CN115150891A - 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 PDFInfo
- Publication number
- CN115150891A CN115150891A CN202210643725.7A CN202210643725A CN115150891A CN 115150891 A CN115150891 A CN 115150891A CN 202210643725 A CN202210643725 A CN 202210643725A CN 115150891 A CN115150891 A CN 115150891A
- Authority
- CN
- China
- Prior art keywords
- base station
- user
- task
- unloading
- optional
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses an interruption probability auxiliary task unloading optimization method based on mobile edge calculation, and aims to reduce energy consumption in a user task unloading process. When the calculation task generated by the user cannot be locally completed or the task is sensitive to time delay, the user divides the total task into a plurality of subtasks which can be independently executed and respectively unloads the subtasks. The method is innovative in that the time delay and the time consumption of unloading are calculated, the extra expense caused by interruption of a wireless link is taken into consideration, the calculation capacity of each base station and the energy consumption condition of each CPU (central processing unit) cycle are considered, the aim of minimizing the system energy consumption is taken, and the task unloading amount of a user is optimized. Compared with the traditional equal task allocation scheme, the scheme has the advantage that the system energy consumption is remarkably reduced.
Description
Technical Field
The invention belongs to the technical field of mobile edge calculation, and particularly relates to an interrupt probability assisted task unloading optimization method aiming at reducing total energy consumption of a system.
Background
The Mobile Edge Computing (MEC) technique plays an important role in the field with strict requirements on delay. In high-definition video, live video and virtual reality technologies, if a traditional cloud computing method is used, the information transmission time may be too long due to the fact that a cloud computing center is far away from a user, and finally the user experience is poor. The mobile edge computing technology is a solution to the above situation, and sinks a part of computing power to a mobile edge node, so that the distance between a user and a cloud server is shortened, the unloading time of a task is reduced, and the purpose of reducing the task processing delay is finally achieved. However, conventional task offloading schemes may result in additional energy consumption due to unreasonable task allocation, and therefore it is important to optimize task allocation for mobile edge computing offloading systems to provide a low-energy task allocation scheme.
In the case where multiple base stations can provide mobile edge computing services to users, the traditional equal-task allocation scheme may allocate too many offload tasks to communication links with undesirable channel conditions, or may offload a large number of tasks to edge servers with high computational power consumption, which may cause additional power consumption for the system. More importantly, the influence caused by unloading link interruption is not considered in the traditional task unloading process, so that further research needs to be carried out on the multi-base-station mobile edge unloading method.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide an interrupt probability assisted task unloading optimization method based on mobile edge calculation so as to solve the problem of high system energy consumption in the prior art.
In order to solve the problems, the invention adopts the following technical scheme:
in a first aspect, the present invention provides an interrupt probability assisted task offloading optimization method, including:
acquiring information of optional unloading base stations and channel state information from a user to all the optional unloading base stations, wherein the information of the optional unloading base stations comprises the computing capacity and the energy consumption condition of the optional unloading base stations;
calculating the channel capacity between the user and each optional unloading base station according to the channel state information, and determining the information transmission rate when the user unloads the sub-tasks to the optional unloading base stations according to the channel statistical characteristics, thereby calculating the interruption 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 And the calculation time delay t of the calculation of the subtask completed at the base station i ci And calculating energy consumption E ci So as to calculate 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 from the user to each optional unloading base station;
and (4) setting constraint conditions by taking the minimization of the total energy consumption function of the system as an optimization target, and performing 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:
wherein C is i Representing the channel capacity from the user to the optional unloading base station i, N representing the total number of the optional base stations, and recording the number of each base station as i (i =1,2, \8230;, N); w is the transmission bandwidth of a single unloading link; r is i The transmission signal-to-noise ratio of the unloading link corresponding to the base station i; b, allocating the total bandwidth for the user to carry out task unloading for the system; zxfoom P s Transmit power when offloading tasks for a user; h is i Is the channel fading coefficient between the user and the base station i; sigma i 2 Is the white noise power of the corresponding channel.
In some embodiments, calculating the outage probability between the user and each optional offload base station comprises:
wherein P is outi Representing the probability of interruption between the user to the optional offload base station i, C i Indicating the channel capacity, R, between the user and the optional offload base station i i Offloading a supervisor to an optional offload base station i for a userInformation transmission rate in service u i For channel fading | h i The variance of l.
