CN110941667A - Method and system for calculating and unloading in mobile edge calculation network - Google Patents

Method and system for calculating and unloading in mobile edge calculation network Download PDF

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CN110941667A
CN110941667A CN201911082719.3A CN201911082719A CN110941667A CN 110941667 A CN110941667 A CN 110941667A CN 201911082719 A CN201911082719 A CN 201911082719A CN 110941667 A CN110941667 A CN 110941667A
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unloading
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CN110941667B (en
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林福宏
李慧霖
周成成
陆月明
许海涛
安建伟
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University of Science and Technology Beijing USTB
Beijing University of Posts and Telecommunications
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Abstract

The invention provides a computing unloading method and a computing unloading system in a mobile edge computing network, which can reduce the energy consumption and time delay of user terminal equipment. The method comprises the following steps: decomposing a calculation unloading problem in a mobile edge calculation network into two sub-problems of user unloading decision solving and calculation resource allocation solving; initializing calculation resource allocation; screening unloading decisions according to different time delay requirements of users to obtain a feasible unloading decision set of each user, and determining an optimal unloading decision according to an initialization result; calculating to obtain an optimal calculation resource allocation scheme under the current optimal unloading decision; and substituting the obtained optimal computing resource allocation scheme into an objective function to solve the unloading strategy, judging whether the obtained unloading strategy is the same as the optimal unloading decision, if so, updating the optimal unloading strategy until the optimal unloading strategy is not changed any more or the maximum iteration number is reached, and stopping iteration. The invention relates to the field of Internet of things and artificial intelligence.

Description

Method and system for calculating and unloading in mobile edge calculation network
Technical Field
The invention relates to the field of Internet of things and artificial intelligence, in particular to a computing unloading method and a computing unloading system in a mobile edge computing network.
Background
With the rapid development of wireless communication technology, people's communication means and methods are greatly changed. On one hand, due to the continuous maturity of the 5G technology and the Internet of things technology, a series of brand new communication concepts such as interconnection of everything and mass machine communication are proposed. With the advent of these emerging communication concepts, there is a massive influx of various network devices. Various terminal devices such as smart phones, smart sensors and wearable smart devices need to be connected into the network, which undoubtedly causes huge influence and impact on the existing network. On the other hand, in order to meet different communication demands from machine to machine, and from person to machine, various new communication services are beginning to enter people's lives, such as unmanned technology, virtual reality technology, augmented reality technology, natural language processing technology, and the like. Unlike the conventional communication services, the above technologies all require real-time and fast processing of a large amount of data generated by an application program, and have relatively strict requirements on time delay.
However, the pursuit of mobility for portable communication devices has resulted in a lack of physical size, battery power, and computing power for these mobile communication devices. Therefore, when the computation tasks with the computation intensity and the time delay sensitivity are executed, the phenomena of long computation period, fast battery power consumption and the like may occur, and the experience at the user side is that the running of the application program is unsmooth, the endurance performance of the communication device is poor, that is, the user experience is greatly influenced.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method and a system for offloading computation in a mobile edge computing network, so as to solve the problems of small battery capacity and limited computation capability of a user terminal device in the prior art.
To solve the foregoing technical problem, an embodiment of the present invention provides a method for offloading computation in a mobile edge computing network, including:
s101, decomposing a calculation unloading problem in a mobile edge calculation network into two sub-problems of user unloading decision solving and calculation resource allocation solving;
s102, initializing calculation resource allocation;
s103, screening unloading decisions according to different time delay requirements of users to obtain a feasible unloading decision set of each user, and determining an optimal unloading decision according to an initialization result;
s104, calculating to obtain an optimal calculation resource allocation scheme under the current optimal unloading decision by taking the sum of the minimized time delay and the energy consumption as a target function;
and S105, substituting the obtained optimal computing resource allocation scheme into an objective function to solve the unloading strategy, judging whether the obtained unloading strategy is the same as the optimal unloading decision, if so, updating the optimal unloading strategy, and returning to execute S104 until the optimal unloading strategy is not changed any more or the maximum iteration number is reached, and stopping iteration.
Further, the initializing a computing resource allocation comprises:
each user is assigned an initial value of available computing resources.
