CN120276866B - A privacy computing task scheduling method and system based on blockchain - Google Patents
A privacy computing task scheduling method and system based on blockchainInfo
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- CN120276866B CN120276866B CN202510748974.6A CN202510748974A CN120276866B CN 120276866 B CN120276866 B CN 120276866B CN 202510748974 A CN202510748974 A CN 202510748974A CN 120276866 B CN120276866 B CN 120276866B
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Abstract
本申请提供了一种基于区块链的隐私计算任务调度方法及系统，从任务进度信息中提取区块链中隐私计算任务的执行状态，进而通过执行状态和区块链在算力资源调度的任务卸载策略确定区块链中调度资源对隐私保护的弹性算力；将区块链中的算力资源划分为多个算力资源节点，进而确定各个算力资源节点之间的资源共享关系，根据资源共享关系确定各个算力资源节点对隐私计算任务的隐私保护代价；通过各个隐私保护代价和弹性算力对区块链中算力分配与隐私保护之间的冲突关系进行评估，得到区块链中调度冲突的平衡值，基于调度冲突的平衡值对区块链中各个算力资源节点进行隐私计算任务的均衡分配。基于上述方案可实现区块链中隐私计算任务的均衡分配。
This application provides a blockchain-based privacy-preserving computing task scheduling method and system. The method extracts the execution status of privacy-preserving computing tasks in the blockchain from task progress information, and then determines the elastic computing power of the blockchain's scheduling resources for privacy protection based on the execution status and the task offloading strategy used in the blockchain's computing resource scheduling. The computing resources in the blockchain are divided into multiple computing resource nodes, and the resource sharing relationships between each computing resource node are determined. Based on the resource sharing relationships, the privacy protection cost of each computing resource node for the privacy-preserving computing task is determined. The conflict between computing power allocation and privacy protection in the blockchain is evaluated using the privacy protection costs and elastic computing power to obtain a balance value for scheduling conflicts in the blockchain. Based on this balance value, privacy-preserving computing tasks are evenly distributed across the computing resource nodes in the blockchain. This scheme can achieve balanced distribution of privacy-preserving computing tasks in the blockchain.
Description
Technical Field
The application relates to the technical field of task scheduling, in particular to a privacy calculation task scheduling method and system based on a blockchain.
Background
The key characteristics of the blockchain include decentralization, data non-falsification, anonymity, transparency and intelligent contract support, so that the blockchain has remarkable advantages in solving trust problems and potential safety hazards in a traditional decentralization system, and can also realize decentralization application programs.
Privacy computing tasks involve the processing of sensitive data, typically involving encryption, computation and analysis of private information of individuals, businesses or organizations, in a decentralized blockchain network, task scheduling needs to take into account how to effectively distribute computing tasks among multiple nodes without exposing or revealing sensitive data, whereas traditional task scheduling approaches can lead to maldistribution of tasks, which can lead to portions of blocknodes carrying processing tasks of too much sensitive data, which can lead to not only unbalanced computing load, but also increase the risk of data leakage, for example, if blocknodes process large amounts of private data for a long time, they can be targets of attacks, or encounter man-in-the-middle attacks during data transmission, thereby exposing private information of users, and therefore, how to achieve balanced distribution of private computing tasks in blockchains has become a problem faced by the industry.
Disclosure of Invention
The application provides a block chain-based privacy calculation task scheduling method and a block chain-based privacy calculation task scheduling system, which can realize balanced distribution of privacy calculation tasks in a block chain.
In a first aspect, the present application provides a blockchain-based privacy calculation task scheduling method, including:
When the block chain executes the privacy calculation task, task progress information of the privacy calculation task in the block chain is monitored in real time;
Extracting the execution state of a privacy calculation task in a blockchain from the task progress information, and further determining the elastic computing power of scheduling resources in the blockchain on privacy protection through the execution state and a task unloading strategy of computing power resource scheduling in the blockchain;
Dividing the computing power resources in the block chain into a plurality of computing power resource nodes by using a hierarchical structure, further determining a resource sharing relation among the computing power resource nodes, and determining privacy protection cost of the computing power resource nodes to the privacy calculation task according to the resource sharing relation and privacy protection characteristics in the block chain;
And evaluating conflict relation between computing power distribution and privacy protection in the block chain through each privacy protection cost and the elastic computing power to obtain a balance value of scheduling conflict in the block chain, and carrying out balanced distribution of privacy computing tasks on each computing power resource node in the block chain based on the balance value of the scheduling conflict.
