CN114338685A - Edge server resource allocation method based on credit-price relationship - Google Patents
Edge server resource allocation method based on credit-price relationship Download PDFInfo
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Abstract
The invention requests to protect an edge server resource allocation method based on credit-price relationship, which comprises the following main steps: s1, acquiring the resource number, the credit and the asking price of the edge server, and acquiring the bid price and the required credit of the user; s2, sorting the edge server and the user according to the credit rating model; s3, matching the user and the edge server according to the sorting result, the price constraint and the credit constraint; s4, judging whether the edge server and the user can carry out price dynamic update, if so, updating the price and then continuing matching; s5, screening the matching result, and screening repeated user requests; s6, calculating a transaction price and transaction utility according to the final successful matching result; and S7, updating the credit of the edge server according to the credit evaluation model. The invention provides a credit degree-price relation and provides a dynamic price updating mechanism based on the credit degree-price relation, thereby integrally improving the reliability of resource allocation and the resource utilization rate.
Description
Technical Field
The invention belongs to the field of mobile edge calculation, and particularly relates to an edge server resource allocation method based on a credit-price relationship.
Background
Driven by ubiquitous wireless communication networks, the popularity of users has led to an increasing growth in information and data, but currently most users are limited in size, resulting in limited computing power and battery life. The strong computing and storage capacity of the cloud server enables tasks generated by a user to be effectively processed, and more effective service is brought to a terminal user. However, with the advancement of 5G, the development of Internet and computer technology, users have exhibited a trend of explosive growth. The number of users connected to remote cloud servers has also increased dramatically, and the demand for high quality services has placed tremendous pressure on the cloud servers. In addition, since the cloud server is deployed in the core network center which is very far away from the user, the task of completing the data generated by the user will cause larger delay and more energy consumption, and the long-distance transmission delay will greatly reduce the operation efficiency of the system. Therefore, it is difficult for the traditional centralized service model based on cloud computing to meet the current business requirements.
Mobile Edge Computing (MEC), an emerging computing paradigm where an edge server processes and stores data near the edge of a network of user terminal devices, is a computing model that provides reliable and stable service to users in the near vicinity. Because the edge servers are deployed at a position close to the user, the edge servers can directly provide more effective service so as to ensure lower delay, and the situation that the server uploads all data to the cloud when processing the data locally is avoided, so that the bandwidth pressure is reduced, and meanwhile, the energy consumption of the cloud server is reduced to a certain extent by the widely distributed edge servers. Thus, moving edge computing can be introduced to handle user-generated data tasks, more efficiently handling user-generated task demands at locations closer to the user. However, while the application of mobile edge computing can work better, it also faces challenges such as limited edge server resources and the disparity in interests between the user and the edge server results in less than optimal allocation and utilization of resources driven by profit. Currently, auctions are a popular form of transaction that efficiently allocates resources of sellers to buyers at competitive prices. However, some dishonest participants inevitably exist in the cloud market, or due to conditions such as network dynamics, resource heterogeneity, and loss of trust of the edge server, the edge server may not reasonably allocate resources or may fail to allocate resources when confronted with a large number of task demand requests. Therefore, how to design a resource allocation mechanism which can improve the resource utilization rate of the edge server, ensure the reliability of the whole edge computing resource allocation process and improve the utility of the edge server and the user as much as possible under the condition of meeting the requirements of the user is still a very worthy of research.
In the face of the problems that the resources of the edge server are limited, but the resources cannot be fully utilized, the resources are interrupted in the resource allocation process due to network dynamics, resource heterogeneity and server loss of confidence, the benefits of the two parties are inconsistent, and the like, an effective and reliable resource allocation method is needed to process highly concurrent edge tasks and heterogeneous edge resources.
