CN115133973A - A satellite lightweight distributed orchestration system and method - Google Patents

Abstract

The invention belongs to the technical field of satellite communication, and relates to a lightweight distributed arrangement system and a lightweight distributed arrangement method for satellites, which comprise the following steps: the scheduling device layer and the task unloading satellite layer; the orchestrator layer comprises a plurality of orchestrators, each orchestrator corresponds to a constellation consisting of a plurality of load satellites in a certain domain in the task unloading satellite layer, and the orchestrators are used for distributing resources to the load satellites in the constellation; the load satellite is used for providing resources for serving ground users or providing shared resources for other satellites, the shared resources among the satellites adopt a market trading mechanism to form contracts, and the contracts are announced to all editors in the editors layer. The light-weight inter-satellite interaction mechanism based on the contract theory can realize reasonable resource distribution of the inter-satellite cooperative task through one inter-satellite interaction, and compared with the existing inter-satellite interaction method mainly based on distributed optimization, the method can greatly reduce inter-satellite communication overhead.

Description

A satellite lightweight distributed orchestration system and method

technical field

The invention relates to a satellite lightweight distributed orchestration system, method and readable storage medium, belonging to the technical field of satellite communications, in particular to a lightweight inter-satellite interaction mechanism based on contract theory in a space-based edge communication scenario.

Background technique

With the rapid development of communication technology, the terrestrial wireless network that has evolved from 1G to 5G has served most users and solved basic communication problems. However, terrestrial wireless networks rely heavily on infrastructure and cannot cover oceans and sparsely populated areas. To this end, satellite networks, as a promising technology with broad coverage capabilities, can achieve on-demand coverage anytime anywhere in the world.

However, the existing satellite network is centrally managed and controlled by the ground control center, which cannot support the rapid development of the future space-based network. The disadvantage is that the invulnerability and stability of the satellite network are greatly reduced. In addition, this is an extremely time-consuming process because the user's request must be generated by the ground control center and transmitted to all satellites around the world. Therefore, the distributed on-board online management and control mechanism should arise from time to time. There are the following cases of traditional distributed resource allocation methods. For example, the method of cooperative multi-agent deep reinforcement learning framework improves radio resources and improves transmission efficiency. However, this method is difficult to ensure stable system performance, and the stability of system performance is poor and depends heavily on On-board decision-making model is offloaded through distributed computing based on particle swarm optimization (PSO) algorithm, but PSO algorithm is a heuristic method lacking theoretical guarantee and poor performance robustness.

SUMMARY OF THE INVENTION

In view of the above problems, the purpose of the present invention is to provide a satellite lightweight distributed orchestration system, method and readable storage medium, which is based on the lightweight inter-satellite interaction mechanism of contract theory, and can be Compared with the existing inter-satellite interaction methods mainly based on distributed optimization, the rational allocation of resources for inter-satellite collaborative tasks can greatly reduce the inter-satellite communication overhead.

In order to achieve the above object, the present invention proposes the following technical solutions: a satellite lightweight distributed scheduling system, comprising: a scheduler layer and a task offloading satellite layer; the scheduler layer includes several schedulers, each scheduler and task offloading The satellite layer corresponds to a constellation composed of several payload satellites in a certain domain. The scheduler is used to allocate resources to the payload satellites in the constellation; payload satellites are used to provide resources for serving ground users or provide shared resources for other satellites. The shared resources between them use the market transaction mechanism to form contracts and announce the contracts to all the orchestrators in the orchestrator layer.

Further, the market transaction mechanism is a bilateral buying and selling relationship between resource demand stars and resource supply stars. The resource demand star requests CPU resources from the resource supply star and pays according to its private type. The resource supply star provides CPU resources from the resource demand star. Get paid.

Further, the contract includes the CPU resources that the resource supply star can provide and the funds that the resource demand star can pay.

Further, the resource demand star utility function is as follows:

U _R (θ,q(θ),t(θ))=θv(q(θ))-t(θ) =θ(1-e- ^q(θ) )-t(θ)

Among them, _UR is the utility function, θ is the private type representing the resource demand star; t(θ) is the funds paid by the resource demand star, and v(q(θ)) is when the resource demand star obtains CPU resources from the resource supply star Profit when the amount is q(θ); q(θ) is the CPU resource provided by the resource supply star.