In some embodiments, extracting the execution state of the privacy calculation task in the blockchain from the task progress information specifically includes:
Acquiring all the executing computing tasks in the block chain;
for each calculation task, extracting the task completion progress and the task completion speed of the calculation task from the task progress information;
Determining the execution characteristics of the computing tasks according to the task completion progress and the task completion speed, and further obtaining the execution characteristics of each computing task;
and extracting the execution state of the privacy calculation task in the blockchain from all the execution characteristics.
In some embodiments, determining the resilience of the scheduled resources in the blockchain to privacy protection by the execution state and the task offloading policy of the scheduling of the resilience resources in the blockchain specifically includes:
Acquiring a task unloading strategy of computing power resource scheduling in a block chain;
Extracting an execution state value of each calculation task from the execution state for each calculation task;
Performing expansibility evaluation on the execution state value through the task unloading strategy to obtain an expandable value of the computing power in the computing task, and further obtaining the expandable value of the computing power in each computing task;
and determining the elasticity computing power of the scheduling resources in the blockchain on privacy protection according to all the extensible values.
In some embodiments, partitioning the computational resources in the blockchain into a plurality of computational resource nodes using a hierarchy specifically includes:
acquiring all resource blocks of a block chain, and further determining the computational power characteristics of each resource block;
carrying out calculation classification on all calculation features through a layered structure to obtain a plurality of calculation grades;
All resource blocks are divided into a plurality of computing power resource nodes according to each computing power level.
In some embodiments, determining the resource sharing relationship between the individual computing power resource nodes specifically includes:
for each computing power resource node, acquiring all shared blocks between the computing power resource node and other computing power resource nodes;
Determining a topological association diagram of the computing power resource nodes through all the shared blocks, and further obtaining the topological association diagram of each computing power resource node;
And generating resource sharing relations among all computing power resource nodes according to all the topological association diagrams.
In some embodiments, determining the privacy protection cost of each computing power resource node to the privacy computing task according to the resource sharing relationship and the privacy protection features in the blockchain specifically includes:
For each computing power resource node, extracting the resource sharing characteristics of the computing power resource node from the resource sharing relation;
Extracting the trust degree of the computing power resource node on the privacy calculation task from the privacy protection features in the blockchain;
And correlating the trust degree through the resource sharing characteristics to obtain the privacy protection cost of the computing resource nodes on the privacy calculation task, and further obtaining the privacy protection cost of each computing resource node on the privacy calculation task.
In some embodiments, the privacy computing task is a distributed computing task based on homomorphic privacy encryption.
In a second aspect, the present application provides a blockchain-based privacy computing task scheduling system, comprising:
the monitoring module is used for monitoring task progress information of the privacy calculation task in the blockchain in real time when the blockchain executes the privacy calculation task;
The processing module is used for extracting the execution state of the privacy calculation task in the blockchain from the task progress information, and further determining the elastic computing power of the scheduling resource in the blockchain on privacy protection through the execution state and a task unloading strategy of computing power resource scheduling in the blockchain;
The processing module is further used for dividing the computing power resources in the blockchain into a plurality of computing power resource nodes by using the hierarchical structure, further determining resource sharing relations among the computing power resource nodes, and determining privacy protection cost of the computing power resource nodes to the privacy calculation task according to the resource sharing relations and privacy protection characteristics in the blockchain;
and the execution module is used for evaluating the conflict relation between the computing power distribution and the privacy protection in the block chain through each privacy protection cost and the elastic computing power to obtain a balance value of the scheduling conflict in the block chain, and carrying out balanced distribution of the privacy computing task on each computing power resource node in the block chain based on the balance value of the scheduling conflict.
