CN116302900B - Computing power reliability assessment method of multi-access edge computing system - Google Patents
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Abstract
The disclosure provides a computing power reliability assessment method of a multi-access edge computing system, and relates to the technical field of cloud computing. The method comprises the following steps: firstly, receiving a task to be processed, then determining at least one target edge computing device for executing the task to be processed in the multi-access edge computing system, further determining the occurrence frequency of a predicted fault corresponding to each target edge computing device in a first preset time period for executing the task to be processed, and finally determining the reliability of the multi-access edge computing system for executing the task to be processed according to the sum of the occurrence frequency of each predicted fault and the first preset time period. Therefore, the reliability of the multi-access edge computing system for successfully completing the task to be processed can be accurately determined according to the occurrence frequency of the corresponding prediction fault of each target edge computing device in the first preset time period.
Description
Technical Field
The disclosure relates to the technical field of cloud computing, in particular to a computing power reliability assessment method of a multi-access edge computing system.
Background
With the continuous development of edge computing technology, multi-access edge computing (Multi-Access Edge Computing, MEC) is widely used, and the main function of MEC is to cooperatively use a plurality of edge devices close to the edge side to complete the edge computing task, so that the advantages of different computing devices are fully exerted, the overall efficiency and performance of the system are enhanced, and the computing task is jointly completed. However, any computing resource device fails during execution of a task, which may result in a task failure. Therefore, how to evaluate the reliability of the MEC to successfully complete the task becomes a urgent issue to be resolved.
Disclosure of Invention
The present disclosure aims to solve, at least to some extent, one of the technical problems in the related art.
An embodiment of a first aspect of the present disclosure provides a computing power reliability evaluation method of a multi-access edge computing system, including:
receiving a task to be processed;
determining at least one target edge computing device in a multi-access edge computing system that performs the task to be processed;
determining the occurrence frequency of the corresponding prediction faults of each target edge computing device in a first preset time period for executing the task to be processed;
and determining the reliability of the multi-access edge computing system for executing the task to be processed according to the sum of the occurrence frequency of each predicted fault and the first preset time period.
An embodiment of a second aspect of the present disclosure proposes a computing power reliability evaluation device of a multi-access edge computing system, including:
the receiving module is used for receiving the task to be processed;
a first determining module, configured to determine at least one target edge computing device in a multi-access edge computing system that performs the task to be processed; the method comprises the steps of carrying out a first treatment on the surface of the
The second determining module is used for determining the occurrence frequency of the corresponding prediction faults of each target edge computing device in a first preset time period for executing the task to be processed;
and the third determining module is used for determining the reliability of the multi-access edge computing system for executing the task to be processed according to the sum of the occurrence frequency of each predicted fault and the first preset time period.
An embodiment of a third aspect of the present disclosure provides an electronic device, including: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the computing force reliability assessment method of the multi-access edge computing system as provided by the embodiment of the first aspect of the disclosure.
An embodiment of a fourth aspect of the present disclosure proposes a computer readable storage medium storing a computer program, which when executed by a processor, implements a method for evaluating the computational power reliability of a multi-access edge computing system as proposed by an embodiment of the first aspect of the present disclosure.
A fifth aspect embodiment of the present disclosure proposes a computer program product comprising a computer program which, when executed by a processor, implements a method for evaluating the computational power reliability of a multi-access edge computing system as proposed by the first aspect embodiment of the present disclosure.
The computing power reliability assessment method of the multi-access edge computing system provided by the disclosure has the following beneficial effects:
in the embodiment of the disclosure, a task to be processed is received first, then at least one target edge computing device for executing the task to be processed in the multi-access edge computing system is determined, further, the occurrence frequency of a predicted fault corresponding to each target edge computing device in a first preset time period for executing the task to be processed is determined, and finally the reliability of executing the task to be processed in the multi-access edge computing system is determined according to the sum of the occurrence frequencies of each predicted fault and the first preset time period. Therefore, the reliability of the multi-access edge computing system for successfully completing the task to be processed can be accurately determined according to the occurrence frequency of the corresponding prediction fault of each target edge computing device in the first preset time period.
