CN115549758A - Satellite-borne edge cloud computing task processing method, system and device and storage medium - Google Patents
Satellite-borne edge cloud computing task processing method, system and device and storage medium Download PDFInfo
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Abstract
The invention discloses a satellite-borne edge cloud computing task processing method, a satellite-borne edge cloud computing task processing system, a computer device and a storage medium. According to the satellite-borne edge cloud computing method, the central cloud and the edge cloud are respectively arranged on the first satellite and the second satellite, the limitation of edge cloud computing on a ground communication network is broken through, satellite-borne edge cloud computing is achieved, and the coverage range of satellite communication is wider than that of ground communication, so that the available range and the application scene of edge cloud computing can be enlarged. The invention is widely applied to the technical field of satellite communication.
Description
Technical Field
The invention relates to the technical field of satellite communication, in particular to a satellite-borne edge cloud computing task processing method, a satellite-borne edge cloud computing task processing system, a computer device and a storage medium.
Background
Edge cloud computing is an extension of the cloud computing concept, reduces service processing delay by deploying computing power at the edge of a network, and simultaneously meets the requirement of on-site computing power. Because the requirement of edge cloud computing on hardware equipment is high, the application of edge cloud computing is limited to a ground communication network at present, and although a satellite communication technology is an increasingly important communication technology, the application of edge cloud computing in the aspect of satellite communication is still lacked in the conventional technology.
Disclosure of Invention
Aiming at the technical problems that edge cloud computing is not applied to satellite communication and the like at present, the invention aims to provide a satellite-borne edge cloud computing task processing method, a satellite-borne edge cloud computing task processing system, a computer device and a storage medium.
In one aspect, an embodiment of the present invention includes a method for processing a satellite-borne edge cloud computing task, including:
acquiring an edge cloud computing task to be processed through a center cloud; the central cloud is arranged on a first satellite;
determining a predicted load corresponding to the edge cloud computing task to be processed according to the timestamp of the edge cloud computing task to be processed; the predicted load is a satellite load generated by the edge cloud computing task to be processed;
distributing the to-be-processed edge cloud computing task to a corresponding edge cloud according to the predicted load; the edge cloud is disposed at a second satellite.
Further, the allocating the to-be-processed edge cloud computing tasks to the corresponding edge clouds according to the predicted loads includes:
when the predicted load reaches a load threshold value, triggering the edge cloud to perform micro-service calculation on the edge cloud calculation task to be processed;
and when the predicted load does not reach the load threshold value, triggering the edge cloud to perform non-service computing on the to-be-processed edge cloud computing task.
Further, the determining the predicted load corresponding to the edge cloud computing task to be processed according to the timestamp of the edge cloud computing task to be processed includes:
processing the time stamp of the edge cloud computing task to be processed by using a prophet algorithm;
and determining the predicted load according to the processing result of the prophet algorithm.
Further, the determining the predicted load corresponding to the edge cloud computing task to be processed according to the timestamp of the edge cloud computing task to be processed further includes:
acquiring a plurality of historical edge cloud computing tasks; the historical edge cloud computing task is an edge cloud computing task processed by the edge cloud;
and performing prophet algorithm modeling by using the historical edge cloud computing task.
Further, the modeling of the prophet algorithm by using the historical edge cloud computing task comprises:
acquiring a time stamp, a resource occupation amount and a computing period of each historical edge cloud computing task;
clustering the historical edge cloud computing tasks according to the resource occupation amount, and determining a plurality of first-class historical edge cloud computing tasks; the first type of historical edge cloud computing task is a historical edge cloud computing task with the largest resource occupation amount;
clustering the first type of historical edge cloud computing tasks according to the respective resource occupation amount of the first type of historical edge cloud computing tasks, and determining a plurality of second type of historical edge cloud computing tasks; the second type of historical edge cloud computing task is a historical edge cloud computing task with the longest computing period;
setting a first mark for historical edge cloud computing tasks belonging to the first type of historical edge cloud computing tasks but not belonging to the second type of historical edge cloud computing tasks;
setting a second mark for the second type of historical edge cloud computing task;
and performing prophet algorithm modeling according to the timestamp corresponding to each first type of historical edge cloud computing task and the first mark or the second mark.
Further, the orbit of the first satellite is higher than the orbit of the second satellite.