In a third aspect, the present application provides a computer device, the computer device including a memory for storing a computer program and a processor for calling and running the computer program from the memory, so that the computer device performs the above-mentioned blockchain-based privacy calculation task scheduling method.
In a fourth aspect, the present application provides a computer readable storage medium having stored therein instructions or code which, when executed on a computer, cause the computer to perform the blockchain-based privacy calculation task scheduling method described above.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
The privacy computing task scheduling method and system based on the blockchain are characterized in that when the blockchain executes the privacy computing task, task progress information of the privacy computing task in the blockchain is monitored in real time, execution states of the privacy computing task in the blockchain are extracted from the task progress information, further, elastic computing power of scheduling resources in the blockchain for privacy protection is determined through the execution states and task unloading strategies of computing power resource scheduling in the blockchain, computing power resources in the blockchain are divided into a plurality of computing power resource nodes through a hierarchical structure, resource sharing relation among the computing power resource nodes is further determined, privacy protection cost of the privacy computing task by the computing power resource nodes is determined according to the resource sharing relation and privacy protection characteristics in the blockchain, conflict relation between computing power distribution and privacy protection in the blockchain is evaluated through the privacy protection cost and the elastic computing power, balance value of scheduling conflict in the blockchain is obtained, and the balance value of the privacy computing task is distributed to the computing power resource nodes in the blockchain based on the balance value of the scheduling conflict.
Therefore, in the application, the conflict relation between the distribution of the computing power in the block chain and the privacy protection is evaluated through each privacy protection cost and the elastic computing power, so that the balance value of the scheduling conflict in the block chain is obtained, the balance value of the scheduling conflict is based on the balance value of the scheduling conflict to carry out the balance distribution of the privacy calculation tasks on each computing power resource node in the block chain, firstly, the elastic computing power is determined, the capability of flexibly adjusting the computing power resources in the block chain in task scheduling is obtained, in the privacy calculation tasks, the complexity and the data sensitivity of the tasks are possibly different along with the change of time and the computing stage, the dynamic adjustment of the elastic computing power ensures the high-efficiency utilization of computing power resources, when the computing power of the computing power resource nodes is insufficient, the elastic computing power can be redistributed in a network to ensure the smooth execution of the tasks and avoid performance bottleneck, meanwhile, the determination of the elastic computing power can also help to preferentially distribute resources for the tasks with high privacy protection requirements, the balance of the privacy protection is realized, the privacy protection capability of the privacy protection can be effectively promoted, in the privacy protection cost is determined, the privacy protection cost is different along with the change of the time and the data sensitivity of the computing power can be satisfied, the privacy protection performance of the computing power can be greatly reduced, the privacy protection can be greatly matched with the privacy protection performance of the computing power nodes, and the privacy protection performance can be greatly reduced by the privacy protection requirements, and the privacy protection can be greatly applied to the privacy protection nodes, and the privacy protection has the performance protection requirements can be greatly reduced by the performance protection. Therefore, the resource utilization efficiency and the privacy protection level are improved, and in sum, the balanced distribution of the privacy calculation tasks in the block chain can be realized based on the scheme.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the application, and that other drawings can be obtained from these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is an exemplary flow chart of a blockchain-based privacy calculation task scheduling method in accordance with some embodiments of the application;
FIG. 2 is a control logic diagram of a blockchain shown in some embodiments in accordance with the present application;
FIG. 3 is a flow diagram illustrating a determination of a balance value for a scheduling conflict in accordance with some embodiments of the application;
FIG. 4 is a schematic diagram of a blockchain-based privacy computing task scheduling system in accordance with some embodiments of the application;
Fig. 5 is a schematic diagram of a computer device implementing a blockchain-based privacy computing task scheduling method in accordance with some embodiments of the application.
Detailed Description
In order to better understand the technical scheme of the present application, the following detailed description will refer to the accompanying drawings and specific embodiments.
