阅读说明：本技术 一种基于期货市场和现货市场的移动边缘计算资源分配和定价方法 (Mobile edge computing resource allocation and pricing method based on futures market and spot market ) 是由 张文杰 郭荣宗 黄晓雯 郑艺峰 于 2021-08-11 设计创作，主要内容包括：本发明属于无线通信技术领域,涉及一种基于期货市场和现货市场的移动边缘计算资源分配和定价方法,包括建立移动边缘计算系统模型、MEC服务器、用户和中间代理,通过预留合同的期货市场最优注册费,代理人收集关于MEC服务器和用户的信息,发布注册费并记录所有用户的注册决策每个用户基于注册决策得到效益信息收集所有边缘服务器的效益,完全信息下的拍卖算法,随机用户对边缘服务器进行估值,并根据估值出价投标代理人对随机用户的投标价降序排序不完全信息下的拍卖算法,将服务器的要价a升序排序,将用户的投标价b降序排序,根据供求模型,可得交点(x,y),并计算用户的中标价和服务器的开价,以达到MEC服务器在不同信息环境下的资源分配和定价策略。(The invention belongs to the technical field of wireless communication, and relates to a mobile edge computing resource allocation and pricing method based on futures markets and spot markets, which comprises the steps of establishing a mobile edge computing system model, an MEC server, users and an intermediate agent, reserving the optimal registration fee of the futures markets of contracts, collecting information about the MEC server and the users by an agent, issuing the registration fee, recording the registration decision of all the users, obtaining benefit information of all the edge servers by each user based on the registration decision, carrying out auction algorithm under complete information, carrying out estimation on the edge servers by random users, carrying out descending ordering on the bid price of the random users by an agent according to the estimation bid, ascending ordering the ask price a of the server, descending ordering the bid price b of the users, obtaining an intersection point (x, y) and calculating the bid price of the user and the offer price of the server so as to achieve the resource allocation and pricing strategy of the MEC server under different information environments.)

1. A method for mobile edge computing resource allocation and pricing based on futures and spot markets, characterized by: the method comprises the following steps:

step 1, establishing a mobile edge computing system model, which comprises a plurality of MEC servers, a plurality of users and an intermediate agent, and comprises the following steps:

step 101, recording all MEC servers in the mobile edge computing system as a set M ═ {1, 2., M }, where a computing resource owned by a jth server is recorded as R_j；

All users in the system are denoted as a set N ═ 1, 2. Contract users and random users, wherein the contract users need to be registered in advance, pay a certain registration fee r, and use reserved resources according to contract agreement; random users purchase resources on demand. Defining a variable alpha_iE {0,1} represents the user type, α_i1 indicates that user i is a contracting user, α_i0 means that user i is a random user; the registration policy of all users in the system is recorded as α ═ α₁,...,α_i,...,α_N}；

The MEC edge server manages resources by hiring an intermediate agent and pays a management fee denoted c₀；

Step 102, constructing a futures market resource allocation model

If the user i is a contract user, contract terms need to be signed with the MEC server and are recorded as R_i＝{D_i,P_i,l_iIn which D is_iRepresenting the resource requirements, P, of a contracting user i_iDenotes a unit price of a resource,/_iRepresenting an indemnity unit price;

definition of beta_ijFor the resource contribution rate of MEC server j to contract user i, i.e. the percentage of resources allocated by server j to contract user i, the resource contribution rate is limited by the constraint

If the user i is a contract user, the user i can negotiate with an intermediate agent to sign a contract and reserve resources to an MEC server, namely futures contracts; the benefit of MEC server j in the futures market can be expressed as:

in the formula B (d)_i,D_i) Resource d for indicating when MEC server is allocated to contract user i_iResources D less than agreed in contract_iAt this time, the MEC server needs to pay the compensation to the contracting user i, expressed as:

if the computing resource allocated to the contract user i is d_iThe profit of the contracting subscriber i can be expressed as

In the formulaRepresenting the marginal benefit of the contract user i unit resource.

