CN113961266B - Task unloading method based on bilateral matching under edge cloud cooperation - Google Patents
Task unloading method based on bilateral matching under edge cloud cooperation Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/445—Program loading or initiating
- G06F9/44594—Unloading
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- 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
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- 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
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
A task unloading method based on bilateral matching under the cooperation of edge cloud comprises the following steps of; 1) Acquiring parameters of an edge server; 2) Acquiring parameters in a task unloading request; 3) Calculating the time for unloading the task to the edge server and the time for unloading the task to the cloud server; 4) Constructing a satisfaction function of the task and the server, and performing initial unloading matching; 5) And carrying out bilateral matching of the best satisfaction degree on the tasks and all the edge servers and the cloud servers. Satisfaction function value. According to the method and the system, the situation that the server simultaneously carries out unloading processing on a plurality of tasks is considered, reasonable price games are carried out on the user tasks so as to price the user tasks, the calculation efficiency of the side/cloud server on the unloading tasks can be effectively improved, and the service quality of the system is effectively improved.
Description
Technical Field
The invention relates to a task unloading method, in particular to a task unloading method based on bilateral matching under the cooperation of edge clouds.
Background
Clouds are widely used because of their powerful computing and storage capabilities. Because of limited network bandwidth, offloading large amounts of data to the cloud center can cause backhaul link congestion and even paralysis. The cloud center is far away from the mobile user end, and for some delay-sensitive tasks, the delay of long-distance transmission of the cloud center is unacceptable to the user. In order to solve the above-described problem, a moving edge calculation is proposed. Mobile edge computing extends the cloud's capabilities to the edges of the network, providing nearby services near the data source, providing faster response demands for the network. The user may choose the offloading policy of the task by himself, but when multiple users choose to offload the task to the same edge server at the same time, this may also result in high latency for the user's task. Therefore, in the current 'end-side-cloud' collaborative background, how to reasonably offload tasks to a server by a user, so that the user time delay limit can be met, and the satisfaction degree of the user and the side/cloud server can be maximized is an important point and difficulty.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a multi-task unloading method based on bilateral matching and game pricing. The method can reasonably price the user, improves the calculation efficiency of the server on the task unloading, and effectively improves the satisfaction degree of the user and the side/cloud server.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
a task unloading method based on bilateral matching under the cooperation of edge cloud comprises the following steps of;
1) Acquiring parameters of the edge servers, wherein the parameters comprise clock frequency, total channel bandwidth of each edge server, channel bandwidth allocated to a user by the edge server and data rate of task unloading to the edge server; the total channel bandwidth of each edge server is constant, and the channel bandwidth allocated to the user by the edge server is variable;
2) Acquiring the size D of task data in a task offloading request m Maximum allowable delay for completing the task, transmission power of user m, preference information of user m for time and price; preference information of time and price satisfies alpha m1 +α m2 ＝1，α m1 For the weight of user m to delay, alpha m2 The weight of the price for the user m;
3) Calculating the time for unloading the task to the edge server and the time for unloading the task to the cloud server;
3.1 Calculating a total time for offloading tasks to the edge server;
3.2 Calculating the total time for offloading tasks to the cloud server;
4) Constructing a satisfaction function of the task and the server, and performing initial unloading matching;
4.1 Initializing and unloading matching of tasks according to satisfaction function values;
satisfaction function valueT m，n Time delay offloaded to the server for user tasks, < >>Receiving the lowest price of a task for a server, wherein kappa is a weight value of time delay in a user satisfaction function, and gamma is a weight value of price in the user satisfaction function; the specific values of kappa and gamma are determined by the users themselves, and kappa and gamma of each user are different;
4.2 The user sorts the servers according to the satisfaction function value, and the user sends an unloading request to the server with the highest current function value for matching initialization, namely, the user initially selects the edge server or the cloud server for initial matching, and no interference is caused in the initial matching;
4.3 Judging whether the server with the highest current satisfaction function value meets the user requirement or not; meeting the demand for step 4.4), and not meeting the demand for step 4.5); the user requirements are whether the offloading of the user tasks to the current server meets the maximum latency constraints of the user, i.eAnd the cost P paid by the user for offloading tasks to the server umn Whether the server is acceptable, i.e. +.> Maximum allowable delay for completing a task;
4.4 The user and the server both reach an initial matching result;
4.5 The user selects the next server in the satisfaction function value ordering to send an unloading request, and the step 4.3) is carried out;
5) And carrying out bilateral matching of the best satisfaction degree on the tasks and all the edge servers and the cloud servers.