In some embodiments, an offload delay t is calculated for a user offloading a computing task to base station i oi And unloading energy consumption E oi The method comprises the following steps:
wherein G is i For the amount of subtasks, P, offloaded by a user to base station i outi Representing the probability of interruption, R, between a user to an optional offload base station i i For the information transmission rate, P, at which the user unloads the subtasks to the optional offload base station i s Transmit power when offloading tasks for the user.
In some embodiments, the computation subtask completes the computed computation delay t at the base station i ci And calculating energy consumption E ci The method comprises the following steps:
E ci ＝G i ke ci (6)
wherein G is i For the amount of subtasks a user offloads to base station i i For the computing power of base station i, k is the user task complexity, e ci Is a unit CPU of a base station i periodic energy consumption situations.
In some embodiments, the total energy consumption E of the system based on the amount of subtasks offloaded by the user to the various optional offload base stations sum The function is represented as:
wherein, E i Total energy consumption, E, required for the user to offload computation tasks to base station i and complete the computation oi Offloading energy consumption for user offloading computing tasks to base station i, E ci Calculation energy consumption, G, for subtasks to complete the calculation at base station i i For the amount of subtasks, P, offloaded by a user to base station i outi Representing the probability of interruption, R, between a user to an optional offload base station i i For the information transmission rate, P, at which the user unloads the subtasks to the optional offload base station i s Transmit power when offloading tasks for a user, k user task complexity, e ci Is a unit CPU of a base station i periodic energy consumption situations.
In some embodiments, the method for optimizing the number of the sub-tasks unloaded from the user to each optional unloading base station by using the total system energy consumption function minimization as an optimization target includes:
wherein s.t. represents a constraint condition, the first constraint condition is a task quantity constraint, which represents that the sum of the sub-task quantities unloaded to each base station is equal to the total task quantity required to be calculated by the user, and G sum The total task quantity required to be calculated for the user is N, and the total number of the optional base stations is represented by N; the second constraint is a delay constraint,the time delay of each subtask completion is limited, namely the completion time of the subtask unloading through each link cannot exceed the maximum time delay T; the third constraint is the subtask constraint, i.e. the task volume per subtask cannot be negative.
In a second aspect, the present invention provides an interrupt probability assisted task offloading optimization apparatus, including 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 invention provides a storage medium having stored thereon a computer program which, when executed by a processor, performs 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 extra expense brought by the link interruption in the unloading process into consideration, and is more suitable for practical application.
The invention comprehensively considers the situation of the channel interruption probability and the situation of the calculated energy consumption of the base station, and calculates a more reasonable task allocation scheme, and compared with the equal task allocation scheme under the same condition, the scheme can obviously reduce the energy consumption of the system.
The invention only carries out one-time calculation of the optimization problem for one task, and does not need to adjust the unloading rate according to the channel change after calculating the task allocation scheme, and the calculation amount and the complexity of the method are lower.
Drawings
FIG. 1 is a system model diagram of an interruption probability-assisted task offloading optimization method based on mobile edge calculation according to the present invention.
FIG. 2 is a flowchart of an interruption probability assisted task offloading optimization method based on mobile edge calculation according to the present invention.
Fig. 3 is a graph of system energy consumption versus the number of optional offload base stations for the proposed method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the following model diagrams, flow charts and actual simulation results. It should be understood that the specific examples described herein are intended only to illustrate the invention and are not intended to limit the invention.
In the description of the present invention, the meaning of a plurality is one or more, the meaning of a plurality is two or more, and the above, below, exceeding, etc. are understood as excluding the present numbers, and the above, below, within, etc. are understood as including the present numbers. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood 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, reference to the description of the terms "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Example 1
An interruption probability assisted task offloading optimization method includes:
acquiring optional unloading base station information and channel state information from a user to all optional unloading base stations, wherein the optional unloading base station information comprises the calculation capacity and the energy consumption condition of the optional unloading base stations;
calculating the channel capacity between the user and each optional unloading base station according to the channel state information, and determining the information transmission rate when the user unloads the sub-tasks to the optional unloading base stations according to the channel statistical characteristics, thereby calculating the interruption 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 And the calculation time delay t of the calculation of the subtask completed at the base station i ci And calculating energy consumption E ci So as to calculate 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 from the user to each optional unloading base station;
and (4) setting constraint conditions by taking the minimization of the total energy consumption function of the system as an optimization target, and performing optimization solution to obtain the subtask quantity unloaded by the user to each optional unloading base station.