Referring to FIG. 1, which is an exemplary flowchart of a blockchain-based privacy computing task scheduling method according to some embodiments of the present application, the blockchain-based privacy computing task scheduling method generally includes the steps of:
In step 101, task progress information of a privacy calculation task in a blockchain is monitored in real time as the blockchain executes the privacy calculation task.
In the application, the task progress information is information describing the completion condition of the privacy calculation task in the blockchain, the privacy calculation task is a distributed calculation task based on homomorphic privacy encryption, when the privacy calculation task is executed by the blockchain, all the calculation tasks which are being executed in the blockchain are obtained, the task completion progress and the task completion speed of each calculation task are monitored in real time, the task completion progress is the percentage of completed tasks, the task completion speed is the progress of completing the tasks per second, and the collection of the task completion progress and the task completion speed of all the calculation tasks can be used as the task progress information of the privacy calculation task in the blockchain.
In some embodiments, the control logic diagram of the blockchain network according to some embodiments of the present application is described with reference to fig. 2, which shows a control logic structure of a blockchain network, including a control center and a plurality of node groups, where the control center is responsible for managing a main chain and a secondary chain of the entire network, and communicating and synchronizing data with each node group through the main chain, and each node group is composed of a plurality of nodes, for example, a node group a and a node group N, and each node group is internally connected through a secondary chain, so that data sharing and communication between nodes in the group can be achieved.
In the control logic structure, the node group A and the node group N respectively comprise four nodes, each node is connected with other nodes in the group through a secondary chain and is connected with a control center through a main chain, the control center sends instructions and data to each node group through the main chain and simultaneously receives information from each node group, the decentralization characteristic of the blockchain network can be ensured, and meanwhile, the effective management and coordination of the whole network are realized through the control center. Through the structure, the blockchain network can realize efficient data transmission and processing, and meanwhile, the safety and the reliability of the blockchain network are ensured.
In step 102, the execution state of the privacy calculation task in the blockchain is extracted from the task progress information, and then the elastic computing power of the scheduling resource in the blockchain for privacy protection is determined according to the execution state and the task unloading strategy of computing power resource scheduling in the blockchain.
In some embodiments, extracting the execution state of the privacy calculation task in the blockchain from the task progress information may be implemented by the following steps:
Acquiring all the executing computing tasks in the block chain;
for each calculation task, extracting the task completion progress and the task completion speed of the calculation task from the task progress information;
Determining the execution characteristics of the computing tasks according to the task completion progress and the task completion speed, and further obtaining the execution characteristics of each computing task;
and extracting the execution state of the privacy calculation task in the blockchain from all the execution characteristics.
In the method, the execution state represents real-time execution progress information of a computing task on a computing resource node, including states of starting, processing, suspending, completing and the like of the task, and when the computing task is concretely implemented, firstly, all the computing tasks which are being executed in a block chain are obtained, secondly, for each computing task, the task completion progress and the task completion speed of the computing task are extracted from the task progress information, then, the execution characteristics of the computing task are determined according to the task completion progress and the task completion speed, and further, the execution characteristics of each computing task are obtained.
In some embodiments, determining the elastic computing power of the scheduled resources in the blockchain for privacy protection by the execution state and the task offloading policy of the computing power resource scheduling in the blockchain may be implemented by:
Acquiring a task unloading strategy of computing power resource scheduling in a block chain;
Extracting an execution state value of each calculation task from the execution state for each calculation task;
Performing expansibility evaluation on the execution state value through the task unloading strategy to obtain an expandable value of the computing power in the computing task, and further obtaining the expandable value of the computing power in each computing task;
and determining the elasticity computing power of the scheduling resources in the blockchain on privacy protection according to all the extensible values.
