CN115150891B - Interrupt probability auxiliary task unloading optimization method based on mobile edge calculation - Google Patents

Interrupt probability auxiliary task unloading optimization method based on mobile edge calculation Download PDF

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CN115150891B
CN115150891B CN202210643725.7A CN202210643725A CN115150891B CN 115150891 B CN115150891 B CN 115150891B CN 202210643725 A CN202210643725 A CN 202210643725A CN 115150891 B CN115150891 B CN 115150891B
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base station
user
task
unloading
energy consumption
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CN115150891A (en
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邹玉龙
李旭冉
王宇靖
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Nanjing University of Posts and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H04W28/086—Load balancing or load distribution among access entities
    • H04W28/0861—Load balancing or load distribution among access entities between base stations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H04W28/09—Management thereof
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H04W28/09—Management thereof
    • H04W28/0917—Management thereof based on the energy state of entities
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention discloses an interruption probability auxiliary task unloading optimization method based on mobile edge calculation, which aims to reduce energy consumption in a user task unloading process. When a computing task generated by a user cannot locally complete the computation or the task is sensitive to time delay, the user respectively uninstalls the total task into a plurality of independently executable subtasks. The method is innovative in that the time delay and the energy consumption of unloading are calculated, the additional overhead caused by the interruption of a wireless link is taken into consideration, meanwhile, the calculation capacity of each base station and the energy consumption condition of each CPU period are taken into account, the energy consumption of a system is minimized, and the task load of user unloading is optimized. Compared with the traditional equal task allocation scheme, the scheme remarkably reduces the energy consumption of the system.

Description

Interrupt probability auxiliary task unloading optimization method based on mobile edge calculation
Technical Field
The invention belongs to the technical field of mobile edge calculation, and particularly relates to an outage probability auxiliary task unloading optimization method aiming at reducing total energy consumption of a system.
Background
The technique of moving edge computation (MEC, mobile Edge Computing) plays a very important role in the field where latency is critical. In high-definition video, live video and virtual reality technologies, if a traditional cloud computing method is used, the information transmission time is too long due to the fact that a cloud computing center is far away from a user, and finally poor user experience is caused. The mobile edge computing technology is a solution for the above situation, and by sinking a part of computing power to the mobile edge node, the distance between the user and the cloud server is shortened, the unloading time of the task is reduced, and finally the purpose of reducing the task processing time delay is achieved. However, conventional task offloading schemes may result in additional energy consumption due to unreasonable task allocation, so optimizing the task allocation of a mobile edge computing offloading system, providing a low-energy-consumption task allocation scheme is important.
In the case where there are multiple base stations that can provide mobile edge computing services to users, conventional equal task allocation schemes may allocate excessive offloading tasks to communication links with undesirable channel conditions, and may offload a large number of tasks to edge servers with greater computing power consumption, which may result in additional power consumption for the system. More importantly, the conventional task offloading process does not consider the influence caused by offloading link interruption, so that further research on a multi-base station mobile edge offloading method is required.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide an interruption probability auxiliary task unloading optimization method based on mobile edge calculation so as to solve the problem of high energy consumption of a system in the prior art.
The invention adopts the following technical scheme for solving the problems:
in a first aspect, the present invention provides a method for optimizing task offloading with outage probability, including:
acquiring information of an optional unloading base station and channel state information between a user and all the optional unloading base stations, wherein the information of the optional unloading base station comprises the computing power and the energy consumption of the optional unloading base station;
calculating channel capacity between the user and each optional unloading base station according to the channel state information, and determining information transmission rate when the user unloads subtasks to the optional unloading base station according to channel statistical characteristics, so as to calculate outage probability between the user and each optional unloading base station;
according to the interruption probability and the optional unloading base station information, calculating the unloading time delay t when the user unloads the calculation task to the base station i oi And unloading energy consumption E oi Calculation time delay t for subtask to complete calculation at base station i ci And calculating energy consumption E ci Thereby calculating the total energy consumption E required by the user to unload the calculation task to the base station i and complete the calculation i Obtaining a system total energy consumption function based on the subtask amount unloaded to each optional base station by a user;
and (3) taking the minimization of the total energy consumption function of the system as an optimization target, setting constraint conditions, and carrying out optimization solution to obtain the subtask quantity unloaded by the user to each optional unloading base station.