Step 103, constructing a spot market resource allocation model

If the user i is a random user, the user i needs to compete with other random users for the remaining computing resources in the spot market. Definition o ═ { o₁₁,...,o_NMIs the match matrix of MEC server and all random users, where o_ij1 represents that the random user i successfully bids to obtain the resource of the MEC server j; the benefit of MEC server j in the spot market can be expressed as:

in the formula p^aPaying a price, phi, for a random user unit resource^aRepresenting the cost per unit of resources paid by the MEC server to the staging agent,represents the marginal cost of allocation of a unit resource by MEC server j,is the transaction amount of the resource;

in the spot market, the random user bids on the resources with the maximum willingness-to-pay, and the user who successfully bids can complete the calculation task by utilizing the allocated resources. The benefit of random user i on the spot market can be expressed as:

in the formula p^bIndicating the price per unit of resource, phi, paid by the user to the MEC server^bCost per unit resource, W, to pay to the intermediate agent_i ^bRepresenting a resource transaction amount;

step 2, determining the total income of the MEC server in futures and spot markets as an objective function, and constructing an optimization model for maximizing the total income of the MEC server under the resource limited constraint and the resource distribution balance constraint;

step 3, constructing a futures market resource allocation algorithm based on a reservation contract to process the optimization problem obtained in the step 2 to obtain the optimal registration fee;

step 4, constructing an auction algorithm of the spot market under complete information to obtain a matching matrix of the MEC server and all random users;

and 5, constructing an auction algorithm of the spot market under incomplete information to obtain a matching matrix.

2. The futures market and spot market based mobile edge computing resource allocation and pricing method according to claim 1, comprising step 2 and step 2 comprising the steps of:

step 201, the overall profit maximization problem of the MEC server is shown as the following formula:

the constraint conditions are as follows:

where C1 represents a resource constraint that ensures that each MEC server accepts a user's purchase request within its capabilities; c2 is a resource allocation balance condition to ensure that the amount of resources allocated to the user by all MEC servers is equal to the total amount of resources received by the user.

3. The futures market and spot market based mobile edge computing resource allocation and pricing method according to claim 1, comprising step 3 and step 3 comprising the steps of:

step 301, in the futures market under the complete information, all MEC servers and intermediate agents have complete contract user information, so that the resource contract provided for contract user i needs to meet individual rationality constraint, and each contract user is guaranteed to have non-negative income, which is expressed as follows:

in the futures market under incomplete information, the MEC server and the user only know limited information (e.g., marginal income of the user and marginal cost of the server), and the resource contract provided to the contracting user i needs to satisfy:

step 303, initializing variables, including registration policies of the userRegistration fee r₀0, MEC maximum profit U_max0, and 0 is the iteration number k;

step 304, the intermediate agent collects the information of the MEC server and the user and publishes registration cost;

step 305, each user calculates the earnings in the spot market and the futures market according to the registration strategy, which are respectively expressed as:

step 306, calculating the total profit of all MEC servers as:

step 307, the total profit of the MEC server and the current maximum value U_maxMake a comparison ifModifying a registration fee r^*＝r_kAnd updating the maximum profit value to

Step 308, if the user i is a contract user, when the contract user is a contract userWhen the user i changes the registration policy to:if the user i is a random user, whenWhen the user i changes the registration policy to:

step 309, new iteration number, new registration fee and total profit of MEC server are:

k＝k+1，r_k+1＝r_k+ τ, where τ is the iteration step;

step 310, step 305 is repeated to step 309 until the total gain increase of the MEC server is less than a given threshold.

4. The futures market and spot market based mobile edge computing resource allocation and pricing method according to claim 1, comprising step 4 and step 4 comprising the steps of:

step 401, each random user purchases computing resources in real time as needed in the spot market. And the random user bids on the computing resource according to the evaluation. Definition v_ij＝g(ζ_j,ρ_ij) Denotes the valuation of the random user i to the MEC server j resource, v_ijIt can also be called the maximum willingness-to-pay of the random user i for the MEC server j resource;for the computational performance of MEC server j, ρ_ijRepresenting the preference of a random user i to an MEC server j; all random users estimate the different MEC server computing resources by Ψ ═ v₁₁,...,v_ij,...,v_NM)；

Step 402, recording the remaining resources of all edge servers as R '═ R'₁,...,R'_MTherein of

In step 403, each edge server has a lowest acceptable price, i.e. an edge server ask price, denoted as a_j；

Step 404, the broker publishes the remaining resource R ' ═ R ' of all MEC servers '₁,...,R'_MAnd each random user estimates v according to the value_ijBidding resources, v_ij∈Ψ；

Step 405: agent pair satisfies v_ij≥a_jThe bids of all random users are sorted;

step 406: depending on the total price, ifEdge server allocating resources to bidsThe largest random user;

step 407, random userThe opportunity to obtain other server resources is lost, i.e.