When the method is specifically implemented, firstly, a task unloading strategy for computing power resource scheduling in a block chain can be achieved by acquiring the task unloading strategy for computing power resource scheduling in the block chain from a control center of the block chain, secondly, for each computing task, extracting the execution state value of the computing task from the execution state can be achieved by taking the execution state value of a state cluster of the computing task in the execution state as the execution state value of the computing task, and then, performing expansibility evaluation on the execution state value through the task unloading strategy to obtain an expandable value of computing power in the computing task, and further, obtaining the expandable value of computing power in each computing task can be achieved by initializing a computing power evaluation model based on a neural network, taking the task unloading strategy as the computing power release strategy of the computing task in the computing power evaluation model, taking the execution state value as an output tag in the computing power evaluation model, using the computing power evaluation model to evaluate the expandable power in the computing task, so that the computing power in the computing power evaluation model can be taken as the expandable value of the computing power in the computing power evaluation model, and finally, the expandable value in the computing power evaluation model can be used as an expandable value of the computing power scheduling block, and the expandable value in the computing power evaluation model can be achieved.
In the application, the elastic computing power represents the task computing capability of a computing power resource node capable of being scheduled under the condition of not reducing the performance when the computing task is processed, the task unloading strategy represents a decision rule for removing the computing task from the computing power resource node in a blockchain, the execution state value represents the current execution progress of the privacy computing task on the computing power resource node, the extensible value represents the extensible capability measurement of the computing power resource node when the computing task is processed, the computing power evaluation model is a model based on a neural network and aims at evaluating the extensibility of the computing task on different computing power resource nodes, in the computing power evaluation model, the task unloading strategy is used as an input feature of the model and represents how to allocate the computing task to different nodes according to the task requirement and the node capability, the execution state value is used as an output tag and reflects the actual execution progress of the task, and the relation between the task unloading strategy and the task execution feature can be learned by training the computing power evaluation model, so that the computing power extensibility of different computing tasks on different nodes can be predicted, and the reasonable scheduling and resource allocation of the task can be guided.
In step 103, the hierarchical structure is used to divide the computing power resource in the blockchain into a plurality of computing power resource nodes, so as to determine the resource sharing relationship among the computing power resource nodes, and the privacy protection cost of each computing power resource node to the privacy calculation task is determined according to the resource sharing relationship and the privacy protection characteristics in the blockchain.
In some embodiments, dividing the computational resources in the blockchain into a plurality of computational resource nodes using a hierarchy may be accomplished by:
acquiring all resource blocks of a block chain, and further determining the computational power characteristics of each resource block;
carrying out calculation classification on all calculation features through a layered structure to obtain a plurality of calculation grades;
All resource blocks are divided into a plurality of computing power resource nodes according to each computing power level.
When the method is concretely implemented, firstly, all resource blocks of a block chain are acquired, and then the calculation power characteristics of each resource block are determined, wherein the calculation power ranges of all the resource blocks and each resource block can be acquired from a control center of the block chain, and each calculation power range can be used as the calculation power characteristics of the corresponding resource block, so that the calculation power characteristics of each resource block can be obtained; the method comprises the steps of obtaining a hierarchical structure from a control center of a block chain, wherein the hierarchical structure comprises a plurality of calculation intervals and levels thereof, the calculation characteristics can be used as calculation levels according to the levels of the calculation intervals to which the calculation ranges belong, the calculation levels can be obtained, and finally, the resource blocks can be divided into a plurality of calculation resource nodes according to the calculation levels.
It should be noted that, in the present application, a computing resource node represents a node that provides computing power in a blockchain network, a resource block represents a computing block in a blockchain that is used to perform a computing task, the computing block represents a node in the blockchain network that is responsible for storing, verifying, and propagating block data, a computing power feature represents a capability feature of the computing resource node when performing the computing task, and a computing power class represents a class division of the computing resource node in providing computing power.
In some embodiments, determining the resource sharing relationship between the various computing force resource nodes may be accomplished by:
for each computing power resource node, acquiring all shared blocks between the computing power resource node and other computing power resource nodes;
Determining a topological association diagram of the computing power resource nodes through all the shared blocks, and further obtaining the topological association diagram of each computing power resource node;
And generating resource sharing relations among all computing power resource nodes according to all the topological association diagrams.