Additional aspects and advantages of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
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The foregoing and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 is a flow chart illustrating a computing power reliability evaluation method of a multi-access edge computing system according to an embodiment of the disclosure;
fig. 2 is a flow chart illustrating a method for evaluating the computational power reliability of a multi-access edge computing system according to another embodiment of the present disclosure;
FIG. 3 is a schematic diagram illustrating a computing power reliability evaluation device of a multi-access edge computing system according to an embodiment of the disclosure;
fig. 4 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present disclosure and are not to be construed as limiting the present disclosure.
The following describes a computing power reliability evaluation method of a multi-access edge computing system according to an embodiment of the present disclosure with reference to the accompanying drawings.
Fig. 1 is a flow chart of a computing power reliability evaluation method of a multi-access edge computing system according to an embodiment of the disclosure.
The embodiment of the disclosure is exemplified by the computing power reliability evaluation method of the multi-access edge computing system being configured in the computing power reliability evaluation device of the multi-access edge computing system, and the computing power reliability evaluation device of the multi-access edge computing system can be applied to the electronic equipment of the multi-access edge computing system, so that the electronic equipment can execute the computing power reliability evaluation function of the multi-access edge computing system.
As shown in fig. 1, the computing power reliability evaluation method of the multi-access edge computing system may include the following steps:
step 101, receiving a task to be processed.
The task to be processed may be a service to be processed by the multi-access edge computing system currently.
The task to be processed may be a task sent by an external terminal device, for example, an algorithm computing task of a Multi-access edge computing system (Multi-Access Edge Computing, MEC) system, or may be any processing task in other service platforms, for example, an image recognition task, an image processing task, an autopilot algorithm task, etc., which is not limited thereto.
Step 102, determining at least one target edge computing device in a multi-access edge computing system that performs a task to be processed.
It will be appreciated that, after receiving a task to be processed sent by any terminal device, the multi-access edge computing system may determine at least one target edge computing device that performs the task to be processed, and the at least one edge computing device processes the task.
The edge computing device may be an edge device that is close to an edge side in a multi-access edge computing system and has computing power.
Alternatively, according to the position of the terminal device sending the task to be processed, the edge computing device closer to the terminal device can be selected to execute the task to be processed. Alternatively, an edge computing device in an idle state is selected to perform the task to be processed. The present disclosure is not limited in this regard.
Optionally, a first position of a terminal device sending a task to be processed and a second position corresponding to an edge computing device in an idle state in the multi-access edge computing system may be obtained, then a distance between the edge computing device in the idle state in the multi-access edge computing system and the terminal device is determined based on the first position and the second position, and finally a preset number of edge computing devices with minimum corresponding distances are determined as target edge computing devices.
Step 103, determining the corresponding predicted fault occurrence frequency of each target edge computing device in a first preset time period for executing the task to be processed.
The first preset time period may be a period of time after starting to execute the task to be processed. For example, one hour, or two hours, etc. after starting to execute the task to be processed. The present disclosure is not limited in this regard.
The predicted failure occurrence frequency may be a failure occurrence frequency per hour or a failure occurrence frequency per minute within a first preset period. The present disclosure is not limited in this regard.
Alternatively, the frequency of occurrence of the predicted failure corresponding to the first preset time period may be determined according to the frequency of occurrence of the failure of each edge computing device.
And step 104, determining the reliability of the multi-access edge computing system for executing the task to be processed according to the sum of the occurrence frequency of each predicted fault and the first preset time period.
It should be noted that the probability of each target edge computing device failing within the first preset time period conforms to poisson distribution, namely
Wherein T is a first preset time period, X j Calculating the number of failures of the device j in the time T for the target edge lambda j The frequency of occurrence of the failure of device j during time T is calculated for the target edge,and representing the probability of X times of faults of the target edge computing device j in the first preset time period.
Because the target edge computing devices do not affect each other when executing the task to be processed, namely the probability that each edge computing device successfully completes the task also accords with poisson distribution.
The determination formula of the reliability of the multi-access edge computing system for executing the task to be processed is as follows:
wherein ,the reliability of the task to be processed is executed for the multi-access edge computing system, namely the probability that the times of faults of a plurality of target edge computing devices in a first preset time period are 0; t is a first preset time period, < >>A sum of the frequency of occurrence of the predicted failure for each of the target edges is calculated for the device.
