation;
and the local equipment acquires an execution result of the cloud computing task sent by the cloud processing equipment.
2. The method of claim 1, wherein obtaining, by a local device, user data associated with the current business application comprises:
the local device acquires user data associated with the current business application from the cloud processing device, wherein the cloud processing device is provided with a tensor database, and various types of data associated with the current business application are stored in the tensor database.
3. The method according to claim 1 or 2, wherein the user data comprises: and the user executes the habit data of the current business application.
4. The method of claim 1, wherein the computational task to be performed comprises a plurality of subtasks; the method further comprises the following steps: the local device determining priorities of the plurality of subtasks;
the local device divides the computation task to be executed into a local computation task and a cloud computation task according to local hardware information and network resource information, and the method comprises the following steps:
and the local equipment divides the subtasks into local computing tasks and cloud computing tasks according to local hardware information, network resource information and the priorities of the subtasks.
5. The method of claim 1 or 4, wherein the local hardware information comprises:
local device heterogeneous computing capability information; local device maximum load handling capacity information;
the network resource information includes: network communication quality information; and the cloud end processes the resource occupation information of the equipment.
6. The method of claim 1, further comprising:
the cloud processing equipment acquires operation information of a user when executing a current business application;
and displaying the operation information on the cloud processing equipment.
7. The method of claim 1, wherein the local device sends the cloud computing task to a cloud processing device, and the obtaining of the execution result of the cloud computing task sent by the cloud processing device comprises:
the local device sends the cloud computing task to a cloud processing device based on a 5G communication technology, and obtains an execution result of the cloud computing task sent by the cloud processing device.
8. A local device, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for task data of current business application and user data related to the current business application;
the processing module is used for determining a to-be-executed computing task according to the task data and the user data and dividing the to-be-executed computing task into a local computing task and a cloud computing task according to local hardware information and network resource information;
the processing module is further configured to execute the local computing task;
the sending module is used for sending the cloud computing task to a cloud processing device;
the acquisition module is further configured to acquire an execution result of the cloud computing task sent by the cloud processing device.
9. An electronic device, comprising:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1 to 7.
10. A non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the method of any one of claims 1 to 7.
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