CN115361392A - Control method, system and storage medium of computing power network based on block chain - Google Patents
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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
Technical Field
The present application relates to the field of a blockchain-based computational power network technology, and more particularly, to a control method, system and computer-readable storage medium for a blockchain-based computational power network.
Background
The computational power network is a novel network architecture, as shown in fig. 1, the core idea is that a multi-computational-power provider and a multi-computational-power demander participate together, the network is used for connecting distributed computational power, computational power resources such as ubiquitous and dynamically distributed computation, network and storage in the network are integrated, and the computational power resources are intelligently scheduled and optimally allocated. At present, the computing network mainly comprises two modes of cloud edge terminal cooperation and computing network fusion. With the gradual transition of services, data and contents from centralized to coordinated and distributed, the ubiquitous computing integrated with computing and networking also puts new demands on the development of computing and networking fusion technology. Therefore, in order to break the current situation of computational power resource isolated island, balance computational power resource distribution, exert the pooling advantage of distributed computational power resources, and research computational power resource scheduling among multiple computational power providers under a computational power network architecture becomes a fundamental and frontier key scientific problem.
Shared allocation of distributed computing power resources in a computing power network is essentially a negotiation cooperation between distributed nodes, however, sharing of resources is hindered by the lack of trust between multiple computing power providers. A block chain is a technology of building, sharing and managing multiple parties, and a consensus mechanism of the block chain is used to ensure that nodes agree on validity and consistency of data and information in a distributed system, so that the block chain has been widely applied to computational networks. In the computing network achieving the trust consensus, by performing optimized scheduling and accurate allocation on the computing resources, the competition of sharing the computing resources can be reduced and the overall efficiency of the computing network can be improved. Therefore, how to realize bilateral matching of the multi-demand tasks and the multi-computing-force resources in the computing-force network has important significance for realizing distributed consensus of the computing-force resources and improving the utilization rate of the overall computing-force resources of the computing-force network.
Disclosure of Invention
The technical problem to be solved by the present application is to provide a control method, system and computer readable storage medium for a blockchain-based computational power network, which can implement bilateral matching of demand task-computational power resources to maximize the computational power resource utilization, in view of the above-mentioned drawbacks of the prior art.
In order to solve the technical problem, in a first aspect, the present application provides a control method for a computational power network based on a block chain, where the method includes: modeling an interactive relation between computing power providers in a computing power network into an optimization problem taking a revenue function with resource utilization rate as a parameter as an optimization target and taking operation cost and task waiting and allocating time as constraint conditions, and performing optimization solution on the optimization problem to obtain optimal matching between a required task and computing power resources, wherein the overall revenue function of each computing power provider in the computing power network is represented as:
the constraint conditions are as follows:
Subject to Time i ≥time i ,i∈(1,n)，
wherein, reward j Providing Service for computing power j J belongs to {1,2,3, \8230; } total yield, qoE is a satisfaction degree measurement index of a calculation power demand side,
in order to account for the electricity charges involved in the operating costs,
cost for environmental processing related to operating costs j Is constant, calculates the power provider Service j J is the cost budget of {1,2,3, \8230; }, n is the number of tasks to be allocated on the power demand side, time i Time, the longest wait to be allocated that can be accepted for a computing power demand party i To calculate the actual latency of the force demand side.
In an embodiment of the method according to the first aspect of the present application, the satisfaction measure QoE of the computing power demander is calculated by the following formula:
wherein s represents the number of computing power providing services obtained by the computing power demand side.
In an embodiment of the control method for a computing power network based on a block chain according to the first aspect of the present application, the computing power provider Service provides a Service to the computing power network j Total yield Reward of j ∈ {1,2,3, \8230; } j Is represented as:
wherein, rev i Effort resources, TR, required for effort demanders j Providing Service for computing power j The sum of resources, com, of j ∈ {1,2,3, \8230; } i And = 1,0 represents the completion of the task.
