CN113783801A - Bandwidth resource allocation method and system based on alliance game - Google Patents
Bandwidth resource allocation method and system based on alliance game Download PDFInfo
- Publication number
- CN113783801A CN113783801A CN202111015613.9A CN202111015613A CN113783801A CN 113783801 A CN113783801 A CN 113783801A CN 202111015613 A CN202111015613 A CN 202111015613A CN 113783801 A CN113783801 A CN 113783801A
- Authority
- CN
- China
- Prior art keywords
- bandwidth
- optimal
- allocation scheme
- bandwidth allocation
- archive set
- 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
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000013468 resource allocation Methods 0.000 title claims abstract description 34
- 238000004590 computer program Methods 0.000 claims description 16
- 238000009826 distribution Methods 0.000 claims description 15
- 238000004891 communication Methods 0.000 claims description 10
- 230000006870 function Effects 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 9
- 238000003860 storage Methods 0.000 claims description 9
- 239000002245 particle Substances 0.000 description 25
- 230000008569 process Effects 0.000 description 10
- 238000005457 optimization Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 8
- 238000004422 calculation algorithm Methods 0.000 description 6
- 239000013598 vector Substances 0.000 description 6
- 230000008901 benefit Effects 0.000 description 5
- 230000007246 mechanism Effects 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 3
- 238000005265 energy consumption Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000003190 augmentative effect Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/70—Admission control; Resource allocation
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/70—Admission control; Resource allocation
- H04L47/76—Admission control; Resource allocation using dynamic resource allocation, e.g. in-call renegotiation requested by the user or requested by the network in response to changing network conditions
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention belongs to the field of bandwidth resource allocation, and provides a bandwidth resource allocation method and a system based on alliance game, which are used for dividing users to be allocated with bandwidth resources into a plurality of alliances and generating an initial global optimal archive set, an initial local optimal archive set and a plurality of initial bandwidth allocation schemes; updating the bandwidth allocation scheme based on the update rule, the optimal archive set and the local optimal archive set; for each updated bandwidth allocation scheme, calculating the dissatisfaction of all alliances, and updating the global optimal archive set and the local optimal archive set by using pareto ordering and congestion entropy; judging whether an end condition is met, and if so, selecting an optimal bandwidth allocation scheme; otherwise, returning to the bandwidth allocation scheme for continuing updating; by summarizing the problem of bandwidth resource allocation with league gaming, bandwidth allocations are found that maximize the utility of the system.
Description
Technical Field
The invention belongs to the technical field of bandwidth resource allocation, and particularly relates to a bandwidth resource allocation method and system based on alliance game.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the rapid development of 5G networks and internet of things, many emerging applications (e.g., augmented reality, virtual reality, and autonomous driving) become more popular, which often have high requirements on latency and energy consumption. Since the user equipment computing resources and storage space often cannot meet the delay and energy consumption requirements of such tasks, Multi-access edge computing (MEC) is proposed and considered as a promising solution, which is a hot topic in the communication field in recent years. Multi-access edge computing is considered a promising technology, and the concept of edge computing is based on cloud computing. Cloud computing refers to decomposing a huge data computing processing program into countless small programs through a network cloud, and then processing and analyzing the small programs through a system consisting of a plurality of servers to obtain results and returning the results to a user. Edge computing differs from cloud computing in that the servers of the edge computing are distributed at the edge of the network near the users. The method solves the problem of insufficient computing resources of user equipment by deploying the edge server close to the edge of the mobile network of the user, and the user unloads the task to the edge server for execution, so that a large amount of computing resources are obtained, the resources are stored, and the execution of the task is accelerated.
Because the types of resources in the edge system are very limited compared to cloud computing, the inefficient resource allocation manner cannot alleviate the conflict between the limited resources and the delay-sensitive task. To alleviate the above-mentioned conflicts, research on resource allocation in mobile edge computing has become attractive. Therefore, how to efficiently allocate computing and communication resources to achieve performance optimization of the whole system is a very critical issue.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a bandwidth resource allocation method and system based on a league game, wherein the league game is used for summarizing the problem of bandwidth resource allocation, and the league game can maximize the system utility and find a bandwidth allocation scheme for maximizing the system utility.
In order to achieve the purpose, the invention adopts the following technical scheme:
the first aspect of the present invention provides a bandwidth resource allocation method based on league gaming, which includes:
dividing users to be allocated with bandwidth resources into a plurality of alliances, and generating an initial global optimal archiving set, an initial local optimal archiving set and a plurality of initial bandwidth allocation schemes;
updating the bandwidth allocation scheme based on the update rule, the global optimal archive set and the local optimal archive set;
for each updated bandwidth allocation scheme, calculating the dissatisfaction of all alliances, and updating the global optimal archive set and the local optimal archive set by using pareto ordering and congestion entropy;
judging whether an end condition is met, and if so, selecting an optimal bandwidth allocation scheme; otherwise, returning to the bandwidth allocation scheme to continue updating.
Further, in the updating process of the bandwidth allocation scheme, if the total bandwidth in the updated bandwidth allocation scheme exceeds the maximum bandwidth, the bandwidth is reallocated in proportion.
Further, the dissatisfaction degree is calculated by the following steps:
for each bandwidth allocation scheme, calculating the utility of each user task;
calculating the value of each alliance according to the users in each alliance and the utility of each user task;
based on the value of each league, the dissatisfaction of each league is calculated by using a Shaapril distribution mode.
Further, the value of the federation is determined by the relationship of the communication delay of the user tasks in the federation to the task deadline.
Further, the specific steps of updating the global optimal archive set and the local optimal archive set by using the pareto sorting and the congestion entropy are as follows:
carrying out pareto sorting on the existing bandwidth allocation schemes in the global optimal archive set and the local optimal archive set and the newly generated bandwidth allocation schemes based on the dissatisfaction degrees of all alliances of each bandwidth allocation scheme;
if the newly generated bandwidth allocation scheme dominates the existing bandwidth allocation scheme, the newly generated bandwidth allocation scheme is used as a global optimal archive set and a local optimal archive set; if the two are mutually non-dominated, calculating congestion entropy, and selecting a bandwidth allocation scheme according to the congestion entropy sequence to be used as a global optimal archive set and a local optimal archive set; if the newly generated bandwidth allocation scheme is dominated, no processing is done.
Further, the congestion entropy considers both the congestion distance and the distribution entropy, and is used for estimating the solution density in the objective function space.
A second aspect of the present invention provides a league gaming-based bandwidth resource allocation system, which includes:
an initialization module configured to: dividing users to be allocated with bandwidth resources into a plurality of alliances, and generating an initial global optimal archiving set, an initial local optimal archiving set and a plurality of initial bandwidth allocation schemes;
a schema update module configured to: updating the bandwidth allocation scheme based on the update rule, the global optimal archive set and the local optimal archive set;
an archive set update module configured to: for each updated bandwidth allocation scheme, calculating the dissatisfaction of all alliances, and updating the global optimal archive set and the local optimal archive set by using pareto ordering and congestion entropy;
a best case selection module configured to: judging whether an end condition is met, and if so, selecting an optimal bandwidth allocation scheme; otherwise, returning to the bandwidth allocation scheme to continue updating.
A third aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in a league game-based bandwidth resource allocation method as described above.
A fourth aspect of the present invention provides a computer device, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the league game-based bandwidth resource allocation method as described above.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a bandwidth resource allocation method based on a coalition game, which summarizes the problem of bandwidth resource allocation by using the coalition game; the alliance game is one of the cooperative games, and can effectively coordinate the strategy of bandwidth allocation, so that the maximization of the utility in the system is realized; the league game may maximize system utility and find bandwidth allocations that maximize system utility.
The invention provides a bandwidth resource allocation method based on alliance game, which considers congestion distance and distribution entropy at the same time; wherein, in order to have good diversity among the non-dominant solutions generated in the external elite archive of fixed size, a good metric is needed to evaluate the degree of congestion around each non-dominant solution, and the congestion distance can measure this degree of congestion; the distribution entropy can measure the distribution condition of a solution space; the two methods not only ensure the diversity in the population, but also can effectively prevent the optimization process from falling into local optimization.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a flowchart of a bandwidth resource allocation method based on league gaming in an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
As shown in fig. 1, the present embodiment provides a bandwidth resource allocation method based on a league game. All users are regarded as participants of the game, the participants are divided into a plurality of alliances, and the value of each alliance is determined by the relation between the communication delay of the user task and the task time limit; after the dissatisfaction degrees of the alliances are calculated through a Sharpley (Sharpley) formula, the dissatisfaction degrees of all the alliances are combined into a vector, and finally, bandwidth allocation corresponding to the pareto non-dominant vector of the vector is found to serve as an allocation method.
For a multi-edge server, multi-user MEC system. Assuming that the number of users in the system is n, the user matrix is denoted by U ═ U1,u2,...,unUsers are distributed in the coverage area of the edge server, each user has one task, each task has three parameters, and Taski ═ d is usedi,wli,deadlineiDenotes wherein diIs the size of the task for user i, wliIs the workload, deadline, required by the task of user iiIs the longest delay to be performed by the task of user i. There are some bandwidth resources in the system that can be allocated.
The specific steps of the bandwidth resource allocation method based on the alliance game are as follows:
step 1, obtaining user information and edge device information in a multi-access edge system, and initializing the system. The system initialization comprises the following steps: the assignable bandwidth resource band, the size of the user task, and the deadline of the user task dealine.
Step 2: and dividing users to be allocated with bandwidth resources into a plurality of alliances.
All users are regarded as participants of the game, and the participants are divided into a plurality of alliances which are specifically composed of the following components: denotes federation by S, SjRepresenting the jth coalition. Assuming n users, the federation can have K types:
for example, when n is 3, there are 7 types of federation, which are {1}, {2}, {3}, {1,2}, {1,3}, {2,3}, and {1,2,3}, respectively.
And step 3: randomly generating a plurality of initial bandwidth allocation schemes, specifically:
assuming n users, the bandwidth resource allocated to user i may be βiSatisfies the conditions
∑βi＜band (2)
And 4, step 4: for each bandwidth allocation scheme, the utility of each user task is calculated.
Assuming that the participants of the league game are all users of the system, for delay sensitive tasks, the benefit of the task is related to the completion delay of the task. For example, for a weather forecast, this information is valid when the weather forecast is pushed earlier than the forecast time; otherwise, this push message is an invalid message. More seriously, the completion of the task not only brings no profit, but also causes loss.
By UiTo represent the utility of the user i task, expressed as:
wherein, profitiRepresenting the benefit of the task of user i in the communication, as a function of a constant or time (delay in completion of the task and revenue generated by the task)Function of the relationship between benefits), w2Represents the cost per unit time, w1Representing a revenue parameter; when the communication time delay is larger than the maximum threshold value of the communication time, the task benefit is 0; when the communication time is less than the maximum threshold value of the transmission time, the utility is the benefit minus the cost, the cost represents the economic cost generated by the operation task, and the profit is the income obtained by completing the task; tti(βi) Representing the time calculated from the bandwidth allocation, the calculation is as follows:
wherein d isiRepresenting the size of the task for user i, p is the average power of the signal transmitted in the channel, and H is the gaussian noise power in the channel.
And 5: each federation has its value in terms of its tasks, the value of each federation being determined by the relationship of the communication delay of the user tasks in the federation to the duration of the task. Calculating the value of each alliance according to the users in each alliance and the utility of each user task, wherein the j-th alliance SjThe value of (d) can be expressed as:
it represents that the value of the federation is equal to the sum of the values of the federation participants.
Step 6: calculating each federation S using Sharpley allocation based on the value of each federationjThe dissatisfaction degree of all the alliances is obtained, namely the dissatisfaction degree array of the alliances.
The league game may be represented as (U, v) and the league value is assigned using the Sharpley distribution formula, which is as follows
Where | S | is the size of the federation, n is the number of participants, v (-) is the value of the federation, | S | is the size of the federation, n is the number of participants, v (-) is the value of the federation! Factorial is represented, { i } represents a set of elements i only, and n/{ i } represents a set of participants without i. The distribution mode refers to the principle of uniformly being equal to the contribution of the user. The goal is to find a suitable bandwidth resource allocation method that maximizes the utility of the overall system.
Because the number of users and devices is too large, it is difficult to judge whether the kernel is empty, and therefore, the concept of kernel is introduced to solve the problem. The nature of the kernel is to minimize the maximum dissatisfaction of the league in the cooperative game. Using dissatisfaction e to measure dissatisfaction of a federation may be described as
Wherein the content of the first and second substances,
is a utility distribution, e denotes a federation SjTo distribution
J is in the range of [1, K ]]；
Use of
To represent the vectors formed by all over-excited behaviors in the game, wherein the dissatisfaction array of the league
Since O is an array, the optimal solution for O needs to be found, i.e.
argmin O (9)
Wherein argmin f (x) represents the value of the independent variable x when the dependent variable f (x) is minimum.
As can be seen, the elements in O
Are a function of the bandwidth distribution. The aim of the present invention is therefore to find a suitable bandwidth distribution aimed at minimizing O, and therefore to summarize this problem as the bandwidth allocation problem of league games (BAPMAE) that minimizes e, and the present invention solves this problem using a multi-objective particle swarm algorithm.
And 7: finding out the global optimal dissatisfaction degree and the local optimal dissatisfaction degree of each iteration by using the pareto sorting; optimizing a bandwidth allocation scheme by using a multi-target particle swarm algorithm according to global optimization, local optimization and updating rules of the multi-target particle swarm algorithm; after a number of iterations, the best bandwidth allocation scheme is recorded.
From the above, the goal is to find the bandwidth distribution to get better O, since O is 2nThe dimension arrays cannot be simply compared when finding their optimal solutions. Therefore, pareto ordering is introduced to solve this problem, and pareto dominance is defined as follows
Defining: for vectors O1 ═ O1,1, O1,2, … … O1, K) and O2 ═ O2,1, O2,2, … … O2, K, if O1,1 ≦ O2,1O1,2 ≦ O2,2 … … O1, K ≦ O2, K, then O1 pareto is better than O2, or O1 pareto is dominated by O2.
The PSO is an evolutionary optimization algorithm, shares an individual optimal value with other particles, searches for an optimal individual optimal value as a current global optimal solution, and adjusts the speed and the position of all the particles according to the current individual optimal value found by the particles and the current global optimal solution shared by a group.
Since O is a vector, Oi and Oj do not dominate each other. Then, in order to better optimize the speed and direction of particle evolution, the present invention introduces an archiving mechanism and a crowded entropy calculation mechanism. The archive mechanism is to record all non-dominant solutions. The use of the archiving mechanism is similar to the elitism mechanism. Due to the optimized direction of the particle swarmGlobal optimal and individual optimal influence, the invention defines two kinds of archives, namely a global optimal archive set f and a local optimal archive set fpi，fpiA locally optimal archive set representing the ith particle.
The archive set has a certain capacity, and in order to have good diversity among the non-dominant solutions generated in the external elite file with a fixed size, a good metric is needed to evaluate the degree of congestion around each non-dominant solution, so that the diversity of the archive set is ensured, and the optimization process can be prevented from falling into local optimization to a great extent. In order to estimate the solution density in the objective function space, the invention considers the congestion distance and the distribution entropy at the same time, which is called congestion entropy, and the calculation of the congestion entropy is shown as the formula:
wherein the content of the first and second substances,
Eij＝-[plijlog2(plij)+puijlog2(puij)] (11)
cij＝dlij+duij (14)
wherein dl isijAnd duijIs the distance of the ith O from its lower and upper neighbors along the jth dimension.
And
are the maximum and minimum values of O along the j-th dimension.
From the above description, the process of solving the BAPMAE algorithm by the particle swarm optimization algorithm can be described as:
(1) initialization: generating a number of initial particles (one particle is a bandwidth allocation scheme), an initial global optimal archive set f and an initial local optimal archive set fpi. From the problem description, a bandwidth allocation scheme can correspond to a federation dissatisfaction array O according to equations (6), (7) and (8).
(2) And (3) evolving each particle by using a particle swarm updating formula, namely updating the bandwidth allocation scheme by combining an updating rule, a global optimal archive set (global optimal solution set) and a local optimal archive set (local optimal solution set).
Wherein the update rule is expressed as
Where w is the inertia factor, c1And c2Is a learning factor, rand is a random number,
the task representing the jth user is the allocation of the band for the particle r in iteration i.
(3) For each newly generated bandwidth allocation scheme, calculating the dissatisfaction degrees of all alliances according to formulas (3) to (8) to obtain the dissatisfaction degrees of all alliances, namely an alliance dissatisfaction degree array O;
(4) updating fp and f with the O generated by the newly generated bandwidth allocation, wherein the fp is locally optimal and the fp retains an optimal solution set known to each particle; f is global optimum, and what remains is the set of solutions that are optimal among all particles. Carrying out pareto sequencing on the newly generated particles and the existing particles in fp and f respectively; replacing newly generated particles if they dominate existing particles; if they are mutually non-dominant, the congestion entropy is calculated, and the reserved particles are determined according to the congestion entropy sorting. If the newly generated particles are dominated, no processing is done. Namely, updating the global optimal archive set and the local optimal archive set by using pareto ordering and congestion entropy, specifically: carrying out pareto sorting on the existing bandwidth allocation schemes and the newly generated bandwidth allocation schemes in the global optimal archiving set and the local optimal archiving set; if the newly generated bandwidth allocation scheme dominates the existing bandwidth allocation scheme, the newly generated bandwidth allocation scheme is used as a global optimal archive set and a local optimal archive set; if the two are mutually non-dominated, calculating congestion entropy, and selecting a bandwidth allocation scheme according to the congestion entropy sequence to be used as a global optimal archive set and a local optimal archive set; if the newly generated bandwidth allocation scheme is dominated, no processing is done.
(5) Judging whether an end condition is met, and if so, selecting an optimal bandwidth allocation scheme; otherwise, returning to the bandwidth allocation scheme to continue updating, namely repeating the steps (2) - (4) until the ending condition is met, ending the iteration, and selecting the final bandwidth allocation strategy from the non-dominant particles in the last iteration.
During the particle update process, the total bandwidth in the updated bandwidth allocation scheme may exceed the maximum bandwidth of the system. If so, reallocating the bandwidth in proportion:
example two
The embodiment provides a bandwidth resource allocation system based on alliance game, which specifically comprises the following modules:
an initialization module configured to: dividing users to be allocated with bandwidth resources into a plurality of alliances, and generating an initial global optimal archiving set, an initial local optimal archiving set and a plurality of initial bandwidth allocation schemes;
a schema update module configured to: updating the bandwidth allocation scheme based on the update rule, the global optimal archive set and the local optimal archive set; in the process of updating the bandwidth allocation scheme, if the total bandwidth in the updated bandwidth allocation scheme exceeds the maximum bandwidth, the bandwidth is reallocated in proportion;
an archive set update module configured to: for each updated bandwidth allocation scheme, calculating the dissatisfaction of all alliances, and updating the global optimal archive set and the local optimal archive set by using pareto ordering and congestion entropy;
a best case selection module configured to: judging whether an end condition is met, and if so, selecting an optimal bandwidth allocation scheme; otherwise, returning to the bandwidth allocation scheme to continue updating.
It should be noted that, each module in the present embodiment corresponds to each step in the first embodiment one to one, and the specific implementation process is the same, which is not described herein again.
EXAMPLE III
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in a league game-based bandwidth resource allocation method as described in the first embodiment.
Example four
The present embodiment provides a computer device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the processor implements the steps in the league game-based bandwidth resource allocation method according to the first embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention 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, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A bandwidth resource allocation method based on alliance game is characterized by comprising the following steps:
dividing users to be allocated with bandwidth resources into a plurality of alliances, and generating an initial global optimal archiving set, an initial local optimal archiving set and a plurality of initial bandwidth allocation schemes;
updating the bandwidth allocation scheme based on the update rule, the global optimal archive set and the local optimal archive set;
for each updated bandwidth allocation scheme, calculating the dissatisfaction of all alliances, and updating the global optimal archive set and the local optimal archive set by using pareto ordering and congestion entropy;
judging whether an end condition is met, and if so, selecting an optimal bandwidth allocation scheme; otherwise, returning to the bandwidth allocation scheme to continue updating.
2. A league gaming based bandwidth resource allocation method as claimed in claim 1, wherein in the updating of the bandwidth allocation scheme, if the total bandwidth in the updated bandwidth allocation scheme exceeds the maximum bandwidth, the bandwidth is reallocated proportionally.
3. The league game-based bandwidth resource allocation method as claimed in claim 1, wherein the dissatisfaction degree is calculated by:
for each bandwidth allocation scheme, calculating the utility of each user task;
calculating the value of each alliance according to the users in each alliance and the utility of each user task;
based on the value of each league, the dissatisfaction of each league is calculated by using a Shaapril distribution mode.
4. A coalition game based bandwidth resource allocation method as claimed in claim 3, wherein the value of the coalition is determined by the relation between the communication delay of the user task in the coalition and the task deadline.
5. The alliance game-based bandwidth resource allocation method as claimed in claim 1, wherein the specific steps of updating the global optimal archive set and the local optimal archive set by using pareto sorting and congestion entropy are as follows:
carrying out pareto sorting on the existing bandwidth allocation schemes in the global optimal archive set and the local optimal archive set and the newly generated bandwidth allocation schemes based on the dissatisfaction degrees of all alliances of each bandwidth allocation scheme;
if the newly generated bandwidth allocation scheme dominates the existing bandwidth allocation scheme, the newly generated bandwidth allocation scheme is used as a global optimal archive set and a local optimal archive set; if the two are mutually non-dominated, calculating congestion entropy, and selecting a bandwidth allocation scheme according to the congestion entropy sequence to be used as a global optimal archive set and a local optimal archive set; if the newly generated bandwidth allocation scheme is dominated, no processing is done.
6. The alliance game-based bandwidth resource allocation method as claimed in claim 5, wherein the congestion entropy considers both the congestion distance and the distribution entropy to estimate the density of the solution in the objective function space.
7. A system for allocating bandwidth resources based on league gaming, comprising:
an initialization module configured to: dividing users to be allocated with bandwidth resources into a plurality of alliances, and generating an initial global optimal archiving set, an initial local optimal archiving set and a plurality of initial bandwidth allocation schemes;
a schema update module configured to: updating the bandwidth allocation scheme based on the update rule, the optimal archive set and the local optimal archive set;
an archive set update module configured to: for each updated bandwidth allocation scheme, calculating the dissatisfaction of all alliances, and updating the global optimal archive set and the local optimal archive set by using pareto ordering and congestion entropy;
a best case selection module configured to: judging whether an end condition is met, and if so, selecting an optimal bandwidth allocation scheme; otherwise, returning to the bandwidth allocation scheme to continue updating.
8. A league gaming-based bandwidth resource allocation system as claimed in claim 7, wherein in updating the bandwidth allocation scheme, if the total bandwidth in the updated bandwidth allocation scheme exceeds the maximum bandwidth, the bandwidth is reallocated proportionally.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of a league game-based bandwidth resource allocation method according to any one of claims 1 to 6.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps in a method for league gaming based bandwidth resource allocation according to any of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111015613.9A CN113783801B (en) | 2021-08-31 | 2021-08-31 | Bandwidth resource allocation method and system based on alliance game |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111015613.9A CN113783801B (en) | 2021-08-31 | 2021-08-31 | Bandwidth resource allocation method and system based on alliance game |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113783801A true CN113783801A (en) | 2021-12-10 |
| CN113783801B CN113783801B (en) | 2024-03-05 |
Family
ID=78840281
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111015613.9A Active CN113783801B (en) | 2021-08-31 | 2021-08-31 | Bandwidth resource allocation method and system based on alliance game |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113783801B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114401037A (en) * | 2022-03-24 | 2022-04-26 | 武汉大学 | Unmanned aerial vehicle communication network flow unloading method and system based on alliance formed game |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102946641A (en) * | 2012-11-27 | 2013-02-27 | 重庆邮电大学 | Heterogeneous converged network bandwidth resource optimizing distribution method |
| CN110062026A (en) * | 2019-03-15 | 2019-07-26 | 重庆邮电大学 | Mobile edge calculations resources in network distribution and calculating unloading combined optimization scheme |
| CN110493800A (en) * | 2019-08-14 | 2019-11-22 | 吉林大学 | Super-intensive networking resources distribution method based on Game with Coalitions in a kind of 5G network |
-
2021
- 2021-08-31 CN CN202111015613.9A patent/CN113783801B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102946641A (en) * | 2012-11-27 | 2013-02-27 | 重庆邮电大学 | Heterogeneous converged network bandwidth resource optimizing distribution method |
| CN110062026A (en) * | 2019-03-15 | 2019-07-26 | 重庆邮电大学 | Mobile edge calculations resources in network distribution and calculating unloading combined optimization scheme |
| CN110493800A (en) * | 2019-08-14 | 2019-11-22 | 吉林大学 | Super-intensive networking resources distribution method based on Game with Coalitions in a kind of 5G network |
Non-Patent Citations (4)
| Title |
|---|
| YANPING LIU 等: "Coalition Game for User Association and Bandwidth Allocation in Ultra-Dense mmWave Networks", 2017 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA (ICCC), 15 April 2018 (2018-04-15) * |
| YONGHUI ZHAO 等: "Bandwidth Allocation Based on Multi-Coalition Game in Wireless Sensor Networks", 2010 SECOND INTERNATIONAL CONFERENCE ON NETWORKS SECURITY, WIRELESS COMMUNICATIONS AND TRUSTED COMPUTING * |
| 刘远祥: "MEC 系统的计算资源分配及任务调度研究", 中国优秀硕士学位论文全文数据库 * |
| 张齐新 等: "车联网中基于联盟合作博弈的带宽分享策略", 电信科学 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114401037A (en) * | 2022-03-24 | 2022-04-26 | 武汉大学 | Unmanned aerial vehicle communication network flow unloading method and system based on alliance formed game |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113783801B (en) | 2024-03-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112286677B (en) | Resource-constrained edge cloud-oriented Internet of things application optimization deployment method | |
| Gao et al. | Hierarchical multi-agent optimization for resource allocation in cloud computing | |
| CN113411369B (en) | Cloud service resource collaborative optimization scheduling method, system, medium and equipment | |
| CN110798849A (en) | Computing resource allocation and task unloading method for ultra-dense network edge computing | |
| CN112380008A (en) | Multi-user fine-grained task unloading scheduling method for mobile edge computing application | |
| CN111813506A (en) | Resource sensing calculation migration method, device and medium based on particle swarm algorithm | |
| CN114585006B (en) | Edge computing task unloading and resource allocation method based on deep learning | |
| Supreeth et al. | Hybrid genetic algorithm and modified-particle swarm optimization algorithm (GA-MPSO) for predicting scheduling virtual machines in educational cloud platforms | |
| Quan et al. | Towards exploring vast mpsoc mapping design spaces using a bias-elitist evolutionary approach | |
| CN116209084A (en) | Task unloading and resource allocation method in energy collection MEC system | |
| CN114741955A (en) | Multi-objective optimization task scheduling method based on security cloud | |
| Sellami et al. | Deep reinforcement learning for energy-efficient task scheduling in SDN-based IoT network | |
| CN113783801B (en) | Bandwidth resource allocation method and system based on alliance game | |
| CN112445617B (en) | Load strategy selection method and system based on mobile edge calculation | |
| Shukla et al. | An efficient offloading technique using DQN for MEC-IoT Networks | |
| CN111488208A (en) | Edge cloud cooperative computing node scheduling optimization method based on variable step length bat algorithm | |
| Li et al. | Batch jobs load balancing scheduling in cloud computing using distributional reinforcement learning | |
| Balusamy et al. | Hybridization of immune with particle swarm optimization in task scheduling on smart devices | |
| CN115220818A (en) | Real-time dependency task unloading method based on deep reinforcement learning | |
| Qi et al. | A task unloading strategy of IoT devices using deep reinforcement learning based on mobile cloud computing environment | |
| CN110689320A (en) | Large-scale multi-target project scheduling method based on co-evolution algorithm | |
| Ya-meng et al. | Deadline-aware Task Scheduling for Cloud Computing using Firefly Optimization Algorithm | |
| CN114928542B (en) | Virtual machine migration method based on dynamic integration and multi-target cuckoo algorithm | |
| CN112328364B (en) | Computing-intensive cloud workflow scheduling method based on farmland fertility algorithm | |
| Chen et al. | Multi-Slot Dynamic Computing Resource Optimization in Edge Computing |
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 |