Further, the screening the offloading decisions according to different time delay requirements of the users to obtain a feasible offloading decision set of each user, and determining the optimal offloading decision according to the initialization result includes:
calculating the time delay when the user selects different unloading modes, comparing the calculated time delay with the maximum time delay tolerance value of the user, if the calculated time delay is less than or equal to the maximum time delay tolerance value, the unloading mode is feasible, and adding the unloading mode into a feasible unloading decision set of the user; if the delay time is larger than the maximum delay tolerance value, the unloading mode is not feasible;
and determining the optimal unloading decision by taking the sum of the minimized time delay and the energy consumption as an objective function according to the initialization result and the feasible unloading decision in the feasible unloading decision set.
Further, the objective function is:
Figure BDA0002264457700000031
Figure BDA0002264457700000032
Figure BDA0002264457700000033
Figure BDA0002264457700000034
Figure BDA0002264457700000035
Figure BDA0002264457700000036
Figure BDA0002264457700000037
C7:α+β＝1
wherein A represents an unloading decision matrix, if element a in matrix Aij1, then represents the task M of user iiSelecting an unloading mode j for unloading calculation; r represents a calculation resource allocation matrix, and the element R in RijRepresenting a task MiAssigned to task M when executed in offload mode jiS represents an unloading mode set, U represents a user set, and U represents a time delay and a proportion of energy consumption in system overhead, wherein S is {0,1,2.., m }, U is a user set, and α and β represent time delay and a proportion of energy consumption in system overhead respectively;
Figure BDA0002264457700000038
representing a task MiExecution time when executed in offload mode j;
Figure BDA0002264457700000039
representing a task MiEnergy consumption when executed in unloaded mode j;
Figure BDA00022644577000000310
representing a task MiA transmission time of the data;
Figure BDA00022644577000000311
representing a transmission task MiEnergy consumption of (2);
Figure BDA00022644577000000312
representing a task MiA maximum delay tolerance value of;
Figure BDA00022644577000000313
the maximum resource quantity which can be allocated by the system when the unloading mode j is selected for unloading calculation; c1, C2, C3, C4, C5, C6, C7 all represent constraints.
Further, the air conditioner is provided with a fan,
Figure BDA00022644577000000314
expressed as:
Figure BDA00022644577000000315
wherein, CijRepresenting a task MiThe computational resources required when executed in offload mode j.
Further, the air conditioner is provided with a fan,
Figure BDA00022644577000000316
expressed as:
Figure BDA00022644577000000317
where k represents the capacitance coefficient.
Further, the air conditioner is provided with a fan,
Figure BDA00022644577000000318
expressed as:
Figure BDA0002264457700000041
wherein P denotes a power allocation matrix, DijRepresenting a task MiAnd selecting the unloading mode j to carry out unloading calculation, wherein the data quantity needs to be uploaded.
Further, the air conditioner is provided with a fan,
Figure BDA0002264457700000042
expressed as:
Figure BDA0002264457700000043
wherein, the elements in P
Figure BDA0002264457700000044
Represents the transmit power of user i; RATEijTask M representing user iiThe rate of transmission of task data when not executing locally.
Further, RATEijExpressed as:
Figure BDA0002264457700000045
wherein W represents the channel bandwidth; elements of P
Figure BDA0002264457700000046
Represents the transmit power of user l; w represents the noise power; hij、HljRespectively representing the channel gains when the user i and the user l select the unloading mode j; a isljIndicating that the user l selects the unload mode j for the unload calculation.
An embodiment of the present invention further provides a system for offloading computation in a mobile edge computing network, including:
the decomposition module is used for decomposing the calculation unloading problem in the mobile edge calculation network into two sub-problems of user unloading decision solving and calculation resource allocation solving;
an initialization module for initializing allocation of computing resources;
the screening module is used for screening the unloading decisions according to different time delay requirements of users to obtain a feasible unloading decision set of each user and determining an optimal unloading decision according to an initialization result;
the determining module is used for calculating to obtain an optimal calculation resource allocation scheme under the current optimal unloading decision by taking the sum of the minimized time delay and the energy consumption as a target function;
and the optimization module is used for substituting the obtained optimal computing resource allocation scheme into the objective function to solve the unloading strategy, judging whether the obtained unloading strategy is the same as the optimal unloading decision, if so, updating the optimal unloading strategy, and returning to the execution determination module to stop iteration until the optimal unloading strategy is not changed any more or the maximum iteration number is reached.