In the invention, the total time of unloading to the edge server comprises transmission time and execution time, and the total timeThe transmission time is +.>The execution time is->
r mn In order for the transmission to be efficient,
W mn the channel bandwidth allocated to user m for edge server n,
P m for the transmission power of user m, H mn For task U m Channel gain performed in edge server N, N being background noise power of the server;
I mn for interference of the influence of other tasks on the edge server n on the current task m,X m decision to offload tasks for user m, a is integer variable, X a For the decision of unloading the task of the user a, judging whether the user a and the user m are unloaded to the same side server, H an Channel gain offloaded to edge server n for task a;
C m to complete task U m F is the total CPU cycles of n Is the clock frequency of edge server n.
In the invention, the total time for offloading the tasks to the cloud server
Wherein the method comprises the steps ofTime for task to be transmitted by user to edge node, < >>For the time of task upload from edge node to cloud server over core network,/for the time of task upload from edge node to cloud server over core network>The execution time of the cloud server;
uploading the data transmission rate of the task to the cloud for user m,/->Transmission speed f allocated to user m for core network to cloud server stage cloud Is the clock frequency of the cloud server.
In the present invention, the specific steps of the step 5) include: 5.1 Updating the bandwidth allocation of the edge server according to the initial matching result of the user; when the edge server is offloaded with k tasks, each task divides all bandwidth of the edge server equally, i.e5.2 Updating task unloading time delay, and updating task unloading time delay according to the bandwidth allocation of the step 5.1);
5.3 Updating the satisfaction function values of the user on all the servers, and sorting; the user selects the server with the highest current function value to send an unloading request;
5.4 Judging whether the server selected in the step 5.3) has a matching object; carrying out step 5.5) with matched objects, and carrying out step 5.6) without matched objects;
5.5 All matchers connected with the server participate in the price game to obtain the suggested price of the current user;
the user price is higher than the suggested price or the user accepts the suggested price and the suggested price is higher than the lowest price of the server, the server accepts the task unloading matching request, otherwise, the server refuses the task unloading matching request;
after receiving the task unloading matching request, the server recalculates the task unloading time delay, and if the maximum time delay limit of the user is met, the matching is kept, so that an optimal satisfaction bilateral matching result is obtained; otherwise, returning to the step 4) to carry out matching again;
5.6 User price higher than the lowest price of the serverThe server receives the task unloading matching request; the price of the user is lower than the lowest price of the server +.>The server feeds back suggested prices to the users, and the users calculate the satisfaction degree of the current server by using the suggested prices and sort the satisfaction degree;
after the user receives the suggested price of the server, the server still is the optimal server, and the server receives the task unloading matching request; otherwise the server refuses the matching request.
In the invention, the price game comprises the following steps:
5.5.1 Constructing satisfaction function matrixes of the server and all matched objects of the server;
5.5.2 Calculating satisfaction function mean values of all matched objects of the server;
5.5.3 Judging whether the satisfaction function value of the server on the current user is higher than the satisfaction function mean value obtained in the previous step; if the average value is higher than the average value, turning to the step 5.5.4); otherwise, turning to step 5.5.5);
5.5.4 The server suggests the price provided for the user, namely the price of the user is unchanged, and the server receives a task unloading matching request; 5.5.5 Server suggests the price as the mean of the satisfaction function for the remaining matching objects.
Compared with the prior art, the invention has the advantages that: according to the invention, the situation that the server simultaneously unloads and processes a plurality of tasks is considered, and reasonable price games are carried out on the user tasks so as to price the user tasks; the computing efficiency of the side/cloud server on the task unloading can be effectively improved, and the service quality of the system is effectively improved.
Drawings
Fig. 1 is a basic framework diagram of a task offloading method based on bilateral matching under Bian Yun cooperation in the present invention.
Fig. 2 is a flowchart of a task offloading method based on bilateral matching under Bian Yun cooperation in the present invention.
FIG. 3 is a flow chart of steps 1) to 3) in example 1.
FIG. 4 is a flow chart of user initiated offloading of tasks in accordance with the present invention.
FIG. 5 is a flow chart of step 5) in example 1;
FIG. 6 is a flow chart of a user price game of embodiment 1;
FIG. 7 is a flow chart of a delay check phase;
fig. 8 is a diagram of the final allocated bandwidth and task offloading decision scenario.
Detailed Description
The present invention will be described more fully hereinafter with reference to the preferred embodiments for the purpose of facilitating understanding of the present invention, but the scope of protection of the present invention is not limited to the specific embodiments described below.