Since the tasks offloaded to the edge server are generated by insufficient storage and computing capabilities of the mobile terminal, and in the case of limited resources, each requesting user would like to have available reliable edge server resources to handle the tasks more efficiently. If the credit factor of the edge server is considered in the resource matching process, and the resource request quantity of all users requesting a certain server resource is compared with the resource ownership quantity of the edge server in the follow-up process to make matching selection, the allocation reliability can be improved, and the resource utilization rate and the successful transaction quantity of the whole cloud market can be improved.
Through retrieval, the method for joint task scheduling and resource allocation supporting D2D-edge server unloading is disclosed as CN107995660B, and is characterized in that: the method comprises the following steps: s1: modeling user joint overhead; s2: modeling time delay required by user task execution; s3: modeling energy consumption required by user task execution; s4: modeling user task scheduling and resource allocation limiting conditions; s5: determining a user task scheduling and resource allocation strategy based on the minimization of user joint overhead under the condition of meeting task scheduling and resource allocation; the S1 specifically includes: modeling user joint cost according to a formula, wherein the user joint cost is the sum of costs of all users for executing tasks in the network, the cost required by the ith user for executing the tasks is more than or equal to 1 and less than or equal to N, and N is the number of the users for executing the tasks in the network; modeling is carried out, wherein t represents the time delay required by the ith user to execute the task, e represents the energy consumption required by the ith user ii to execute the task, represents the weighting coefficient of the time delay cost of the ith user, and represents the weighting coefficient of the energy consumption cost of the ith user.
The invention patent with publication number CN107995660B is different from the invention in the following points:
1. the invention considers task unloading, the invention considers resource allocation, although the overall concept is not greatly different, the actually made content is greatly different from the research method due to the difference of the research direction;
2. the invention aims at minimizing the combined overhead of time delay, energy consumption and the like, and the research focuses on the aspect of economic benefit, aims at the factors of credit and price, and considers the reliability of resource allocation and the waste condition of resources;
3. the invention overcomes the condition that the node is unreliable, and avoids the condition that the edge server does not act after obtaining the profit as much as possible;
4, the invention provides a price dynamic updating mechanism, and overcomes the resource waste situation as much as possible by improving the algorithm.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. An edge server resource allocation method based on a credit-price relationship is provided. The technical scheme of the invention is as follows:
an edge server resource allocation method based on credit-price relationship comprises the following steps:
s1, acquiring the resource number, asking price and credit degree of the edge server, and acquiring the bid price and the required credit degree of the user; s2, sorting the edge server and the user according to the credit rating model; s3, matching the resources owned by the user and the edge server according to the sorting result, the price constraint, the credit constraint and the like; s4, judging whether the edge server and the user can carry out price dynamic update, if so, updating the price and then continuing matching; s5, screening the matching result, and screening repeated user requests; s6, calculating a transaction price and transaction utility according to the final successful matching result; and S7, updating the credit of the edge server according to the credit evaluation model.
Further, the step S1 of obtaining the number of resources of the edge server, the asking price and the credit, and obtaining the bid price of the user and the required credit specifically includes:
acquiring information of the edge server and the user, if the information is matched for the first time, initializing the prices of the edge server and the user according to a credit-price relation mechanism in a credit evaluation model, and calculating an initial asking price Ask of the edge server iiWith initial request price of user j to edge server i
Otherwise, directly acquiring the ask price of the edge server and the bid price of the user.
Further, the step of initializing the asking price of the edge server and the bidding of the user according to the credit-price relationship mechanism is as follows:
(1) first, the ask AskS of the edge server is initialized according to the following formulaiBidding with a user
AskSi＝Crei*10 (1)
Wherein Crei∈[0,1]，
AskSiIndicates the initial ask, Cre, for the edge server iiRepresenting the initial credit of the edge server i;
representing j opposite sides of a userThe initial bid of the edge server i,
representing the credit requirement of the user j on the edge server i;
(2) randomly generating a final marginal server ask and a user bid according to the following formula;
Aski＝AskSi+Random(-a,a) (3)
wherein, AskiRepresenting the initial final price of the edge server i,
represents the final bid of user j on edge server i, and Random (-a, a) represents the Random number of [ -a, a []Generates a Random number in the interval of (1), and similarly, Random (0, b) is represented as [0, b ]]Generating a random number in the interval;
at each iteration later, according to the credit-price relationship, for the edge server, an initial price AskS is firstly generated according to the current credit value of the edge serveri(ii) a Then, Random price fluctuation is carried out according to Random (-a, a), and finally the generated AskiThe asking price of the edge server at the final matching time is obtained; for the user, the price is similar to the initial asking price; firstly, randomly generating a credit degree requirement value of a user j to an edge server i
Get initial bid
Then Random price fluctuation is carried out according to Random (0, b); obtained finally
Indicating that user j is to edge server i at this time of matchingAnd finally bidding.
Further, the
Also indicates the credit Cre of the edge server i when matching is performediNeeds to be greater than the credit requirement of user j for edge server i
At initial initialization, a credit value of 0.5 is set for each edge server, and a credit requirement is set for user j for edge server i
In order to better meet the random situation in real life, the setting is carried out according to the following formula. That is, randomly generating a decimal number from 0 to 1
Is started.
Further, the method for allocating resources of an edge server based on a credit-price relationship is characterized in that, in step S2, the edge server and the user are ranked according to a credit evaluation model, and specifically includes: and carrying out priority ordering on the edge server and the user according to the credit rating model. Aiming at the edge server, by judging RankiSize sorts them in ascending order. For the user, then according to
And sorting the requests of the users in a descending order. Ranki，
The definition is shown in the formula.
Ranki＝Aski/Crei (6)
Wherein CreiIndicating the initial credit of the edge server i,
representing the credit requirement of user j for edge server i.
Further, the step S3 matches the credit required by the user and the resources owned by the edge server according to the sorting result, and specifically includes: and matching the user with the edge server according to price constraint, credit constraint and the like, wherein the matching cannot be performed if the resources of the edge server are insufficient during matching. Since each user may bid on a different edge server, one user may appear multiple times and one edge server may appear multiple times.
Further, the step S4 is to determine whether the edge server and the user can perform price dynamic update, and if yes, the price is updated and then matching is continued, which specifically includes: on the basis of the proposed credit-price relationship, a dynamic price updating mechanism is proposed; the dynamic price updating mechanism formula is shown as the following formula:
Ask'i＝Aski-a' (8)
wherein Ask'iUpdating the value of the asking price of the edge server for the updated asking price of the edge server;
to request a price for the updated user, b' bids the updated value for the user. For the user, in the section
Iterating the price upwards
No more iteration boundaries for price; for edge Server, in the interval [ Ask'i-a,Ask'i+a]Inner progress price iteration down to Ask'iA is the price no-more-iteration boundary.
Further, the step S5 is to filter matching results, and filter repeated user requests, which specifically includes: after matching is completed, the matching result needs to be screened; because only one edge server can be requested by one user at the same time, but one edge server can process the requests of a plurality of users under the condition of sufficient resources at the same time; because the request repeatedly sent by the user needs to be screened out, the screening rule is to discard the user request with lower effectiveness.
Further, in step S6, after the final matching is completed, the transaction price and the utility are calculated according to the prices of both parties, and the calculation formula is shown in the formula.
V＝Vj+Vi (14)
Wherein
Is represented byThe trade price, Vi, obtained when the edge server i and the user j are successfully matchedjRepresenting the utility of the edge server when the edge server i is successfully matched with the user j; viExpressed as the total utility of the matching edge server i at this time, the edge server i exists to provide service to a plurality of users, so the total utility V of the edge server iiShould be Vi jAccumulating; and VjThe total utility of the current matching of the user j is shown, and V is the total utility of the single matching.