In an embodiment of the control method for a blockchain-based computational power network according to the first aspect of the present application, the method further includes: allocating profits with corresponding weights to all calculation force providers based on contribution values of all calculation force providers participating in the consensus process, wherein one calculation force provider Service j J ∈ {1,2,3, \8230 } the contribution value to a task i is:
wherein, rev i Resource, the computing power Resource required by the computing power demander j Providing Service for computing power j J ∈ {1,2,3, \8230; } available resources of the current cooperative member.
In an embodiment of the control method for a blockchain-based computational power network according to the first aspect of the present application, the method further includes: according to a certain period T, the calculation power is provided to the Service j J ∈ {1,2,3, \8230; } and using the task average contribution value as a basis for dynamically updating the credit value of the computing power provider, wherein the task average contribution value is calculated as follows:
in an embodiment of the control method for a blockchain-based computational power network according to the first aspect of the present application, the method further includes: the method comprises the steps of dynamically updating real-time credit values of all computing power providers according to participation of all computing power providers in a consensus process in a computing power network, dividing all computing power providers into a main control layer, a coordination layer and a peripheral layer based on the credit values, and selecting a new main node from the main control layer, wherein the main control layer consists of trusted high-performance computing nodes, the coordination layer consists of nodes which are dynamically selected and marked by the main control layer based on the credit values, and the peripheral layer consists of nodes except the main control layer and the coordination layer.
In an embodiment of the control method for a block chain-based computational power network according to the first aspect of the present application, a credit value of a node l (l ≧ 1) in a k (k ≧ 1) th round of consensus process is defined as replication l,k Then the credit value is calculated l,k The polynomial weighting formula of (a) is:
Reputation l,k ＝αA l,k +βB l,k +γC l,k +ηD l,k +μE l,k +Reputation l,0 ,
wherein A is l Representing computing power, B l Representing memory capacity, C l Representative of bandwidth level, D l Represents the on-line stability, E l Representing interaction scoring trust l,0 Representing the initial credit values, α, β, γ, η, μ represent the weights of these five dimensions, respectively.
In order to solve the technical problem, in a second aspect, the present application provides a control system for a blockchain-based computational power network, the system including a processor and a memory, the memory storing a computer program, when executed by the processor, implementing the control method for a blockchain-based computational power network as described above.
In order to solve the technical problem, in a third aspect, a computer-readable storage medium is provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the control method for a blockchain-based computational power network as described above.
The block chain-based computational power network control method, the block chain-based computational power network control system and the computer-readable storage medium have the following beneficial effects:
(1) The method is based on a cooperative game theory, simultaneously considers relevant influence factors such as available computing power, required computing power of required tasks and task completion cost in the computing power network, researches the matching problem between the required tasks and the computing power resources, provides an alliance forming algorithm based on the cooperative game, and achieves the maximum target of the utilization rate of the computing power resources in the computing power network by analyzing, solving and optimizing a target income function under a balance strategy.
(2) The method and the device further provide a hierarchical distributed credit consensus mechanism aiming at the distributed cooperation and resource sharing requirements among the computing power providers, improve the distributed consensus efficiency of the computing power network and reduce the waste of computing power resources.
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The present application will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a schematic diagram of a computing power network architecture in the prior art;
fig. 2 is a logical block diagram of a control system of a blockchain-based computational power network according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. Also, the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The computing power network based on the block chain solves the problem of achieving trust consensus among computing power providers, and under the guarantee of distributed computing power resource sharing distribution, in order to maximize the utilization rate of computing power resources widely distributed in the computing power network, the bilateral supply-demand relationship between demand tasks and the computing power resources needs to be fully considered. Since different tasks have different requirements, such as low-latency response type tasks or high-computational resource type tasks, and the resources of a single computational provider are limited, and cannot simultaneously respond to multiple intensive service requests, the computational provider still needs to cooperate with other computational providers to realize resource sharing and cooperative work. Therefore, the method is based on a cooperative game theory, an interactive relation between computing power providers in the computing power network is modeled into an optimization problem which takes a revenue function with a resource utilization rate as a parameter as an optimization target and takes operation cost and task waiting and allocation time as constraint conditions, and the optimization problem is optimized and solved to obtain the optimal matching between the required task and the computing power resource.