The technical scheme of the invention has the following beneficial effects:
in the scheme, the calculation unloading problem in the mobile edge calculation network is decomposed into two sub-problems of user unloading decision solving and calculation resource allocation solving; initializing calculation resource allocation; screening unloading decisions according to different time delay requirements of users to obtain a feasible unloading decision set of each user, and determining an optimal unloading decision according to an initialization result; calculating to obtain an optimal calculation resource allocation scheme under the current optimal unloading decision by taking the sum of the minimized time delay and the energy consumption as a target function; and substituting the obtained optimal computing resource allocation scheme into an objective function to solve the unloading strategy, judging whether the obtained unloading strategy is the same as the optimal unloading decision, if so, updating the optimal unloading strategy until the optimal unloading strategy is not changed any more or the maximum iteration number is reached, and stopping iteration. Therefore, by optimizing the calculation unloading strategy in the mobile edge calculation network, the user terminal equipment can unload the calculation task to the edge server which is closer to the user side for execution, thereby reducing the energy consumption (short for energy consumption) and the time delay (short for time delay) of the user terminal equipment.
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Fig. 1 is a flowchart illustrating a computation offloading method in a mobile edge computing network according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a communication structure of a mobile edge computing network according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computing offload system in a mobile edge computing network according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
The invention provides a method and a system for calculating and unloading in a mobile edge calculating network, aiming at the problems of smaller battery capacity and limited calculating capacity of the existing user terminal equipment.
To better understand the computation offload method in the mobile edge computing network according to the embodiment of the present invention, a mobile edge computing architecture is briefly described:
in a mobile edge computing architecture, there are many servers with some computing power deployed at the edge of the network. The user terminal equipment can unload the calculation task to the edge server for execution, the user only needs to provide various parameters required by calculation, the specific calculation process is executed by the edge server, and after a final calculation result is obtained, the edge server feeds back the calculation result to the user. Through the calculation unloading mechanism, the user terminal equipment does not need to process a large amount of data frequently any more, so that the operation burden of the user terminal equipment is reduced, part of energy consumption is reduced, and the endurance performance is improved to a certain extent. In addition, the physical distance between the edge servers and the user terminal equipment is relatively short, the user does not need to transfer the task to a remote cloud computing center any more, and the edge servers can meet most of computing requirements of the user, so that the communication distance is greatly shortened, and the problem of long transmission delay can be well solved.
Example one
As shown in fig. 1, a method for offloading computation in a mobile edge computing network according to an embodiment of the present invention includes:
s101, decomposing a calculation unloading problem in a mobile edge calculation network into two sub-problems of user unloading decision solving and calculation resource allocation solving;
s102, initializing calculation resource allocation;
s103, screening unloading decisions according to different time delay requirements of users to obtain a feasible unloading decision set of each user, and determining an optimal unloading decision according to an initialization result;
s104, calculating to obtain an optimal calculation resource allocation scheme under the current optimal unloading decision by taking the sum of the minimized time delay and the energy consumption as a target function;
and S105, substituting the obtained optimal computing resource allocation scheme into an objective function to solve the unloading strategy, judging whether the obtained unloading strategy is the same as the optimal unloading decision, if so, updating the optimal unloading strategy, and returning to execute S104 until the optimal unloading strategy is not changed any more or the maximum iteration number is reached, and stopping iteration.
The calculation unloading method in the mobile edge calculation network according to the embodiment of the invention decomposes the calculation unloading problem in the mobile edge calculation network into two sub-problems of user unloading decision solving and calculation resource allocation solving; initializing calculation resource allocation; screening unloading decisions according to different time delay requirements of users to obtain a feasible unloading decision set of each user, and determining an optimal unloading decision according to an initialization result; calculating to obtain an optimal calculation resource allocation scheme under the current optimal unloading decision by taking the sum of the minimized time delay and the energy consumption as a target function; and substituting the obtained optimal computing resource allocation scheme into an objective function to solve the unloading strategy, judging whether the obtained unloading strategy is the same as the optimal unloading decision, if so, updating the optimal unloading strategy until the optimal unloading strategy is not changed any more or the maximum iteration number is reached, and stopping iteration. In this way, by optimizing the computation offload policy in the mobile edge computing network, the user terminal device can offload the computation task to the edge server closer to the user side for execution, so that the energy consumption and time delay of the user terminal device can be reduced.