Further, in step S7, the credit rating update formula is updated by using the credit rating update formula in the credit rating model, and the credit rating update formula is as follows:
wherein CrekiRepresenting the credit value of the server after the k-th auction is finished, and updating the credit of the edge server according to the matching result; alpha is a variable factor having a value of
The influence of the credit value on the updating at this time is reduced; numkiThe representative is the total transaction amount, namely the total matching amount, in the auction of the edge server; prikiRepresentative is the total transaction price, NumS, in this auction for the i-edge serverkRepresentative is the total transaction volume, PrIS, of all edge servers in the auction roundkThe representative is the total transaction price of all edge servers in the current round of auction.
The invention has the following advantages and beneficial effects:
1. the invention introduces the demand attribute of the credit degree by combining the double-beat selling algorithm process, considers the factors of the credit degree and the price, and provides a credit degree evaluation model based on the credit degree-price relationship, so that the credit degree of the model improves the reliability of resource allocation by influencing resource matching. The proposed credit rating model based on credit-price relationship is specifically set forth in claims 2-5 and claim 10, and the innovative advantages are embodied in: in the prior inventions, when credit rating is used in auction, no definition is made on the relationship between credit and price. When the price is initialized, if the credit and the price are randomly generated, the edge server with better credit obtains a lower asking price, which is obviously unreasonable, and the influence of the updated credit value on the price in the next matching cannot be clearly obtained. Therefore, the invention provides a credit evaluation model based on the credit-price relationship, and the proposed correlation formulas (1), (2), (3), (4), (5), (6) and (15) are explained in the claims.
2. The invention provides a dynamic price updating mechanism on the basis of 1, and improves a double-clap selling algorithm by using dynamic price updating. All resources are matched as much as possible, and waste of resources of the edge server is reduced. The proposed dynamic price updating mechanism is specified in claim 8, and the innovative advantages are specified in: because there are residual resources and users who are not matched with the resources after each matching, in order to utilize the resources, the invention takes the price interval in the relation as the no-iteration boundary of the price based on the credit-price relation proposed in the step 1, and the price is matched after being updated, thereby avoiding the resource waste as much as possible. The present invention therefore proposes a dynamic price update mechanism and the proposed correlation equations (8) (9) are explained in the claims.
Drawings
FIG. 1 is a diagram of a preferred embodiment moving edge calculation model provided by the present invention;
fig. 2 is a flow chart of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
the invention provides a resource allocation strategy based on credit-price relationship, which comprises the following steps:
and S1, acquiring the information of the edge server and the user. If the matching is the first time, initializing the prices of the edge server and the user according to a credit-price relation mechanism in the credit evaluation model, and calculating the initial Ask of the edge server iiWith initial request price of user j to edge server i
Otherwise, directly acquiring the ask price of the edge server and the bid price of the user.
And S2, sorting the edge server and the user according to the credit rating model. Aiming at the edge server, by judging RankiSize sorts them in ascending order. For the user, then according to
And sorting the requests of the users in a descending order. Ranki，
The definition is shown in the formula.
Ranki＝Aski/Crei
Wherein CreiIndicating the initial credit of the edge server i,
representing the credit requirement of user j for edge server i.
And S3, matching the user request with the edge server, wherein if the resources of the edge server are insufficient during matching, the matching cannot be performed. Upon matching, each user may bid on a different edge server. Thus, a user may appear multiple times and an edge server may appear multiple times.
And S4, if the edge server has resources after the matching is finished, updating the prices of the edge server having the remaining resources and the users who do not finish the matching, and matching again after the updating is finished. Until the edge server has no resources left or the user cannot update the price with the edge server.
S5, after the matching is completed, the matching result needs to be screened. Because only one edge server can be requested by one user at a time, but one edge server can process the requests of a plurality of users under the condition of sufficient resources at the same time. Because the request repeatedly sent by the user needs to be screened out, the screening rule is to discard the user request with lower effectiveness.
