CN115361392A - Control method, system and storage medium of blockchain-based computing power network - Google Patents

Control method, system and storage medium of blockchain-based computing power network Download PDF

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CN115361392A
CN115361392A CN202210989950.6A CN202210989950A CN115361392A CN 115361392 A CN115361392 A CN 115361392A CN 202210989950 A CN202210989950 A CN 202210989950A CN 115361392 A CN115361392 A CN 115361392A
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CN115361392B (en
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郭利
黄典
冯圣中
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NATIONAL SUPERCOMPUTING CENTER IN SHENZHEN (SHENZHEN CLOUD COMPUTING CENTER)
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network arrangements or protocols for supporting network services or applications
    • H04L67/01Protocols
    • H04L67/10Protocols in which an application is distributed across nodes in the network
    • H04L67/1001Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
    • H04L67/1004Server selection for load balancing
    • H04L67/1023Server selection for load balancing based on a hash applied to IP addresses or costs
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network arrangements or protocols for supporting network services or applications
    • H04L67/01Protocols
    • H04L67/10Protocols in which an application is distributed across nodes in the network
    • H04L67/1001Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
    • H04L67/1004Server selection for load balancing
    • H04L67/1008Server selection for load balancing based on parameters of servers, e.g. available memory or workload
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network arrangements or protocols for supporting network services or applications
    • H04L67/01Protocols
    • H04L67/10Protocols in which an application is distributed across nodes in the network
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    • Y02DCLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract

The application relates to a control method, a system and a computer readable storage medium of a block chain-based computational power network. The control method of the computational power network based on the block chain models the interactive relation between each computational power provider in the computational power network into an optimization problem which takes a revenue function with resource utilization rate as a parameter as an optimization target and takes operation cost and task waiting and allocating time as constraint conditions, and the optimization problem is optimized and solved to obtain the optimal matching between a required task and the computational power resources, so that the maximization target of the computational power resource utilization rate in the computational power network is realized.

Description

基于区块链的算力网络的控制方法、系统和存储介质Control method, system and storage medium of blockchain-based computing power network

技术领域technical field

本申请涉及基于区块链的算力网络技术领域,更具体地说,涉及一种基于区块链的算力网络的控制方法、系统和计算机可读存储介质。The present application relates to the technical field of blockchain-based computing power network, and more specifically, to a control method, system and computer-readable storage medium for a blockchain-based computing power network.

背景技术Background technique

算力网络作为一种新型网络架构,如图1所示,其核心思想是通过多算力提供方和多算力需求方共同参与,用网络连接分布式算力,整合网络中泛在且动态分布的计算、网络、存储等算力资源,并将其进行智能化调度与最优化分配。算力网络目前主要包括云边端协同和算网融合两种模式。随着服务、数据、内容逐渐由集中式向协调分布式转变,算网一体的泛在计算也对算网融合技术发展提出了新需求。因此,为了打破算力资源孤岛的现状,平衡算力资源分布,发挥分布式算力资源池化优势,研究算力网络架构下多算力提供方之间的算力资源调度成为基础性和前沿性的关键科学问题。As a new type of network architecture, computing power network, as shown in Figure 1, its core idea is to connect distributed computing power with the network through the joint participation of multiple computing power providers and multiple computing power demanders, and integrate the ubiquitous and dynamic Distributed computing, network, storage and other computing resources, and intelligently schedule and optimally allocate them. At present, the computing power network mainly includes two modes: cloud-edge-device collaboration and computing-network integration. With the gradual transformation of services, data, and content from centralized to coordinated distribution, the ubiquitous computing integrating computing and network has also put forward new requirements for the development of computing and network integration technology. Therefore, in order to break the status quo of isolated islands of computing power resources, balance the distribution of computing power resources, and leverage the advantages of distributed computing power resource pooling, research on computing power resource scheduling among multiple computing power providers under the computing power network architecture has become fundamental and cutting-edge. key scientific questions.

算力网络中分布式算力资源的共享分配,本质上是分布式节点之间的协商合作,然而多个算力提供方之间的无信任阻碍了资源的共享。区块链作为一种多方共建、共享和共管的技术,其共识机制用于保证节点在分布式系统中对数据和信息的有效性和一致性达成共识,因而已被广泛运用于算力网络。在达成信任共识的算力网络中,通过对算力资源进行优化调度和精准分配,可以减少共享算力资源的竞争和提高算力网络的整体效能。因此,如何实现算力网络中多需求任务和多算力资源的双边匹配,对实现算力资源的分布式共识以及提高算力网络整体算力资源利用率具有重要意义。The sharing and allocation of distributed computing power resources in the computing power network is essentially the negotiation and cooperation among distributed nodes, but the lack of trust among multiple computing power providers hinders the sharing of resources. Blockchain is a multi-party co-construction, sharing and co-management technology. Its consensus mechanism is used to ensure that nodes reach a consensus on the validity and consistency of data and information in a distributed system, so it has been widely used in computing power networks. . In a computing power network that has reached a trust consensus, through optimal scheduling and precise allocation of computing power resources, competition for shared computing power resources can be reduced and the overall performance of the computing power network can be improved. Therefore, how to realize the bilateral matching of multi-demand tasks and multi-computing resources in the computing power network is of great significance for realizing the distributed consensus of computing power resources and improving the overall utilization of computing power resources in the computing power network.

发明内容Contents of the invention

本申请要解决的技术问题在于,针对现有技术的上述缺陷,提供一种能实现需求任务-算力资源双边匹配以最大化算力资源利用率的基于区块链的算力网络的控制方法、系统和计算机可读存储介质。The technical problem to be solved in this application is to provide a blockchain-based computing power network control method that can realize bilateral matching of demand tasks-computing power resources to maximize the utilization of computing power resources in view of the above-mentioned defects of the existing technology , systems and computer readable storage media.

本申请为解决其技术问题在第一方面提出一种基于区块链的算力网络的控制方法,所述方法包括:将算力网络中各算力提供方之间的交互关系建模为以将资源利用率作为参数的收益函数为优化目标、以运营成本和任务等待分配时间为约束条件的最优化问题,对该最优化问题进行优化求解以得到需求任务与算力资源之间的最优匹配,其中,所述算力网络中各算力提供方的整体收益函数表示为:In order to solve its technical problems, the present application proposes a blockchain-based computing power network control method in the first aspect. The method includes: modeling the interaction relationship between computing power providers in the computing power network as follows: It is an optimization problem that takes the resource utilization rate as a parameter, the profit function as the optimization objective, and the operating cost and task waiting time as the constraints. The optimization problem is solved to obtain the optimal balance between the required tasks and the computing power resources. Matching, where the overall revenue function of each computing power provider in the computing power network is expressed as:

Figure BDA0003801939610000021
Figure BDA0003801939610000021

所述约束条件为:The constraints are:

Subject to Timei≥timei,i∈(1,n),Subject to Time i ≥ time i , i∈(1,n),

Figure BDA0003801939610000022
Figure BDA0003801939610000022

其中,Rewardj为算力提供方Servicej,j∈{1,2,3,…}的总收益,QoE为算力需求方的满意程度衡量指标,

Figure BDA0003801939610000023
为运营成本涉及的电费,
Figure BDA0003801939610000024
为运营成本涉及的环境处理费,Costj为常数,指算力提供方Servicej,j∈{1,2,3,…}的成本预算,n为算力需求方的待分配任务个数,Timei为算力需求方能接受的最长等待被分配时间,timei为算力需求方的实际等待时间。Among them, Reward j is the total income of Service j , j ∈ {1,2,3,…} of the computing power provider, and QoE is the satisfaction measure index of the computing power demander.
Figure BDA0003801939610000023
Electricity charges involved in operating costs,
Figure BDA0003801939610000024
is the environmental processing fee involved in the operating cost, Cost j is a constant, and refers to the cost budget of the computing power provider Service j , j∈{1,2,3,…}, n is the number of tasks to be allocated by the computing power demander, Time i is the longest waiting time that the computing power demander can accept to be allocated, and time i is the actual waiting time of the computing power demander.

根据本申请第一方面所述的方法的一个实施例中,所述算力需求方的满意程度衡量指标QoE通过下式计算得到:According to an embodiment of the method described in the first aspect of the present application, the satisfaction measure index QoE of the computing power demander is calculated by the following formula:

Figure BDA0003801939610000025
Figure BDA0003801939610000025

其中,s表示算力需求方得到算力提供服务的个数。Among them, s represents the number of services provided by the computing power demand side.

根据本申请第一方面所述的基于区块链的算力网络的控制方法的一个实施例中,所述算力提供方Servicej,j∈{1,2,3,…}的总收益Rewardj被表示为:According to an embodiment of the control method for a blockchain-based computing power network described in the first aspect of the present application, the total revenue Reward of the computing power provider Service j , j∈{1,2,3,...} j is represented as:

Figure BDA0003801939610000026
Figure BDA0003801939610000026

其中,Revi为算力需求方所需要的算力资源,TRj为算力提供方Servicej,j∈{1,2,3,…}的资源总和,Comi={1,0}表示任务的完成情况。Among them, Rev i is the computing power resource required by the computing power demander, TR j is the sum of resources of the computing power provider Service j , j∈{1,2,3,…}, Com i ={1,0} means The completion of the task.

根据本申请第一方面所述的基于区块链的算力网络的控制方法的一个实施例中,所述方法还包括:基于参与共识过程的各算力提供方在共识过程中的贡献值给各算力提供方分配相应权重的收益,其中,一个算力提供方Servicej,j∈{1,2,3,…}对于一个任务i的贡献值为:According to an embodiment of the method for controlling a blockchain-based computing power network described in the first aspect of the present application, the method further includes: based on the contribution value of each computing power provider participating in the consensus process in the consensus process to Each computing power provider distributes the income of the corresponding weight. Among them, the contribution value of a computing power provider Service j , j∈{1,2,3,…} to a task i is:

Figure BDA0003801939610000031
Figure BDA0003801939610000031

其中,Revi为算力需求方所需要的算力资源,Resourcej为算力提供方Servicej,j∈{1,2,3,…}当前合作成员的可用资源。Among them, Rev i is the computing power resource required by the computing power demander, and Resource j is the available resources of the computing power provider Service j , j∈{1,2,3,…} current cooperative members.

根据本申请第一方面所述的基于区块链的算力网络的控制方法的一个实施例中,所述方法还包括:按一定的周期T对算力提供方Servicej,j∈{1,2,3,…}的任务平均贡献值进行计算,并将所述任务平均贡献值作为动态更新算力提供方的信用值的依据,其中,任务平均贡献值计算如下:According to an embodiment of the method for controlling a computing power network based on blockchain described in the first aspect of the present application, the method further includes: providing service j to the computing power provider Service j ,j∈{1, 2,3,…}, and use the average contribution value of the task as the basis for dynamically updating the credit value of the computing power provider, where the average contribution value of the task is calculated as follows:

Figure BDA0003801939610000032
Figure BDA0003801939610000032

根据本申请第一方面所述的基于区块链的算力网络的控制方法的一个实施例中,所述方法还包括:根据算力网络中各算力提供方参与共识过程动态更新各算力提供方的实时信用值,基于信用值的高低将各算力提供方分为主控层、协调层和外围层并从主控层选取新的主节点,其中,主控层由可信的高性能计算节点组成,协调层由主控层基于信用值动态选取并标记的节点组成,外围层由除了主控层和协调层之外的节点组成。According to an embodiment of the method for controlling a blockchain-based computing power network described in the first aspect of the present application, the method further includes: dynamically updating each computing power according to the participation of each computing power provider in the computing power network in the consensus process Based on the real-time credit value of the provider, each computing power provider is divided into the main control layer, the coordination layer and the peripheral layer based on the level of the credit value, and a new master node is selected from the main control layer. The coordination layer is composed of nodes dynamically selected and marked by the main control layer based on the credit value, and the peripheral layer is composed of nodes other than the main control layer and the coordination layer.

根据本申请第一方面所述的基于区块链的算力网络的控制方法的一个实施例中,定义节点l(l≥1)在第k(k≥1)轮共识过程中的信用值为Reputationl,k,则信用值Reputationl,k的多项加权公式为:According to an embodiment of the control method of the blockchain-based computing power network described in the first aspect of the application, the credit value of node l (l≥1) in the k-th (k≥1) round of consensus process is defined as Reputation l, k , the multiple weighting formula of credit value Reputation l, k is:

Reputationl,k=αAl,k+βBl,k+γCl,k+ηDl,k+μEl,k+Reputationl,0,Reputation l,k =αA l,k +βB l,k +γC l,k +ηD l,k +μE l,k +Reputation l,0 ,

其中Al代表计算能力,Bl代表内存能力,Cl代表带宽水平,Dl代表在线稳定性,El代表交互评分信任度,Reputationl,0代表初始信用值,α,β,γ,η,μ分别代表这五个维度的权重。Among them, A l represents the computing power, B l represents the memory capacity, C l represents the bandwidth level, D l represents the online stability, E l represents the trust degree of interactive scoring, Reputation l, 0 represents the initial credit value, α, β, γ, η , μ represent the weights of these five dimensions respectively.

本申请为解决其技术问题在第二方面提出一种基于区块链的算力网络的控制系统,所述系统包括处理器和存储器,所述存储器存储有计算机程序,所述计算机程序被处理器执行时实现如上所述的基于区块链的算力网络的控制方法。In order to solve its technical problems, the present application proposes a block chain-based control system of computing power network in the second aspect, the system includes a processor and a memory, the memory stores a computer program, and the computer program is controlled by the processor During execution, the control method of the computing power network based on the block chain as described above is realized.

本申请为解决其技术问题在第三方面提出一种计算机可读存储介质,存储有计算机程序,所述计算机程序被处理器执行时实现如上所述的基于区块链的算力网络的控制方法。In order to solve its technical problems, the present application proposes a computer-readable storage medium in the third aspect, which stores a computer program, and when the computer program is executed by a processor, the control method of the computing power network based on blockchain as described above is realized. .

实施本申请的基于区块链的算力网络的控制方法、系统和计算机可读存储介质,具有以下有益效果:Implementing the control method, system and computer-readable storage medium of the blockchain-based computing power network of the present application has the following beneficial effects:

(1)本申请基于合作博弈理论,同时考虑算力网络中的可用算力、需求任务的所需算力以及任务完成成本等相关影响因子,研究需求任务与算力资源之间的匹配问题,提出了基于合作博弈的联盟形成算法,通过分析求解并优化均衡策略下的目标收益函数,实现了算力网络中算力资源利用率的最大化目标。(1) This application is based on the cooperative game theory, and considers the available computing power in the computing power network, the computing power required for the required task, and the cost of task completion and other related factors to study the matching problem between the required task and the computing power resource. A coalition formation algorithm based on cooperative game is proposed. By analyzing, solving and optimizing the target revenue function under the equilibrium strategy, the goal of maximizing the utilization of computing power resources in the computing power network is realized.

(2)本申请还进一步针对算力提供方之间的分布式合作和资源共享需求,提出层级化分布式信用共识机制,提高了算力网络分布式共识效率,减少算力资源的浪费。(2) This application further addresses the distributed cooperation and resource sharing requirements among computing power providers, and proposes a hierarchical distributed credit consensus mechanism, which improves the efficiency of distributed consensus in the computing power network and reduces the waste of computing power resources.

附图说明Description of drawings

下面将结合附图及实施例对本申请作进一步说明,附图中:The application will be further described below in conjunction with the accompanying drawings and embodiments, in the accompanying drawings:

图1是现有技术中的算力网络架构示意图;Figure 1 is a schematic diagram of a computing power network architecture in the prior art;

图2是本申请一个实施例的基于区块链的算力网络的控制系统的逻辑结构图。Fig. 2 is a logical structure diagram of a control system of a blockchain-based computing power network according to an embodiment of the present application.

具体实施方式Detailed ways

为了使本申请的目的、技术方案及优点更加清楚明白,以下结合附图及实施例,对本申请进行进一步详细说明。应当理解,此处所描述的具体实施例仅仅用以解释本申请,并不用于限定本申请。并且,在不冲突的情况下,本申请中的实施例及实施例中的特征可以相互组合。In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application. Moreover, the embodiments in the present application and the features in the embodiments can be combined with each other under the condition of no conflict.

基于区块链的算力网络解决了算力提供方之间达成信任共识的问题,在分布式算力资源共享分配的保证下,为了算力网络中广泛分布的算力资源利用率最大化,需要充分考虑需求任务和算力资源的双边供需关系。又由于不同的任务有不同的需求,如低延迟响应型任务或者高计算资源型任务,而单个算力提供方的资源是有限的,无法同时应对多项密集服务请求,所以算力提供方依然需要与其他算力提供方进行合作以实现资源共享和协同工作。因此,本申请基于合作博弈理论,提出一种基于区块链的算力网络的控制方法,将算力网络中各算力提供方之间的交互关系建模为以将资源利用率作为参数的收益函数为优化目标、以运营成本和任务等待分配时间为约束条件的最优化问题,对该最优化问题进行优化求解以得到需求任务与算力资源之间的最优匹配。The blockchain-based computing power network solves the problem of reaching a consensus among computing power providers. Under the guarantee of distributed computing power resource sharing and distribution, in order to maximize the utilization of widely distributed computing power resources in the computing power network, It is necessary to fully consider the bilateral supply and demand relationship between demand tasks and computing power resources. And because different tasks have different requirements, such as low-latency response tasks or high computing resource tasks, and the resources of a single computing power provider are limited, it cannot cope with multiple intensive service requests at the same time, so the computing power provider still Cooperation with other computing power providers is required to achieve resource sharing and collaborative work. Therefore, based on the cooperative game theory, this application proposes a blockchain-based computing power network control method, modeling the interaction relationship between computing power providers in the computing power network as a model with resource utilization as a parameter The profit function is an optimization problem with the optimization objective and the constraint conditions of operating cost and task waiting time for allocation. The optimization problem is solved to obtain the optimal match between the required tasks and the computing power resources.

具体来说,在算力网络中进行资源调度与算力共享,各算力提供方需要提供闲置算力以共同协作完成需求任务,实现整体算力网络效能最大化。即,每个算力提供方都拥有相同的目标函数,彼此之间形成了一种合作关系,因此本申请的基于区块链的算力网络的控制方法选择合作博弈对各个算力提供方之间的关系进行建模。该合作博弈的具体定义如下:Specifically, for resource scheduling and computing power sharing in the computing power network, each computing power provider needs to provide idle computing power to work together to complete required tasks and maximize the overall computing power network performance. That is, each computing power provider has the same objective function and forms a cooperative relationship with each other. Therefore, the control method of the block chain-based computing power network in this application chooses a cooperative game for each computing power provider. Model the relationship between them. The specific definition of the cooperative game is as follows:

1)参与者:各个算力提供方;1) Participants: various computing power providers;

2)策略:是否加入新的联盟或者是否离开所在联盟;2) Strategy: whether to join a new alliance or whether to leave the current alliance;

3)收益函数:一个以整体算力资源利用率为参数的函数。3) Profit function: a function that takes the utilization rate of the overall computing resource as a parameter.

同时,为了进行任务和资源的双边匹配,算力需求方对于任务的完成满意度是必须考虑的,本申请的基于区块链的算力网络的控制方法将算力需求方的满意度形式化为任务等待分配的所用时间。又由于每个算力提供方Servicej,j∈{1,2,3,…}需要考虑自身的建设成本和运营成本,其中建设成本是恒定的,运营成本涉及到电费

Figure BDA0003801939610000051
和环境处理费
Figure BDA0003801939610000052
等,于是各方的收益情况取决于运营成本和自身算力资源的使用情况。At the same time, in order to carry out the bilateral matching of tasks and resources, the satisfaction of the computing power demander with regard to the completion of the task must be considered. The control method of the blockchain-based computing power network of this application formalizes the satisfaction of the computing power demander The amount of time spent waiting for a task to be assigned. And because each computing power provider Service j ,j∈{1,2,3,…} needs to consider its own construction cost and operating cost, where the construction cost is constant, and the operating cost involves electricity
Figure BDA0003801939610000051
and environmental treatment fees
Figure BDA0003801939610000052
etc., so the income of all parties depends on the operating costs and the use of their own computing power resources.

当前某个算力需求方有n个待分配任务,任务Taski,i∈(1,n)能接受的最长等待被分配时间为Timei,实际等待时间为timei,所需要的算力资源为Revi,算力提供方Servicej,j∈{1,2,3,…}的资源总和为TRj,根据任务的完成情况Comi={1,0},该算力提供方的总收益Rewardj可以被表示为:At present, a computing power demander has n tasks to be allocated. The longest waiting time for Task i , i∈(1,n) to be assigned is Time i , and the actual waiting time is time i . The required computing power The resource is Rev i , the sum of the resources of the computing power provider Service j , j∈{1,2,3,…} is TR j , according to the completion of the task Com i ={1,0}, the computing power provider’s The total revenue Reward j can be expressed as:

Figure BDA0003801939610000061
Figure BDA0003801939610000061

另外,各算力提供方的可用资源信息是相互独立的,只有当某算力提供方决定与其他算力提供方进行合作时,该信息才会被彼此分享。而每个算力提供方当前承担任务的数量Xj则是一个公开信息。于是,算力提供方Servicej,j∈{1,2,3,…}可以根据当前合作成员的可用资源Resourcej以及各算力提供方的任务承担情况,基于贝叶斯公式推断得到其他算力提供方的可用资源情况的概率,即:In addition, the available resource information of each computing power provider is independent of each other, and only when a computing power provider decides to cooperate with other computing power providers, the information will be shared with each other. The number X j of tasks currently undertaken by each computing power provider is public information. Therefore, the computing power provider Service j , j∈{1,2,3,…} can infer other computing power based on the Bayesian formula based on the available resource Resource j of the current cooperative members and the task commitment of each computing power provider. The probability of the resource availability of the force provider, namely:

Figure BDA0003801939610000062
Figure BDA0003801939610000062

其中,X={X1,X2,X3,…}代表所有算力提供方各自承担任务数量的集合,上式(1)的概率推断在博弈论中也被信念(Belief)。P(X|Resourcej)和P(Resourcej)可以根据合作后分享的数据直接进行计算得到,为了避免数据集可能过小导致的下溢,因此,由上式(1)改为计算下式:Among them, X={X 1 ,X 2 ,X 3 ,…} represents the set of the number of tasks undertaken by all computing power providers, and the probability inference of the above formula (1) is also believed in game theory. P(X|Resource j ) and P(Resource j ) can be directly calculated based on the data shared after the cooperation. In order to avoid underflow caused by the possible small data set, the above formula (1) is changed to the following formula :

Figure BDA0003801939610000063
Figure BDA0003801939610000063

最终,算力网络中各算力提供方的整体收益函数可以表示为:Finally, the overall revenue function of each computing power provider in the computing power network can be expressed as:

Figure BDA0003801939610000064
Figure BDA0003801939610000064

约束条件为:The constraints are:

Subject to Timei≥timei,i∈(1,n) (4)Subject to Time i ≥ time i , i∈(1,n) (4)

Figure BDA0003801939610000065
Figure BDA0003801939610000065

其中,Costj为常数,指算力提供方Servicej,j∈{1,2,3,…}的成本预算,n为算力需求方的待分配任务个数,QoE(Quality of Experience)为算力需求方的满意程度衡量指标。具体来说,QoE通过下式计算得到:Among them, Cost j is a constant, which refers to the cost budget of Service j , j ∈ {1,2,3,…} of the computing power provider, n is the number of tasks to be assigned by the computing power demander, and QoE (Quality of Experience) is Satisfaction indicator of computing power demand side. Specifically, QoE is calculated by the following formula:

Figure BDA0003801939610000066
Figure BDA0003801939610000066

其中,s表示算力需求方得到算力提供服务的个数。Among them, s represents the number of services provided by the computing power demand side.

以上优化目标式(3)要求算力提供方的资源利用率和算力需求方对于任务完成情况的满意程度最大,同时还要求算力提供方的成本支出最小化。约束条件式(4)要求算力需求方对于每个任务的等待时间不能超过相应的等待上界,约束条件式(5)要求算力提供方的电费和环境费成本不能超过成本预算Costj。结合对于优化目标式(3)和约束条件式(4)-(5)所构成的最优化问题,理论证明合作联盟形成算法可以收敛到稳定均衡点,并以较快的收敛速度达到次优解。根据需求任务的算力需求、算力提供方的闲置算力、等待时延等,优化目标找到均衡点后,则认为任务和需求之间是最优匹配,按照相应的算力提供方来完成相应需求任务即可实现算力网络中算力资源利用率的最大化目标。The above optimization objective formula (3) requires the resource utilization rate of the computing power provider and the satisfaction degree of the computing power demander for the completion of the task to be the largest, and also requires the cost of the computing power provider to be minimized. Constraint (4) requires that the computing power demander's waiting time for each task cannot exceed the corresponding waiting upper bound, and constraint (5) requires that the computing power provider's electricity and environmental costs cannot exceed the cost budget Cost j . Combined with the optimization problem composed of the optimization objective formula (3) and the constraint condition formula (4)-(5), it is theoretically proved that the cooperative alliance formation algorithm can converge to a stable equilibrium point, and reach a suboptimal solution with a faster convergence speed . According to the computing power demand of the demand task, the idle computing power of the computing power provider, the waiting delay, etc., after the optimization goal finds the equilibrium point, it is considered that the task and the demand are the best match, and the corresponding computing power provider is used to complete The corresponding demand tasks can achieve the goal of maximizing the utilization of computing power resources in the computing power network.

进一步地,本申请的基于区块链的算力网络的控制方法还基于参与共识过程的各算力提供方在共识过程中的贡献值给各算力提供方分配相应权重的收益。为了衡量一个算力提供方Servicej,j∈{1,2,3,…}对于一个任务i的贡献程度,本申请提出以下贡献度衡量指标以完成最终的收益分配:Further, the blockchain-based computing power network control method of the present application also distributes corresponding weighted income to each computing power provider based on the contribution value of each computing power provider participating in the consensus process in the consensus process. In order to measure the contribution of a computing power provider Service j , j∈{1,2,3,…} to a task i, this application proposes the following contribution measurement indicators to complete the final revenue distribution:

Figure BDA0003801939610000071
Figure BDA0003801939610000071

其中,

Figure BDA0003801939610000072
为算力提供方Servicej,j∈{1,2,3,…}对于一个任务i的贡献值,Revi为算力需求方所需要的算力资源,Resourcej为算力提供方Servicej,j∈{1,2,3,…}当前合作成员的可用资源。in,
Figure BDA0003801939610000072
is the contribution value of the computing power provider Service j , j∈{1,2,3,…} to a task i, Rev i is the computing power resource required by the computing power demander, and Resource j is the computing power provider Service j , j ∈ {1,2,3,…} the available resources of the current cooperative member.

进一步地,本申请的基于区块链的算力网络的控制方法还按一定的周期T对算力提供方Servicej,j∈{1,2,3,…}的任务平均贡献值进行计算如下:Further, the control method of the blockchain-based computing power network of this application also calculates the average contribution value of the task of the computing power provider Service j , j∈{1,2,3,...} at a certain period T as follows :

Figure BDA0003801939610000073
Figure BDA0003801939610000073

该任务平均贡献值将作为动态更新算力提供方的信用值的一个依据,以鼓励各个算力提供方在一定周期内积极参与算力资源的协同分配。The average contribution value of this task will be used as a basis for dynamically updating the credit value of computing power providers to encourage each computing power provider to actively participate in the collaborative allocation of computing power resources within a certain period.

进一步地,根据本申请上述实施例的基于区块链的算力网络的控制方法针对算力网络中分布式算力提供方合作共享算力资源的需求,基于共识节点产生有效区块并上链的动态过程,提出层级化分布式信用共识模型,促成算力提供方之间的信任合作和资源共享。Further, according to the control method of the blockchain-based computing power network according to the above-mentioned embodiments of the present application, in order to meet the needs of distributed computing power providers in the computing power network to cooperate and share computing power resources, valid blocks are generated based on consensus nodes and uploaded to the chain. A hierarchical distributed credit consensus model is proposed to promote trust cooperation and resource sharing among computing power providers.

具体来说,本申请的基于区块链的算力网络的控制方法根据算力网络中各算力提供方参与共识过程动态更新各算力提供方的实时信用值,基于信用值的高低将各算力提供方分为三层,即主控层、协调层和外围层。其中,主控层由事先安排的可信的高性能计算节点组成,新的主节点优先从主控层选取,以降低选取时产生的消耗。协调层由主控层基于信用值动态选取并标记的节点组成,外围层由除了主控层和协调层之外的节点组成,由于信任值较低,这些节点并不参与共识过程,但他们可以通过提升信任值被主控层标记入协调层。Specifically, the blockchain-based computing power network control method of this application dynamically updates the real-time credit value of each computing power provider according to the participation of each computing power provider in the computing power network in the consensus process. The computing power provider is divided into three layers, namely the master control layer, the coordination layer and the peripheral layer. Among them, the master control layer is composed of trusted high-performance computing nodes arranged in advance, and new master nodes are selected from the master control layer first to reduce the consumption of selection. The coordination layer is composed of nodes dynamically selected and marked by the main control layer based on the credit value. The peripheral layer is composed of nodes other than the main control layer and the coordination layer. Due to the low trust value, these nodes do not participate in the consensus process, but they can By increasing the trust value, it is marked into the coordination layer by the master control layer.

每一个进入的新算力提供方都会得到一个唯一的ID和初始信用值,信用值随着该算力提供方与其他算力提供方的共识行为进行累积统计。正面信用的回报可以激励算力提供方更高程度地参与共识行为,而消极信用则让该成员在共识中的权重降低。定义节点l(l≥1)在第k(k≥1)轮共识过程中的信用值为Reputationl,k,则计算信用值Reputationl,k的多项加权公式为:Each incoming new computing power provider will get a unique ID and initial credit value, and the credit value will be accumulated and counted along with the consensus behavior of the computing power provider and other computing power providers. The return of positive credit can motivate computing power providers to participate in the consensus behavior to a higher degree, while negative credit can reduce the weight of the member in the consensus. Define the credit value of node l (l ≥ 1) in the k (k ≥ 1) round of consensus process as Reputation l, k , then the multiple weighted formula for calculating credit value Reputation l, k is:

Reputationl,k=αAl,k+βBl,k+γCl,k+ηDl,k+μEl,k+Reputationl,0, (9)Reputation l,k =αA l,k +βB l,k +γC l,k +ηD l,k +μE l,k +Reputation l,0 , (9)

其中Al代表计算能力,Bl代表内存能力,Cl代表带宽水平,Dl代表在线稳定性,El代表交互评分信任度,Reputationl,0代表初始信用值,α,β,γ,η,μ分别代表这五个维度的权重。该表达式的前四项体现该节点的客观处理能力,第五项体现节点分布式交互过程中完成的转发和通信情况。根据以上式(9)计算得到的信用值的高低,本申请可以将节点分为主控层、协调层、外围层三层。Among them, A l represents the computing power, B l represents the memory capacity, C l represents the bandwidth level, D l represents the online stability, E l represents the trust degree of interactive scoring, Reputation l, 0 represents the initial credit value, α, β, γ, η , μ represent the weights of these five dimensions respectively. The first four items of the expression reflect the objective processing capability of the node, and the fifth item reflects the forwarding and communication completed during the distributed interaction process of the node. According to the level of the credit value calculated by the above formula (9), the application can divide the nodes into three layers: the main control layer, the coordination layer, and the peripheral layer.

随着算力提供方数量的增加,交互的信息会呈指数型增长,共识算法的性能会有所下降,可拓展性受到一定限制,造成资源的大量浪费。因此,本申请的基于区块链的算力网络的控制方法进一步考虑使用合适的并行分布式架构对算法进行改进,以提出更具有拓展性和更加轻量级的共识算法。对于基于区块链的算力网络,可以通过多主节点并行同时计算同一个视图单元任务,当其中一个主节点最先完成一个完整共识过程后,主控层则通知其他主节点停止相应进程,以降低额外增加的共识代价。As the number of computing power providers increases, the interactive information will increase exponentially, the performance of the consensus algorithm will decline, and the scalability will be limited to a certain extent, resulting in a large waste of resources. Therefore, the blockchain-based computing power network control method of this application further considers using an appropriate parallel distributed architecture to improve the algorithm, so as to propose a more scalable and lightweight consensus algorithm. For the computing power network based on the blockchain, the same view unit task can be calculated in parallel through multiple master nodes. When one of the master nodes first completes a complete consensus process, the master control layer will notify other master nodes to stop the corresponding process. To reduce the additional consensus cost.

基于本申请前述的基于区块链的算力网络的控制方法,本申请还提出一种基于区块链的算力网络的控制系统10。参见图2所示,基于区块链的算力网络的控制系统10包括处理器11和存储器12,处理器11和存储器12通信连接。存储器12存储有计算机程序,所述计算机程序被处理器11执行时使处理器11实现本申请前述实施例的基于区块链的算力网络的控制方法。Based on the control method of the blockchain-based computing power network described above in this application, this application also proposes a control system 10 for a blockchain-based computing power network. As shown in FIG. 2 , the control system 10 of the blockchain-based computing power network includes a processor 11 and a memory 12 , and the processor 11 and the memory 12 are connected in communication. The memory 12 stores a computer program, and when the computer program is executed by the processor 11, the processor 11 realizes the control method of the computing power network based on blockchain in the foregoing embodiments of the present application.

本申请还提出一种计算机可读存储介质,存储有计算机程序,所述计算机程序被处理器执行时实现本申请前述实施例的基于区块链的算力网络的控制方法。The present application also proposes a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the method for controlling a computing power network based on blockchain in the foregoing embodiments of the present application is implemented.

以上所述仅为本申请的较佳实施例而已,并不用以限制本申请,凡在本申请的精神和原则之内所作的任何修改、等同替换和改进等,均应包含在本申请的保护范围之内。The above descriptions are only preferred embodiments of the application, and are not intended to limit the application. Any modifications, equivalent replacements and improvements made within the spirit and principles of the application should be included in the protection of the application. within range.

Claims (9)

1.一种基于区块链的算力网络的控制方法,其特征在于,所述方法包括:将算力网络中各算力提供方之间的交互关系建模为以将资源利用率作为参数的收益函数为优化目标、以运营成本和任务等待分配时间为约束条件的最优化问题,对该最优化问题进行优化求解以得到需求任务与算力资源之间的最优匹配,其中,所述算力网络中各算力提供方的整体收益函数表示为:1. A method for controlling a blockchain-based computing power network, characterized in that the method includes: modeling the interaction between computing power providers in the computing power network as taking resource utilization as a parameter The revenue function of is an optimization problem with the optimization objective and the constraint conditions of operating cost and task waiting time for allocation. The optimization problem is solved to obtain the optimal match between the required tasks and the computing power resources. Among them, the The overall revenue function of each computing power provider in the computing power network is expressed as:
Figure FDA0003801939600000011
Figure FDA0003801939600000011
所述约束条件为:The constraints are: Subject to Tim ei≥tim ei,i∈(1,n),Subject to Tim e i ≥tim e i ,i∈(1,n),
Figure FDA0003801939600000012
Figure FDA0003801939600000012
其中,Rewardj为算力提供方Servicej,j∈{1,2,3,…}的总收益,QoE为算力需求方的满意程度衡量指标,
Figure FDA0003801939600000013
为运营成本涉及的电费,
Figure FDA0003801939600000014
为运营成本涉及的环境处理费,Costj为常数,指算力提供方Servicej,j∈{1,2,3,…}的成本预算,n为算力需求方的待分配任务个数,Timei为算力需求方能接受的最长等待被分配时间,tim ei为算力需求方的实际等待时间。
Among them, Reward j is the total income of Service j , j ∈ {1,2,3,…} of the computing power provider, and QoE is the satisfaction measure index of the computing power demander.
Figure FDA0003801939600000013
Electricity charges involved in operating costs,
Figure FDA0003801939600000014
is the environmental processing fee involved in the operating cost, Cost j is a constant, and refers to the cost budget of the computing power provider Service j , j∈{1,2,3,…}, n is the number of tasks to be allocated by the computing power demander, Time i is the longest waiting time that the demander of computing power can accept to be allocated, and time i is the actual waiting time of the demander of computing power.
2.根据权利要求1所述的方法,其特征在于,所述算力需求方的满意程度衡量指标QoE通过下式计算得到:2. The method according to claim 1, characterized in that, the satisfaction index QoE of the computing power demand side is calculated by the following formula:
Figure FDA0003801939600000015
Figure FDA0003801939600000015
其中,s表示算力需求方得到算力提供服务的个数。Among them, s represents the number of services provided by the computing power demand side.
3.根据权利要求1所述的方法,其特征在于,所述算力提供方Servicej,j∈{1,2,3,…}的总收益Rewardj被表示为:3. The method according to claim 1, wherein the total revenue Reward j of the computing power provider Service j , j∈{1,2,3,…} is expressed as:
Figure FDA0003801939600000016
Figure FDA0003801939600000016
其中,Revi为算力需求方所需要的算力资源,TRj为算力提供方Servicej,j∈{1,2,3,…}的资源总和,Comi={1,0}表示任务的完成情况。Among them, Rev i is the computing power resource required by the computing power demander, TR j is the sum of resources of the computing power provider Service j , j∈{1,2,3,…}, Com i ={1,0} means The completion of the task.
4.根据权利要求1所述的方法,其特征在于,所述方法还包括:基于参与共识过程的各算力提供方在共识过程中的贡献值给各算力提供方分配相应权重的收益,其中,一个算力提供方Servicej,j∈{1,2,3,…}对于一个任务i的贡献值为:4. The method according to claim 1, characterized in that the method further comprises: based on the contribution value of each computing power provider participating in the consensus process in the consensus process, assigning corresponding weighted income to each computing power provider, Among them, the contribution value of a computing power provider Service j ,j∈{1,2,3,…} to a task i is:
Figure FDA0003801939600000021
Figure FDA0003801939600000021
其中,Revi为算力需求方所需要的算力资源,Resourcej为算力提供方Servicej,j∈{1,2,3,…}当前合作成员的可用资源。Among them, Rev i is the computing power resource required by the computing power demander, and Resource j is the available resources of the computing power provider Service j , j∈{1,2,3,…} current cooperative members.
5.根据权利要求4所述的方法,其特征在于,所述方法还包括:按一定的周期T对算力提供方Servicej,j∈{1,2,3,…}的任务平均贡献值进行计算,并将所述任务平均贡献值作为动态更新算力提供方的信用值的依据,其中,任务平均贡献值计算如下:5. The method according to claim 4, characterized in that the method further comprises: according to a certain period T, the average contribution value of the tasks of the computing power provider Service j , j∈{1,2,3,...} Perform calculations, and use the average contribution value of the task as the basis for dynamically updating the credit value of the computing power provider, where the average contribution value of the task is calculated as follows:
Figure FDA0003801939600000022
Figure FDA0003801939600000022
6.根据权利要求1所述的方法,其特征在于,所述方法还包括:根据算力网络中各算力提供方参与共识过程动态更新各算力提供方的实时信用值,基于信用值的高低将各算力提供方分为主控层、协调层和外围层并从主控层选取新的主节点,其中,主控层由可信的高性能计算节点组成,协调层由主控层基于信用值动态选取并标记的节点组成,外围层由除了主控层和协调层之外的节点组成。6. The method according to claim 1, further comprising: dynamically updating the real-time credit value of each computing power provider according to the participation of each computing power provider in the computing power network in the consensus process, based on the credit value High and low divide each computing power provider into the main control layer, coordination layer and peripheral layer and select a new master node from the main control layer. Among them, the main control layer is composed of trusted high-performance computing nodes, and the coordination layer is composed of the main control layer The nodes are dynamically selected and marked based on the credit value, and the peripheral layer is composed of nodes other than the control layer and the coordination layer. 7.根据权利要求6所述的方法,其特征在于,定义节点l(l≥1)在第k(k≥1)轮共识过程中的信用值为Reputationl,k,则信用值Reputationl,k的多项加权公式为:7. The method according to claim 6, characterized in that, defining the credit value of node l (l≥1) in the k (k≥1) round consensus process is Reputation l,k , then the credit value Reputation l, The polynomial weighting formula for k is: Reputationl,k=αAl,k+βBl,k+γCl,k+ηDl,k+μEl,k+Reputationl,0,Reputation l,k =αA l,k +βB l,k +γC l,k +ηD l,k +μE l,k +Reputation l,0 , 其中Al代表计算能力,Bl代表内存能力,Cl代表带宽水平,Dl代表在线稳定性,El代表交互评分信任度,Reputationl,0代表初始信用值,α,β,γ,η,μ分别代表这五个维度的权重。Among them, A l represents the computing power, B l represents the memory capacity, C l represents the bandwidth level, D l represents the online stability, E l represents the trust degree of interactive scoring, Reputation l, 0 represents the initial credit value, α, β, γ, η , μ represent the weights of these five dimensions respectively. 8.一种基于区块链的算力网络的控制系统,其特征在于,所述系统包括处理器和存储器,所述存储器存储有计算机程序,所述计算机程序被处理器执行时实现如权利要求1-7中任一项所述的基于区块链的算力网络的控制方法。8. A control system based on a blockchain computing power network, characterized in that the system includes a processor and a memory, the memory stores a computer program, and when the computer program is executed by the processor, it realizes The control method of the computing power network based on blockchain described in any one of 1-7. 9.一种计算机可读存储介质,其特征在于,存储有计算机程序,所述计算机程序被处理器执行时实现如权利要求1-7中任一项所述的基于区块链的算力网络的控制方法。9. A computer-readable storage medium, characterized in that a computer program is stored, and when the computer program is executed by a processor, the blockchain-based computing power network according to any one of claims 1-7 is realized control method.
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