Step 408, repeating the steps 405 to 407 until any one of the following termination conditions is met:

1) the edge server has no residual resource to be distributed; 2) all random users have been allocated resources.

Step 409, under complete information, the MEC server does not need to pay service fee to the agent, and the income of unit resource of the server is the estimation of the user, and the transaction amount is equal to the demand amount of the random user i, so that phi exists^a＝0，p^a＝v_ij,The benefits of the edge server are:

5. the futures market and spot market based mobile edge computing resource allocation and pricing method according to claim 1, comprising step 5 and step 5 comprising the steps of:

step 501, defining the bid price of the random user as b ═ b 1., bn }, defining the server ask price as a ═ a 1., am }, and representing the computing resource supply amount submitted to the agent by the server j as

Step 502, arranging a to-be-priced a in a non-descending order_m≥a_m-1≥...≥a₁；

Step 503, arranging the bids b in a non-ascending order b₁≤b₂≤...≤b_n；

Step 504, ifAnd isThe transaction amount of buyer i is the resource demand amount when it bids, i.e. the amount of the resource demandSeller j has a transaction amount of

Step 505, ifAnd isThe transaction amount of seller j is the resource supply amount at the time of bid by buyer, i.e.Transaction of buyer i

If buyer i successfully obtains computing resources in the auction, the benefit is step 506Otherwise V_i＝0；

Step 507, if the seller j successfully sells the computing resource in the auction, the benefit isOtherwise U_j＝0；

Step 508, the edge server benefits in the two markets are:

Technical Field

The invention belongs to the technical field of wireless communication, and relates to a mobile edge computing resource allocation and pricing method based on futures markets and spot markets.

Background

With the development of 5G networks and mobile internet, the continuous emergence of massive applications such as automatic driving, augmented reality and intelligent transportation, and the mobile terminal equipment is required to be capable of timely processing high-energy-consumption, high-magnitude and high-complexity computing tasks. To solve the above problem, a concept of Mobile Edge Computing (MEC) is proposed. The MEC allows the mobile device to offload computing tasks to servers deployed near the edge of the network, localizing traffic, reducing the amount of remote data transmitted, and reducing energy consumption and transmission latency. At present, the MEC becomes one of the key technologies of the 5G network, and research and application of the MEC is helpful for solving the problems of congestion, time delay and the like of the future network.

The resource allocation is one of key technologies of the MEC, and has great research significance in shortening task delay and improving energy efficiency. In the existing research, the resource allocation problem of MEC is mainly analyzed from two aspects: namely a technical and theoretical level and an economic level. The former mainly aims at minimizing energy consumption and task delay, or balances two optimization objectives according to the requirements of different applications to allocate resources. For example, there are literature studies on the problem of minimizing delay of joint allocation of communication and computing resources in a multi-user time division multiple access meco (mobile Edge computing offloading) system, three different computing models are studied, and finally an optimal joint communication and computing resource allocation algorithm is proposed. The latter uses an economic method to allocate and price the resources, and stimulates the MEC server to share the resources to maximize the profit. For another example, there is a document that studies competition behavior among multiple MEC servers through game theory, and proposes an iterative algorithm to dynamically adjust resource amount and pricing of the MEC servers to attract more users.

The existing resource allocation method based on economic analysis considers the problems of user diversity, information asymmetry and the like less. There are many types of users in the MEC system, and different users have different service requirements, and need to make different resource allocation and pricing strategies according to different user types. In addition, in the resource market activity, the knowledge of the information by each participant is different, and the resource allocation and pricing strategy of the MEC server under different information environments needs to be considered.

Disclosure of Invention

The embodiment of the application provides a mobile edge computing resource allocation and pricing method based on futures market and spot market, aims to comprehensively consider the problems of user diversity and information asymmetry of a resource trading market in MEC, provides a resource trading mechanism based on a mixed market, and considers two different types of users, namely contract users and random users. In the futures market, the contract users use reserved resources according to the signed contract, and in the spot market, random users purchase resources as needed.

In order to achieve the purpose, the invention provides the following technical scheme:

step 1, establishing a mobile edge computing system model, which comprises a plurality of MEC servers, a plurality of users and an intermediate agent, and comprises the following steps:

step 101, recording all MEC servers in the mobile edge computing system as a set M ═ {1, 2., M }, where a computing resource owned by a jth server is recorded as R_j；

All users in the system are denoted as a set N ═ 1, 2. Contract users and random users. The contract user needs to register in advance, pay a certain registration fee r, and use the reserved resource according to the contract agreement; purchasing resources by random users according to needs;

defining a variable alpha_iE {0,1} represents the user type, α_i1 indicates that user i is a contracting user, α_i0 means that user i is a random user; the registration policy of all users in the system is recorded as α ═ α₁,...,α_i,...,α_N}；

The MEC edge server manages resources by hiring an intermediate agent and pays a management fee denoted c₀；

Step 102, constructing a futures market resource allocation model

If the user i is a contract user, contract terms need to be signed with the MEC server and are recorded as R_i＝{D_i,P_i,l_iIn which D is_iIndicating the resource requirements of the contracting subscriber, P_iDenotes a unit price of a resource,/_iRepresenting an indemnity unit price;

definition of beta_ijFor the resource contribution rate of MEC server j to contract user i, i.e. the percentage of resources allocated by server j to contract user i, the resource contribution rate is limited by the constraint

If the user i is a contract user, the user i can negotiate with an intermediate agent to sign a contract and reserve resources to an MEC server, namely futures contracts; the benefit of MEC server j in the futures market can be expressed as:

in the formula B (d)_i,D_i) Indicating the resource d allocated to user i when MEC server_iResources D less than agreed in contract_iAt this time, the MEC server needs to pay the compensation to the contracting user i, expressed as:

if the computing resource allocated to the contract user i is d_iThe profit of the contracting subscriber i can be expressed as

In the formulaThe marginal profit of the contract user i unit resource.

Step 103, constructing a spot market resource allocation model

If the user i is a random user, the user i needs to compete with other random users for the remaining computing resources in the spot market. Definition o ═ { o₁₁,...,o_NMIs the match matrix of MEC server and all random users, where o_ij1 represents that the random user i successfully bids to obtain the resource of the MEC server j; the benefit of MEC server j in the spot market can be expressed as:

in the formula p^aPaying a price, phi, for a random user unit resource^aRepresenting the cost per unit of resources paid by the MEC server to the staging agent,represents the marginal cost of allocation of a unit resource by MEC server j,is the transaction amount of the resource;

in the spot market, the random user bids on the resources with the maximum willingness-to-pay, and the user who successfully bids can complete the calculation task by utilizing the allocated resources. The benefit of random user i on the spot market can be expressed as:

in the formula p^bIndicating the price per unit of resource, phi, paid by the user to the MEC server^bTo pay the unit resource cost to the staging agent,representing a resource transaction amount;

step 2, determining the total income of the MEC server in futures and spot markets as an objective function, and constructing an optimization model for maximizing the total income of the MEC server under the resource limited constraint and the resource distribution balance constraint;

step 3, constructing a futures market resource allocation algorithm based on a reservation contract to process the optimization problem obtained in the step 2 to obtain the optimal registration fee;

step 4, constructing an auction algorithm of the spot market under complete information to obtain a matching matrix of the MEC server and all random users;

step 5, constructing an auction algorithm of the spot market under incomplete information to obtain a matching matrix;

preferably, the step 2 comprises the steps of:

step 201, the overall profit maximization problem of the MEC server is shown as the following formula:

the constraint conditions are as follows:

where C1 represents a resource constraint that ensures that each MEC server accepts a user's purchase request within its capabilities; c2 is a resource allocation balance condition to ensure that the amount of resources allocated to the user by all MEC servers is equal to the total amount of resources received by the user;

preferably, the step 3 comprises the steps of:

step 301, in the futures market under the complete information, all MEC servers and intermediate agents have complete contract user information, so that the resource contract provided for contract user i needs to meet individual rationality constraint, and each contract user is guaranteed to have non-negative income, which is expressed as follows:

in the futures market under incomplete information, the MEC server and the user only know limited information (e.g., marginal income of the user and marginal cost of the server), and the resource contract provided to the contracting user i needs to satisfy:

step 303, initializing variables, including registration policies of the userRegistration fee r₀0, MEC maximum profit U_max0, and 0 is the iteration number k;

step 304, the intermediate agent collects the information of the MEC server and the user and publishes registration cost;

step 305, each user calculates the earnings in the spot market and the futures market according to the registration strategy, which are respectively expressed as:

step 306, calculating the total profit of all MEC servers as:

step 307, the total profit of the MEC server and the current maximum value U_maxMake a comparison ifModifying a registration fee r^*＝r_kAnd updating the maximum profit value to

Step 308, if the user i is a contract user, when the contract user is a contract userWhen the user i changes the registration policy to:if the user i is a random user, whenWhen the user i changes the registration policy to:

step 309, new iteration number, new registration fee and total profit of new MEC server are:

k＝k+1，r_k+1＝r_k+ τ, where τ is the iteration step;

step 310, the steps 305 to 309 are repeated until the total income increment of the MEC server is smaller than a given threshold value;

preferably, the step 4 comprises the steps of:

step 401, each random user purchases computing resources in real time as needed in the spot market. And the random user bids on the computing resource according to the evaluation. Definition v_ij＝g(ζ_j,ρ_ij) To indicate random useValuation of household i to MEC server j resource, v_ijIt can also be called the maximum willingness-to-pay of the random user i for the MEC server j resource;for the computational performance of MEC server j, ρ_ijRepresenting the preference of a random user i to an MEC server j; all random users estimate the different MEC server computing resources by Ψ ═ v₁₁,...,v_ij,...,v_NM)；

Step 402, recording the remaining resources of all edge servers as R '═ R'₁,...,R'_MTherein of

In step 403, each edge server has a lowest acceptable price, i.e. an edge server ask price, denoted as a_j；

Step 404, the broker publishes the remaining resource R ' ═ R ' of all MEC servers '₁,...,R'_MAnd each random user estimates v according to the value_ijBidding resources, v_ij∈Ψ；

Step 405: agent pair satisfies v_ij≥a_jThe bids of all random users are sorted;

step 406: depending on the total price, ifEdge server allocating resources to bidsThe largest random user;

step 407, random userThe opportunity to obtain other server resources is lost, i.e.

Step 408, repeating the steps 405 to 407 until any one of the following termination conditions is met: 1) the edge server has no residual resource to be distributed; 2) all random users have been allocated resources.

Step 409, under complete information, the MEC server does not need to pay service fee to the agent, and the income of unit resource of the server is the estimation of the user, and the transaction amount is equal to the demand amount of the random user i, so that phi exists^a＝0，p^a＝v_ij，The benefits of the edge server are:

preferably, the step 5 comprises the steps of:

step 501, defining the bid price of the random user as b ═ b 1., bn }, defining the server ask price as a ═ a 1., am }, and representing the computing resource supply amount submitted to the agent by the server j as

Step 502, arranging a to-be-priced a in a non-descending order_m≥a_m-1≥...≥a₁；

Step 503, arranging the bids b in a non-ascending order b₁≤b₂≤...≤b_n；

Step 504, ifAnd isThe transaction amount of buyer i is the resource demand amount when it bids, i.e. the amount of the resource demandSeller j has a transaction amount of

Step 505, ifAnd isThe transaction amount of seller j is the resource supply amount at the time of bid by buyer, i.e.Transaction amount of buyer i is

If buyer i successfully obtains computing resources in the auction, the benefit is step 506Otherwise V_i＝0；

Step 507, if the seller j successfully sells the computing resource in the auction, the benefit isOtherwise U_j＝0；

Step 508, the edge server benefits in the two markets are:

drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

FIG. 1 is a resource allocation diagram of an MEC system for multiple types of users according to the present invention;

fig. 2 is a flow chart of the futures market optimal registration fee based on reservation contracts in the present invention;

FIG. 3 is a flow chart of an auction algorithm under full information in the present invention;

FIG. 4 is a flow chart of the auction algorithm under incomplete information in the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.