The computation offloading method in the mobile edge computing network provided in this embodiment can be used to deal with challenges brought by computation-intensive and delay-sensitive computation tasks to the computation capability and battery capacity of the user terminal device, and implement low-energy-consumption and low-delay efficient processing of the computation tasks.
In order to better understand the computation offloading method in the mobile edge computing network according to the embodiment of the present invention, the detailed description thereof may specifically include the following steps:
a1, optimizing variable decoupling, decomposing the original calculation unloading problem into two subproblems
In this embodiment, the computation offload problem (two optimization variables, i.e., the user offload decision and the computation resource allocation with low correlation) in the mobile edge computing network is decomposed into two sub-problems, i.e., the user offload decision solution and the computation resource allocation solution, which are respectively solved.
A2, initializing computing resource allocation
In this embodiment, each user is allocated with a feasible initial value of computing resources, for example, existing computing resources are evenly allocated to each user.
A3, screening the unloading decision of each user to obtain the feasible unloading decision set of each user and determining the optimal unloading decision
In this embodiment, screening the offloading decisions according to different time delay requirements of the users to obtain a feasible offloading decision set of each user, and determining an optimal offloading decision according to an initialization result, may specifically include the following steps:
a31, calculating the time delay when the user selects different unloading modes, comparing the calculated time delay with the maximum time delay tolerance value of the user, if the calculated time delay is less than or equal to the maximum time delay tolerance value, the unloading mode is feasible, and adding the unloading mode into the feasible unloading decision set of the user; if the delay time is larger than the maximum delay tolerance value, the unloading mode is not feasible; in this way, a feasible offloading decision set of all users is obtained;
and A32, determining the optimal unloading decision by taking the sum of the minimized time delay and the energy consumption as an objective function according to the initialization result and the feasible unloading decision in the feasible unloading decision set.
In the embodiment, when the user unloading decision is calculated, the feasible unloading decision of each user terminal device is screened in advance according to different time delay requirements of each user terminal device, so that the complexity of the algorithm can be greatly reduced, and the calculation efficiency is improved.
In this embodiment, a communication system of a single base station shown in fig. 2 is taken as an example for explanation:
an edge Server (MEC Server) is deployed at the Base Station (Base Station) side to provide computing service for users. There are n users in the coverage of the base station, and the user set is denoted as U ═ 1,2.., n }; wherein, assuming that each user has a task to perform the off-load calculation, the task of each user can be represented as MiI belongs to U, if the time delay needed by the calculation unloading exceeds the maximum time delay (namely, the maximum time delay tolerance value) T which can be tolerated by the taskMAXThen the unload is considered to have failed.
The inter-device communication can also be used as a means for computation offloading, that is, through the communication means such as inter-device communication among the users, the user with idle computation resources can provide computation service for the user who needs computation offloading, therefore, for each task M that needs to be offloadediFor example, there will be an (m +1) kind of unload mode, denoted as S ═ {0,1,2.., m }; for different unload patterns j, j ∈ S, the explanation is as follows:
when j is 0, it indicates the task MiIs unloaded to the edge server for calculation;
when j is equal to i, it represents task MiPerforming local calculation without unloading;
when j takes other values, the task M representing the user iiIs offloaded to user j for computation.
Based on the unloading mode, the unloading decision of the user is defined as a matrix A of i multiplied by j, i belongs to U and j belongs to S, if an element a in the matrix A belongs to Sij1, then represents the task M of user iiThe unload mode j is selected for unload calculations. Taking the system model of FIG. 2 as an example, it can be seen that user 1(U1) and user 2(U2) choose to offload tasks to edge server execution, user 3(U3) choose to offload tasks to user 2(U2) execution, and user 4(U4) choose to locally execute computing tasks. Thus, in the offload decision matrix of the user, a10,a10,a10,a10,a10,a20,a32,a44Is set to 1. In addition, the computing resource allocation matrix is defined as a matrix R of i x j, i belongs to U and j belongs to S, and the element R in the matrix RijRepresenting a task MiAssigned to task M when executed in offload mode jiThe computing resources of (1). Defining the power distribution matrix as i multiplied by 1, i belongs to the matrix P of U, and the elements in the matrix P
Figure BDA0002264457700000081
Representing the transmit power of user i.
Task M when user iiWhen executed in offload mode j, the execution time is expressed as:
Figure BDA0002264457700000082
wherein, CijRepresenting a task MiThe computational resources required to be executed in the offload mode j are the number of CPU cycles occupied by the computation task.
