CN116366661A - Collaborative edge user allocation method based on blockchain and auction theory - Google Patents
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Abstract
The invention relates to a collaborative edge user allocation method based on a blockchain and auction theory, which comprises the following steps: firstly, under an edge computing environment, acquiring physical positions of a user and an edge server; then calculating the distance according to the physical position to form a connectable subset; then, users are allocated based on a preset allocation rule, and edge resources are scheduled; and finally, according to the number of users and the task amount, estimating the accumulated calculation time and load condition in the server, setting a task amount estimated value of the server, comparing the estimated value, and distributing the user request with large task amount to the server with high service rate. According to the method, the influence of the user demand difference and the edge facility isomerism is considered, the user requests with large task quantity are distributed to the servers with high service rate to maximize social utility, so that the method is suitable for the edge demand response scene of mismatching of the user and the service facility quantity, improves the resource utilization rate on the basis of guaranteeing the basic user experience quality, and reduces the system energy consumption.
Description
Technical Field
The invention relates to the technical field of edge calculation, in particular to a collaborative edge user allocation method based on a blockchain and auction theory.
Background
With the development of the internet of things and network communication technology, a large amount of information is sent, transmitted and processed in various forms, which affects the aspects of people's production and life. The development of mobile edge computing is driven by the rise of 5G technology, computing offloading is a very active topic in edge computing, and the process of transferring a resource-intensive computing task from a resource-constrained user end to a cloud end or an edge end for processing is studied, which involves the allocation of a lot of resources.
At the user equipment side, different user requirements present new challenges to the edge devices. The requirements such as automatic driving, cloud games and the like have higher requirements on task processing time delay and user side energy consumption; the internet of things equipment such as intelligent camera shooting, temperature sensing and the like needs to process or upload real data to an edge end, and needs larger emergency processing and computing capacity; the requirements such as video transceiving, hot content query and the like provide new requirements for the edge cache.
Improving load balancing of an edge system is beneficial to improving resource utilization rate, reducing system energy consumption and improving the capability of the system for resisting abnormal traffic attacks (such as DDoS attacks), which is necessary in a large-area and high-density 5G ultra-dense cellular network. Because of limited edge resources, servers belonging to different edge facility providers lack motivation to assist other servers in fulfilling user requests. And the edge collaboration among different application providers may cause problems of user privacy, user experience quality, etc. due to external factors or malicious competition among merchants. Therefore, the cooperation process needs to be recorded to trace back the execution process of the user task, and rewards are carried out according to the execution effect of the user task. In order to facilitate traceability and improve the credibility of the recorded content, the recorded content should be public and non-tamperable, and the traditional centralized storage mode is obviously not suitable.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a cooperative edge user allocation method based on a blockchain and auction theory, which can synthesize the problems of efficiency, excitation and trust in the process of cooperatively processing calculation tasks among edge servers, and improve the utilization rate of edge resources on the basis of ensuring the quality of user experience, thereby reducing the energy consumption of a system and improving the robustness of the system.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a collaborative edge user allocation method based on a blockchain and auction theory is used for allocating user requests with large task quantity to servers with high service rate to maximize social utility by considering the influence of user demand variability and edge facility isomerism so as to adapt to an edge demand response scene of unmatched user and service facility quantity, improve resource utilization rate and reduce system energy consumption on the basis of guaranteeing basic user experience quality, and concretely comprises the following steps:
s1: acquiring physical positions of a user and an edge server in an edge computing environment;
s2: calculating the distance between the user and the edge server according to the physical position obtained in the step S1, and forming a connectable subset between the user and the edge server according to the calculated distance;
s3: distributing users based on a preset distribution rule, and scheduling edge resources;
s4: estimating accumulated calculation time and load conditions in the edge server according to the current user quantity and task quantity to obtain an estimated value of the task quantity of the edge server, comparing the estimated value of the task quantity of the edge server with a threshold value of the task processing quantity of the edge server, and executing step S5 if the estimated value of the task quantity of the edge server exceeds the threshold value of the task processing quantity of the edge server; if the task quantity predicted value of the edge server does not exceed the threshold value of the task processing quantity of the edge server, scheduling task demands to physical facilities in the edge server, completing service, recording execution time, and ending the service;
s5: the edge server enters an edge cooperative state, and user tasks with the maximum task quantity are screened out from the edge server task set;
s6: according to the reputation value ranking of the current collaboration server, comparing the maximum task quantity with task processing quantity thresholds of the collaboration servers in sequence, unloading the task with the maximum task quantity from an edge server to the collaboration server if the maximum task quantity does not exceed the task processing quantity threshold of a certain collaboration server, scheduling task demands to physical facilities in the collaboration server, completing service, recording execution time, and returning to the step S4; otherwise, executing the step S7;
s7: and the original edge server overrates to complete the task, record the execution time and finish the service.