The calculation formula of (2) can be as follows:
wherein ,for the failure occurrence frequency corresponding to the first target edge computing device, m is the number of target edge computing devices executing the task to be processed in the multi-access edge computing system, < +.>And representing the occurrence frequency of the fault corresponding to the mth target edge computing device.
In the embodiment of the disclosure, a task to be processed is received first, then at least one target edge computing device for executing the task to be processed in the multi-access edge computing system is determined, further, the occurrence frequency of a predicted fault corresponding to each target edge computing device in a first preset time period for executing the task to be processed is determined, and finally the reliability of executing the task to be processed in the multi-access edge computing system is determined according to the sum of the occurrence frequencies of each predicted fault and the first preset time period. Therefore, the reliability of the multi-access edge computing system for successfully completing the task to be processed can be accurately determined according to the occurrence frequency of the corresponding prediction fault of each target edge computing device in the first preset time period.
Fig. 2 is a flow chart of a computing power reliability evaluation method of a multi-access edge computing system according to an embodiment of the disclosure, as shown in fig. 2, the computing power reliability evaluation method of the multi-access edge computing system may include the following steps:
step 201, a task to be processed is received.
Step 202, determining at least one target edge computing device in a multi-access edge computing system that performs a task to be processed.
Step 203, determining a first preset time period according to the calculated amount of the task to be processed and the calculation capability of each target edge calculation device.
In the embodiment of the disclosure, the time required by the multi-access edge computing system to execute the task to be processed can be predicted according to the calculated amount of the task to be processed and the calculation capability of each target edge computing device, and then the first preset time period is determined according to the time required by the task to be processed, so that the reliability of the predicted multi-access edge computing system to execute the task to be processed is more accurate.
It should be noted that, the time length of the first preset time period needs to be longer than the predicted time required for executing the task to be processed.
Step 204, obtaining the historical faults and the occurrence time of each historical fault in a second preset time period before the moment of receiving the task to be processed by each target edge computing device, wherein the first preset time period is larger than the second preset time period.
In the embodiment of the disclosure, the time of each occurrence of the fault, that is, the occurrence time of the historical fault, of each target edge computing device in a second preset time period before receiving the task to be processed may be obtained from the database.
Wherein the second preset time period may be 1 month, 3 months, etc. The present disclosure is not limited in this regard.
Step 205, determining the occurrence frequency of the predicted fault corresponding to each target edge computing device according to the first preset time period, the second preset time period and the occurrence time of each historical fault.
Optionally, the second preset time may be divided into a plurality of sub-time periods based on the first preset time period, where the duration of each sub-time period is the same as the duration of the first preset time period, and then the occurrence times of the historical faults in each sub-time period are determined according to the occurrence times of the historical faults in each sub-time period, the occurrence frequency of the reference faults corresponding to each sub-time period is determined according to the occurrence times of the historical faults in each sub-time period, and finally the occurrence frequency of the predicted faults is determined according to the occurrence frequency of the reference faults corresponding to each sub-time period.
The dividing the second preset time into a plurality of sub-time periods according to the first preset time period may be sequentially dividing the second preset time period into a plurality of sub-time periods identical to the first preset time period.
For example, if the first preset time period is 2 hours and the second preset time period is 1 month, the second preset time period is sequentially divided into sub-time periods with a duration of 2 hours. And then determining the occurrence times of the historical faults in each 2 hours according to the occurrence time of each historical fault, and further determining the reference fault frequency according to the occurrence times of the historical faults in each 2 hours, namely dividing the occurrence times of the historical faults by 2 hours.
Optionally, the reference fault occurrence frequency corresponding to each sub-time period may be statistically analyzed, and the reference fault frequency with the largest occurrence number is determined as the fault occurrence frequency corresponding to the first preset time period.
Alternatively, an average value of the occurrence frequencies of the reference faults corresponding to each sub-period may be determined first, and then the average value may be determined as the predicted occurrence frequency of the faults.
In the embodiment of the disclosure, the number of times of occurrence of the fault in the second preset time period may be determined according to the occurrence time of each historical fault, so as to determine the quotient of the number of occurrence of the fault and the second preset time period as the occurrence frequency of the fault.
And step 206, determining the reliability of the multi-access edge computing system for executing the task to be processed according to the sum of each predicted fault occurrence frequency and the first preset time period.
According to the method, a task to be processed is received firstly, at least one target edge computing device for executing the task to be processed in the multi-access edge computing system is determined, a first preset time period is determined according to the calculated amount of the task to be processed and the calculating capability of each target edge computing device, then historical faults and occurrence time of each historical fault are obtained in a second preset time period before the moment of receiving the task to be processed of each target edge computing device, the occurrence frequency of the predicted fault corresponding to each target edge computing device is determined according to the first preset time period, the second preset time period and the occurrence time of each historical fault, and finally the reliability of the multi-access edge computing system for executing the task to be processed is determined according to the occurrence frequency of the fault and the first preset time period. Therefore, the occurrence frequency of the predicted faults of each target edge computing device can be determined according to the occurrence time of each historical fault of each target edge computing device in a second preset time period, so that the accuracy of the determined occurrence probability of the predicted faults is improved, and the reliability of the multi-access edge computing system for successfully completing the task to be processed is further accurately determined.
In order to implement the above embodiments, the present disclosure further proposes a computing power reliability evaluation device of a multi-access edge computing system.
Fig. 3 is a schematic structural diagram of a computing power reliability evaluation device of a multi-access edge computing system according to an embodiment of the disclosure.
As shown in fig. 3, the computing power reliability assessment apparatus 300 of the multi-access edge computing system may include:
a receiving module 310, configured to receive a task to be processed;
a first determining module 320, configured to determine at least one target edge computing device in the multi-access edge computing system that performs a task to be processed;
a second determining module 330, configured to determine a predicted failure occurrence frequency corresponding to each target edge computing device in a first preset time period for executing the task to be processed;
and a third determining module 340, configured to determine the reliability of the multi-access edge computing system for executing the task to be processed according to the sum of each predicted failure occurrence frequency and the first preset time period.
Optionally, the second determining module 330 is configured to:
acquiring historical faults and occurrence time of each historical fault in a second preset time period before the moment of receiving a task to be processed of each target edge computing device, wherein the first preset time period is larger than the second preset time period;
and determining the occurrence frequency of the predicted faults corresponding to each target edge computing device according to the first preset time period, the second preset time period and the occurrence time of each historical fault.
Optionally, the second determining module 330 is further configured to:
dividing the second preset time into a plurality of sub-time periods based on the first preset time period, wherein the duration of each sub-time period is the same as the duration of the first preset time period;
determining the occurrence times of the historical faults in each sub-time period according to the occurrence time of each historical fault;
determining the occurrence frequency of the reference fault corresponding to each sub-time period according to the occurrence times of the historical fault in each sub-time period;
and determining the predicted fault occurrence frequency according to the reference fault occurrence frequency corresponding to each sub-time period.
Optionally, the second determining module 330 is further configured to:
determining an average value of the occurrence frequency of the reference fault corresponding to each sub-time period;
the average value is determined as the predicted failure occurrence frequency.
Optionally, the determination formula of the reliability of the multi-access edge computing system in executing the task to be processed is as follows:
wherein ,the reliability of performing the task to be processed for the multi-access edge computing system, T is a first preset time period,a sum of the frequency of occurrence of the predicted failure for each of the target edges is calculated for the device.
Optionally, the method further comprises a fourth determining module for:
and determining a first preset time period according to the calculated amount of the task to be processed and the calculation capability of each target edge calculation device.
Optionally, the first determining module is configured to:
acquiring a first position of terminal equipment for sending a task to be processed and a second position corresponding to each edge computing equipment in a multi-access edge computing system;
determining a distance between an edge computing device in an idle state and a terminal device in a multi-access edge computing system based on the first position and the second position;
and determining a preset number of edge computing devices with minimum corresponding distances as target edge computing devices.
The computing power reliability evaluation device of the multi-access edge computing system of the embodiment of the disclosure firstly receives a task to be processed, then determines at least one target edge computing device for executing the task to be processed in the multi-access edge computing system, further determines the occurrence frequency of a corresponding prediction fault of each target edge computing device in a first preset time period for executing the task to be processed, and finally determines the reliability of the multi-access edge computing system for executing the task to be processed according to the sum of the occurrence frequencies of each prediction fault and the first preset time period. Therefore, the reliability of the multi-access edge computing system for successfully completing the task to be processed can be accurately determined according to the occurrence frequency of the corresponding prediction fault of each target edge computing device in the first preset time period.
In order to achieve the above embodiments, the present disclosure further proposes an electronic device including: the system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the computing power reliability evaluation method of the multi-access edge computing system as proposed by the previous embodiment of the disclosure.
In order to implement the above embodiments, the present disclosure further proposes a computer-readable storage medium storing a computer program, which when executed by a processor, implements a method for evaluating the computational power reliability of a multi-access edge computing system as proposed in the foregoing embodiments of the present disclosure.
To achieve the above embodiments, the present disclosure also proposes a computer program product comprising a computer program which, when executed by a processor, implements a method for evaluating the computational power reliability of a multi-access edge computing system as proposed in the foregoing embodiments of the present disclosure.
Fig. 4 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present disclosure. The electronic device 12 shown in fig. 4 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
As shown in fig. 4, the electronic device 12 is in the form of a general purpose computing device. Components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include industry Standard architecture (Industry Standard Architecture; hereinafter ISA) bus, micro channel architecture (Micro Channel Architecture; hereinafter MAC) bus, enhanced ISA bus, video electronics standards Association (Video Electronics Standards Association; hereinafter VESA) local bus, and peripheral component interconnect (Peripheral Component Interconnection; hereinafter PCI) bus.
Electronic device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by electronic device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
Memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (Random Access Memory; hereinafter: RAM) 30 and/or cache memory 32. The electronic device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard disk drive"). Although not shown in fig. 4, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a compact disk read only memory (Compact Disc Read Only Memory; hereinafter CD-ROM), digital versatile read only optical disk (Digital Video Disc Read Only Memory; hereinafter DVD-ROM), or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the various embodiments of the disclosure.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods in the embodiments described in this disclosure.
The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the electronic device 12, and/or any devices (e.g., network card, modem, etc.) that enable the electronic device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Also, the electronic device 12 may communicate with one or more networks, such as a local area network (Local Area Network; hereinafter: LAN), a wide area network (Wide Area Network; hereinafter: WAN) and/or a public network, such as the Internet, via the network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 over the bus 18. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, implementing the methods mentioned in the foregoing embodiments.
According to the technical scheme, a task to be processed is received firstly, then at least one target edge computing device for executing the task to be processed in the multi-access edge computing system is determined, the occurrence frequency of a predicted fault corresponding to each target edge computing device in a first preset time period for executing the task to be processed is further determined, and finally the reliability of the multi-access edge computing system for executing the task to be processed is determined according to the sum of the occurrence frequency of each predicted fault and the first preset time period. Therefore, the reliability of the multi-access edge computing system for successfully completing the task to be processed can be accurately determined according to the occurrence frequency of the corresponding prediction fault of each target edge computing device in the first preset time period.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," 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 present disclosure. In this specification, schematic representations of the above terms are not necessarily directed 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, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, the meaning of "a plurality" is at least two, such as two, three, etc., unless explicitly specified 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 specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present disclosure 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 disclosure.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
Furthermore, each functional unit in the embodiments of the present disclosure may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. Although embodiments of the present disclosure have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the present disclosure, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the present disclosure.
Claims (6)
1. A method for computing power reliability assessment for a multi-access edge computing system, comprising:
receiving a task to be processed;
acquiring a first position of terminal equipment for transmitting the task to be processed and a second position corresponding to edge computing equipment in an idle state in a multi-access edge computing system, determining the distance between the edge computing equipment in the idle state in the multi-access edge computing system and the terminal equipment based on the first position and the second position, determining at least one target edge computing equipment for executing the task to be processed in the multi-access edge computing system by a preset number of edge computing equipment with the minimum corresponding distance;
acquiring a historical fault occurring in each target edge computing device in a second preset time period before the moment of receiving the task to be processed and the occurrence time of each historical fault, determining the corresponding predicted fault occurrence frequency of each target edge computing device in the first preset time period for executing the task to be processed according to a first preset time period, the second preset time period and the occurrence time of each historical fault, wherein the time length of the first preset time period is longer than the time required by the task to be processed, dividing the second preset time period into a plurality of sub-time periods, the duration of each sub-time period is the same as the duration of the first preset time period, determining the occurrence times of the historical faults in each sub-time period according to the occurrence time of each historical fault, determining the corresponding reference fault occurrence frequency of each sub-time period according to the occurrence times of the historical faults in each sub-time period, and determining the predicted fault occurrence frequency according to the corresponding reference fault occurrence frequency of each sub-time period;
according to the sum of the occurrence frequencies of each predicted fault and the first preset time period, determining the reliability of the multi-access edge computing system for executing the task to be processed, wherein the determination formula of the reliability of the multi-access edge computing system for executing the task to be processed is as follows:
wherein ,for the reliability of the multi-access edge computing system executing the task to be processed, T is the first preset time period,/for the first preset time period>Calculating the sum of the corresponding predicted failure occurrence frequencies of the devices for the target edge>The calculation formula of (2) is as follows:
wherein ,for the failure occurrence frequency corresponding to the first target edge computing device, m is the number of target edge computing devices executing the task to be processed in the multi-access edge computing system, < +.>And representing the occurrence frequency of the fault corresponding to the mth target edge computing device.
2. The method of claim 1, wherein said determining said predicted failure occurrence frequency from said reference failure occurrence frequency for each of said sub-time periods comprises:
determining an average value of the reference fault occurrence frequency corresponding to each sub-time period;
and determining the average value as the predicted fault occurrence frequency.
3. The method according to claim 1 or 2, further comprising:
and determining the first preset time period according to the calculated amount of the task to be processed and the calculation capability of each target edge calculation device.
4. A computing power reliability assessment device for a multi-access edge computing system, comprising:
the receiving module is used for receiving the task to be processed;
a first determining module, configured to obtain a first position of a terminal device that sends the task to be processed and a second position corresponding to an edge computing device in an idle state in a multi-access edge computing system, determine, based on the first position and the second position, a distance between the edge computing device in the idle state in the multi-access edge computing system and the terminal device, and determine at least one target edge computing device in the multi-access edge computing system that executes the task to be processed, where the preset number of edge computing devices with the minimum corresponding distance;
a second determining module, configured to obtain a historical fault occurring in a second preset time period before a time of receiving the task to be processed and an occurrence time of each historical fault, determine, according to a first preset time period, the second preset time period, and the occurrence time of each historical fault, a predicted fault occurrence frequency corresponding to each target edge computing device in the first preset time period for executing the task to be processed, where a time length of the first preset time period is greater than a time required by the task to be processed, divide the second preset time period into a plurality of sub-time periods, where a time length of each sub-time period is the same as a time length of the first preset time period, determine, according to the occurrence time of each historical fault, a frequency of occurrence of each historical fault in the sub-time period, determine, according to the occurrence time of each historical fault in the sub-time period, a reference fault occurrence frequency corresponding to each sub-time period, and determine, according to the reference fault occurrence frequency corresponding to each sub-time period;
the third determining module is configured to determine, according to the sum of the occurrence frequencies of each of the predicted faults and the first preset time period, reliability of the multi-access edge computing system executing the task to be processed, where a determination formula of the reliability of the multi-access edge computing system executing the task to be processed is as follows:
wherein ,for the reliability of the multi-access edge computing system executing the task to be processed, T is the first preset time period,/for the first preset time period>Calculating the sum of the corresponding predicted failure occurrence frequencies of the devices for the target edge>The calculation formula of (2) is as follows:
wherein ,for the failure occurrence frequency corresponding to the first target edge computing device, m is the number of target edge computing devices executing the task to be processed in the multi-access edge computing system, < +.>Representing the mth target edgeAnd calculating the occurrence frequency of the fault corresponding to the equipment.
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a method of computing power reliability assessment of a multi-access edge computing system according to any one of claims 1-3 when the program is executed by the processor.
6. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the method for evaluating the computational reliability of a multi-access edge computing system according to any of claims 1-3.
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