The additional overhead caused by the interruption of the wireless link is considered when the unloading delay and the unloading energy consumption are calculated. The specific method is that different punishments are carried out on unloading delay and unloading energy consumption according to the interruption probability of different links, meanwhile, the calculation capability and the energy consumption condition of the target base station are considered, the total task is divided into a plurality of subtasks to be respectively unloaded to different base stations to complete calculation, and the total energy consumption minimization of the system is realized under the condition that certain delay constraint is met.
In some embodiments, as shown in fig. 1, the system includes a user and five optional offload base stations, each base station is equipped with an MEC server to perform MEC offload computation, when a task generated by the user requires MEC auxiliary computation, the task is divided into five subtasks and offloaded to the corresponding base station respectively in an OFDM manner, after each subtask is offloaded, computation is performed immediately, and finally, the base station returns the subtask computation result to the user, and the user integrates the result 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 the calculation capacity and energy consumption condition of each base station before the arrival of an unloading time slot;
determining the unloading rate of each unloading link according to the received channel state information, and further calculating the interruption probability of the corresponding channel;
further, the time delay and the energy consumption in the unloading process are calculated by integrating the calculation result of the previous 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 provided by the patent, and unloading subtasks according to the task distribution scheme;
and the base station immediately performs calculation after receiving the task, when the MEC server completes the calculation, the base station returns the result to the user again, and the user integrates the calculation results returned by the base stations to obtain the result of the original task, so that the whole process is finished.
The specific calculation formula in the important steps is given below and explained in more detail:
1. and acquiring the channel state information from the user to the optional unloading base station, and calculating the channel capacity and the interruption probability according to the channel state information. The channel capacity is calculated as follows:
wherein C is i Indicating the channel capacity between the user and the optional offload base station i, N indicating the total number of optional base stations, where the number of each base station is denoted as i (i =1,2, \8230;, N); w is the transmission bandwidth of each offload link in Hertz (Hz), r i The transmission signal-to-noise ratio of the unloading link corresponding to the base station i; b is the total bandwidth which is allocated to the user by the system for task unloading, when the user has N base stations which can be used for task unloading, an equal bandwidth allocation method is adopted, and the bandwidth allocated to each link is B/N; p s Transmit power when offloading tasks for a user; h is i Is the channel fading coefficient between the user and the base station i, which is taken as an example of the rayleigh channel here, but is not limited to the rayleigh channel, i.e. | h i I obeys Rayleigh distribution; sigma i 2 Is the white noise power of the corresponding channel.
Probability of interruption P between user and each optional unloading base station outi The calculation is as follows:
wherein R is i The user offloading rate determined from the channel statistics; u. of i Is Rayleigh fading | h i The variance of | is given.
2. And acquiring the 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 capacity a of the base station i Expressed in cycles/s as the number of CPU cycles running per second, the second component is the energy consumption situation e for each base station ci Expressed as energy consumed per CPU cycle, it is expressed in joules/cycle and is denoted as J/cycle. The base station information is sent to the user by the corresponding base station before the unloading time slot arrives. Combining the information of the last step, 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 The calculation is as follows:
wherein G is i The unit of the task amount unloaded from the user to the base station i is bit, which is recorded as bit, (1-P) in the denominator outi ) The probability of uninterrupted normal communication of a radio channel is represented, the higher the interruption probability of a corresponding channel is, the smaller the value is, the larger the time delay required by a corresponding link offloading sub-task is, and the larger the energy consumption required under the condition that the transmission power of a user is not changed is.
After the subtask is unloaded to the corresponding base station, the base station completes the calculation of the subtask, and the calculation time delay t of the calculation of the subtask is completed at the base station i ci And calculating energy consumption E ci The calculation is as follows:
E ci ＝G i ke ci
k is task complexity, namely the number of CPU cycles required by each bit of calculation task is in cycles/bit, and the larger the value of k is, the more complex the task is, the larger the energy consumption is when the same base station completes calculation; a is i Is the computational power of base station i (the number of CPU cycles per second a base station can run); e.g. of the type ci Is the energy consumption condition of unit CPU cycle of the base station i, i.e. the energy consumed by the base station for operating one CPU cycle each time.