On the other hand, the embodiment of the invention also comprises a satellite-borne edge cloud computing task processing system, wherein the satellite-borne edge cloud computing task processing system comprises:
a first satellite; the first satellite sets a central cloud;
a second satellite; the second satellite sets an edge cloud;
the center cloud is used for obtaining an edge cloud computing task to be processed, determining a predicted load corresponding to the edge cloud computing task to be processed according to a timestamp of the edge cloud computing task to be processed, wherein the predicted load is a satellite load expected to be generated by the edge cloud computing task to be processed, and distributing the edge cloud computing task to be processed to a corresponding edge cloud according to the predicted load.
Further, the orbit of the first satellite is higher than the orbit of the second satellite.
In another aspect, the embodiment of the present invention further includes a computer apparatus, including a memory and a processor, where the memory is used to store at least one program, and the processor is used to load the at least one program to perform the method for processing the satellite-borne edge cloud computing task in the embodiment.
In another aspect, embodiments of the present invention further include a storage medium having stored therein a program executable by a processor, where the program executable by the processor is configured to execute the method for processing the task of the satellite-borne edge cloud computing in the embodiments.
The invention has the beneficial effects that: according to the satellite-borne edge cloud computing task processing method in the embodiment, the center cloud and the edge cloud are respectively arranged on the first satellite and the second satellite, the limitation of edge cloud computing on a ground communication network is broken through, satellite-borne edge cloud computing is achieved, and the coverage range of satellite communication is wider than that of ground communication, so that the available range and the application scene of edge cloud computing can be enlarged.
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Fig. 1 is a schematic diagram illustrating steps of a satellite-borne edge cloud computing task processing method in an embodiment;
fig. 2 is a schematic diagram of a satellite communication system of a satellite-borne edge cloud computing task processing method in an application embodiment;
FIG. 3 is a schematic diagram of a historical edge cloud computing task in an embodiment.
Detailed Description
In this embodiment, referring to fig. 1, the method for processing the satellite-borne edge cloud computing task includes the following steps:
s1, acquiring an edge cloud computing task to be processed through a center cloud;
s2, determining a prediction load corresponding to the edge cloud computing task to be processed according to the timestamp of the edge cloud computing task to be processed; the predicted load is a satellite load generated by an edge cloud computing task to be processed;
and S3, distributing the edge cloud computing tasks to be processed to corresponding edge clouds according to the predicted load.
In this embodiment, steps S1-S3 may be applied to the satellite communication system shown in fig. 2. Referring to fig. 2, a mobile phone, an internet of things device, a dedicated communication device installed in a vehicle such as an airplane and a ship, and the like may be used as a terminal, and the terminal may establish a connection with a communication satellite such as a low-orbit satellite or a high-orbit satellite.
In this embodiment, the first satellite is one of high orbit satellites, and a server device serving as a central cloud, such as a large data center, is installed on the first satellite; the second satellite is one of the low orbit satellites, and the second satellite has installed thereon a server device, such as a micro data center, serving as an edge cloud. The high-low standards of the high-orbit satellite and the low-orbit satellite may be relative, and are not necessarily limited to a specific standard. For example, a communication satellite having an orbit of 2000 to 20000km from the earth surface is conventionally referred to as a "medium orbit satellite", a communication satellite having an orbit of 200 to 2000km from the earth surface is conventionally referred to as a "low orbit satellite", and when the on-board edge cloud computing task processing method in the present embodiment is applied, the conventionally used "medium orbit satellite" is relatively a high orbit satellite in the present embodiment, and the conventionally used "low orbit satellite" is relatively a low orbit satellite in the present embodiment.
When the step S1 is executed, that is, the step of obtaining the to-be-processed edge cloud computing task through the center cloud, the to-be-processed edge cloud computing task may be established by the terminal, the terminal sends the to-be-processed edge cloud computing task to the outside, and the first satellite receives the to-be-processed edge cloud computing task and processes the to-be-processed edge cloud computing task by the center cloud.
And after the central cloud acquires the edge cloud computing task to be processed, acquiring a timestamp of the edge cloud computing task to be processed. The timestamp of the to-be-processed edge cloud computing task can be a timestamp added by the terminal and reflects the time when the terminal establishes the to-be-processed edge cloud computing task, and can also be added by the center cloud and reflects the time when the center cloud receives the to-be-processed edge cloud computing task.