Step S3, allocating users based on a preset allocation rule, and scheduling edge resources, wherein the specific process is as follows:
order the
Representing mobile user and internet of things device +.>
Representing collaboration server->
Representing physical facilities within the collaboration server; the allocation rules between the user and the collaboration server are as follows:
wherein,,
representing user +.>
Select collaboration server +.>
Utility of (2); i represents the ith user and j represents the jth collaboration server; />
Representation except for user->
Besides, other users select collaboration server +.>
I represents other users than the ith user; />
Representing user +.>
Select collaboration server +.>
Probability of (2); delta represents the selectable set of all users and servers;
through the distribution process, the price to be paid by the user needs to be further calculated; the user bidding rules meet Nash equilibrium and weak dominance strategies to ensure that the user truly transmits task types and task amounts.
Further, the price paid by the user is the external influence brought to other participants by the choice of the user participating in the bid or not participating in the bid, if the external influence is income acquired by regular users, if the external influence is negative, the price paid by the user is indicated to bring negative external influence to others, and the price is paid, which is specifically expressed as follows:
if the user
Successful bid, user->
The price to be paid is user +.>
The sum of utility values available to all other participants not participating in the bid or bidding at 0 minus the user +.>
The difference of the sum of utility values available to all other participants after participating in the bid; user->
Social welfare involved in bidding->
And social welfare not participating in the auction->
The calculation is as follows:
wherein,,
representing user +.>
Selecting a server under an optimal allocation rule>
Probability of (2); />
Representation of the user
Besides, other users select server ++under optimal allocation rules>
Probability of (2);
from the above, the user
Payment rule->
The settings were as follows:
user' s
Balanced benefit of->
The method comprises the following steps:
in a multi-user-multi-server scenario, users
Balanced benefit of->
Expressed as:
in step S6, the reputation value ranking of the current collaboration server is updated according to the task execution effect of the collaboration server, the reputation value and the task execution effect in the collaboration process are recorded in the blockchain, and the rewards are exchanged according to the reputation value ranking of the collaboration server after a service period is over, so as to achieve the incentive effect.
Further, the updating and recording process of the reputation value is as follows:
in order to ensure traceability of the collaboration process between collaboration servers, specific information of each user task subjected to edge collaboration processing is recorded as a block, a server for assisting in completing the edge tasks has accounting authority and updates a reputation value, and the reputation value updating method comprises the following steps:
wherein,,
representing collaboration Server->
First->
Secondarily processing the reputation value updated by the user task; />
A weight value representing the efficiency of the completion of the task by the collaboration server; />
Representing a unit reputation for processing each task; />
For user->
Is calculated at the current collaboration server/>
Calculation time estimated with original edge server +.>
Is a ratio of (2);
when the collaboration server performs edge collaboration, the collaboration server with high utility value is preferentially selected, and the computing method is as follows:
wherein,,
representing edge server +.>
Utility values updated after the nth service; />
Representing edge server +.>
A reputation value updated after the nth service; α and β represent weights of reputation value and distance, α+β=1, respectively; />
Representing a selection action; />
Representing a physical distance between the requestor and a collaboration server that is scheduled to receive the task; />
A server representing the initial selection of the user, +.>
A server representing the actual selection of the user;
the billing and verification modes set by the collaboration server are as follows:
after the task is completed, the collaboration server with accounting authority records the task information as
Wherein,,
、/>
respectively represent user +>
A hash value corresponding to a specific task and the task amount of the task; />
、
Respectively representing the collaboration servers initially selected by the user and the corresponding processing time thereof; />
、/>
Respectively representing the actual selection of the collaboration server and the actual processing time of the user,/->
Hash value representing task execution result; />
A hash value representing information recorded in a previous block; after accounting, the collaboration server publishes the content, and after checking the content, other collaboration servers copy an account book content, and at this time, the new block which has been verified can be added to the blockchain.