It will be understood that when an element is referred to as being "fixed, affixed, connected, or in communication with" another element, it can be directly fixed, affixed, connected, or in communication with the other element or intervening elements may be present.
Unless defined otherwise, all technical and scientific terms used hereinafter have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the scope of the present invention.
Example 1
A task offloading method based on bilateral matching under the cooperation of Bian Yun shown in fig. 1 and fig. 2, comprising the following steps;
1) Acquiring parameters of an edge server and a cloud server, including clock frequency f of each edge server m Total channel bandwidth w n Channel bandwidth w allocated to users by edge server mn Data rate r of task offloading to edge server mn The method comprises the steps of carrying out a first treatment on the surface of the The total channel bandwidth of each edge server is constant and the channel bandwidth allocated to the user by the edge server is variable. The parameters of the cloud server comprise the clock frequency f of the cloud server cloud Upstream bandwidth of cloud serverChannel gain of cloud server->User m upload task data transfer rate to cloud +.>Transmission rate allocated to user m in core network-to-cloud server stage
2) Acquiring the size D of task data in a task offloading request m The number of cycles of the CPU (central processingunit ) required for task processing (bits), the maximum allowable delay for completing the task, the transmission power of user m, the preference information of user m for time and price; preference information of time and price satisfies alpha m1 +α m2 ＝1，α m1 For the weight of user m to delay, alpha m2 The weight of user m on price.
3) The time when the task is offloaded to the edge server and the time when the task is offloaded to the cloud server are calculated.
3.1 Total time of task offloading to edge server including transfer time and execution time, total timeThe transmission time is +.>The execution time is->
r mn In order for the transmission to be efficient,
W mn the channel bandwidth allocated to user m for edge server n,
P m for the transmission power of user m, H mn For task U m Channel gain performed in edge server N, N being background noise power of the server;
I mn for interference of the influence of other tasks on the edge server n on the current task m,X m decision to offload tasks for user m, a is integer variable, X a For the decision of unloading the task of the user a, judging whether the user a and the user m are unloaded to the same side server, H an Channel gain offloaded to edge server n for task a;
C m to complete task U m F is the total CPU cycles of n Is the clock frequency of edge server n.
3.2 Calculating the total time for offloading tasks to the cloud server;
total time of task offloading to cloud serverWherein->Time for task to be transmitted by user to edge node, < >>For the time of task upload from edge node to cloud server over core network,/for the time of task upload from edge node to cloud server over core network>The execution time of the cloud server;
uploading the data transmission rate of the task to the cloud for user m,/->Transmission speed f allocated to user m for core network to cloud server stage cloud Is the clock frequency of the cloud server. The flow chart of steps 1) -3) is shown in fig. 3.
4) Constructing a satisfaction function of the task and the server, and performing initial unloading matching; the specific steps are shown in fig. 4.
4.1 Initializing and unloading matching of tasks according to satisfaction function values;
satisfaction function valueT m，n The latency of offloading user tasks to the server,accepting a minimum price for the task for the server; wherein kappa is a weight value of time delay in a user satisfaction function, and gamma is a weight value of price in the user satisfaction function; the specific values of kappa and gamma are determined by the user themselves, with kappa and gamma being different for each user.
4.2 The user sorts the servers according to the satisfaction function value, and sorting methods such as bubbling sorting, inserting sorting, quick sorting and the like can be used; the server with the largest satisfaction function value is arranged at the first position of the sequence, the server with the second function value is arranged at the second position, and so on. And the user sends an unloading request to the server with the highest current function value for carrying out matching initialization, namely the user initially selects the edge server or the cloud server for carrying out initial matching, and no interference influence is caused in the initial matching, namely no other tasks on the edge server and the cloud server are operated.
4.3 Judging whether the server with the highest current satisfaction function value meets the user requirement or not; meeting the demand for step 4.4), and not meeting the demand for step 4.5); the user requirements are whether the offloading of the user tasks to the current server meets the maximum latency constraints of the user, i.eAnd the cost P paid by the user for offloading tasks to the server umn Whether the server is acceptable, i.e. +.> To achieve maximum allowable delay for the task.
4.4 The user and the server both achieve an initial matching result.
4.5 The user selects the next server in the satisfaction function value order to send an unloading request, and the step 4.3 is performed).
5) And carrying out optimal satisfaction bilateral matching on the task and all the edge servers and the cloud servers, namely considering the optimal bilateral matching under the condition of interference, as shown in fig. 5.