In some embodiments, the method for calculating the channel capacity includes:
Figure BDA0003685096190000021
wherein C is i Representing the channel capacity between the user and the optional offloading base station i, N represents the total number of optional base stations, and the number of each base station is denoted as i (i=1, 2, …, N); w is the transmission bandwidth of a single offload link; r is (r) i A transmission signal-to-noise ratio of a corresponding unloading link of the base station i; b is the total bandwidth allocated to the user by the system for task unloading; p (P) s The transmission power when the task is unloaded for the user; h is a i Channel fading coefficients between the user and the base station i; sigma (sigma) i 2 Is the white noise power of the corresponding channel.
In some embodiments, calculating outage probabilities between users to various optional offloading base stations includes:
Figure BDA0003685096190000031
wherein P is outi Representing outage probability between user and optional offload base station i, C i Representing channel capacity between a user to an optional offloading base station i, R i Information transmission rate u when offloading subtasks to optional offload base station i for user i For channel fading |h i Variance of I.
In some embodiments, the offloading delay t is calculated when the computing user offloads the computing task to base station i oi And unloading energy consumption E oi Comprising:
Figure BDA0003685096190000032
Figure BDA0003685096190000033
wherein G is i For the amount of subtasks the user offloads to base station i, P outi Representing outage probability between user and optional offload base station i, R i Information transmission rate, P, for user offloading subtasks to optional offload base station i s The transmit power when the task is offloaded for the user.
In some embodiments, the computation subtask completes the computed computation delay t at base station i ci And calculating energy consumption E ci Comprising the following steps:
Figure BDA0003685096190000034
E ci ＝G i ke ci (6)
wherein G is i Subtask amount, a, offloaded for user to base station i i For the computing power of base station i, k is the user task complexity, e ci The unit CPU cycle energy consumption condition of the base station i.
In some embodiments, total system energy consumption E based on the amount of subtasks users offload to each optional offload base station sum The function is expressed as:
Figure BDA0003685096190000041
Figure BDA0003685096190000042
wherein E is i Offloading calculation tasks to base station i for user and completing total energy consumption required for calculation, E oi Unloading energy consumption for unloading computing task to base station i for user, E ci The calculation energy consumption for completing calculation in the base station i for the subtasks is G i For the amount of subtasks the user offloads to base station i, P outi Representing outage probability between user and optional offload base station i, R i Information transmission rate, P, for user offloading subtasks to optional offload base station i s Transmitting power when unloading task for user, k is user task complexity, e ci The unit CPU cycle energy consumption condition of the base station i.
In some embodiments, with the minimization of the total energy consumption function of the system as an optimization target, setting constraint conditions, and performing optimization solution to obtain the subtask amount unloaded by the user to each optional unloading base station, including:
Figure BDA0003685096190000043
Figure BDA0003685096190000044
Figure BDA0003685096190000045
Figure BDA0003685096190000046
wherein s.t. represents constraint conditions, the first constraint condition is task amount constraint, the sum of sub-task amounts unloaded to each base station is equal to the total task amount required to be calculated by a user, G sum N represents the total number of optional base stations for the total task amount that the user needs to calculate; the second constraint condition is a time delay constraint, which limits the time delay of completing each subtask, namely the time of completing the subtask unloaded through each link cannot exceed the maximum time delay T; the third constraint is a subtask constraint, i.e., the amount of tasks per subtask cannot be negative.
In a second aspect, the invention provides an interrupt probability auxiliary task unloading optimization device, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to the first aspect.
In a third aspect, the present invention provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of the first aspect.