Preferably, when the edge server and the collaboration server belong to different edge facility providers, a bifurcation attack scheme needs to be designed, and the bifurcation attack scheme is specific:
the bifurcation attack comprises the following three forms:
a. because a certain server of the edge facility provider A has a problem when cooperatively processing tasks, in order to avoid adverse effects, the edge facility provider actively gives up rewards brought by the tasks, and initiates bifurcation attack to cover records;
b. edge facility provider B performs excellently in collaborative processing of tasks, and its reputation value increases greatly, and edge facility provider a as a competitor maliciously initiates bifurcation attack to cover records;
c. in the reputation value accumulation process, as the computing resources owned by the edge facility provider A are larger, the opportunity of participating in edge collaboration is more than that of other edge facility providers, and the reputation value is increased in the collaborative computing process, so that the reputation value of a collaboration server under the flag of the edge facility provider A is increased over time, and the number of times of participating in collaborative computing is increased until the edge facility provider A fully owns the control right of the edge collaborative computing;
aiming at the three types of bifurcation attacks, a corresponding bifurcation attack plan needs to be designed:
a1. when selecting the collaboration server, fully considering the edge facility provider to which the collaboration server belongs, enabling each edge facility provider to provide collaboration servers with the number similar to the task processing performance, and reducing the possibility of bifurcation attack from the hardware level;
b1. in the algorithm scheduling process of the edge collaborative computing, limiting the collaborative computing times of a single edge facility provider in a single service period, and preventing a collaborative server of a certain edge facility provider from monopolizing or occupying a collaborative computing rewards;
c1. and the period of credit value exchange rewards is reduced, and excessive accumulation of credit values is avoided.
Compared with the prior art, the invention has the beneficial effects that:
1. the collaborative edge computing method based on the blockchain fully considers the influence of the difference of the user demands and the isomerism of edge facilities, and based on the VCG auction theory, ensures that the bidding rules of the users meet Nash equilibrium and weak dominance strategies, improves the load balancing of edge nodes, and has positive significance in improving the utilization rate of edge resources and reducing the energy consumption of the system.
2. According to the collaborative edge computing method based on the blockchain, social utility is maximized by distributing the user requests with large task quantity to the server with high service rate, the collaborative edge computing method can adapt to edge demand response scenes with unmatched user and service facility quantity, improves the resource utilization rate on the basis of guaranteeing basic user experience quality, and has positive significance in reducing system energy consumption and improving system robustness.
3. The collaborative edge computing method based on the block chain further optimizes the excitation and trust mechanism of edge collaboration by combining with the block chain, and considers three scenes of no collaboration, internal collaboration and excitation collaboration. And the influence of the transmission distance of the tasks in the edge coordination on the user experience quality is considered. Furthermore, the present disclosure is described and presented in terms of a bifurcated attack that may occur in the application of blockchains in edge collaboration.
Drawings
FIG. 1 is a flow chart of a collaborative edge user allocation method based on blockchain and auction theory of the present invention;
FIG. 2 is a generalized application scenario diagram of the method of the present invention;
fig. 3 is a network topology diagram corresponding to a general application scenario.