In the present embodiment, as shown in fig. 1 to 4, a method for allocating and pricing mobile edge computing resources based on futures market and spot market includes the following steps:

1) step 1, building a mobile edge computing system;

step 101, the mobile edge computing system comprises a plurality of MEC servers, a plurality of users and an intermediate agent;

all MEC servers in the mobile edge computing system are recorded as a set M ═ {1, 2., M }, where the computing resource owned by the jth server is recorded as R_j；

All users in the system are denoted as a set N ═ 1, 2. Contract users and random users. The contract user needs to register in advance, pay a certain registration fee r, and use the reserved resource according to the contract agreement; purchasing resources by random users according to needs; defining a variable alpha_iE {0,1} represents the user type, α_i1 indicates that user i is a contracting user, α_i0 means that user i is a random user; the registration policy of all users in the system is recorded as α ═ α₁,...,α_i,...,α_N}；

The MEC edge server manages resources by hiring an intermediate agent and pays a management fee denoted c₀；

102, constructing a futures market resource allocation model, and simultaneously establishing a profit model of the MEC server and a contract user in the futures market;

if the user i is a contract user, contract terms need to be signed with the MEC server and are recorded as R_i＝{D_i,P_i,l_iIn which D is_iIndicating the resource requirements of the contracting subscriber, P_iDenotes a unit price of a resource,/_iRepresenting an indemnity unit price;

definition of beta_ijFor the resource contribution rate of MEC server j to contract user i, i.e. the percentage of resources allocated by server j to contract user i, the resource contribution rate is limited by the constraint

If the user i is a contract user, the user i can negotiate with an intermediate agent to sign a contract and reserve resources to an MEC server, namely futures contracts; the benefit of MEC server j in the futures market can be expressed as:

in the formula B (d)_i,D_i) Indicating the resource d allocated to user i when MEC server_iResources D less than agreed in contract_iAt this time, the MEC server needs to pay the compensation to the contracting user i, expressed as:

if the computing resource allocated to the contract user i is d_iThe profit of the contracting subscriber i can be expressed as

In the formulaThe marginal profit of the contract user i unit resource.

103, constructing a spot market resource allocation model, and simultaneously establishing a profit model of the MEC server and the random user in the spot market;

if the user i is a random user, the user i needs to compete with other random users for the remaining computing resources in the spot market. Definition o ═ { o₁₁,...,o_NMIs the match matrix of MEC server and all random users, where o_ij1 represents that the random user i successfully bids to obtain the resource of the MEC server j; the benefit of MEC server j in the spot market can be expressed as:

in the formula p^aPaying a price, phi, for a random user unit resource^aRepresenting the cost per unit of resources paid by the MEC server to the staging agent,represents the marginal cost of allocation of a unit resource by MEC server j,is the transaction amount of the resource;

in the spot market, the random user bids on the resources with the maximum willingness-to-pay, and the user who successfully bids can complete the calculation task by utilizing the allocated resources. The benefit of random user i on the spot market can be expressed as:

in the formula p^bRepresenting a userPrice per unit of resource, phi, paid to MEC server^bTo pay the unit resource cost to the staging agent,representing a resource transaction amount;

2) step 2, determining the total income of the MEC server in futures and spot markets as an objective function, and constructing an optimization model for maximizing the total income of the MEC server under the resource limited constraint and the resource distribution balance constraint;

the MEC server total revenue maximization problem is shown as follows:

the constraint conditions are as follows:

where C1 represents a resource constraint that ensures that each MEC server accepts a user's purchase request within its capabilities; c2 is a resource allocation balance condition to ensure that the amount of resources allocated to the user by all MEC servers is equal to the total amount of resources received by the user;