Specifically, resource scheduling and computing power sharing are performed in the computing power network, and each computing power provider needs to provide idle computing power to cooperatively complete a required task, so that the efficiency of the overall computing power network is maximized. Namely, each computing power provider has the same objective function and forms a cooperative relationship with each other, so that the control method of the computing power network based on the block chain selects the cooperative game to model the relationship between the computing power providers. The cooperative game is specifically defined as follows:
1) The participants: each computing power provider;
2) Strategy: whether to join a new alliance or whether to leave the existing alliance;
3) The gain function: a function with the overall computational resource utilization as a parameter.
Meanwhile, in order to perform bilateral matching of tasks and resources, the satisfaction degree of the computing power demander to the completion of the tasks must be considered, and the control method based on the computing power network of the block chain formalizes the satisfaction degree of the computing power demander into the time for the tasks to wait for distribution. And each computing power provider Service j J ∈ {1,2,3, \8230; } requires consideration of its own construction cost, which is constant, and operation cost, which relates to electricity charge
And environmental disposal fees
Etc., the revenue situation of the parties then depends on the operating costs and the usage of the self-computing resources.
At present, a certain calculation force demand side has n tasks to be distributed, namely Task i The longest waiting allocated Time that i ∈ (1, n) can accept is Time i The actual waiting time is time i The required computing power resource is Rev i Service for computing power provider j The sum of the resources of j ∈ {1,2,3, \8230; } is TR j According to the completion Com of the task i = 1,0, total revenue Reward for the computing power provider j Can be expressed as:
in addition, the available resource information of each computing power provider is independent of each other, and the information can be shared with each other only when a certain computing power provider decides to cooperate with other computing power providers. And the number X of tasks currently undertaken by each computing power provider j It is a public message. Thus, the computing power provider Service j J ∈ {1,2,3, \8230; } may be based on the Resource available for the current cooperative member j And the task undertaking conditions of each computing power provider, and deducing the probability of the available resource conditions of other computing power providers based on a Bayesian formula, namely:
wherein, X = { X 1 ,X 2 ,X 3 \8230; } represents the set of numbers of tasks each undertaken by all computing power providers, and the probabilistic inference of equation (1) above is also Belief (Belief) in game theory. P (X | Resource) j ) And P (Resource) j ) The calculation can be directly carried out according to the shared data after cooperation, and in order to avoid underflow caused by the fact that the data set is possibly too small, the formula (1) is changed into the following formula：
Finally, the overall revenue function for each computing force provider in the computing force network can be expressed as:
the constraint conditions are as follows:
Subject to Time i ≥time i ,i∈(1,n) (4)
among them, cost j As a constant, the calculation power provider Service j J belongs to the cost budget {1,2,3, \8230; }, n is the number of tasks to be allocated of the power demand side, and QoE (Quality of Experience) is a satisfaction degree measurement index of the power demand side. Specifically, qoE is calculated by the following formula:
wherein s represents the number of computing power providing services obtained by the computing power demander.
The optimization objective formula (3) requires that the resource utilization rate of the computing power provider and the satisfaction degree of the computing power demander on the task completion condition are maximum, and simultaneously requires that the cost expenditure of the computing power provider is minimized. The constraint equation (4) requires that the waiting time of the computing power demand side for each task cannot exceed the corresponding waiting upper bound, and the constraint equation (5) requires that the Cost of the power fee and the environmental fee of the computing power supply side cannot exceed the Cost budget Cost j . Combining the optimization problems formed by the optimization target formula (3) and the constraint conditions (4) to (5), the theory proves that the cooperative alliance forming algorithm can converge to a stable equilibrium point and can compare the stable equilibrium pointThe fast convergence rate reaches a suboptimal solution. After the optimization target finds a balance point according to the computing power demand of the demand task, the idle computing power of the computing power provider, the waiting time delay and the like, the task and the demand are considered to be optimal matching, and the maximum goal of the computing power resource utilization rate in the computing power network can be achieved by completing the corresponding demand task according to the corresponding computing power provider.