After the calculation of the subtask is completed by the base station, the base station returns the calculation result to the user, and because the calculation result of the subtask is usually very simple, the delay and the energy consumption of the returned result are ignored in the method. Further, the user unloads the task to the base station i and completes the calculated energy E required by the whole process i Energy consumption mainly by task offloading E oi And task computing energy consumption E ci The two parts are composed by the following specific calculation:
on the basis, the total energy consumption of the system for the user to complete the whole calculation task is calculated as follows:
finally, the interruption probability assisted task unloading optimization method based on the mobile edge calculation is as follows:
wherein, the first constraint condition indicates that the sum of the subtask amount unloaded to each base station should be equal to the total task amount required to be calculated by the user, wherein G sum The total task amount required to be calculated for the user; the second constraint conditions limit the time delay of the completion of each subtask, namely the completion time of each subtask cannot exceed the maximum time delay T; the third constraint limits the task volume of each subtask to not be negative.
The invention provides an interruption probability auxiliary task unloading optimization method based on mobile edge calculation, which is applied to an unloading model of multiple MECs and can save the energy consumption of a system, and the effect of the invention is verified through a specific comparison experiment as follows:
the comparison scheme adopts an equal task allocation scheme, namely, the user unloads equal sub-tasks to each optional unloading base station, and the residual conditions are consistent with the patent adoption method.
The specific simulation parameters are as follows, the transmit power P of a given user s Is 1w, the channel between the user and each optional offload base station is a rayleigh channel, | h i I variance u i The information transmission rate R of the unloading link is taken according to the statistical characteristics of each channel according to the uniform distribution between 0.5 and 1 i 3Mbits/s, white noise power σ of the channel i 2 Is 5 x 10 -10 w, the total channel bandwidth B allocated to the user by the system is 1MHz, and the computing power a of each base station i Obey 1 x 10 9 cycles/s to 2X 10 9 Uniform distribution between cycles/s with energy consumption e per CPU cycle ci Are all 1 × 10 -9 J/cycle。
Corresponding number of optional offload base stationsThe number N is increased from 1 to 10, and the total number G of the unloading tasks of the user sum Keeping 1Mbits unchanged, setting the task complexity k as 100cycles/bit and the time delay constraint T of the task as 0.45s, and simulating the method by using MATALB according to the given parameters. The simulation program calculates ten thousand times of each of the two unloading methods, and the average value of ten thousand simulation results of each method is plotted, and the final result is shown in fig. 3. It can be seen from the figure that the energy consumption of the two 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 offloading link, the probability of interruption increases, and finally the energy consumption of the system increases, which is very obvious when the number of base stations is large. However, the method provided by the patent increases with the number of optional uninstalled base stations, and allocates different calculation tasks to different base stations by comprehensively considering the outage probability and the calculation capacity of the target base station, so that the system energy consumption is obviously reduced, and the energy saving effect is better when the number of the base stations is larger.
Example 2
In a second aspect, the present embodiment provides an interrupt probability assisted task offloading optimization apparatus, including a processor and a storage medium;
the storage medium is to store instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method of 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, performs the steps of the method of embodiment 1.
As will be appreciated by one skilled in the art, 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 computer program product. Although the method of the present invention has been described in detail by way of examples, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. An interruption probability assisted task offloading optimization method, comprising:
acquiring information of optional unloading base stations and channel state information from a user to all the optional unloading base stations, wherein the information of the optional unloading base stations comprises the computing capacity and the energy consumption condition of the optional unloading base stations;
calculating the channel capacity between the user and each optional unloading base station according to the channel state information, and determining the information transmission rate when the user unloads the sub-tasks to the optional unloading base stations according to the channel statistical characteristics, thereby calculating the interruption 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 And the calculation time delay t of the calculation of the subtask completed at the base station i ci And calculating energy consumption E ci So as to calculate 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 from the user to each optional unloading base station;
and (4) setting constraint conditions by taking the minimization of the total energy consumption function of the system as an optimization target, and performing optimization solution to obtain the subtask quantity unloaded by the user to each optional unloading base station.