3) step 3, constructing a futures market resource allocation algorithm based on a reservation contract to process the optimization problem obtained in the step 2 to obtain the optimal registration fee;

step 301, in the futures market under the complete information, all MEC servers and intermediate agents have complete contract user information, so that the resource contract provided for contract user i needs to meet individual rationality constraint, and each contract user is guaranteed to have non-negative income, which is expressed as follows:

in the futures market under incomplete information, the MEC server and the user only know limited information (e.g., marginal income of the user and marginal cost of the server), and the resource contract provided to the contracting user i needs to satisfy:

step 303, initializing variables, including registration policies of the userRegistration fee r₀0, MEC maximum profit U_max0, and 0 is the iteration number k;

step 304, the intermediate agent collects the information of the MEC server and the user and publishes registration cost;

step 305, each user calculates the earnings in the spot market and the futures market according to the registration strategy, which are respectively expressed as:

step 306, calculating the total profit of all MEC servers as:

step 307, the total profit of the MEC server and the current maximum value U_maxMake a comparison ifModifying a registration fee r^*＝r_kAnd updating the maximum profit value to

Step 308, if the user i is a contract user, when the contract user is a contract userWhen the user i changes the registration policy to:if the user i is a random user, whenWhen the user i changes the registration policy to:

step 309, new iteration number, new registration fee and total profit of MEC server are:

k＝k+1，r_k+1＝r_k+ τ, where τ is the iteration step;

step 310, the steps 305 to 309 are repeated until the total income increment of the MEC server is smaller than a given threshold value;

4) step 4, constructing an auction algorithm of the spot market under complete information to obtain a matching matrix of the MEC server and all random users, and calculating a benefit model of the edge server;

step 401, each random user purchases computing resources in real time as needed in the spot market. And the random user bids on the computing resource according to the evaluation. Definition v_ij＝g(ζ_j,ρ_ij) Representing random usersi valuation of MEC server j resources, v_ijIt can also be called the maximum willingness-to-pay of the random user i for the MEC server j resource;for the computational performance of MEC server j, ρ_ijRepresenting the preference of a random user i to an MEC server j; all random users estimate the different MEC server computing resources by Ψ ═ v₁₁,...,v_ij,...,v_NM)；

Step 402, recording the remaining resources of all edge servers as R '═ R'₁,...,R'_MTherein of

In step 403, each edge server has a lowest acceptable price, i.e. an edge server ask price, denoted as a_j；

Step 404, the broker publishes the remaining resource R ' ═ R ' of all MEC servers '₁,...,R'_MAnd each random user estimates v according to the value_ijBidding resources, v_ij∈Ψ；

Step 405: agent pair satisfies v_ij≥a_jThe bids of all random users are sorted;

step 406: depending on the total price, ifEdge server allocating resources to bidsThe largest random user;

step 407, random userThe opportunity to obtain other server resources is lost, i.e.

Step 408, repeating the steps 405 to 407 until any one of the following termination conditions is met: (1) the edge server has no residual resource to be distributed; (2) all random users have been allocated resources.

Step 409, under complete information, the MEC server does not need to pay service fee to the agent, and the income of unit resource of the server is the estimation of the user, and the transaction amount is equal to the demand amount of the random user i, so that phi exists^a＝0，p^a＝v_ij，The benefits of the edge server are:

5) step 5, constructing an auction algorithm of the spot market under incomplete information to obtain a matching matrix, and meanwhile, calculating a benefit model of the edge server;

step 501, defining the bid price of the random user as b ═ b 1., bn }, defining the server ask price as a ═ a 1., am }, and representing the computing resource supply amount submitted to the agent by the server j as

Step 502, arranging a to-be-priced a in a non-descending order_m≥a_m-1≥...≥a₁；

Step 503, arranging the bids b in a non-ascending order b₁≤b₂≤...≤b_n；

Step 504, ifAnd isThe transaction amount of buyer i is the resource demand amount when it bids, i.e. the amount of the resource demandSeller j has a transaction amount of

Step 505, ifAnd isThe transaction amount of seller j is the resource supply amount at the time of bid by buyer, i.e.Transaction amount of buyer i is

If buyer i successfully obtains computing resources in the auction, the benefit is step 506Otherwise V_i＝0；

Step 507, if the seller j successfully sells the computing resource in the auction, the benefit isOtherwise U_j＝0；

Step 508, the edge server benefits in the two markets are:

the above description is only a preferred embodiment of the present invention, and is not intended to limit the technical scope of the present invention, so that any minor modifications, equivalent changes and modifications made to the above embodiment according to the technical spirit of the present invention are within the technical scope of the present invention.