Detailed Description
The invention aims to provide a coordinated edge user allocation method based on a blockchain and auction theory, which can integrate the efficiency, excitation and trust problems of coordinated processing of calculation tasks between an edge server and a coordinated server, and improve the utilization rate of edge resources on the basis of guaranteeing the quality of user experience, thereby reducing the energy consumption of a system and improving the robustness of the system. The technical scheme of the invention will be clearly and completely described below with reference to the accompanying drawings.
Basic principle: edge calculation is a distributed operation architecture, which is to move the operation of application programs, data and services to edge nodes on network logic for processing by a network center node, and the edge calculation is to decompose the large service processed by the center node completely, cut into smaller and easier to manage parts and distribute to the edge nodes for processing; the edge node is closer to the user terminal device, so as to increase the data processing and transmission speed and reduce the delay.
Referring to fig. 1, the collaborative edge user allocation method based on blockchain and auction theory provided by the invention maximizes social utility by allocating user requests with large task amount to servers with high service rate in consideration of the influence of user demand variability and edge facility isomerism, so as to adapt to the edge demand response scene of unmatched user and service facility amount, improve resource utilization rate and reduce system energy consumption on the basis of guaranteeing basic user experience quality, and specifically comprises the following steps:
s1: acquiring physical positions of a user and an edge server in an edge computing environment;
s2: calculating the distance between the user and the edge server according to the physical position obtained in the step S1, and forming a connectable subset between the user and the edge server according to the calculated distance;
s3: distributing users based on a preset distribution rule, and scheduling edge resources;
s4: estimating accumulated calculation time and load conditions in the edge server according to the current user quantity and task quantity to obtain an estimated value of the task quantity of the edge server, comparing the estimated value of the task quantity of the edge server with a threshold value of the task processing quantity of the edge server, and executing step S5 if the estimated value of the task quantity of the edge server exceeds the threshold value of the task processing quantity of the edge server; if the task quantity predicted value of the edge server does not exceed the threshold value of the task processing quantity of the edge server, scheduling task demands to physical facilities in the edge server, completing service, recording execution time, and ending the service;
s5: the edge server enters an edge cooperative state, and user tasks with the maximum task quantity are screened out from the edge server task set;
s6: according to the reputation value ranking of the current collaboration server, comparing the maximum task quantity with task processing quantity thresholds of the collaboration servers in sequence, unloading the task with the maximum task quantity from an edge server to the collaboration server if the maximum task quantity does not exceed the task processing quantity threshold of a certain collaboration server, scheduling task demands to physical facilities in the collaboration server, completing service, recording execution time, and returning to the step S4; otherwise, executing the step S7;
s7: and the original edge server overrates to complete the task, record the execution time and finish the service.
Example 1
Referring to fig. 2 and 3, the collaborative edge user allocation method according to the present invention is further described below by way of an example.
For more specific description of the scene, as shown in FIG. 2, the present embodiment is described in the following
Representing a mobile user or an internet of things device +.>
Representing collaboration server->
Representing physical facilities within the collaboration server; the collaborative edge computing method based on the blockchain comprises the following steps:
s1, acquiring physical positions of a user and an edge server in an edge computing environment;
s2, calculating the distance between the user and the edge server according to the physical position obtained in the step S1, further forming a connectable subset between the user and the edge server according to the calculated distance based on the scene shown in the figure 2, and obtaining a network topological graph shown in the figure 3, wherein the figure 3 shows that the edge server and the user have a many-to-many relationship, namely, a plurality of edge servers can be selected by each user, and each edge server can serve a plurality of users;
s3, distributing users based on a preset distribution rule, and scheduling edge resources:
is known to be
Representing mobile user and internet of things device +.>
Representing collaboration server->
Representing the physical facilities within the collaboration server, the allocation rules between the user and the collaboration server are as follows:
wherein,,
representing user +.>
Select collaboration server +.>
Utility of (2); i represents the ith user and j represents the jth collaboration server; />
Representation except for user->
Besides, other users select collaboration server +.>
Utility of (2); -i represents a user other than the i-th user; />
Representing user +.>
Select collaboration server +.>
Probability of (2); delta represents the selectable set of all users and servers;
s4, estimating accumulated calculation time and load conditions in the edge server according to the current user quantity and task quantity, obtaining an estimated value of the task quantity of the edge server, comparing the estimated value of the task quantity of the edge server with a threshold value of the task quantity of the edge server, and executing a step S5 if the estimated value of the task quantity of the edge server exceeds the threshold value of the task quantity of the edge server; if the task quantity predicted value of the edge server does not exceed the threshold value of the task processing quantity of the edge server, scheduling task demands to physical facilities in the edge server, completing service, recording execution time, and ending the service;
s5: the edge server enters an edge cooperative state, and user tasks with the maximum task quantity are screened out from the edge server task set;
s6: according to the reputation value ranking of the current collaboration server, comparing the maximum task quantity with task processing quantity thresholds of the collaboration servers in sequence, unloading the task with the maximum task quantity from an edge server to the collaboration server if the maximum task quantity does not exceed the task processing quantity threshold of a certain collaboration server, scheduling task demands to physical facilities in the collaboration server, completing service, recording execution time, and returning to the step S4; otherwise, executing the step S7;
the reputation value ranking is updated according to the task execution effect of the collaboration server, the reputation value and the task execution effect in the collaboration process are recorded in the blockchain, and ranking is carried out again according to the updated reputation value after one service period is finished;
further, the updating and recording process of the reputation value is as follows:
in order to ensure traceability of the collaboration process between collaboration servers, specific information of each user task subjected to edge collaboration processing is recorded as a block, a server for assisting in completing the edge tasks has accounting authority and updates a reputation value, and the reputation value updating method comprises the following steps:
wherein,,
representing collaboration Server->
First->
Secondarily processing the reputation value updated by the user task; />
A weight value representing the efficiency of the completion of the task by the collaboration server; />
Representing a unit reputation for processing each task; />
For user->
Is the calculation time of the task of the current collaboration server +.>
Calculation time estimated with original edge server +.>
Is a ratio of (2);
when the collaboration server performs edge collaboration, the collaboration server with high utility value is preferentially selected, and the computing method is as follows:
wherein,,
representing edge server +.>
Utility values updated after the nth service; />
Representing edge server +.>
A reputation value updated after the nth service; α and β represent weights of reputation value and distance, α+β=1, respectively; />
Representing a selection action; />
Representing a physical distance between the requestor and a collaboration server that is scheduled to receive the task; />
A server representing the initial selection of the user, +.>
A server representing the actual selection of the user;
the billing and verification modes set by the collaboration server are as follows:
after the task is completed, the collaboration server with accounting authority records the task information as
Wherein,,
、/>
respectively represent user +>
A hash value corresponding to a specific task and the task amount of the task; />
、
Respectively representing the collaboration servers initially selected by the user and the corresponding processing time thereof; />
、/>
Respectively representing the actual selection of the collaboration server and the actual processing time of the user,/->
Hash value representing task execution result; />
A hash value representing information recorded in a previous block; after accounting, the collaboration server publishes the content, and after other collaboration servers check the content for errors, the collaboration server copies an account book content, and at the moment, a new block which is verified can be added into the blockchain;
s7: and the original edge server overrates to complete the task, record the execution time and finish the service.
Example 2
In embodiment 1, the edge user allocation process is performed on the premise of the internal cooperation of the edge server and the cooperation server, and considering that in the actual application process, the edge resources are limited and belong to different edge facility providers, and the gratuitous internal cooperation mode obviously cannot well mobilize the enthusiasm of the edge facility providers, so that in the embodiment, the cooperation mode with an incentive mechanism is adopted for the situation that the servers belong to different edge facility providers, and the incentive mechanism is combined with the credit value record, thereby guaranteeing the efficiency of cooperation.