And S6, after the final matching is finished, calculating the transaction price and the utility according to the prices of the two parties. The calculation formula is shown in the formula.
V＝Vj+Vi
Wherein
Representing the transaction price, Vi, achieved when the edge server i successfully matches the user jjIndicating the utility of the edge server when the edge server i is successfully matched with the user j. ViIs shown asThe total utility of the edge server i is matched at this time, and the edge server i provides services for a plurality of users, so the total utility V of the edge server iiShould be Vi jAnd (4) accumulating. And VjThe total utility of the current matching of the user j is shown, and V is the total utility of the single matching.
And S7, after the transaction price and the effectiveness are calculated, the credit degree of the edge server is updated according to a credit degree updating formula in the credit degree evaluation model, and the updated credit degree value is used as an initial value of the credit degree of the edge server for the next matching.
And S8, completing the matching and completing the whole auction process.
In this embodiment, the step of initializing the asking price of the edge server and the bidding of the user according to the credit-price relationship mechanism in step S1 is as follows:
(1) first, the ask AskS of the edge server is initialized according to the following formulaiBidding with a user
AskSi＝Crei*10
Wherein Crei∈[0,1]，
AskSiIndicates the initial ask, Cre, for the edge server iiRepresenting the initial credit of the edge server i.
Indicating an initial bid by user j for edge server i,
representing the credit requirement of user j for edge server i. Confidence of edge server i when matchingCreiNeeds to be greater than the credit requirement of user j for edge server i
Therefore, the user can not be matched with the edge server with lower credit degree on the premise of having a requirement on the credit degree of the edge server.
At the very beginning of initialization, a credit value of 0.5 is set for each edge server. And for the credit requirement of user j to edge server i
In order to better meet the random situation in real life, the setting is carried out according to the following formula. That is, randomly generating a decimal number from 0 to 1
Is started.
(2) After the initial price of the edge server and the user is initialized, a fixed value is generated, which is not in accordance with the actual situation. In order to better meet the competitive auction condition in real life, a price fluctuation interval is set according to the price interval in the experimental environment, and the final asking price of the edge server and the user bidding price are randomly generated according to the following formula.
Aski＝AskSi+Random(-a,a)
Wherein, AskiRepresenting the initial final price of the edge server i,
representing the final bid of user j on edge server i. And Random (-a, a) means Random at [ -a,a]A random number is generated in the interval (2). When Random (-a, a) takes-a, the calculated Ask at this timeiRepresents the lowest price that the edge server can accept, when Random (-a, a) takes a, the calculated AskiRepresenting the edge server's highest price boundary. In the same way, Random (0, b) is represented as [0, b ]]The interval generates a random number. When Random (0, b) takes b, it is calculated
Indicating the highest bid the user is willing to accept. By adding random numbers on the basis of the formula, the edge server can dynamically float the price up and down on the basis of the credit rating without fixing the price. And the user bids are randomly changed to meet the actual situation.
Therefore, at each iteration later, according to the credit-price relationship, for the edge server, an initial price AskS is first generated according to the current credit value of the edge serveri(ii) a Then, Random price fluctuation is carried out according to Random (-a, a), and finally the generated AskiI.e. the price charged by the edge server at the final matching time. For the user, it is similar to the initial asking price. Firstly, randomly generating a credit degree requirement value of a user j to an edge server i
Random price volatility is then performed according to Random (0, b). Obtained finally
Representing the final bid of user j on edge server i at this match.
In this embodiment, in step S4, there may be remaining resources after each round of matching is completed, and these resources are used for utilization. On the basis of the proposed credit-price relationship, a dynamic price update mechanism is proposed. The dynamic price updating mechanism formula is shown as the formula.
Ask'i＝Aski-a'
Wherein Ask'iFor updated asking prices, a' is the edge server asking price update value.