Compared with the prior art, the invention has the following technical effects:
the invention takes the additional overhead caused by the link interruption in the unloading process into consideration, and is more suitable for practical application.
The invention comprehensively considers the channel interruption probability condition and the calculation energy consumption condition of the base station, calculates a more reasonable task allocation scheme, and can obviously reduce the system energy consumption compared with an equal task allocation scheme under the same condition.
The invention only carries out the calculation of the optimization problem once for one task, and the unloading rate does not need to be adjusted according to the channel change after the task allocation scheme is calculated.
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FIG. 1 is a system model diagram of an outage probability assisted task offload optimization method based on mobile edge computation in accordance with the present invention.
FIG. 2 is a flow chart of an outage probability assisted task offload optimization method based on mobile edge calculation in accordance with the present invention.
Fig. 3 is a graph of system power consumption versus the number of alternative unloaded base stations for the proposed method.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the following model diagrams, flowcharts and real simulation results. It should be understood that the specific examples described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, the meaning of a number is one or more, the meaning of a number is two or more, and greater than, less than, exceeding, etc. are understood to exclude the present number, and the meaning of a number is understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, the descriptions of the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., mean 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 invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Example 1
An interrupt probability assisted task offload optimization method comprising:
acquiring information of an optional unloading base station and channel state information between a user and all the optional unloading base stations, wherein the information of the optional unloading base station comprises the computing power and the energy consumption of the optional unloading base station;
calculating channel capacity between the user and each optional unloading base station according to the channel state information, and determining information transmission rate when the user unloads subtasks to the optional unloading base station according to channel statistical characteristics, so as to calculate outage probability between the user and each optional unloading base station;
according to the interruption probability and the optional unloading base station information, calculating the unloading time delay t when the user unloads the calculation task to the base station i oi And unloading energy consumption E oi Calculation time delay t for subtask to complete calculation at base station i ci And calculating energy consumption E ci Thereby calculating the total energy consumption E required by the user to unload the calculation task to the base station i and complete the calculation i Obtaining a system total energy consumption function based on the subtask amount unloaded to each optional base station by a user;
and (3) taking the minimization of the total energy consumption function of the system as an optimization target, setting constraint conditions, and carrying out optimization solution to obtain the subtask quantity unloaded by the user to each optional unloading base station.
The overhead due to radio link disruption is taken into account in calculating the offload latency and the offload energy time-consuming. The method comprises the steps of punishing unloading delay and unloading energy consumption to different degrees according to the outage probability of different links, meanwhile, taking account of the calculation capability and the energy consumption condition of a target base station, dividing a total task into a plurality of subtasks, respectively unloading the subtasks to different base stations to finish calculation, and minimizing the total energy consumption of the system under the condition of meeting certain delay constraint.
In some embodiments, as shown in fig. 1, the system includes a user and five optional offloading base stations, each base station is equipped with an MEC server to perform MEC offloading calculation, when the user generates a task and needs MEC assistance calculation, the task is divided into five sub-tasks by adopting an OFDM mode to be offloaded to the corresponding base station respectively, each sub-task is calculated immediately after offloading, and finally the base station returns the sub-task calculation result to the user, and the user performs integration to obtain the original task result.
In some embodiments, as shown in fig. 2, the method includes the steps of:
firstly, acquiring channel state information between a user and each optional unloading base station and computing capacity and energy consumption conditions of each base station before an unloading time slot arrives;
determining the unloading rate of each unloading link according to the received channel state information, thereby further calculating the interruption probability of the corresponding channel;
further, the time delay and the energy consumption of the unloading process are calculated by combining the calculation result of the last step and the acquired base station information, and finally, the energy consumption of each part is summed to represent the energy consumption of the system;
calculating a task unloading scheme with minimum system energy consumption according to the method disclosed by the patent, and unloading subtasks according to the task allocation scheme;
and the base station immediately calculates after receiving the task, and after the MEC server finishes the calculation, the base station returns the result to the user again, and the user integrates the calculation results returned by each base station to obtain the original task result, so that the whole process is finished.