Further, the utility function of the resource supply star is as follows:

U _P (q(θ),t(θ))=t(θ)-c(q(θ))=t(θ)-c ₀ q ² (θ)

Among them, U _p is the utility function, t(θ) is the funds paid by the resource demand star, c(q(θ)) is the reduction in utility when the resource supply star provides resources to the resource demand star; q(θ) is the resource CPU resources provided by the supply star; c ₀ is a constant coefficient.

The present invention also discloses an optimization method for satellite lightweight distributed scheduling, which is used in any of the above-mentioned satellite lightweight distributed scheduling systems, comprising the following steps: when the scheduling system needs to perform the scheduling among multiple schedulers During the collaborative task, determine the parameters of the collaborative task and initialize the contract of each user; determine whether the information between the resource demand star and the resource supply star is symmetrical; if the information is symmetrical, solve the optimization equation under the condition of only considering individual rational constraints; If the information is asymmetric, the optimization equation is solved considering both the individual rational constraints and the incentive compatibility constraints; the optimal cooperative task resource allocation contract is obtained according to the solution results.

Further, the optimization equation is:

是资源需求星支付的资金，c(q(θ))是当资源供应星向资源需求星提供资源时效用的降低量；q(θ)是资源供应星提供的CPU资源；c₀是常系 数，IR为个体理性约束条件；IC为激励相容约束条件；s.t.为约束条件；

是类型为

的资源需求星所支付的定金；

是资源需求星的类型为

时，资源供应星提供给资 源供应星的CPU资源；

与θ均为资源需求星的私有类型，且

f(θ)是θ的概率 密度函数。where U _p is the utility function,

is the funds paid by the resource demand star, c(q(θ)) is the reduction in utility when the resource supply star provides resources to the resource demand star; q(θ) is the CPU resource provided by the resource supply star; c ₀ is a constant coefficient , IR is the individual rational constraint; IC is the incentive compatibility constraint; st is the constraint;

is of type

The deposit paid by the resource demand star;

is the type of resource demand star

, the resource supply star provides CPU resources to the resource supply star;

and θ are private types of resource demand stars, and

f(θ) is the probability density function of θ.

Further, information symmetry means that the resource supply star knows θ, and the final solution of the cooperative task resource allocation contract is:

Among them, {q*(θ), t*(θ)} is the final solution of the cooperative task resource allocation contract problem when the information is symmetric, and W(θ) is the Lambert W function.

Further, information asymmetry information symmetry means that the resource supply star knows f(θ), and the final solution of the cooperative task resource allocation contract is:

是信息不对称时协作任务资源分配合同问题中的最终解，W(θ)是Lambert W函数，τ是积分变量，λ是指数分布的参数。in,

is the final solution in the cooperative task resource allocation contract problem when information is asymmetric, W(θ) is the Lambert W function, τ is the integral variable, and λ is the parameter of the exponential distribution.

The present invention also discloses a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and the computer program is executed by a processor to implement any one of the above-mentioned light-weight distributed scheduling methods for satellites.

Due to adopting the above technical solutions, the present invention has the following advantages: the present invention is based on the contract theory, adopts a novel lightweight interaction method, solves the task coordination problem in the space-based edge scenario, realizes distributed resource negotiation and lightweight The interactive mode greatly reduces the communication overhead on the satellite.

Description of drawings

1 is a schematic diagram of a satellite lightweight distributed orchestration system according to an embodiment of the present invention;

Fig. 2 is the variation diagram of CPU resource q(θ) provided by resource supply star with θ in an embodiment of the present invention;

Fig. 3 is the variation diagram of the fund t(θ) paid by the resource demand star with θ in an embodiment of the present invention;

的变化图；FIG. 4 is the utility function of the resource demand star in an embodiment of the present invention as a function of

change diagram;

Fig. 5 is the variation diagram of the utility function of resource supply star with θ in an embodiment of the present invention;

6 is a graph showing the variation of the utility function of the resource demand star with θ in an embodiment of the present invention;

FIG. 7 is a graph showing the change of social welfare with θ in an embodiment of the present invention.

Detailed ways

In order to make those skilled in the art better understand the technical solutions of the present invention, the present invention is described in detail through specific embodiments. It should be understood, however, that the specific embodiments are provided only for a better understanding of the present invention, and they should not be construed to limit the present invention. In describing the present invention, it is to be understood that the terms used are for the purpose of description only and should not be construed to indicate or imply relative importance.