To request a price for the updated user, b' bids the updated value for the user. For the user, in the section
Iterating the price upwards
The boundaries are not iterated for prices. For edge Server, in the interval [ Ask'i-a,Ask'i+a]Inner progress price iteration down to Ask'iA is the price no-more-iteration boundary.
In order to reduce the time complexity of the invention, a 'is a' ═ a/2, and b 'is b' ═ b/2.
In this embodiment, in the step S7, the credit update formula is updated by using the credit update formula in the credit evaluation model, and the credit update formula is shown as the formula.
Wherein CrekiThe representative is the credit value of the server after the k-th auction is finished, and the credit of the edge server needs to be updated according to the matching result. Alpha is a variable factor having a value of
Num is to reduce the influence of the credit value of the last time on the update, andkiand the representative is the total transaction amount, namely the total matching amount, of the auction of the i edge server. PrikiRepresentative is the total transaction price in this auction for the i-edge server,NumSkrepresentative is the total transaction volume, PrIS, of all edge servers in the auction roundkThe representative is the total transaction price of all edge servers in the current round of auction.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (10)
1. An edge server resource allocation method based on credit-price relationship is characterized by comprising the following steps:
s1, acquiring the resource number, asking price and credit degree of the edge server, and acquiring the bid price and the required credit degree of the user; s2, sorting the edge server and the user according to the credit rating model; s3, matching the resources owned by the user and the edge server according to the sorting result, the price constraint, the credit constraint and the like; s4, judging whether the edge server and the user can carry out price dynamic update, if so, updating the price and then continuing matching; s5, screening the matching result, and screening repeated user requests; s6, calculating a transaction price and transaction utility according to the final successful matching result; and S7, updating the credit of the edge server according to the credit evaluation model.
2. The method for allocating edge server resources based on a credit-price relationship according to claim 1, wherein the step S1 of obtaining the number of edge server resources, the asking price and the credit, and obtaining the user' S bid price and the required credit comprises:
acquiring information of the edge server and the user, if the information is matched for the first time, initializing the prices of the edge server and the user according to a credit-price relation mechanism in a credit evaluation model, and calculating an initial asking price Ask of the edge server iiWith initial request price of user j to edge server i
Otherwise, directly acquiring the ask price of the edge server and the bid price of the user.
3. The method for edge server resource allocation based on credit-price relationship according to claim 2, wherein the step of initializing ask price of the edge server and user's bid according to the credit-price relationship mechanism is as follows:
(1) first, the ask AskS of the edge server is initialized according to the following formulaiBidding with a user
AskSi＝Crei*10 (1)
Wherein Crei∈[0,1]，
AskSiIndicates the initial ask, Cre, for the edge server iiRepresenting the initial credit of the edge server i;
indicating an initial bid by user j for edge server i,
representing the credit requirement of the user j on the edge server i;
(2) randomly generating a final marginal server ask and a user bid according to the following formula;
Aski＝AskSi+Random(-a,a) (3)
wherein, AskiRepresenting the initial final price of the edge server i,
represents the final bid of user j on edge server i, and Random (-a, a) represents the Random number of [ -a, a []Generates a Random number in the interval of (1), and similarly, Random (0, b) is represented as [0, b ]]Generating a random number in the interval;
at each iteration later, according to the credit-price relationship, for the edge server, an initial price AskS is firstly generated according to the current credit value of the edge serveri(ii) a Then, Random price fluctuation is carried out according to Random (-a, a), and finally the generated AskiThe asking price of the edge server at the final matching time is obtained; for the user, the price is similar to the initial asking price; firstly, randomly generating a credit degree requirement value of a user j to an edge server i
Get initial bid
Then Random price fluctuation is carried out according to Random (0, b); obtained finally
Representing the final bid of user j on edge server i at this match.