Specific calculation formulas in the important steps are given below, and are described in more detail:
1. and obtaining channel state information of the user to the optional unloading base station, and calculating channel capacity and outage probability according to the channel state information. The channel capacity is calculated as follows:
Figure BDA0003685096190000081
wherein C is i Representing the channel capacity between the user to the optional offloading base station i, N representing the total number of optional base stations, where the number of each base station is denoted i (i=1, 2, …, N); w is the transmission bandwidth of each offload link in hertz (Hz), r i A transmission signal-to-noise ratio of a corresponding unloading link of the base station i; b is the total bandwidth allocated to the user by the system for task unloading, when the user has N base stations for task unloading, an equal bandwidth allocation method is adopted, and the bandwidth allocated to each link is B/N; p (P) s The transmission power when the task is unloaded for the user; h is a i For the channel fading coefficients between the user and the base station i, here, a rayleigh channel is taken as an example, but not limited to, a rayleigh channel, i.e., |h i Obeying the rayleigh distribution; sigma (sigma) i 2 Is the white noise power of the corresponding channel.
Probability of outage P between user and each optional offload base station outi The calculation is as follows:
Figure BDA0003685096190000082
Figure BDA0003685096190000091
wherein R is i Unloading rate for the user determined based on the channel statistics; u (u) i Is Rayleigh fading |h i Variance of I.
2. And acquiring base station information and calculating the energy consumption of the system. The base station information mainly comprises two parts, wherein the first part is the computing power a of the base station i Expressed in cycles of CPU running per second, the second part is the energy consumption e of each base station ci Expressed in terms of energy consumed per CPU cycle, in units of joules per cycle, denoted J/cycle. The above base station information is sent to the user by the corresponding base station before the arrival of the offload slot. Combining the information of the previous step, calculating the unloading of the user to the base station iLoad-off delay t when carrying a computing task oi And unloading energy consumption E oi The calculation is as follows:
Figure BDA0003685096190000092
Figure BDA0003685096190000093
wherein G is i For the user to offload tasks to base station i in bits, noted bit, (1-P in denominator outi ) The probability that normal communication of the wireless path is not interrupted is represented, the higher the interruption probability of the corresponding channel is, the smaller the value is, the larger the time delay required for unloading the subtask of the corresponding link is, and the larger the energy consumption required under the condition that the transmission power of a user is unchanged is.
After the subtasks are unloaded to the corresponding base stations, the base stations complete the calculation of the subtasks, and the subtasks complete the calculated calculation time delay t in the base station i ci And calculating energy consumption E ci The calculation is as follows:
Figure BDA0003685096190000094
E ci ＝G i ke ci
wherein k is the task complexity, namely the CPU cycle number required by each bit of calculation task, the unit is cycles/bit, and the larger the value is, the more complex the task is, the larger the energy consumption is when the calculation is completed in the same base station; a, a i Computing power (number of CPU cycles the base station can run per second) for base station i; e, e ci The energy consumption condition of the base station i in a unit CPU period is that the base station runs the energy consumed by one CPU period.
The subtasks are returned to the user by the base station after the base station finishes the calculation, and the time delay and the energy consumption for returning the results are ignored in the method because the calculation result of the subtasks is usually very simple. Further, the user unloads tasks to the base station i and completes the whole process of calculationEnergy E required i Unloading energy consumption E mainly by tasks oi Energy consumption E calculated with task ci The two parts consist of the following concrete calculation:
Figure BDA0003685096190000101
on this basis, the total system energy consumption for the user to complete the entire computing task is calculated as follows:
Figure BDA0003685096190000102
finally, the interrupt probability auxiliary task unloading optimization method based on the mobile edge calculation is as follows:
Figure BDA0003685096190000103
Figure BDA0003685096190000104
Figure BDA0003685096190000105
Figure BDA0003685096190000106
wherein the first constraint indicates that the sum of the sub-task amounts offloaded to the respective base stations is equal to the total task amount that the user needs to calculate, wherein G sum The total task amount required to be calculated for the user; the second constraint limits the time delay of completion of each subtask, i.e., the completion time of each subtask cannot exceed the maximum time delay T; the third constraint limits the amount of tasks per subtask from being negative.