5.1 Updating the bandwidth allocation of the edge server according to the initial matching result of the user; when the edge server is offloaded with k tasks, each task divides all bandwidth of the edge server equally, i.eW is the bandwidth of the server;
5.2 Update task offload latency: updating task unloading delay according to the bandwidth allocation of the step 5.1);
5.3 Updating the satisfaction function values of the user on all the servers, and sorting; the user selects the server with the highest current function value to send an unloading request;
5.4 Judging whether the server selected in the step 5.3) has a matching object; step E) is carried out with matched objects, and step 5.6) is carried out without matched objects;
5.5 All matched participant prices connected with the server are game, and the suggested price of the current user is obtained;
the user price is higher than the suggested price or the user accepts the suggested price, and the server accepts the task unloading matching request; otherwise, the server refuses the task unloading matching request;
as shown in fig. 7, after receiving the task unloading matching request, the server recalculates the task unloading time delay, and if the maximum time delay limit of the user is met, the matching is kept, so as to obtain an optimal satisfaction bilateral matching result; otherwise, returning to the step 4) to carry out matching again.
5.6 User price higher than the lowest price of the serverThe server receives the task unloading matching request; the price of the user is lower than the lowest price of the server +.>The server feeds back suggested prices to the users, and the users calculate the satisfaction degree of the current server by using the suggested prices and sort the satisfaction degree;
after the user receives the suggested price of the server, the server still is an optimal server, and the server receives the task unloading matching request to obtain an optimal satisfaction bilateral matching result; otherwise the server refuses the matching request.
6) All that is next done is to calculate the final bandwidth each server allocates to the user based on the optimal satisfaction bilateral matching results and to determine the user's final offloading decision, as shown in fig. 8. Specifically comprising steps 6.1) to 6.4), wherein: and 6.1), obtaining the best satisfaction bilateral matching result capable of meeting the time delay requirement through the steps.
Step 6.2), calculating the total number of tasks unloaded to each edge server according to the matching result.
Step 6.3), the tasks on the same edge server equally divide the total bandwidth of the edge server, and calculate the bandwidth allocated by the tasks. And updating the offloading decisions for these tasks to the edge server number.
And 6.4), distributing bandwidth to the task unloaded to the cloud server according to the task size, and updating the unloading decision of the task to the cloud server.
In this embodiment, the price game is shown in fig. 6, and includes the following steps:
5.5.1 A satisfaction function matrix of the server and all matching objects of the server is constructed.
5.5.2 Calculating satisfaction function mean values of all matched objects of the server;
5.5.3 Judging whether the satisfaction function value of the server on the current user is higher than the satisfaction function mean value obtained in the previous step; if the average value is higher than the average value, turning to the step 5.5.4); otherwise, turning to step 5.5.5);
5.5.4 The server suggests the price provided for the user, namely the price of the user is unchanged, and the server receives a task unloading matching request; 5.5.5 Server suggests the price as the mean of the satisfaction function for the remaining matching objects.
According to the invention, the situation that the server simultaneously unloads and processes a plurality of tasks is considered, and reasonable price games are carried out on the user tasks so as to price the user tasks; the computing efficiency of the side/cloud server on the task unloading can be effectively improved, and the service quality of the system is effectively improved.
Claims (5)
1. A task unloading method based on bilateral matching under the cooperation of edge cloud is characterized in that: comprises the following steps of;
1) Acquiring parameters of the edge servers, wherein the parameters comprise clock frequency, total channel bandwidth of each edge server, channel bandwidth allocated to a user by the edge server and data rate of task unloading to the edge server; the total channel bandwidth of each edge server is constant, and the channel bandwidth allocated to the user by the edge server is variable;
2) Acquiring the size D of task data in a task offloading request m Maximum allowable delay for completing the task, transmission power of user m, preference information of user m for time and price; preference information of time and price satisfies alpha m1 +α m2 ＝1，α m1 For the weight of user m to delay, alpha m2 The weight of the price for the user m;
3) Calculating the time for unloading the task to the edge server and the time for unloading the task to the cloud server;
3.1 Calculating a total time for offloading tasks to the edge server;
3.2 Calculating the total time for offloading tasks to the cloud server;
4) Constructing a satisfaction function of the task and the server, and performing initial unloading matching;
4.1 Initializing and unloading matching of tasks according to satisfaction function values;
satisfaction function valueT m,n Time delay offloaded to the server for user tasks, < >>Receiving the lowest price of a task for a server, wherein kappa is a weight value of time delay in a user satisfaction function, and gamma is a weight value of price in the user satisfaction function;
4.2 The user sorts the servers according to the satisfaction function value, and the user sends an unloading request to the server with the highest current function value for matching initialization, namely, the user initially selects the edge server or the cloud server for initial matching, and no interference is caused in the initial matching;
4.3 Judging whether the server with the highest current satisfaction function value meets the user requirement or not; meeting the demand for step 4.4), and not meeting the demand for step 4.5); the user requirements are whether the offloading of the user tasks to the current server meets the maximum latency constraints of the user, i.eAnd the cost P paid by the user for offloading tasks to the server umn Whether the server is acceptable, i.e. +.> Maximum allowable delay for completing a task;
4.4 The user and the server both reach an initial matching result;
4.5 The user selects the next server in the satisfaction function value ordering to send an unloading request, and the step 4.3) is carried out;
5) And carrying out bilateral matching of the best satisfaction degree on the tasks and all the edge servers and the cloud servers.