4. The method of claim 3, wherein the method comprises allocating resources to the edge server based on a credit-price relationship
Also indicates the credit Cre of the edge server i when matching is performediNeeds to be greater than the credit requirement of user j for edge server i
At initial initialization, a credit value of 0.5 is set for each edge server, and a credit requirement is set for user j for edge server i
In order to better meet the random situation in real life, the setting is carried out according to the following formula. That is, randomly generating a decimal number from 0 to 1
Is started.
5. The method for allocating resource of an edge server based on a credit-price relationship according to claim 4, wherein the step S2 is to rank the edge server and the user according to a credit evaluation model, and specifically comprises: and carrying out priority ordering on the edge server and the user according to the credit rating model. Aiming at the edge server, by judging RankiSorting the data in ascending order according to size, and aiming at the user, sorting the data according to size
Sorting the requests of the users in descending order, Ranki，
The definition is shown in the formula.
Ranki＝Aski/Crei (6)
Wherein CreiIndicating the initial credit of the edge server i,
representing the credit requirement of user j for edge server i.
6. The method for allocating resource of edge server based on credit-price relationship as claimed in claim 5, wherein said step S3 matches the credit required by the user and the resource owned by the edge server according to the sorting result, specifically comprising: and matching the user with the edge server according to price constraint, credit constraint and the like, wherein the matching cannot be performed if the resources of the edge server are insufficient during matching. Since each user may bid on a different edge server, one user may appear multiple times and one edge server may appear multiple times.
7. The method for allocating resource to an edge server based on a credit-price relationship according to claim 6, wherein the step S4 is performed to determine whether the edge server and the user can perform a price dynamic update, and if so, the method continues to match after updating the price, and specifically includes: on the basis of the proposed credit-price relationship, a dynamic price updating mechanism is proposed; the dynamic price updating mechanism formula is shown as the following formula:
Ask'i＝Aski-a' (8)
wherein
Updating the value of the asking price of the edge server for the updated asking price of the edge server;
to request a price for the updated user, b' bids the updated value for the user. For the user, in the section
Iterating the price upwards
No more iteration boundaries for price; for edge Server, in the interval [ Ask'i-a,Ask'i+a]Inner progress price iteration down to Ask'iA is the price no-more-iteration boundary.
8. The method for allocating resource to an edge server based on a credit-price relationship according to claim 7, wherein the step S5 is performed to filter matching results and filter repeated user requests, and specifically includes: after matching is completed, the matching result needs to be screened; because only one edge server can be requested by one user at the same time, but one edge server can process the requests of a plurality of users under the condition of sufficient resources at the same time; because the request repeatedly sent by the user needs to be screened out, the screening rule is to discard the user request with lower effectiveness.
9. The method for allocating resource to an edge server based on credit-price relationship of claim 8, wherein in step S6, after the final matching is completed, the transaction price and the utility are calculated according to the prices of both parties, and the calculation formula is shown as the formula.
V＝Vj+Vi (14)
Wherein
Representing the transaction price, Vi, achieved when the edge server i successfully matches the user jjRepresenting the utility of the edge server when the edge server i is successfully matched with the user j; viExpressed as the total utility of the matching edge server i at this time, the edge server i exists to provide service to a plurality of users, so the total utility V of the edge server iiShould be Vi jAccumulating; and VjThe total utility of the current matching of the user j is shown, and V is the total utility of the single matching.
10. The method for allocating edge server resources based on credit-price relationship of claim 9, wherein in step S7, the edge server credit is updated by using a credit update formula in the credit evaluation model, wherein the credit update formula is as follows:
wherein CrekiRepresenting the credit value of the server after the k-th auction is finished, and updating the credit of the edge server according to the matching result; alpha is a variable factor having a value of
The influence of the credit value on the updating at this time is reduced; numkiThe representative is the total transaction amount, namely the total matching amount, in the auction of the edge server; prikiRepresentative is the total transaction price, NumS, in this auction for the i-edge serverkRepresentative is the total transaction volume, PrIS, of all edge servers in the auction roundkThe representative is the total transaction price of all edge servers in the current round of auction.
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