The invention provides an interrupt probability auxiliary task unloading optimization method based on mobile edge calculation, which is applied to an unloading model of a multi-MEC, can save system energy consumption, and is verified by a specific comparison experiment:
the comparison scheme adopts an equal task allocation scheme, namely, a user unloads equal amounts of subtasks to each selectable unloading base station, and the residual conditions are consistent with the patent adoption method.
The specific simulation parameters are as follows, the transmission power P of a given user s The channel between the user and each optional offloading base station is a Rayleigh channel, 1w, |h i Variance u of I i Obeying a uniform distribution between 0.5 and 1, taking the information transmission rate R of the unloading link according to the statistical characteristics of each channel i White noise power sigma of channel of 3Mbits/s i 2 Is 5 multiplied by 10 -10 w, the total channel bandwidth B allocated by the system for the user is 1MHz, and the computing power a of each base station i Obeys 1 x 10 9 cycles/s to 2X 10 9 Uniform distribution between cycles/s with unit CPU cycle energy consumption e ci Are all 1X 10 -9 J/cycle。
The number N of the corresponding optional unloading base stations increases from 1 to 10, and the total amount G of the unloading tasks of the user sum The method is simulated by MATALB according to the given parameters, wherein 1Mbits is kept unchanged, the task complexity k is 100cycles/bit, the time delay constraint T of the task is 0.45 s. The simulation program calculates ten thousands of times each of the two unloading methods, and maps the average value of ten thousands of simulation results of each method, and the final result is shown in fig. 3. It can be seen that the energy consumption of both task allocation schemes increases with the increase of the number of base stations, because the increase of the number of base stations reduces the bandwidth allocated to each unloading link, the outage probability increases, and finally the energy consumption of the system increases, which is obvious when the number of base stations is large. However, the method provided by the patent increases with the optional unloading base stations, and by comprehensively considering the outage probability and the calculation capability of the target base station, different calculation tasks are distributed to different base stations, so that the energy consumption of the system is obviously reduced, and the energy-saving effect is better as the number of the base stations is larger.
Example 2
In a second aspect, the present embodiment provides an interrupt probability auxiliary task offloading optimization device, including a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is operative according to the instructions to perform the steps of the method according to embodiment 1.
Example 3
In a third aspect, the present embodiment provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method described in embodiment 1.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method or a computer program product. The above examples are only for illustrating the method proposed by the present invention and not for limiting the same, although the above patent method is described in detail by way of examples, it should be understood by those skilled in the art that only the equivalent substitution of the specific implementation of the present invention is possible without changing the nature thereof and that the solution is included in the scope of protection of the claims of the present invention.