Specifically, in embodiment 1, users are allocated according to a preset allocation rule, and after the edge resources are scheduled, the price to be paid by the users needs to be calculated additionally; the user bidding rules meet Nash equilibrium and weak dominance strategies to ensure that the user truly transmits task types and task amounts.
Further, the price paid by the user is the external influence brought to other participants by the choice of the user participating in the bid or not participating in the bid, if the external influence is income acquired by regular users, if the external influence is negative, the price paid by the user is indicated to bring negative external influence to others, and the price is paid, which is specifically expressed as follows:
if the user
Successful bid, user->
The price to be paid is user +.>
The sum of utility values available to all other participants not participating in the bid or bidding at 0 minus the user +.>
The difference of the sum of utility values available to all other participants after participating in the bid; user->
Social welfare involved in bidding->
And social welfare not participating in the auction->
The calculation is as follows:
wherein,,
representing user +.>
Selecting a server under an optimal allocation rule>
Probability of (2); />
Representation of the user
Besides, other users select server ++under optimal allocation rules>
Probability of (2);
from the above, the user
Payment rule->
The settings were as follows:
user' s
Balanced benefit of->
The method comprises the following steps:
in a multi-user-multi-server scenario, users
Balanced benefit of->
Expressed as:
example 3
On the basis of embodiment 2, since the edge server and the collaboration server belong to different edge facility providers, and there is an incentive mechanism combining the reputation values, the possibility of bifurcation attack between different edge facility providers is greatly improved, and therefore, a corresponding bifurcation attack scheme needs to be designed according to the specific form of bifurcation attack.
Specifically, the bifurcation attack includes the following three forms:
a. because a certain server of the edge facility provider A has a problem when cooperatively processing tasks, in order to avoid adverse effects, the edge facility provider actively gives up rewards brought by the tasks, and initiates bifurcation attack to cover records;
b. edge facility provider B performs excellently in collaborative processing of tasks, and its reputation value increases greatly, and edge facility provider a as a competitor maliciously initiates bifurcation attack to cover records;
c. in the reputation value accumulation process, as the computing resources owned by the edge facility provider A are larger, the opportunity of participating in edge collaboration is more than that of other edge facility providers, and the reputation value is increased in the collaborative computing process, so that the reputation value of a collaboration server under the flag of the edge facility provider A is increased over time, and the number of times of participating in collaborative computing is increased until the edge facility provider A fully owns the control right of the edge collaborative computing;
in this embodiment, according to the above three forms of bifurcation attack, a corresponding bifurcation attack scheme is given:
a1. when selecting the collaboration server, fully considering the edge facility provider to which the collaboration server belongs, enabling each edge facility provider to provide collaboration servers with the number similar to the task processing performance, and reducing the possibility of bifurcation attack from the hardware level;
b1. in the algorithm scheduling process of the edge collaborative computing, limiting the collaborative computing times of a single edge facility provider in a single service period, and preventing a collaborative server of a certain edge facility provider from monopolizing or occupying a collaborative computing rewards;
c1. and the period of credit value exchange rewards is reduced, and excessive accumulation of credit values is avoided.
In summary, the invention fully considers the influence of the user demand difference and the edge facility isomerism, and provides an edge user allocation method which adapts to the edge demand response scene of mismatching of the number of users and service facilities; according to possible excitation collaboration problems and bifurcation attacks in practical application, while an excitation mechanism is established, bidding rules of users meet Nash equilibrium and weak dominance strategies, bifurcation attack plans are assisted, so that user requests with large task quantity are ensured to be distributed to servers with high service rate to maximize social utility, resource utilization rate is improved on the basis of ensuring basic user experience quality, system energy consumption is reduced, and system robustness is improved.
While the preferred embodiments of the present invention have been illustrated and described, the present invention is not limited to the embodiments, and various equivalent modifications and substitutions can be made by one skilled in the art without departing from the spirit of the present invention, and these modifications and substitutions are intended to be included in the scope of the present invention as defined in the appended claims.