In order to solve the deficiencies in the prior art, the present invention proposes a satellite lightweight distributed orchestration system and method. Compared with traditional distributed optimization, its lightweight contract mechanism does not require frequent interaction with other orchestrators, and the satellite network There are multiple control centers distributed in the center, and each control center can determine the resource allocation requirements. Its advantages are reflected in the nearest decision-making, nearest response, and low propagation delay. The CPU resource allocation problem for cooperative tasks can be solved without frequently interacting with satellites. The requester only needs to choose one of the contracts according to its own type (private variable), and then the provider provides the resource directly. During the above period, the communication between the requester and the provider is only once, which greatly reduces the inter-satellite communication overhead. Below in conjunction with the accompanying drawings, the solution of the present invention will be described in detail through embodiments.

Example 1

This embodiment discloses a satellite lightweight distributed orchestration system, as shown in FIG. 1, including: an orchestrator layer and a task offloading satellite layer; Corresponding to a constellation composed of several payload satellites in a certain domain in the layer, the scheduler is used to allocate resources to the payload satellites in the constellation; payload satellites are used to provide resources for serving ground users or to provide shared resources for other satellites. The shared resources of the network adopt the market transaction mechanism to form contracts and announce the contracts to all the orchestrators in the orchestrator layer.

The market transaction mechanism is a bilateral buying and selling relationship between resource demand stars and resource supply stars. The resource demand star requests CPU resources from the resource supply star and pays according to its private type. The resource supply star obtains rewards from the resource demand star by providing CPU resources. . The contract includes the CPU resources that the resource supply star can provide and the funds that the resource demand star can pay.

The system in this embodiment can solve the problem of resource allocation for satellite task offloading. It is assumed that users offload tasks to satellites with limited CPU resources, especially for collaborative tasks that require cross-domain communication between multiple satellites. As shown in FIG. 1 , the satellite node a located in the organizer 1 needs to collect images from the satellite node b of the organizer N. In this embodiment, user tasks that must occupy tasks in other schedulers to offload satellite resources are called cooperative tasks.

均是随机变量Θ中的一个取值，θ是资源需求星 的私有类型，是一个标量；

也是资源需求星的私有类型，且

它是W的递增函 数。假设提供者只知道请求者的私有类型θ的分布，而不知道θ的准确值。假设 s(W)＝-λln(1-W)且W～U[0,1]，则私有类型θ服从指数分布，其中λ为正常数。Let the random variable W denote the urgency or importance of the task requested by the requester. Let Θ=s(W) denote a random variable of the private type of the resource requester, Θ and

Both are a value in the random variable Θ, and Θ is the private type of the resource demand star, which is a scalar;

is also a private type of resource requirement stars, and

It is an increasing function of W. It is assumed that the provider only knows the distribution of the requester's private type θ, but does not know the exact value of θ. Assuming s(W)=-λln(1-W) and W～U[0,1], the private type θ obeys an exponential distribution, where λ is a positive number.

The resource demand star utility function is as follows:

U _R (θ,q(θ),t(θ))=θv(q(θ))-t(θ) =θ(1-e- ^q(θ) )-t(θ)

Among them, _UR is the utility function, θ is the private type representing the resource demand star; t(θ) is the funds paid by the resource demand star, and v(q(θ)) is when the resource demand star obtains CPU resources from the resource supply star The profit of the requester when the amount is q(θ); q(θ) is the CPU resource provided by the resource supply star. It is assumed here that v(q(θ)) is a concave function of q(θ). Due to marginal utility, v'(q)>0 and v'(q)<0. It is worth mentioning that the more urgent the task, the more The larger the type, the higher the utility.

The utility function of the resource supply star is as follows:

U _P (q(θ),t(θ))=t(θ)-c(q(θ))=t(θ)-c ₀ q ² (θ)

Among them, U _p is the utility function, t(θ) is the funds paid by the resource demand star, considering that too much resource contribution will reduce the profit of the provider, because the provider also needs to provide services for its own users, c(q( θ)) is the reduction in utility when the resource supply star provides resources to the resource demand star, obviously c'(q)>0 and c'(q)>0 are more reasonable, this setting can prevent the provider from contributing too much resources And it reduces its own income excessively; q(θ) is the CPU resource provided by the resource supply star; c ₀ is a constant coefficient.