Claims (5)

1. An outage probability aided task offload optimization method, comprising the steps of:
acquiring information of an optional unloading base station and channel state information between a user and all the optional unloading base stations, wherein the information of the optional unloading base station comprises the computing power and the energy consumption of the optional unloading base station;
calculating channel capacity between the user and each optional unloading base station according to the channel state information, and determining information transmission rate when the user unloads subtasks to the optional unloading base station according to channel statistical characteristics, so as to calculate outage probability between the user and each optional unloading base station;
according to the interruption probability and the optional unloading base station information, calculating the unloading time delay t when the user unloads the calculation task to the base station i oi And unloading energy consumption E oi Calculation time delay t for subtask to complete calculation at base station i ci And calculating energy consumption E ｃi Thereby calculating the total energy consumption E required by the user to unload the calculation task to the base station i and complete the calculation i Obtaining a system total energy consumption function based on the subtask amount unloaded to each optional base station by a user;
setting constraint conditions by taking minimization of a system total energy consumption function as an optimization target, and carrying out optimization solution to obtain the subtask amount unloaded by a user to each selectable unloading base station;
wherein calculating the outage probability between the user and each of the selectable offload base stations comprises:
Figure FDA0004100090720000011
wherein P is ouｔｉ Representing outage probability between user and optional offload base station i, C i Representing channel capacity between a user to an optional offloading base station i, R i Information transmission rate u when offloading subtasks to optional offload base station i for user i For channel fading |h i Variance of I;
wherein the unloading time delay t when the computing user unloads the computing task to the base station i oi And unloading energy consumption E oi Comprising:
Figure FDA0004100090720000021
Figure FDA0004100090720000022
wherein G is i For the amount of subtasks the user offloads to base station i, P ouｔｉ Representing outage probability between user and optional offload base station i, R i Information transmission rate, P, for user offloading subtasks to optional offload base station i s The transmission power when the task is unloaded for the user;
wherein the calculation sub-task completes the calculation of the calculation time delay t at the base station i ci And calculating energy consumption E ｃi Comprising the following steps:
Figure FDA0004100090720000023
E ci ＝G i ke ci
wherein G is i Subtask amount, a, offloaded for user to base station i i For the computing power of base station i, k is the user task complexity, e ci The energy consumption condition of a unit CPU period of the base station i;
wherein total system energy consumption E is based on the amount of subtasks the user offloads to the various optional base stations ｓum The function is expressed as:
Figure FDA0004100090720000024
Figure FDA0004100090720000025
wherein E is i Offloading calculation tasks to base station i for user and completing total energy consumption required for calculation, E oi Unloading energy consumption for unloading computing task to base station i for user, E ｃi The calculation energy consumption for completing calculation in the base station i for the subtasks is G i For the amount of subtasks the user offloads to base station i, P ouｔｉ Representing outage probability between user and optional offload base station i, R i Information transmission rate, P, for user offloading subtasks to optional offload base station i s Transmitting power when unloading task for user, k is user task complexity, e ci The unit CPU cycle energy consumption condition of the base station i.
2. The outage probability assist task load optimization method according to claim 1, wherein said method for calculating channel capacity comprises:
Figure FDA0004100090720000031
wherein C is i Representing channel capacity between the user to the optional offload base station i, N representing the total number of optional base stations, the number of each base station being noted as i (i=1, 2,., N); w is the transmission bandwidth of a single offload link; r is (r) i A transmission signal-to-noise ratio of a corresponding unloading link of the base station i; b is the total bandwidth allocated to the user by the system for task unloading; p (P) s The transmission power when the task is unloaded for the user; h is a i Channel fading coefficients between the user and the base station i; sigma (sigma) i 2 Is the white noise power of the corresponding channel.
3. The method for optimizing task offloading with the aid of outage probability based on mobile edge calculation according to claim 1, wherein the optimizing solution to obtain the subtask amount offloaded from the user to each optional offloading base station by setting constraint conditions with minimization of a total energy consumption function of the system as an optimization target comprises:
Figure FDA0004100090720000032
Figure FDA0004100090720000033
Figure FDA0004100090720000034
Figure FDA0004100090720000035
wherein s.t. represents constraint conditions, the first constraint condition is task amount constraint, which represents sum of subtask amounts unloaded to each base station and user need to calculateIs equal in total task amount G sum N represents the total number of optional base stations for the total task amount that the user needs to calculate; the second constraint condition is a time delay constraint, which limits the time delay of completing each subtask, namely the time of completing the subtask unloaded through each link cannot exceed the maximum time delay T; the third constraint is a subtask constraint, i.e., the amount of tasks per subtask cannot be negative.
4. The interrupt probability auxiliary task unloading optimizing device is characterized by comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1 to 3.
5. A storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the method of any of claims 1 to 3.
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