Claims (6)
1. A collaborative edge user allocation method based on a blockchain and auction theory is characterized in that social utility is maximized by allocating user requests with large task quantity to servers with high service rate by considering the influence of user demand variability and edge facility isomerism so as to adapt to an edge demand response scene of unmatched user and service facility quantity, thereby improving resource utilization rate and reducing system energy consumption on the basis of guaranteeing basic user experience quality, and the method is characterized by comprising the following steps:
s1: acquiring physical positions of a user and an edge server in an edge computing environment;
s2: calculating the distance between the user and the edge server according to the physical position obtained in the step S1, and forming a connectable subset between the user and the edge server according to the calculated distance;
s3: distributing users based on a preset distribution rule, and scheduling edge resources;
s4: estimating accumulated calculation time and load conditions in the edge server according to the current user quantity and task quantity to obtain an estimated value of the task quantity of the edge server, comparing the estimated value of the task quantity of the edge server with a threshold value of the task processing quantity of the edge server, and executing step S5 if the estimated value of the task quantity of the edge server exceeds the threshold value of the task processing quantity of the edge server; if the task quantity predicted value of the edge server does not exceed the threshold value of the task processing quantity of the edge server, scheduling task demands to physical facilities in the edge server, completing service, recording execution time, and ending the service;
s5: the edge server enters an edge cooperative state, and user tasks with the maximum task quantity are screened out from the edge server task set;
s6: according to the reputation value ranking of the current collaboration server, comparing the maximum task quantity with task processing quantity thresholds of the collaboration servers in sequence, unloading the task with the maximum task quantity from an edge server to the collaboration server if the maximum task quantity does not exceed the task processing quantity threshold of a certain collaboration server, scheduling task demands to physical facilities in the collaboration server, completing service, recording execution time, and returning to the step S4; otherwise, executing the step S7;
s7: and the original edge server overrates to complete the task, record the execution time and finish the service.
2. The method for allocating collaborative edge users based on blockchain and auction theory according to claim 1, wherein step S3 allocates users based on a preset allocation rule, and schedules edge resources, comprising the following steps:
order the
Representing mobile user and internet of things device +.>
The representation of the collaboration server is provided as,
representing physical facilities within the collaboration server; the allocation rules between the user and the collaboration server are as follows:
wherein,,
representing user +.>
Select collaboration server +.>
Utility of (2); i represents the ith user and j represents the jth collaboration server; />
Representation except for user->
Besides, other users select collaboration server +.>
Utility of (2); -i represents a user other than the i-th user; />
Representing user +.>
Select collaboration server +.>
Probability of (2); delta represents the selectable set of all users and servers;
through the distribution process, the price to be paid by the user needs to be further calculated; the user bidding rules meet Nash equilibrium and weak dominance strategies to ensure that the user truly transmits task types and task amounts.
3. The method for allocating collaborative edge users based on blockchain and auction theory according to claim 2, wherein the price paid by the user is the external influence of the choice of the user to participate in or not participating in the bidding on other participants, if the external influence is that regular users obtain income, if the external influence is negative, the participation of the user is indicated to bring negative external influence to others, and payment is needed, which is specifically shown as follows:
if the user
Successful bid, user->
The price to be paid is user +.>
The sum of utility values available to all other participants not participating in the bid or bidding at 0 minus the user +.>
The difference of the sum of utility values available to all other participants after participating in the bid; user->
Social welfare involved in bidding->
And social welfare not participating in the auction->
The calculation is as follows:
wherein,,
representing user +.>
Selecting a server under an optimal allocation rule>
Probability of (2); />
Representation except for user->
Besides, other users are in optimal allocation rulesDown selection server->
Probability of (2);
from the above, the user
Payment rule->
The settings were as follows:
user' s
Balanced benefit of->
The method comprises the following steps:
in a multi-user-multi-server scenario, users
Balanced benefit of->
Expressed as:
4. the method for allocating collaborative edge users based on blockchain and auction theory according to claim 1, wherein in step S6, the reputation value of the current collaborative server is ranked, the reputation value is updated according to the task execution effect of the collaborative server, the reputation value and the task execution effect in the collaborative process are recorded in the blockchain, and the rewards are exchanged according to the reputation value of the collaborative server after one service period is finished, so as to achieve the incentive effect.