In the step S2, the central cloud determines the predicted load corresponding to the edge cloud computing task to be processed according to the timestamp of the edge cloud computing task to be processed. The predicted load is a satellite load expected to be generated by the edge cloud computing task to be processed by the center cloud, and specifically, may be a satellite load expected to be generated by the edge cloud when the edge cloud computing task to be processed is allocated to the edge cloud for processing.
In the step S3, the center cloud distributes the edge cloud computing tasks to be processed to the corresponding edge clouds according to the predicted load.
The principle of steps S1-S3 includes: the central cloud is arranged on a high orbit satellite with a wide signal coverage range, and can more easily receive the edge cloud computing task to be processed sent by the terminal; the edge cloud is arranged on the low orbit satellite, and edge cloud computing tasks to be processed can be uniformly distributed to the low orbit satellite by the high orbit satellite according to the load condition of each low orbit satellite, for example, the edge cloud computing tasks to be processed with high predicted load are preferentially distributed to the low orbit satellite with low current load, the edge cloud computing tasks to be processed with low predicted load are preferentially distributed to the low orbit satellite with high current load, and the execution efficiency of the edge cloud computing tasks to be processed is improved; the edge cloud computing task to be processed is executed by the edge cloud arranged on the low-orbit satellite, so that the communication with the terminal can be maintained with better communication quality, the execution efficiency of the edge cloud computing task to be processed is improved, and the execution result of the edge cloud computing task to be processed can be returned to the terminal in time.
In this embodiment, when the central cloud performs step S2, that is, determines the predicted load corresponding to the edge cloud computing task to be processed according to the timestamp of the edge cloud computing task to be processed, the following steps may be specifically performed:
s203, processing the time stamp of the edge cloud computing task to be processed by using a prophet algorithm;
and S204, determining the predicted load according to the processing result of the prophet algorithm.
By executing steps S203-S204, a prophet algorithm may be used to predict, according to the timestamp of the edge cloud computing task to be processed, a satellite load, i.e., a predicted load, of which the load is expected to be generated by the edge cloud computing task to be processed.
When step S2 is executed, before steps S203-S204 are executed, the following steps may also be executed:
s201, obtaining a plurality of historical edge cloud computing tasks; the historical edge cloud computing task is an edge cloud computing task processed by an edge cloud;
s202, modeling a prophet algorithm by using a historical edge cloud computing task.
Steps S201 to S202 are steps of performing prophet algorithm modeling, that is, establishing a prophet algorithm model having a capability of predicting a load according to a timestamp of an edge cloud computing task to be processed.
In step S201, the center cloud may query the working log from the edge cloud, query the edge cloud computing tasks processed by the edge cloud, and obtain a plurality of historical edge cloud computing tasks. Information for each historical edge cloud computing task may be as shown in table 1.
TABLE 1
| Time stamp | User ID | Computing task ID | Occupancy of resources | Calculating period |
| 20220102-10:00:00 | 1 | 1 | 10M | 1hr |
| 20220102-10:00:02 | 1 | 2 | 20M | 3hr |
| 20220102-10:00:03 | 1 | 3 | 30M | 10hr |
The user ID and the computing task ID of each historical edge cloud computing task are recorded in table 1, and different historical edge cloud computing tasks can be distinguished through the user ID and the computing task ID. Each historical edge cloud computing task has a respective timestamp, and the timestamp of the historical edge cloud computing task can have the same meaning as the timestamp of the edge cloud computing task to be processed.
In table 1, each historical edge cloud computing task has its own resource occupation amount and computing period, where the resource occupation amount reflects the number of resources such as hardware or software occupied by the edge cloud when executing the historical edge cloud computing task, and the computing period reflects the time length consumed by the edge cloud when executing the historical edge cloud computing task.
The resource occupation amount and the computing period represent the load size of the historical edge cloud computing task on the edge cloud from different dimensions, for example, the larger the resource occupation amount is, the larger the load generated on the edge cloud is, and the longer the computing period is, the larger the load generated on the edge cloud is.