Task MiThe energy consumption when the offload mode j is executed is:
Figure BDA0002264457700000091
wherein k >0 is the capacitance coefficient.
Computing task M when user iiIs executed in an offload mode j, j ≠ i, i.e., when the task is not executed locally, it is requiredTo transmit task data, the transmission RATE RATEijIs represented as follows:
Figure BDA0002264457700000092
wherein W represents the channel bandwidth;represents the transmit power of user i; elements of P
Figure BDA0002264457700000094
Represents the transmit power of user l; w represents the noise power; hij、HljRespectively representing the channel gains when the user i and the user l select the unloading mode j; a isljRepresenting that the user l selects the unloading mode j to carry out unloading calculation;
Figure BDA0002264457700000095
representing interference between users using the same channel.
After the task is executed, the calculation result needs to be returned to the user who initiated the offload request, but the data volume of the calculation result is negligible compared with the input data, so the system overhead may not be counted in the downlink data transmission process. The transmission time of the task data is as follows:
Figure BDA0002264457700000096
wherein P denotes a power allocation matrix, DijRepresenting a task MiAnd selecting the unloading mode j to carry out unloading calculation, wherein the data quantity needs to be uploaded.
The energy consumption of the transmission task is as follows:
Figure BDA0002264457700000097
combining the above analysis, the objective function can be expressed as:
Figure BDA0002264457700000101
Figure BDA0002264457700000102
Figure BDA0002264457700000103
Figure BDA0002264457700000104
Figure BDA0002264457700000105
Figure BDA0002264457700000106
Figure BDA0002264457700000107
C7:α+β＝1
wherein A represents an unloading decision matrix, if element a in matrix Aij1, then represents the task M of user iiSelecting an unloading mode j for unloading calculation; r represents a calculation resource allocation matrix, and the element R in RijRepresenting a task MiAssigned to task M when executed in offload mode jiS represents an unloading mode set, U represents a user set, and U represents a time delay and a proportion of energy consumption in system overhead, wherein S is {0,1,2.., m }, U is a user set, and α and β represent time delay and a proportion of energy consumption in system overhead respectively;
Figure BDA0002264457700000108
representing a task MiExecution time when executed in offload mode j;
Figure BDA0002264457700000109
representing a task MiIs executed in an unload mode jEnergy consumption in time;
Figure BDA00022644577000001010
representing a task MiA transmission time of the data;
Figure BDA00022644577000001011
representing a transmission task MiEnergy consumption of (2);
Figure BDA00022644577000001012
representing a task MiA maximum delay tolerance value of;
Figure BDA00022644577000001013
the maximum resource quantity which can be allocated by the system when the unloading mode j is selected for unloading calculation; c1, C2, C3, C4, C5, C6, C7 all represent constraints.
In this embodiment, C1 ensures that every task will be executed; c2 indicates that each user can only accept one computing task at a time; c3 indicates that the resulting allocation of computing resources cannot exceed
Figure BDA00022644577000001014
C4 and C5 represent compute tasks M if user iiThe method comprises the steps of executing in an unloading mode j, allocating computing resources for the unloading mode j, C6, if computing unloading is carried out by a user, meeting a time delay condition, wherein α and β in C7 respectively represent the proportion of time delay and energy consumption in system overhead, and the sum of the time delay and the energy consumption is 1.
A4, calculating to obtain an optimal calculation resource allocation scheme under the current optimal unloading decision by taking the sum of the minimized time delay and the energy consumption as an objective function;
in this embodiment, the obtained current optimal offloading decision is substituted into the objective function and the constraint inequality, a convex optimization means is adopted, a non-convex problem which is difficult to solve is converted into a quasi-convex problem or other forms which are easy to solve, and an optimal computing resource allocation scheme under the current optimal offloading decision is obtained through calculation.
And A5, substituting the obtained optimal calculation resource allocation scheme into the objective function to solve the unloading strategy, judging whether the obtained unloading strategy is the same as the optimal unloading decision, if so, updating the optimal unloading strategy, and returning to execute A4 until the optimal unloading strategy is not changed any more or the maximum iteration number is reached, and stopping iteration.
In this embodiment, the iterative update is performed on the offloading decision and the allocation of the computing resources through an iterative heuristic algorithm until the optimal offloading policy does not change any more, which indicates that the current solution is the optimal solution of the original problem, i.e., the optimal computing offloading scheme, and the iteration is ended.
It is to be emphasized that:
the calculation unloading method in the mobile edge calculation network provided by the embodiment of the invention is not only suitable for a communication system consisting of a single base station and a single edge server, but also suitable for a communication system consisting of a plurality of base stations and a plurality of edge servers; and each user may have multiple tasks that require offload computation.
Example two
The present invention further provides a specific embodiment of a computing offloading system in a mobile edge computing network, and since the computing offloading system in the mobile edge computing network provided by the present invention corresponds to the specific embodiment of the computing offloading method in the mobile edge computing network, and the computing offloading system in the mobile edge computing network can achieve the object of the present invention by executing the flow steps in the specific embodiment of the method, the explanation in the specific embodiment of the computing offloading method in the mobile edge computing network is also applicable to the specific embodiment of the computing offloading system in the mobile edge computing network provided by the present invention, and will not be described again in the following specific embodiment of the present invention.
As shown in fig. 3, an embodiment of the present invention further provides a computing offload system in a mobile edge computing network, including:
the decomposition module 11 is configured to decompose a computation offload problem in the mobile edge computing network into two sub-problems, namely, a user offload decision solution and a computation resource allocation solution;
an initialization module 12 for initializing the allocation of computing resources;
the screening module 13 is configured to screen the offloading decisions according to different time delay requirements of the users to obtain a feasible offloading decision set of each user, and determine an optimal offloading decision according to an initialization result;
the determining module 14 is configured to calculate, with the sum of the minimum time delay and the energy consumption as an objective function, to obtain an optimal calculation resource allocation scheme under the current optimal offloading decision;
and the optimization module 15 is configured to substitute the obtained optimal computing resource allocation scheme into an objective function to solve the unloading strategy, determine whether the obtained unloading strategy is the same as the optimal unloading decision, update the optimal unloading strategy if the obtained unloading strategy is different from the optimal unloading decision, and return to the execution determination module to stop the iteration until the optimal unloading strategy is not changed any more or the maximum iteration number is reached.
The computation unloading system in the mobile edge computing network of the embodiment of the invention decomposes the computation unloading problem in the mobile edge computing network into two sub-problems of user unloading decision solving and computation resource allocation solving; initializing calculation resource allocation; screening unloading decisions according to different time delay requirements of users to obtain a feasible unloading decision set of each user, and determining an optimal unloading decision according to an initialization result; calculating to obtain an optimal calculation resource allocation scheme under the current optimal unloading decision by taking the sum of the minimized time delay and the energy consumption as a target function; and substituting the obtained optimal computing resource allocation scheme into an objective function to solve the unloading strategy, judging whether the obtained unloading strategy is the same as the optimal unloading decision, if so, updating the optimal unloading strategy until the optimal unloading strategy is not changed any more or the maximum iteration number is reached, and stopping iteration. In this way, by optimizing the computation offload policy in the mobile edge computing network, the user terminal device can offload the computation task to the edge server closer to the user side for execution, so that the energy consumption and time delay of the user terminal device can be reduced.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (10)

1. A method of computation offload in a mobile edge computing network, comprising:
s101, decomposing a calculation unloading problem in a mobile edge calculation network into two sub-problems of user unloading decision solving and calculation resource allocation solving;
s102, initializing calculation resource allocation;
s103, screening unloading decisions according to different time delay requirements of users to obtain a feasible unloading decision set of each user, and determining an optimal unloading decision according to an initialization result;
s104, calculating to obtain an optimal calculation resource allocation scheme under the current optimal unloading decision by taking the sum of the minimized time delay and the energy consumption as a target function;
and S105, substituting the obtained optimal computing resource allocation scheme into an objective function to solve the unloading strategy, judging whether the obtained unloading strategy is the same as the optimal unloading decision, if so, updating the optimal unloading strategy, and returning to execute S104 until the optimal unloading strategy is not changed any more or the maximum iteration number is reached, and stopping iteration.
2. The method of claim 1, wherein initializing allocation of computing resources comprises:
each user is assigned an initial value of available computing resources.
3. The method of claim 1, wherein the screening offloading decisions according to different latency requirements of users to obtain a set of feasible offloading decisions for each user, and determining an optimal offloading decision according to an initialization result comprises:
calculating the time delay when the user selects different unloading modes, comparing the calculated time delay with the maximum time delay tolerance value of the user, if the calculated time delay is less than or equal to the maximum time delay tolerance value, the unloading mode is feasible, and adding the unloading mode into a feasible unloading decision set of the user; if the delay time is larger than the maximum delay tolerance value, the unloading mode is not feasible;
and determining the optimal unloading decision by taking the sum of the minimized time delay and the energy consumption as an objective function according to the initialization result and the feasible unloading decision in the feasible unloading decision set.
4. The method of claim 1, wherein the objective function is:
Figure FDA0002264457690000021
s.t.C1:
Figure FDA0002264457690000022
C2:
Figure FDA0002264457690000023
C3:
Figure FDA0002264457690000024
C4:
Figure FDA0002264457690000025
C5:
Figure FDA0002264457690000026
C6:
Figure FDA0002264457690000027
C7:α+β＝1
wherein A represents an unloading decision matrix, if element a in matrix Aij1, then represents the task M of user iiSelecting an unloading mode j for unloading calculation; r represents a calculation resource allocation matrix, and the element R in RijRepresenting a task MiAssigned to task M when executed in offload mode jiS represents an unloading mode set, U represents a user set, and U represents a time delay and a proportion of energy consumption in system overhead, wherein S is {0,1,2.., m }, U is a user set, and α and β represent time delay and a proportion of energy consumption in system overhead respectively;
Figure FDA0002264457690000028
representing a task MiExecution time when executed in offload mode j;
Figure FDA0002264457690000029
representing a task MiEnergy consumption when executed in unloaded mode j;
Figure FDA00022644576900000210
representing a task MiA transmission time of the data;
Figure FDA00022644576900000211
representing a transmission task MiEnergy consumption of (2); t isi MAXRepresenting a task MiA maximum delay tolerance value of;
Figure FDA00022644576900000212
the maximum resource quantity which can be allocated by the system when the unloading mode j is selected for unloading calculation; c1, C2, C3, C4, C5, C6, C7 all represent constraints.
5. Method for computation offload in a mobile edge computing network according to claim 4, characterized in thatIn that,
Figure FDA00022644576900000213
expressed as:
Figure FDA00022644576900000214
wherein, CijRepresenting a task MiThe computational resources required when executed in offload mode j.
6. The method of computing offload in a mobile edge computing network according to claim 5,
Figure FDA00022644576900000215
expressed as:
Figure FDA00022644576900000216
where k represents the capacitance coefficient.
7. The method of computing offload in a mobile edge computing network according to claim 6,
Figure FDA0002264457690000031
expressed as:
Figure FDA0002264457690000032
wherein P denotes a power allocation matrix, DijRepresenting a task MiAnd selecting the unloading mode j to carry out unloading calculation, wherein the data quantity needs to be uploaded.
8. The method of computing offload in a mobile edge computing network of claim 7,
Figure FDA0002264457690000033
expressed as:
Figure FDA0002264457690000034
wherein, the elements in P
Figure FDA0002264457690000035
Represents the transmit power of user i; RATEijTask M representing user iiThe rate of transmission of task data when not executing locally.
9. Method for computation offload in a mobile edge computing network according to claim 7 or 8, characterized in that RATEijExpressed as:
Figure FDA0002264457690000036
wherein W represents the channel bandwidth; elements of P
Figure FDA0002264457690000037
Represents the transmit power of user l; w represents the noise power; hij、HljRespectively representing the channel gains when the user i and the user l select the unloading mode j; a isljIndicating that the user l selects the unload mode j for the unload calculation.
10. A computing offload system in a mobile edge computing network, comprising:
the decomposition module is used for decomposing the calculation unloading problem in the mobile edge calculation network into two sub-problems of user unloading decision solving and calculation resource allocation solving;
an initialization module for initializing allocation of computing resources;
the screening module is used for screening the unloading decisions according to different time delay requirements of users to obtain a feasible unloading decision set of each user and determining an optimal unloading decision according to an initialization result;
the determining module is used for calculating to obtain an optimal calculation resource allocation scheme under the current optimal unloading decision by taking the sum of the minimized time delay and the energy consumption as a target function;
and the optimization module is used for substituting the obtained optimal computing resource allocation scheme into the objective function to solve the unloading strategy, judging whether the obtained unloading strategy is the same as the optimal unloading decision, if so, updating the optimal unloading strategy, and returning to the execution determination module to stop iteration until the optimal unloading strategy is not changed any more or the maximum iteration number is reached.
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