5. The method for assigning collaborative edge users based on blockchain and auction theory according to claim 4, wherein the ranking of reputation values is updated according to the task execution effect of the collaboration server, and the reputation values and the task execution effect in the collaboration process are recorded in the blockchain, specifically:
in order to ensure traceability of the collaboration process between collaboration servers, specific information of each user task subjected to edge collaboration processing is recorded as a block, a server for assisting in completing the edge tasks has accounting authority and updates a reputation value, and the reputation value updating method comprises the following steps:
wherein,,
representing collaboration Server->
First->
Secondarily processing the reputation value updated by the user task; />
A weight value representing the efficiency of the completion of the task by the collaboration server; />
Representing a unit reputation for processing each task; />
For user->
Is the calculation time of the task of the current collaboration server +.>
Calculation time estimated with original edge server +.>
Is a ratio of (2);
when the collaboration server performs edge collaboration, the collaboration server with high utility value is preferentially selected, and the computing method is as follows:
wherein,,
representing collaboration Server->
Utility values updated after the nth service; />
Representing collaboration Server->
A reputation value updated after the nth service; />
And->
Weights representing reputation value and distance, respectively, +.>
+/>
=1；/>
Representing a selection action; />
Representing a physical distance between the requestor and a collaboration server that is scheduled to receive the task; />
A collaboration server representing an initial selection by a user, +.>
A collaboration server representing the actual selection of the user;
the billing and verification modes set by the collaboration server are as follows:
after the task is completed, the collaboration server with accounting authority records the task information as
Wherein,,
、/>
respectively represent user +>
A hash value corresponding to a specific task and the task amount of the task; />
、/>
Respectively representing the collaboration servers initially selected by the user and the corresponding processing time thereof; />
、/>
Respectively representing the actual selection of the collaboration server and the actual processing time of the user,/->
Hash value representing task execution result; />
A hash value representing information recorded in a previous block; after accounting, the collaboration server publishes the content, and after checking the content, other collaboration servers copy an account book content, and at this time, the new block which has been verified can be added to the blockchain.
6. The method for collaborative edge user allocation based on blockchain and auction theory according to claim 1, wherein when the edge server and the collaborative server belong to different edge facility providers, a bifurcation attack scheme is designed, and the method is specifically:
the bifurcation attack comprises the following three forms:
a. because a certain server of the edge facility provider A has a problem when cooperatively processing tasks, in order to avoid adverse effects, the edge facility provider actively gives up rewards brought by the tasks, and initiates bifurcation attack to cover records;
b. edge facility provider B performs excellently in collaborative processing of tasks, and its reputation value increases greatly, and edge facility provider a as a competitor maliciously initiates bifurcation attack to cover records;
c. in the reputation value accumulation process, as the computing resources owned by the edge facility provider A are larger, the opportunity of participating in edge collaboration is more than that of other edge facility providers, and the reputation value is increased in the collaborative computing process, so that the reputation value of a collaboration server under the flag of the edge facility provider A is increased over time, and the number of times of participating in collaborative computing is increased until the edge facility provider A fully owns the control right of the edge collaborative computing;
aiming at the three types of bifurcation attacks, a corresponding bifurcation attack plan needs to be designed:
a1. when selecting the collaboration server, fully considering the edge facility provider to which the collaboration server belongs, enabling each edge facility provider to provide collaboration servers with the number similar to the task processing performance, and reducing the possibility of bifurcation attack from the hardware level;
b1. in the algorithm scheduling process of the edge collaborative computing, limiting the collaborative computing times of a single edge facility provider in a single service period, and preventing a collaborative server of a certain edge facility provider from monopolizing or occupying a collaborative computing rewards;
c1. and the period of credit value exchange rewards is reduced, and excessive accumulation of credit values is avoided.
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