Embodiment 2

Considering that the cooperative mission requires multiple satellites to communicate across domains, the following optimization problem (P1) needs to be formulated, which aims to maximize the utility of providers to motivate them to actively contribute CPU resources. Meanwhile, the contract {q(θ), t(θ)} is designed based on the optimization problem P ₁ in this embodiment.

Based on the same inventive concept, this embodiment discloses an optimization method for satellite lightweight distributed orchestration, which is used in any of the above-mentioned satellite lightweight distributed orchestration systems, including the following steps:

When the orchestration system needs to perform collaborative tasks among multiple orchestrators, determine the parameters of the collaborative tasks, and initialize each user's contract;

Determine whether the information between the resource demand star and the resource supply star is symmetrical;

If the information is symmetric, the optimization equation is solved under the condition that only individual rational constraints are considered;

If the information is asymmetric, the optimization equation is solved considering both the individual rational constraints and the incentive compatibility constraints; the optimal cooperative task resource allocation contract is obtained according to the solution results.

The optimization equation is:

是资源需求星支付的资金，c(q(θ))是当资源供应星向资源需求星提供资源时效用的降低量；q(θ)是资源供应星提供的CPU资源；c₀是常系 数，IR为个体理性约束条件，个体理性约束表示请求者的效用是非负的，这意味着请 求者意愿参与协作任务并选择合约；IC为激励相容约束条件，其表示每个请求者必须 更偏好于选择为其私有类型θ而设计的合约，这意味着选择偏离私有类型的合约会降 低自身的效用。s.t.为约束条件；

是类型为

的资源需求星所支付的定金；

是资 源需求星的类型为

时，资源供应星提供给资源供应星的CPU资源；；

与θ均为资源 需求星的私有类型，且

f(θ)是θ的概率密度函数。where U _p is the utility function,

is the funds paid by the resource demand star, c(q(θ)) is the reduction in utility when the resource supply star provides resources to the resource demand star; q(θ) is the CPU resource provided by the resource supply star; c ₀ is a constant coefficient , IR is the individual rationality constraint, the individual rationality constraint indicates that the utility of the requester is non-negative, which means that the requester is willing to participate in the cooperative task and choose the contract; IC is the incentive compatibility constraint, which indicates that each requester must prefer The choice of contracts designed for their private type θ means that choosing a contract that deviates from the private type reduces its own utility. st is the constraint condition;

is of type

The deposit paid by the resource demand star;

is the type of resource demand star

When , the resource supply star provides CPU resources to the resource supply star;

and θ are private types of resource demand stars, and

f(θ) is the probability density function of θ.

In the case _of information symmetry, the IC constraint in the optimization problem P1 does not exist, and the exact value of θ can be obtained by the provider. It is obvious that IR is a tight constraint without IC (i.e. equals and holds), so the optimization problem P ₁ can be reduced to P ₂ .

Information symmetry means that the resource supply star knows θ. At this time, the final solution of the cooperative task resource allocation contract is:

Among them, {q*(θ), t*(θ)} is the final solution of the cooperative task resource allocation contract problem when the information is symmetric, and W(θ) is the Lambert W function.

A feasible resource allocation mechanism needs to satisfy that the provider can design the contract even in the case of asymmetric information, and the requester can maximize his utility through the contract designed by the provider. Therefore, the optimization problem P ₁ in the case of information asymmetry is solved in this embodiment. Considering that θ is a continuous random variable, resulting in an infinite number of IR constraints and IC constraints. In order to simplify the problem P ₁ , the following equivalent transformations are performed in this embodiment:

Furthermore, the IR constraint can be simplified to u _R (θ, θ) ≥ u _R (θ, 0) ≥ u _R (0, 0) according to the IC constraint and the monotonicity of v(q(θ)). If u _R (0,0)=0 is assumed, then the IR constraint must be maintained for any θ∈[0,+∞). Based on the above derivation, this embodiment transforms the optimization problem P ₁ into an optimization problem P ₃ , as follows:

Considering that there are two decision variables q(θ) and t(θ) in problem _P3 , the decision variables of the above optimization problem will be simplified. First, the requester's target function, which looks like this:

Then, it can be concluded that u' _R (θ, θ)=1-e- ^q(θ) , and the variable t(θ) is eliminated by the equation constraint below.