2. The method for task offloading based on bilateral matching under the cooperation of Bian Yun according to claim 1, wherein the method comprises the steps of: the total time of unloading to the edge server comprises transmission time and execution time, and the total timeThe transmission time is +.>The execution time is->
r mn In order for the transmission to be efficient,
W mn the channel bandwidth allocated to user m for edge server n,
P m for the transmission power of user m, H mn For task U m Channel gain performed in edge server N, N being background noise power of the server;
I mn for interference of the influence of other tasks on the edge server n on the current task m,X m decision to offload tasks for user m, a is integer variable, X a For the decision of unloading the task of the user a, judging whether the user a and the user m are unloaded to the same side server, H an Channel gain offloaded to edge server n for task a;
C m to complete task U m F is the total CPU cycles of n Is the clock frequency of edge server n.
3. The method for task offloading based on bilateral matching under the cooperation of Bian Yun according to claim 1, wherein the method comprises the steps of: total time for offloading the task to cloud server
Wherein the method comprises the steps ofTime for task to be transmitted by user to edge node, < >>For the time of task upload from edge node to cloud server over core network,/for the time of task upload from edge node to cloud server over core network>The execution time of the cloud server;
uploading the data transmission rate of the task to the cloud for user m,/->Transmission speed f allocated to user m for core network to cloud server stage cloud Is the clock frequency of the cloud server.
4. The method for task offloading based on bilateral matching under the cooperation of Bian Yun according to claim 1, wherein the method comprises the steps of: the specific steps of the step 5) comprise: 5.1 Updating the bandwidth allocation of the edge server according to the initial matching result of the user; when the edge server is offloaded with k tasks, each task divides all bandwidth of the edge server equally, i.e
5.2 Updating task unloading time delay, and updating task unloading time delay according to the bandwidth allocation of the step 5.1);
5.3 Updating the satisfaction function values of the user on all the servers, and sorting; the user selects the server with the highest current function value to send an unloading request;
5.4 Judging whether the server selected in the step 5.3) has a matching object; carrying out step 5.5) with matched objects, and carrying out step 5.6) without matched objects;
5.5 All matchers connected with the server participate in the price game to obtain the suggested price of the current user;
the user price is higher than the suggested price or the user accepts the suggested price and the suggested price is higher than the lowest price of the server, the server accepts the task unloading matching request, otherwise, the server refuses the task unloading matching request;
after receiving the task unloading matching request, the server recalculates the task unloading time delay, and if the maximum time delay limit of the user is met, the matching is kept, so that an optimal satisfaction bilateral matching result is obtained; otherwise, returning to the step 4) to carry out matching again;
5.6 User price higher than the lowest price of the serverThe server receives the task unloading matching request; the price of the user is lower than the lowest price of the server +.>The server feeds back suggested prices to the users, and the users calculate the satisfaction degree of the current server by using the suggested prices and sort the satisfaction degree;
after the user receives the suggested price of the server, the server still is the optimal server, and the server receives the task unloading matching request; otherwise the server refuses the matching request.
5. The method for task offloading based on bilateral matching under the cooperation of Bian Yun as claimed in claim 4, wherein: the price game comprises the following steps:
5.5.1 Constructing satisfaction function matrixes of the server and all matched objects of the server;
5.5.2 Calculating satisfaction function mean values of all matched objects of the server;
5.5.3 Judging whether the satisfaction function value of the server on the current user is higher than the satisfaction function mean value obtained in the previous step; if the average value is higher than the average value, turning to the step 5.5.4); otherwise, turning to step 5.5.5);
5.5.4 The server suggests the price provided for the user, namely the price of the user is unchanged, and the server receives a task unloading matching request;
5.5.5 Server suggests the price as the mean of the satisfaction function for the remaining matching objects.
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