2. The outage probability assisted task offloading optimization method of claim 1, wherein the channel capacity calculation method comprises:
wherein C is i Representing the channel capacity from the user to the optional unloading base station i, N representing the total number of the optional base stations, and recording the number of each base station as i (i =1,2, \8230;, N); w is the transmission bandwidth of a single unloading link; r is i The transmission signal-to-noise ratio of the unloading link corresponding to the base station i; b is the total bandwidth which is allocated to the user by the system for task unloading; p s Transmit power when offloading tasks for a user; h is i Is the channel fading coefficient between the user and the base station i; sigma i 2 Is the white noise power of the corresponding channel.
3. The outage probability assisted task offloading optimization method of claim 2, wherein calculating outage probabilities between users and various optional offloading base stations comprises:
wherein P is outi Representing the probability of interruption, C, between the user and the optional offload base station i i Indicating the channel capacity, R, between the user and the optional offload base station i i For the information transmission rate u when the user unloads the subtasks to the optional unloading base station i i For channel fading | h i The variance of | is given.
4. The outage probability-aided task offloading optimization method based on mobile edge computing (MHC) of claim 1, wherein the offloading time delay t when the user offloads the computing task to the base station i is computed oi And unloading energy consumption E oi The method comprises the following steps:
wherein G is i For the amount of subtasks, P, offloaded by a user to base station i outi Representing the probability of interruption, R, between a user to an optional offload base station i i Offloading information in subtasks to optional offload base station i for userTransmission rate, P s Transmit power when offloading tasks for the user.
5. The outage probability assisted task offloading optimization method of claim 1, wherein computing the computing time delay t for a subtask to complete computation at base station i ci And calculating energy consumption E ci The method comprises the following steps:
E ci ＝G i ke ci (6)
wherein G is i For the amount of subtasks a user offloads 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 is shown.
6. The outage probability assisted task offloading optimization method of claim 1, wherein total system energy consumption E is based on an amount of subtasks offloaded by a user to each optional offloading base station sum The function is represented as:
wherein E is i Total energy consumption, E, required for the user to offload computation tasks to base station i and complete the computation oi Offloading energy consumption for offloading computing tasks to base station i for users, E ci Calculation energy consumption, G, for subtasks to complete the calculation at base station i i For the amount of subtasks, P, offloaded by a user to base station i outi Representing the probability of interruption, R, between a user to an optional offload base station i i Information transfer for user in offloading subtasks to optional offload base station iRate of delivery, P s Transmit power when offloading tasks for a user, k user task complexity, e ci Is the unit CPU cycle energy consumption situation of the base station i.
7. The method for optimizing task offloading based on outage probability calculation of claim 6, wherein the method for optimizing task offloading based on mobile edge calculation sets constraint conditions with a system total energy consumption function minimization as an optimization objective, and performs optimization solution to obtain the subtask amount offloaded by the user to each optional offloading base station, comprises:
wherein, s.t. represents constraint condition, the first constraint condition is task quantity constraint, which represents that the sum of sub-task quantities unloaded to each base station should be equal to the total task quantity required to be calculated by user, G sum The total task quantity required to be calculated for the user is N, and the total number of the optional base stations is represented by N; the second limiting condition is a time delay constraint, and the time delay for completing each subtask is limited, namely the time for completing the subtask for unloading through each link cannot exceed the maximum time delay T; the third constraint is the subtask constraint, i.e. the task volume per subtask cannot be negative.
8. An interrupt probability assisted task offloading optimization device, comprising 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 any one of claims 1 to 7.
9. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, performing the steps of the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210643725.7A CN115150891B (en) | 2022-06-09 | 2022-06-09 | Interrupt probability auxiliary task unloading optimization method based on mobile edge calculation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210643725.7A CN115150891B (en) | 2022-06-09 | 2022-06-09 | Interrupt probability auxiliary task unloading optimization method based on mobile edge calculation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115150891A true CN115150891A (en) | 2022-10-04 |
| CN115150891B CN115150891B (en) | 2023-04-21 |
Family
ID=83407904
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210643725.7A Active CN115150891B (en) | 2022-06-09 | 2022-06-09 | Interrupt probability auxiliary task unloading optimization method based on mobile edge calculation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115150891B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115866691A (en) * | 2023-02-27 | 2023-03-28 | 南京邮电大学 | Task unloading method for heterogeneous network mobile edge computing system |
| CN116249143A (en) * | 2023-05-11 | 2023-06-09 | 南京邮电大学 | Time delay optimization method and system for unmanned aerial vehicle auxiliary movement edge calculation |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111988806A (en) * | 2020-09-01 | 2020-11-24 | 重庆邮电大学 | Mobile edge computing network resource allocation method based on full-duplex relay |
| CN112004239A (en) * | 2020-08-11 | 2020-11-27 | 中国科学院计算机网络信息中心 | Computing unloading method and system based on cloud edge cooperation |
| CN112399482A (en) * | 2020-10-09 | 2021-02-23 | 西北工业大学 | Mobile edge calculation method, storage medium and equipment |
-
2022
- 2022-06-09 CN CN202210643725.7A patent/CN115150891B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112004239A (en) * | 2020-08-11 | 2020-11-27 | 中国科学院计算机网络信息中心 | Computing unloading method and system based on cloud edge cooperation |
| CN111988806A (en) * | 2020-09-01 | 2020-11-24 | 重庆邮电大学 | Mobile edge computing network resource allocation method based on full-duplex relay |
| CN112399482A (en) * | 2020-10-09 | 2021-02-23 | 西北工业大学 | Mobile edge calculation method, storage medium and equipment |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115866691A (en) * | 2023-02-27 | 2023-03-28 | 南京邮电大学 | Task unloading method for heterogeneous network mobile edge computing system |
| CN115866691B (en) * | 2023-02-27 | 2023-06-20 | 南京邮电大学 | Task unloading method for heterogeneous network mobile edge computing system |
| CN116249143A (en) * | 2023-05-11 | 2023-06-09 | 南京邮电大学 | Time delay optimization method and system for unmanned aerial vehicle auxiliary movement edge calculation |
| CN116249143B (en) * | 2023-05-11 | 2023-09-01 | 南京邮电大学 | Time delay optimization method and system for unmanned aerial vehicle auxiliary movement edge calculation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115150891B (en) | 2023-04-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108509276B (en) | Video task dynamic migration method in edge computing environment | |
| JP7162385B2 (en) | Multi-User Multi-MEC Task Unload Resource Scheduling Method Based on Edge-Terminal Collaboration | |
| CN115150891A (en) | Interrupt probability auxiliary task unloading optimization method based on mobile edge calculation | |
| CN109951869B (en) | Internet of vehicles resource allocation method based on cloud and mist mixed calculation | |
| CN111949409B (en) | Method and system for unloading computing task in power wireless heterogeneous network | |
| CN110489176B (en) | Multi-access edge computing task unloading method based on boxing problem | |
| CN110519370B (en) | Edge computing resource allocation method based on facility site selection problem | |
| CN110740473A (en) | management method for mobile edge calculation and edge server | |
| CN111935677B (en) | Internet of vehicles V2I mode task unloading method and system | |
| CN112153145A (en) | Method and device for unloading calculation tasks facing Internet of vehicles in 5G edge environment | |
| CN113315669B (en) | Cloud edge cooperation-based throughput optimization machine learning inference task deployment method | |
| CN112231085A (en) | Mobile terminal task migration method based on time perception in collaborative environment | |
| CN115237580B (en) | Intelligent calculation-oriented flow parallel training self-adaptive adjustment system and method | |
| CN105450784B (en) | The device and method of message distribution consumption node into MQ | |
| CN115473896A (en) | Electric power internet of things unloading strategy and resource configuration optimization method based on DQN algorithm | |
| CN110167031B (en) | Resource allocation method, equipment and storage medium for centralized base station | |
| CN110780986B (en) | Internet of things task scheduling method and system based on mobile edge computing | |
| CN110266757B (en) | Edge computing resource optimization method for task collaborative scene | |
| CN113747450B (en) | Service deployment method and device in mobile network and electronic equipment | |
| CN107203256B (en) | Energy-saving distribution method and device under network function virtualization scene | |
| CN113778675A (en) | Calculation task distribution system and method based on block chain network | |
| CN117931459A (en) | Elasticity evaluation method and system for computing power resources | |
| CN112486685B (en) | Computing task distribution method and device of electric power Internet of things and computer equipment | |
| Chen et al. | Joint optimization of task caching, computation offloading and resource allocation for mobile edge computing | |
| CN111580943B (en) | Task scheduling method for multi-hop unloading in low-delay edge calculation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |