CN115133973A - Lightweight distributed arrangement system and method for satellite - Google Patents
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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
Technical Field
The invention relates to a lightweight distributed arrangement system, a method and a readable storage medium for satellites, belongs to the technical field of satellite communication, and particularly relates to a lightweight inter-satellite interaction mechanism based on contract theory under a space-based edge communication scene.
Background
With the rapid development of communication technology, a terrestrial wireless network evolved from 1G to 5G has served most users, solving the problem of basic communication. However, terrestrial wireless networks rely heavily on infrastructure and cannot cover oceans and sparsely populated areas. For this reason, satellite networks, a promising technology with extensive coverage capability, can achieve on-demand coverage at any time in any region of the world.
However, the existing satellite network is centrally controlled by a ground control center, and cannot support the rapid development of the future space-based network, and the disadvantage is that the survivability and stability of the satellite network are greatly reduced. Furthermore, this is an extremely time consuming process, as the user's request must be generated by the ground control center and transmitted to all satellites around the world. Therefore, the distributed online onboard control mechanism is generated in time. The following cases exist in the conventional distributed resource allocation method, for example: radio resources are improved and transmission efficiency is improved by a method of a collaborative multi-agent deep reinforcement learning framework, but the method is difficult to ensure stable system performance, the stability of the system performance is poor and the method depends heavily on parameter selection; an on-satellite decision model is unloaded through distributed computing based on a Particle Swarm Optimization (PSO) algorithm, but the PSO algorithm belongs to an initiating method, lacks of theoretical guarantee and is poor in performance robustness.
Disclosure of Invention
In view of the foregoing problems, an object of the present invention is to provide a lightweight distributed arrangement system and method for satellites, and a readable storage medium, which can implement reasonable resource allocation for inter-satellite cooperative tasks through one inter-satellite interaction based on a lightweight inter-satellite interaction mechanism of a contract theory, and can greatly reduce inter-satellite communication overhead compared to the existing inter-satellite interaction method mainly based on distributed optimization.
In order to achieve the purpose, the invention provides the following technical scheme: a lightweight distributed orchestration system of satellites comprising: 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 transaction mechanism to form contracts, and the contracts are announced to all schedulers in the orchestrator layer.
Further, the market transaction mechanism is the amount bilateral buying and selling relationship between the resource demand star and the resource supply star, the resource demand star requests CPU resources from the resource supply star and pays according to the private type of the resource demand star, and the resource supply star obtains payment from the resource demand star by providing the CPU resources.
Further, the contract includes the CPU resources that the resource provisioning 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)
wherein, U R As a utility function, θ is a private type representing a resource demand star; t (theta) is the fund paid by the resource demand star, and v (q (theta)) is the profit when the resource demand star obtains the amount of CPU resource q (theta) from the resource supply star; q (θ) is the CPU resource provided by the resource provisioning 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)
wherein, U p As a utility function, t (θ) is the funding paid by the resource demand star, c (q (θ)) is the amount of reduction in utility when the resource supply star provides the resource to the resource demand star; q (θ) is a CPU resource provided by the resource provider; c. C 0 Is a constant coefficient.
The invention also discloses an optimization method for the lightweight distributed arrangement of the satellite, which is used for the lightweight distributed arrangement system of any one of the satellites and comprises the following steps: when the orchestration system needs to perform the collaborative tasks among a plurality of orchestrators, determining parameters of the collaborative tasks and initializing contracts of each user; judging whether the information between the resource demand star and the resource supply star is symmetrical or not; if the information is symmetrical, solving an optimization equation under the condition of only considering individual rational constraint; if the information is asymmetric, solving the optimization equation by simultaneously considering individual rational constraint conditions and excitation compatibility constraint conditions; and obtaining an optimal cooperative task resource allocation contract according to the solving result.
Further, the optimization equation is:
wherein, U p In order to be a function of the utility,
is the funds paid by the resource demand star, and c (q (θ)) is the reduction in utility when the resource supply star provides the resource to the resource demand star; q (θ) is a CPU resource provided by the resource provider; c. C 0 Is a constant coefficient, IR is an individual rational constraint; IC is an excitation compatibility constraint; s.t. is a constraint condition;
is of type
The resource demand star paid a subscription;
the resource requirement star is of the type
Then the resource supply star provides the CPU resource of the resource supply star;
theta is a proprietary type of resource requirement star, and
f (θ) is a probability density function of θ.
Further, the information symmetry means that the resource supply star knows θ, and the final solution of the resource allocation contract of the cooperative task is as follows:
wherein, q (theta), t (theta) is the final solution of the collaborative task resource allocation contract problem when the information is symmetrical, and W (theta) is a Lambert W function.
Further, the information asymmetry information symmetry means that the resource supply satellite knows f (θ), and the final solution of the cooperative task resource allocation contract is as follows:
wherein,
the final solution of the cooperative task resource allocation contract problem when the information is asymmetric is shown, wherein W (theta) is a Lambert W function, tau is an integral variable, and lambda is a parameter of exponential distribution.
The invention also discloses a computer readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to realize the satellite lightweight distributed arrangement method.
Due to the adoption of the technical scheme, the invention has the following advantages: based on the contract theory, the invention adopts a novel lightweight interaction mode, solves the task cooperation problem under the space-based edge scene, realizes the distributed resource cooperation and lightweight interaction mode, and greatly reduces the on-satellite communication overhead.
Drawings
FIG. 1 is a schematic diagram of a lightweight distributed orchestration system for satellites according to an embodiment of the invention;
FIG. 2 is a diagram of the variation of CPU resource q (θ) provided by the resource provider with θ according to one embodiment of the present invention;
FIG. 3 is a graph of the funding t (θ) paid by the resource demand star as a function of θ in accordance with an embodiment of the present invention;
FIG. 4 is a utility function of a resource demand star in an embodiment of the present invention
A variation graph of (2);
FIG. 5 is a graph of utility function of a resource provider star as a function of θ in accordance with an embodiment of the present invention;
FIG. 6 is a graph of the utility function of the resource demand star as a function of θ in accordance with an embodiment of the present invention;
FIG. 7 is a graph of socio-welfare as a function of θ in accordance with an embodiment of the invention.
Detailed Description
The present invention is described in detail with reference to specific embodiments for better understanding of the technical solutions of the present invention. It should be understood, however, that the detailed description is provided for better understanding of the present invention only and should not be taken as limiting the present invention. In describing the present invention, it is to be understood that the terminology used is for the purpose of description only and is not intended to be interpreted as indicating or implying any relative importance.
Compared with the traditional distributed optimization, the lightweight contract mechanism of the invention does not need to frequently interact with other organizers, a plurality of control centers are distributed in a satellite network, each control center can determine the resource distribution requirement, and the invention has the advantages of being embodied in the aspects of nearby decision, nearby response, low propagation delay and the like. The problem of CPU resource allocation of cooperative tasks can be solved under the condition of not frequently interacting satellites. The requester only needs to select 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, and the inter-satellite communication overhead is greatly reduced. The invention will be described in detail below by way of examples with reference to the accompanying drawings.
Example one
The embodiment of the present invention discloses a lightweight distributed arrangement system for satellites, as shown in fig. 1, including: a orchestrator layer and a task offload 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 market transaction mechanism is a bilateral buying and selling relation between the resource demand star and the resource supply star, the resource demand star requests CPU resources from the resource supply star and pays according to the private type of the resource demand star, and the resource supply star obtains payment from the resource demand star by providing the CPU resources. The contract includes the CPU resources that the resource supplying star can provide and the funds that the resource requiring star can pay.
The system in the embodiment can solve the problem of resource allocation of satellite task unloading. Suppose a user offloads tasks to a satellite with limited CPU resources, particularly for collaborative tasks that require multiple satellites to communicate across domains. As shown in fig. 1, a satellite node a at the orchestrator 1 needs to collect images from a satellite node b of the orchestrator N. User tasks that must occupy task offload satellite resources within other organizers are referred to as collaborative tasks in this embodiment.
Let random variable WIndicating the urgency or importance of the task requested by the requester. Let Θ(s) denote a private type of random variable of the resource requestor, θ and
the theta is a value in a random variable theta, is a private type of the resource demand star and is a scalar;
is also a private type of resource demand star, and
it is an increasing function of W. It is assumed that the provider only knows the distribution of the private type θ of the requester and does not know the exact value of θ. Let s (W) — λ ln (1-W) and W to U [0, 1%]Then the private type θ follows an exponential distribution, where λ is a positive constant.
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)
wherein, U R As a utility function, θ is a private type representing a resource demand star; t (θ) is the funds paid by the resource demand star, and v (q (θ)) is the profit of the requester when the resource demand star obtains the amount of CPU resource from the resource supply star as q (θ); q (θ) is the CPU resource provided by the resource provisioning 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 larger the type of θ, the higher the utility.
The utility function of the resource provisioning star is the following equation:
U P (q(θ),t(θ))＝t(θ)-c(q(θ)) ＝t(θ)-c 0 q 2 (θ)
wherein, U p For the utility function, t (θ) is the capital paid by the resource demand star, and considering that excessive resource contribution will reduce the profit of the provider, since the provider also needs to provide services for its own users, c (q (θ)) is the time-dependent of when the resource supply star provides the resource demand star with the resourceDecrease in amount of use, apparently c' (q)>0 and c "(q)>0 is more reasonable, so the arrangement can prevent the provider from contributing too much resources and excessively reducing the self income; q (θ) is a CPU resource provided by the resource provider; c. C 0 Is a constant coefficient.
Example two
Considering that a cooperative mission requires multiple satellite cross-domain communications, the following optimization problem (P1) needs to be listed, aiming to maximize the utility of the provider, to motivate the provider to actively contribute CPU resources. Meanwhile, the optimization problem P is based on in the embodiment 1 A contract q (θ), t (θ) is designed.
Based on the same inventive concept, the embodiment discloses an optimization method for lightweight distributed arrangement of satellites, which is used for any lightweight distributed arrangement system of satellites and comprises the following steps:
when the orchestration system needs to perform a collaborative task among a plurality of orchestrators, determining parameters of the collaborative task, and initializing a contract of each user;
judging whether the information between the resource demand star and the resource supply star is symmetrical or not;
if the information is symmetrical, solving an optimization equation under the condition of only considering individual rational constraint;
if the information is asymmetric, simultaneously considering individual rational constraint conditions and excitation compatibility constraint conditions to solve the optimization equation; and obtaining an optimal cooperative task resource allocation contract according to the solving result.
The optimization equation is as follows:
wherein, U p In order to be a function of the utility,
is the capital paid by the resource demand star, and c (q (θ)) is the amount of reduction in utility when the resource supply star provides the resource to the resource demand star; q (θ) is a CPU resource provided by the resource provider; c. C 0 Is a constant, IR is an individual rational constraint condition, which means that the utility of the requester is non-negative, which means that the requester wants to participate in the collaborative task and select a contract; the IC is an incentive compliance constraint that indicates that each requester must prefer to select a contract designed for its private type θ, meaning that selecting a contract that deviates from the private type reduces its utility. s.t. is a constraint condition;
is of type
The resource demand star paid a subscription;
is the type of resource demand star is
Then the resource supply star provides the CPU resource of the resource supply star; (ii) a
Theta is a proprietary type of resource requirement star, and
f (θ) is a probability density function of θ.
In the case of information symmetry, the optimization problem P 1 The IC constraint in (a) does not exist and the exact value of θ can be obtained by the provider. It is clear that IR is tightly constrained (i.e., equal sign equal and true) without IC, so the optimization problem P 1 Can be simplified to P 2 。
The information symmetry means that the resource supply satellite knows theta, and the final solution of the resource allocation contract of the cooperative task is as follows:
wherein, q (theta), t (theta) is the final solution of the collaborative task resource allocation contract problem when the information is symmetrical, and W (theta) is a Lambert W function.
A feasible resource allocation mechanism needs to satisfy the requirement that the provider can design contracts even if the information is not symmetric, and the requester can maximize its utility through the contracts designed by the provider. Therefore, the optimization problem P in the case of information asymmetry is solved in the present embodiment 1 . Considering that θ is a continuous random variable, the number of IR constraints and IC constraints is infinite. To simplify problem P 1 In the present embodiment, the following equivalent conversion is performed:
furthermore, the IR constraint can be simplified to u according to the IC constraint and monotonicity of v (q (θ)) R (θ,θ)≥u R (θ,0)≥u R (0,0). If u is assumed R (0,0) ═ 0, then the IR constraint must be maintained for any θ e [0, + ∞). Based on the above derivation, the present embodiment will optimize the problem P 1 Conversion to optimization problem P 3 The following are:
in view of problem P 3 There are two decision variables q (theta) and t (theta) and so the decision variables of the above optimization problem will be simplified. First, the objective function of the requester is as follows:
subsequently, a conclusion u 'can be obtained' R (θ,θ)＝1-e -q(θ) The variable t (θ) is eliminated below with an equality constraint.
Therefore, the optimization problem P4 is finally obtained:
wherein H (q (θ), θ) ═ θ - λ (1-e) -q(θ) )-c 0 q 2 (theta). In order to solve the above optimization problem, it is assumed that constraint conditions are constantly satisfied, and an objective function is directly solved.
The information asymmetry information symmetry means that the resource supply satellite knows f (theta), and the final solution of the cooperative task resource allocation contract is as follows:
wherein,
the final solution of the cooperative task resource allocation contract problem when the information is asymmetric is shown, wherein W (theta) is a Lambert W function, tau is an integral variable, and lambda is a parameter of exponential distribution.
EXAMPLE III
In order to verify the scheme of the present invention, the present embodiment introduces a simulation example, wherein the simulation parameters are shown in table 1:
TABLE 1 simulation parameter Table
In this embodiment, a "linear price mechanism" is selected as a baseline scene for comparison, and the bid expression is as follows: t is t l (θ)＝p(θ)q(θ)＝p 0 q (θ) θ. The performance simulation curves of the mechanism are shown in fig. 2 and fig. 3.
FIG. 2 illustrates the relationship between q (θ) and θ, i.e., the demand for CPU resources increases with increasing private type θ, which demonstrates that P 4 The monotonicity mentioned in (1) holds. In addition, as can be seen from fig. 2, the CPU resource q (θ) with the upper limit of the information symmetry mechanism is higher than that of the information asymmetry mechanism, which shows that the information asymmetry reduces the transaction efficiency to some extent. It is worth mentioning that the contract mechanism in this embodiment performs better than the traditional linear price mechanism due to the difference in the payment t (θ).
FIG. 3 shows the relationship between t (θ) and θ, the payment t (θ) increases with the increase of the private type θ, and
as a result of (A) being
Upper bound of (i.e. the
Is greater than
Always true). HealdAs described above, monotonicity of q (θ) and t (θ) can be verified according to the graph (a) and the graph (b).
FIG. 4 shows that the incentive compatibility constraint, i.e., the IC constraint mentioned above, holds, as explained below: the value of the selected private type θ in the simulation is equal to 3. Calculating the utility of the requester with the type theta and taking the value of the contract from
To:
at this time, from the simulation result diagram, it can be seen that when θ is equal to 3, the maximum value of the requester utility is taken
Then, we change the value of θ, and let θ equal to 6 and 9, respectively, the maximum value that can be obtained for the requestor utility is taken at
And
here, therefore, the IC constraint mentioned above is explained to be established.
As shown in fig. 5, 6, and 7, the utility of the provider increases as the privacy type θ increases, which means that the provider can earn more if the requester type is larger. The utility of the provider in the first optimization is higher than the second optimization, and the linear bidding mechanism is still the lowest utility compared to the second optimization under the previous parameter settings. However, the utility of the requester in FIG. 6 is very different from that of FIG. 5, the performance of the linear baseline bidding mechanism in FIG. 6 is better than but worse than the information symmetry mechanism, and the result of the information symmetry mechanism is no longer an upper bound on the information asymmetry mechanism. This is because the goal herein is to maximize the utility of the provider to incentivize the provider to actively contribute CPU resources, rather than maximizing the utility of the requester. Finally, comparing the social benefits in FIG. 7, it can be seen that the mechanism performs better in this embodiment than the linear bidding mechanism.
Example four
Based on the same inventive concept, the present embodiment discloses a computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the satellite lightweight distributed orchestration method according to any one of the above.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims. The above disclosure is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A lightweight distributed satellite orchestration system, comprising: 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 a contract, and the contract is announced to all editors in the editors layer.
2. The satellite lightweight distributed orchestration system according to claim 1, wherein the market trading mechanism is a bilateral trading relationship between a resource demand star and a resource supply star, the resource demand star requesting CPU resources from the resource supply star and paying according to a proprietary type of the resource demand star, the resource supply star receiving payment from the resource demand star by providing CPU resources.
3. The satellite lightweight distributed orchestration system according to claim 2, wherein the contracts comprise CPU resources that the resource supply star can provide and funds that the resource demand star can pay.
4. The satellite lightweight distributed orchestration system according to claim 2, wherein the utility function of the resource demand star is the following equation:
U R (θ,q(θ),t(θ))＝θv(q(θ))-t(θ)
＝θ(1-e -q(θ) )-t(θ)
wherein, U R () As a utility function, θ is a private type representing a resource demand star; t (θ) is the funds paid by the resource demand star, and v (q (θ)) is the profit of the requester when the resource demand star obtains the amount of CPU resource from the resource supply star as q (θ); q (θ) is the CPU resource provided by the resource provisioning star.
5. The satellite lightweight distributed orchestration system according to claim 2, wherein the utility function of the resource supply star is the following equation:
U P (q(θ),t(θ))＝t(θ)-c(q(θ))
＝t(θ)-c 0 q 2 (θ)
wherein, U p () As a utility function, t (θ) is the fund paid by the resource demand star, c (q (θ)) is the reduction in utility when the resource supply star provides the resource to the resource demand star; q (θ) is a CPU resource provided by the resource provisioning star; c. C 0 Is a constant coefficient.
6. An optimization method for a lightweight distributed satellite orchestration system according to any one of claims 1-5, comprising the steps of:
when the orchestration system needs to perform a collaborative task among a plurality of orchestrators, determining parameters of the collaborative task, and initializing a contract of each user;
judging whether the information between the resource demand satellite and the resource supply satellite is symmetrical or not based on the initialized contract;
if the information is symmetrical, solving an optimization equation under the condition of only considering individual rational constraint;
if the information is asymmetric, simultaneously considering individual rational constraint conditions and excitation compatibility constraint conditions to solve the optimization equation;
and obtaining an optimal cooperative task resource allocation contract according to the solving result.
7. The method of lightweight distributed orchestration of satellites according to claim 6, wherein the optimization equation is:
P 1 :
wherein, U p () In order to be a function of the utility,
is the capital paid by the resource demand star, and c (q (θ)) is the amount of reduction in utility when the resource supply star provides the resource to the resource demand star; q (θ) is a CPU resource provided by the resource provisioning star; c. C 0 Is a constant coefficient, and IR is an individual rational constraint condition; IC is an excitation compatibility constraint; s.t. is a constraint condition;
is of type
The resource demand star paid a subscription;
is a proprietary type of resource demand star of
Then the resource supply star provides the CPU resource of the resource supply star;
theta is a proprietary type of resource requirement star, and
f (theta) is a probability density function of theta, f (theta) is a probability density function of a private type theta, P 1 Is the first optimization problem.
8. The satellite lightweight distributed orchestration method according to claim 7, wherein the information symmetry means that a resource supply satellite knows the private type θ, and a final solution of the cooperative task resource allocation contract is:
wherein, q (theta), t (theta) is the final solution of the collaborative task resource allocation contract problem when the information is symmetrical, and W (theta) is a Lambert W function.
9. The method according to claim 7, wherein the information asymmetry refers to a probability density function f (θ) that a resource supply satellite only knows a private type θ, and the final solution of the cooperative task resource allocation contract is:
wherein,
the final solution of the contract problem of the resource allocation of the cooperative task when the information is asymmetric is provided, wherein W (theta) is a Lambert W function, tau is an integral variable, and lambda is a parameter of exponential distribution.
10. A computer-readable storage medium, having stored thereon a computer program for execution by a processor to perform the method of lightweight distributed orchestration of satellites according to any of claims 6-9.
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