Therefore, the optimization problem P4 is finally obtained:

Wherein, H(q(θ), θ)=(θ−λ)(1−e− ^q(θ) )−c ₀ q ² (θ). In order to solve the above optimization problem, it is assumed that the constraints are always established, and then the objective function is directly solved.

Information asymmetry Information symmetry means that the resource supply star knows f(θ), and the final solution of the cooperative task resource allocation contract is:

是信息不对称时协作任务资源分配合同问题中的最终解，W(θ)是Lambert W函数，τ是积分变量，λ是指数分布的参数。in,

is the final solution in the cooperative task resource allocation contract problem when information is asymmetric, W(θ) is the Lambert W function, τ is the integral variable, and λ is the parameter of the exponential distribution.

Embodiment 3

In order to verify the scheme in the present invention, a simulation example is introduced in this embodiment, and the simulation parameters are shown in Table 1:

Table 1 Simulation parameter table

Among them, in this embodiment, "linear price mechanism" is selected as the baseline scenario for comparison, and its bidding expression is: t _l (θ)=p(θ)q(θ)=p ₀ q(θ)θ. The performance simulation curves of this mechanism are shown in Figures 2 and 3.

Figure 2 illustrates the relationship between q(θ) and θ, that is, the demand for CPU resources increases with the increase of the private type θ, which proves that the _monotonicity mentioned in P4 holds. In addition, it can be seen from Figure 2 that the CPU resource q(θ) with the information symmetry mechanism as the upper limit is higher than that of the information asymmetry mechanism, indicating that the information asymmetry reduces the transaction efficiency to a certain extent. It is worth mentioning that due to the difference in payment t(θ), the contract mechanism in this embodiment performs better than the traditional linear price mechanism.

的结果是

的上界(即

大于

恒成立)。综上所述，可以根据图(a)和图(b)验 证q(θ)和t(θ)的单调性。Figure 3 shows the relationship between t(θ) and θ, the payout t(θ) increases with the private type θ, and

The result is

the upper bound of (i.e.

more than the

Heng established). To sum up, the monotonicity of q(θ) and t(θ) can be verified according to Figures (a) and (b).

变化到：Figure 4 shows that the incentive compatibility constraint, that is, the IC constraint mentioned above, holds, and is explained as follows: The value of the private type θ is chosen to be equal to 3 in the simulation. Computes the requester utility of type θ and converts the value of the contract from

changes to:

随后，我们变化θ的取值，分别令θ等于6和9时，可以得到请求者效用的最大值取 在

及

之处，因此，说明前文所提的IC约束是成立的。At this time, from the simulation result graph, it can be seen that when θ is equal to 3, the maximum value of the requester's utility is taken as

Then, we change the value of θ, and when θ is equal to 6 and 9, respectively, the maximum value of the requester's utility can be obtained at

and

Therefore, it shows that the IC constraint mentioned above is established.

As shown in Figures 5, 6, 7, the utility of the provider increases with the private type θ, which means that the provider can earn more if the type of the requester is larger. The utility of the provider in the first best is higher than that of the second best, and under the previous parameter settings, the linear bidding mechanism is still the lowest utility compared with the second best. However, the utility of the requester in Fig. 6 is very different from that in Fig. 5, the bidding mechanism of the linear baseline in Fig. 6 outperforms the information-symmetric mechanism but worse than the information-symmetric mechanism, and the result of the information-symmetric mechanism is no longer information The upper limit of the asymmetric mechanism. This is because the goal of this paper is to maximize the utility of the provider to incentivize the provider to actively contribute CPU resources, not to maximize the utility of the requester. Finally, comparing the social benefits in Figure 7, it can be seen that the mechanism in this embodiment performs better than the linear bidding mechanism.