When step S202 is executed, that is, the step of performing prophet algorithm modeling by using the historical edge cloud computing task, the following steps may be specifically executed:
s20201, acquiring time stamps, resource occupation amounts and computing periods of historical edge cloud computing tasks;
s20202, clustering each historical edge cloud computing task according to the resource occupation amount, and determining a plurality of first-class historical edge cloud computing tasks; the first type of historical edge cloud computing task is the historical edge cloud computing task with the largest resource occupation;
s20203, clustering the first type of historical edge cloud computing tasks according to the respective resource occupation amount of the first type of historical edge cloud computing tasks, and determining a plurality of second type of historical edge cloud computing tasks; the second type of historical edge cloud computing task is the historical edge cloud computing task with the longest computing period;
s20204, setting a first mark for the historical edge cloud computing tasks which belong to the first type of historical edge cloud computing tasks but not the second type of historical edge cloud computing tasks;
s20205, setting a second mark for the second type of historical edge cloud computing task;
s20206, performing prophet algorithm modeling according to the time stamp and the first mark or the second mark corresponding to each first type of historical edge cloud computing task.
In step S20201, information such as time stamps, resource occupation amounts, and calculation cycles of the respective historical edge cloud calculation tasks is shown in table 1.
In step S20202, a first clustering is performed, that is, clustering is performed on each historical edge cloud computing task. Specifically, the first clustering may adopt a K-means clustering algorithm, and perform the following steps:
A1. setting a value K, for example, K =4, and randomly selecting 4 resource occupancy amounts as an initial clustering center (centroid) for clustering;
A2. computing per historical edge cloud computingThe distance from the resource occupation amount corresponding to the task to the center of mass is distributed to the cluster represented by the closest center of mass according to the distance from the resource occupation amount corresponding to the task to the center of mass i Where i e (1,k);
A3. recalculating centroids of all clusters, and updating each centroid to be current cluster i The mean value of all historical edge cloud computing tasks;
and continuously repeating the steps A2 and A3 until the mass center is not changed any more.
After steps A1-A3 are performed (where A2 and A3 may be performed multiple times), as shown in fig. 3, the historical edge cloud computing tasks are clustered, where the historical edge cloud computing task corresponding to the type with the largest resource occupation amount, in this embodiment, the first type of historical edge cloud computing task, is the clustered.
In step S20203, performing a second clustering, that is, clustering each first-class historical edge cloud computing task. Specifically, the second clustering may also adopt a K-means clustering algorithm, and perform the following steps:
B1. setting a value K, for example, K =3, randomly selecting 4 calculation cycles as an initial clustering center (centroid) for clustering;
B2. calculating the distance from the computing cycle corresponding to each first-class historical edge cloud computing task to the centroid, and distributing each first-class historical edge cloud to the cluster represented by the closest centroid according to the distance from the first-class historical edge cloud computing task to the centroid j Where j ∈ (1,k);
B3. recalculating centroids of all clusters, and updating each centroid to be current cluster j The average value of all the first-class historical edge cloud computing tasks;
and continuously repeating the steps B2 and B3 until the mass center is not changed any more.
After steps B1-B3 are performed (where B2 and B3 may be performed multiple times), as shown in fig. 3, the first type of historical edge cloud computing task is clustered, where the historical edge cloud computing task with the largest computing cycle corresponds to the second type of historical edge cloud computing task in this embodiment.
In step S20204, referring to fig. 3, a first flag is set for a historical edge cloud computing task that belongs to the first type of historical edge cloud computing task but does not belong to the second type of historical edge cloud computing task; in step S20205, referring to fig. 3, a second flag is set for the second type of history edge cloud computing task.
Specifically, the first flag may be a value of 0, and the first flag is set for the first type of historical edge cloud computing task, which indicates that the load generated by the first type of historical edge cloud computing task is small; the second flag may be a value of 1, and the second flag is set for the second type of historical edge cloud computing task, indicating that the load generated by the second type of historical edge cloud computing task is large.
In step S20206, prophet algorithm modeling is performed according to the timestamp and the first marker or the second marker corresponding to each first type of historical edge cloud computing task. Specifically, a timestamp corresponding to the first type of historical edge cloud computing task is used as an input of the prophet algorithm model, and a first label (if not belonging to the second type of historical edge cloud computing task) or a second label (if belonging to the second type of historical edge cloud computing task) corresponding to the first type of historical edge cloud computing task is used as an expected output of the prophet algorithm model, so that the training process is executed.