Further, the control method of the computational power network based on the block chain further allocates the profit of the corresponding weight to each computational power provider based on the contribution value of each computational power provider participating in the consensus process. To measure a computing power provider Service j J e {1,2,3, \8230; } the contribution degree to one task i, the application proposes the following contribution degree metrics to accomplish the final profit distribution:
wherein,
providing Service for computing power j J ∈ {1,2,3, \8230; } contribution to a task i, rev i Resource for the computing resources required by the computing power demander j Providing Service for computing power j J ∈ {1,2,3, \8230; } available resources of the current cooperative member.
Furthermore, the block chain-based computing power network control method further provides Service for the computing power provider according to a certain period T j The task average contribution value of j ∈ {1,2,3, \8230; } is calculated as follows:
the task average contribution value is used as a basis for dynamically updating the credit value of the computing power provider, so that each computing power provider is encouraged to actively participate in the collaborative allocation of the computing power resources in a certain period.
Further, according to the control method of the computing power network based on the block chain in the embodiment of the application, a hierarchical distributed credit consensus model is provided for the requirement that the distributed computing power providers in the computing power network cooperate to share computing power resources based on the dynamic process that the consensus node generates the effective blocks and links the chains, and trust cooperation and resource sharing among the computing power providers are facilitated.
Specifically, the block chain-based computing power network control method dynamically updates the real-time credit value of each computing power provider according to the participation consensus process of each computing power provider in the computing power network, and divides each computing power provider into three layers, namely a main control layer, a coordination layer and a peripheral layer, based on the credit value. The master control layer is composed of credible high-performance computing nodes which are arranged in advance, and a new master node is selected from the master control layer preferentially to reduce consumption generated in selection. The coordination layer is composed of nodes which are dynamically selected and marked by the main control layer based on the credit value, the peripheral layer is composed of nodes except the main control layer and the coordination layer, and the nodes do not participate in the consensus process because of the low trust value, but can be marked into the coordination layer by the main control layer through improving the trust value.
Each entering new computing provider receives a unique ID and an initial credit value that is cumulatively counted as the computing provider agrees with other computing providers. The reward of positive credits may encourage the incentive provider to participate in consensus activities to a higher degree, while negative credits allow the member to weigh less in consensus. Defining the credit value of the node l (l is more than or equal to 1) in the k (k is more than or equal to 1) round consensus process as the reiteration l,k Then calculate the credit value regeneration l,k The polynomial weighting formula of (a) is:
Reputation l,k ＝αA l,k +βB l,k +γC l,k +ηD l,k +μE l,k +Reputation l,0 , (9)
wherein A is l Representing computing power, B l Representing memory capacity, C l Representative of the bandwidth level, D l Represents the on-line stability, E l Representing interaction score confidence l,0 Representing an initial credit valueα, β, γ, η, μ represent the weights of these five dimensions, respectively. The first four items of the expression represent the objective processing capacity of the node, and the fifth item represents the forwarding and communication conditions completed in the distributed interaction process of the node. According to the credit value calculated by the formula (9), the node can be divided into three layers, namely a main control layer, a coordination layer and a peripheral layer.
Along with the increase of the number of calculation force providers, the interactive information can grow exponentially, the performance of the consensus algorithm is reduced, the expansibility is limited to a certain extent, and a large amount of resources are wasted. Therefore, the block chain-based computational power network control method further considers the use of a proper parallel distributed architecture to improve the algorithm, so as to provide a consensus algorithm with higher expansibility and lighter weight. For the computational power network based on the block chain, the same view unit task can be simultaneously computed through a plurality of main nodes in parallel, and after one main node completes a complete consensus process firstly, the main control layer informs other main nodes to stop corresponding processes so as to reduce the additionally increased consensus cost.
Based on the foregoing control method of the computational power network based on the block chain, the present application further provides a control system 10 of the computational power network based on the block chain. Referring to fig. 2, a control system 10 of a force network based on a block chain 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 that, when executed by the processor 11, causes the processor 11 to implement the control method of the blockchain-based computational power network according to the foregoing embodiment of the present application.
The present application also proposes a computer-readable storage medium storing a computer program which, when executed by a processor, implements the control method of the blockchain-based computational power network of the foregoing embodiment of the present application.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (9)
1. A control method of a block chain-based computational power network is characterized by comprising the following steps: modeling an interactive relation among computing power providers in a computing power network into an optimization problem which takes a profit 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 carrying out optimization solution on the optimization problem to obtain optimal matching between a required task and computing power resources, wherein the overall profit function of each computing power provider in the computing power network is expressed as:
the constraint conditions are as follows:
Subject to Tim e i ≥tim e i ,i∈(1,n)，
wherein, reward j Providing Service for computing power j J belongs to {1,2,3, \8230 }, total yield, qoE is a satisfaction degree measuring index of a calculation power demand party,
in order to account for the electricity charges involved in the operating costs,
cost for environmental processing related to operating costs j As a constant, the calculation power provider Service j J belongs to {1,2,3, \8230 }, the cost budget, n is the number of tasks to be allocated of a calculation power demand side, and Time i Time, the maximum wait allotted time that a computing power demander can accept i To calculate the actual latency of the force demand side.
2. The method of claim 1, wherein the satisfaction measure QoE of the computing power demander is calculated by the following formula:
wherein s represents the number of computing power providing services obtained by the computing power demand side.
3. The method of claim 1, wherein the computing power provider Service j Total yield Reward of j ∈ {1,2,3, \8230; } j Is represented as:
wherein, rev i Effort resources, TR, required for effort demanders j Providing Service for computing power j Resource sum of j ∈ {1,2,3, \8230; }, com i = 1,0 represents the completion of the task.
4. The method of claim 1, further comprising: allocating profits with corresponding weights to all calculation force providers based on contribution values of all calculation force providers participating in the consensus process, wherein one calculation force provider Service j J ∈ {1,2,3, \8230; } the contribution value to a task i is:
wherein, rev i Resource for the computing resources required by the computing power demander j Providing Service for computing power j J ∈ {1,2,3, \8230; } available resources of the current cooperative member.
5. The method of claim 4, whereinCharacterized in that said method further comprises: according to a certain period T, the calculation power is provided to the Service j And j ∈ {1,2,3, \8230; } and using the task average contribution value as a basis for dynamically updating the credit value of the computing power provider, wherein the task average contribution value is calculated as follows:
6. the method of claim 1, further comprising: the method comprises the steps of dynamically updating real-time credit values of computing power providers according to the participation of the computing power providers in a consensus process in a computing power network, dividing the computing power providers into a main control layer, a coordination layer and a peripheral layer based on the credit values, and selecting a new main node from the main control layer, wherein the main control layer consists of trusted high-performance computing nodes, the coordination layer consists of nodes which are dynamically selected and marked by the main control layer based on the credit values, and the peripheral layer consists of nodes except the main control layer and the coordination layer.
7. The method of claim 6, wherein the credit value of node l (l ≧ 1) in the k (k ≧ 1) round consensus process is defined as replication l,k Then credit value recovery l,k The polynomial weighting formula of (a) is:
Reputation l,k ＝αA l,k +βB l,k +γC l,k +ηD l,k +μE l,k +Reputation l,0 ,
wherein A is l Representing computing power, B l Representing memory capacity, C l Representative of bandwidth level, D l Represents the on-line stability, E l Representing interaction scoring trust l,0 Representing the initial credit values, α, β, γ, η, μ represent the weights of these five dimensions, respectively.
8. A control system for a blockchain based computing power network, characterized in that the system comprises a processor and a memory, the memory storing a computer program which, when executed by the processor, implements the control method for a blockchain based computing power network according to any one of claims 1 to 7.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out a method of controlling a blockchain-based computational power network according to any one of claims 1 to 7.
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|---|---|---|---|---|
| CN117055818A (en) * | 2023-08-25 | 2023-11-14 | 南京合宜信息技术有限公司 | Client information storage management method and system based on block chain |
| CN117055818B (en) * | 2023-08-25 | 2024-05-10 | 南京合宜信息技术有限公司 | Client information storage management method and system based on block chain |
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