Embodiment 4

Based on the same inventive concept, this embodiment discloses a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement any of the above-mentioned lightweight distributed orchestration of satellites method.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: Modifications or equivalent replacements are made to the specific embodiments of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention shall be included within the protection scope of the claims of the present invention. The above contents are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the technical scope disclosed in the present application can easily think of changes or replacements, which should cover within the scope of protection of this application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (10)

1. a satellite light-weight distributed scheduling system, is characterized in that, comprises: arranger layer and task unloading satellite layer; The orchestrator layer includes a plurality of orchestrators, each of which corresponds to a constellation composed of a number of payload satellites in a certain domain in the task offloading satellite layer, and the orchestrator is used to perform a constellation on the payload satellites in the constellation. Resource allocation; the load satellites are used to provide resources for serving ground users or to provide shared resources for other satellites, and the shared resources between satellites use a market transaction mechanism to form contracts, and report to all arrangements in the orchestrator layer. The device declares the contract. 2. The satellite lightweight distributed orchestration system as claimed in claim 1, wherein the market transaction mechanism is a bilateral buying and selling relationship between the resource demand star and the resource supply star, and the resource demand star requests the CPU from the resource supply star Resources, and pay according to the private type of resource demand stars, and resource supply stars get paid from resource demand stars by providing CPU resources. 3 . The satellite lightweight distributed orchestration system according to claim 2 , wherein the contract includes CPU resources that can be provided by the resource supply star and funds that can be paid by the resource demand star. 4 . 4. The satellite lightweight distributed orchestration system according to claim 2, wherein the utility function of the resource requirement satellite is the following formula: U _R (θ,q(θ),t(θ))=θv(q(θ))-t(θ) =θ(1-e- ^q(θ) )-t(θ) Among them, U _R () is the utility function, θ is the private type representing the resource demand star; t(θ) is the funds paid by the resource demand star, and v(q(θ)) is when the resource demand star is obtained from the resource supply star. The profit of the requester when the amount of CPU resource is q(θ); q(θ) is the CPU resource provided by the resource supply star. 5. The satellite lightweight distributed orchestration system as claimed in claim 2, wherein the utility function of the resource supply star is the following formula: U _P (q(θ),t(θ))=t(θ)-c(q(θ)) =t(θ)-c ₀ q ² (θ) Among them, U _p () is the utility function, t(θ) is the funds paid by the resource demand star, c(q(θ)) is the reduction in utility when the resource supply star provides resources to the resource demand star; q(θ) is the CPU resource provided by the resource supply star; c ₀ is a constant coefficient. 6. An optimization method for satellite lightweight distributed orchestration, characterized in that, for the satellite lightweight distributed orchestration system as described in any one of claims 1-5, comprising the following steps: When the orchestration system needs to perform a collaborative task between multiple orchestrators, determine the parameters of the collaborative task, and initialize each user's contract; Based on the initialized contract, determine whether the information between the resource demand star and the resource supply star is symmetrical; If the information is symmetric, the optimization equation is solved under the condition that only individual rational constraints are considered; If the information is asymmetric, the optimization equation is solved considering both the individual rational constraints and the incentive compatibility constraints; The optimal cooperative task resource allocation contract is obtained according to the solution result. 7. The satellite lightweight distributed orchestration method according to claim 6, wherein the optimization equation is: P ₁ :

Among them, U _p () is the utility function,

is the funds paid by the resource demand star, c(q(θ)) is the reduction in utility when the resource supply star provides resources to the resource demand star; q(θ) is the CPU resource provided by the resource supply star; c ₀ is a constant coefficient , IR is the individual rational constraint; IC is the incentive compatibility constraint; st is the constraint;

is of type

The deposit paid by the resource demand star;

is the private type of the resource requirement star is

, the resource supply star provides CPU resources to the resource supply star;

and θ are private types of resource demand stars, and

f(θ) is the probability density function of θ, f(θ) is the probability density function of private type θ, and P1 is the _first optimization problem. 8. The satellite lightweight distributed orchestration method according to claim 7, wherein the information symmetry means that the resource supply star knows the private type θ, and the final solution of the cooperative task resource allocation contract is:

Among them, {q*(θ), t*(θ)} is the final solution of the cooperative task resource allocation contract problem when the information is symmetric, and W(θ) is the Lambert W function. 9. The satellite lightweight distributed orchestration method according to claim 7, wherein the information asymmetry means that the information asymmetry means that the resource supply star only knows the probability density function f(θ) of the private type θ, The final solution of the cooperative task resource allocation contract is:

in,

is the final solution in the cooperative task resource allocation contract problem when information is asymmetric, W(θ) is the Lambert W function, τ is the integral variable, and λ is the parameter of the exponential distribution. 10. A computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and the computer program is executed by a processor to realize the satellite according to any one of claims 6-9. A lightweight distributed orchestration approach.

Images