By executing the steps S20101-S20106, a prophet algorithm model with the capability of predicting according to the time stamp of the edge cloud computing task to be processed to obtain the predicted load can be established, and specifically, two indexes which are easy to measure and cluster are used as the basis for classification in the training process, namely the resource occupation amount and the computing period, so that the training speed can be accelerated.
In this embodiment, when the center cloud performs step S3, that is, the step of allocating the edge cloud computing task to be processed to the corresponding edge cloud according to the predicted load, the following steps may be specifically performed:
s301, when the predicted load reaches a load threshold value, triggering an edge cloud to perform micro-service calculation on the edge cloud calculation task to be processed;
s302, when the predicted load does not reach the load threshold, triggering the edge cloud to perform non-service computing on the edge cloud computing task to be processed.
When steps S301 to S302 are executed, the central cloud may compare the predicted load with a set load threshold, and if the predicted load reaches the load threshold, it may be determined that the predicted load is large enough and the edge cloud computing task to be processed does not need to be computed frequently, and in step S301, the central cloud may trigger the edge cloud to perform micro-service computation on the edge cloud computing task to be processed; if the predicted load does not reach the load threshold, it can be judged that the predicted load is small, and in step S301, the center cloud can trigger the edge cloud to perform non-service computing on the to-be-processed edge cloud computing task.
By executing steps S301 to S302, the to-be-processed edge cloud computing task may be subjected to shunting processing according to the predicted load of the to-be-processed edge cloud computing task, for example, the to-be-processed edge cloud computing task with a smaller predicted load is allocated to an edge cloud suitable for performing non-service computing, and the to-be-processed edge cloud computing task with a larger predicted load is allocated to an edge cloud suitable for performing micro-service computing, so as to balance loads of the edge cloud and the low orbit satellite.
The processing method of the satellite-borne edge cloud computing task in the embodiment can be implemented by writing a computer program for implementing the processing method of the satellite-borne edge cloud computing task in the embodiment, writing the computer program into a computer device or a storage medium, and executing the processing method of the satellite-borne edge cloud computing task in the embodiment when the computer program is read out to run, so that the same technical effect as that of the processing method of the satellite-borne edge cloud computing task in the embodiment is achieved.
It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature or indirectly fixed or connected to the other feature. Furthermore, the descriptions of upper, lower, left, right, etc. used in the present disclosure are only relative to the mutual positional relationship of the constituent parts of the present disclosure in the drawings. As used in this disclosure, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. In addition, unless defined otherwise, all technical and scientific terms used in this example have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in the description of the embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this embodiment, the term "and/or" includes any combination of one or more of the associated listed items.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element of the same type from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present disclosure. The use of any and all examples, or exemplary language ("e.g.," such as "or the like") provided with this embodiment is intended merely to better illuminate embodiments of the invention and does not pose a limitation on the scope of the invention unless otherwise claimed.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, operations of processes described in this embodiment can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described in this embodiment (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated onto a computing platform, such as a hard disk, optically read and/or write storage media, RAM, ROM, etc., so that it is readable by a programmable computer, which when read by the computer can be used to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described in this embodiment includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein.
A computer program can be applied to input data to perform the functions described in the present embodiment to convert the input data to generate output data that is stored to a non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
The present invention is not limited to the above embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means. The invention is capable of other modifications and variations in its technical solution and/or its implementation, within the scope of protection of the invention.
Claims (10)
1. A satellite-borne edge cloud computing task processing method is characterized by comprising the following steps: acquiring an edge cloud computing task to be processed through a center cloud; the central cloud is arranged at a first satellite;
determining a predicted load corresponding to the edge cloud computing task to be processed according to the timestamp of the edge cloud computing task to be processed; the predicted load is a satellite load generated by the edge cloud computing task to be processed;
distributing the to-be-processed edge cloud computing task to a corresponding edge cloud according to the predicted load; the edge cloud is disposed at a second satellite.
2. The on-board edge cloud computing task processing method according to claim 1, wherein the allocating the to-be-processed edge cloud computing task to a corresponding edge cloud according to the predicted load includes:
when the predicted load reaches a load threshold value, triggering the edge cloud to perform micro-service computation on the edge cloud computing task to be processed;
and when the predicted load does not reach the load threshold value, triggering the edge cloud to perform non-service computing on the to-be-processed edge cloud computing task.
3. The on-board edge cloud computing task processing method according to claim 1, wherein the determining the predicted load corresponding to the edge cloud computing task to be processed according to the timestamp of the edge cloud computing task to be processed includes:
processing the time stamp of the edge cloud computing task to be processed by using a prophet algorithm;
and determining the predicted load according to a processing result of a prophet algorithm.
4. The method for processing the satellite-borne edge cloud computing task according to claim 3, wherein the determining of the predicted load corresponding to the edge cloud computing task to be processed according to the timestamp of the edge cloud computing task to be processed further includes:
acquiring a plurality of historical edge cloud computing tasks; the historical edge cloud computing task is an edge cloud computing task processed by the edge cloud;
and performing prophet algorithm modeling by using the historical edge cloud computing task.
5. The on-board edge cloud computing task processing method according to claim 4, wherein the prophet algorithm modeling using the historical edge cloud computing tasks includes:
acquiring a time stamp, a resource occupation amount and a computing period of each historical edge cloud computing task;
clustering the historical edge cloud computing tasks according to the resource occupation amount, and determining a plurality of first-class historical edge cloud computing tasks; the first type of historical edge cloud computing task is the historical edge cloud computing task with the largest resource occupation;
clustering the first type of historical edge cloud computing tasks according to the respective resource occupation amount of the first type of historical edge cloud computing tasks, and determining a plurality of second type of historical edge cloud computing tasks; the second type of historical edge cloud computing task is a historical edge cloud computing task with the longest computing period;
setting a first mark for historical edge cloud computing tasks belonging to the first type of historical edge cloud computing tasks but not belonging to the second type of historical edge cloud computing tasks;
setting a second mark for the second type of historical edge cloud computing task;
and performing prophet algorithm modeling according to the timestamp corresponding to each first type of historical edge cloud computing task and the first mark or the second mark.
6. The on-board edge cloud computing task processing method according to any one of claims 1 to 5, wherein an orbit of the first satellite is higher than an orbit of the second satellite.
7. A satellite-borne edge cloud computing task processing system is characterized by comprising:
a first satellite; the first satellite is provided with a central cloud;
a second satellite; the second satellite sets an edge cloud;
the center cloud is used for obtaining an edge cloud computing task to be processed, determining a predicted load corresponding to the edge cloud computing task to be processed according to a timestamp of the edge cloud computing task to be processed, wherein the predicted load is a satellite load expected to be generated by the edge cloud computing task to be processed, and distributing the edge cloud computing task to be processed to a corresponding edge cloud according to the predicted load.
8. The on-board edge cloud computing task processing system of claim 7, wherein the orbit of the first satellite is higher than the orbit of the second satellite.
9. A computer apparatus comprising a memory for storing at least one program and a processor for loading the at least one program to perform the on-board edge cloud computing task processing method of any of claims 1-6.
10. A computer-readable storage medium in which a processor-executable program is stored, wherein the processor-executable program, when executed by a processor, is configured to perform the method for processing the satellite-borne edge cloud computing task according to any one of claims 1 to 6.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20210072956A (en) * | 2019-12-10 | 2021-06-18 | 한국전자기술연구원 | Edge Cloud Computing Offloading Method in the IoT environment |
| CN114026834A (en) * | 2019-04-30 | 2022-02-08 | 英特尔公司 | Multi-entity resource, security, and service management in edge computing deployments |
| CN114356582A (en) * | 2022-01-12 | 2022-04-15 | 陈银 | Expansion method, system, equipment and medium based on edge cloud computing |
| CN115022894A (en) * | 2022-06-08 | 2022-09-06 | 西安交通大学 | Task unloading and computing resource allocation method and system for low-earth-orbit satellite network |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114026834A (en) * | 2019-04-30 | 2022-02-08 | 英特尔公司 | Multi-entity resource, security, and service management in edge computing deployments |
| KR20210072956A (en) * | 2019-12-10 | 2021-06-18 | 한국전자기술연구원 | Edge Cloud Computing Offloading Method in the IoT environment |
| CN114356582A (en) * | 2022-01-12 | 2022-04-15 | 陈银 | Expansion method, system, equipment and medium based on edge cloud computing |
| CN115022894A (en) * | 2022-06-08 | 2022-09-06 | 西安交通大学 | Task unloading and computing resource allocation method and system for low-earth-orbit satellite network |
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