In the application, the resource sharing relationship is a relationship diagram for measuring the cooperation capacity and the resource transfer efficiency between nodes; when the method is concretely implemented, firstly, for each computing resource node, all shared blocks between the computing resource node and other computing resource nodes can be obtained by adopting the following way that for each computing resource node, a part which is repeated with the resource blocks in the computing resource node is screened out from all the resource blocks in other computing resource nodes to be used as all the shared blocks; then, determining a topology association graph of the computing power resource nodes through all the shared blocks, and further obtaining a topology association graph of each computing power resource node, wherein the topology association graph is realized in such a way that all the shared blocks are arranged and connected according to communication coordinates in a blockchain and then used as the topology association graph of the computing power resource nodes, and the topology association graph of each computing power resource node can be obtained through the method, wherein the topology association graph is a network layout graph reflecting the dependency relationship of the nodes in the resource sharing and task unloading processes; and finally, generating the resource sharing relation among all the computing power resource nodes according to all the topological association diagrams can be realized by carrying out co-coordinate fusion on all the topological association diagrams according to communication coordinates in a block chain, thereby taking the fused topological association diagram as the resource sharing relation among all the computing power resource nodes.
In some embodiments, determining the privacy protection cost of each computing resource node for the privacy computing task according to the resource sharing relationship and the privacy protection features in the blockchain may be implemented by:
For each computing power resource node, extracting the resource sharing characteristics of the computing power resource node from the resource sharing relation;
Extracting the trust degree of the computing power resource node on the privacy calculation task from the privacy protection features in the blockchain;
And correlating the trust degree through the resource sharing characteristics to obtain the privacy protection cost of the computing resource nodes on the privacy calculation task, and further obtaining the privacy protection cost of each computing resource node on the privacy calculation task.
When the method is concretely implemented, firstly, for each computing power resource node, the resource sharing characteristic of the computing power resource node can be extracted from the resource sharing relation, namely, for each computing power resource node, the set of all associated values corresponding to the computing power resource node in a topological association graph in the resource sharing relation can be used as the resource sharing characteristic of the computing power resource node, then, the trust degree of the computing power resource node to the privacy computing task can be extracted from the privacy protection characteristic in a blockchain, namely, the set of the privacy protection means of each computing power resource node in the blockchain can be obtained from the control center of the blockchain and used as the privacy protection characteristic in the blockchain, the privacy protection means comprises homomorphic encryption, multi-party calculation, zero knowledge proof and other privacy protection technologies, so that the protection degree of the privacy protection technology can be used in the privacy protection characteristic by the computing power resource node can be quantified, the quantized protection degree can be used as the trust degree of the computing power resource node to the privacy computing task, in other embodiments, the trust degree of the privacy computing task can also be regulated in combination with the trust degree of the privacy computing power resource node, and finally, the privacy protection cost of the privacy computing resource can be realized by the privacy protection mode of the computing power resource node, namely, the privacy protection mode can be realized by combining the privacy protection cost of the privacy protection nodes, the privacy protection mode can be realized by the privacy protection cost of the computing resource nodes, the privacy protection mode, the privacy protection of the computing resource nodes can be realized by the associated with the privacy protection cost, and the privacy protection mode can be realized, privacy protection cost of each computing power resource node on the privacy calculation task=a×resource sharing characteristic+b×trust degree+c, wherein a, b and c are parameters of a multi-variable regression model, and are obtained through historical operation data training.
In the application, the privacy protection cost represents the computing power required by the computing power resource node for privacy protection of the privacy calculation task, the resource sharing characteristic represents the sharing degree of the resources among the computing power resource nodes, and the trust degree represents the trust degree of the privacy calculation task on the computing power resource nodes.
In step 104, the conflict relation between the computing power distribution and the privacy protection in the blockchain is evaluated through each privacy protection cost and the elastic computing power, so that a balance value of the scheduling conflict in the blockchain is obtained, and the balanced distribution of the privacy computing tasks is carried out on each computing power resource node in the blockchain based on the balance value of the scheduling conflict.
In some embodiments, the conflict relationship between the computing power distribution and the privacy protection in the blockchain is evaluated through each privacy protection cost and the elastic computing power to obtain the balance value of the scheduling conflict in the blockchain, and the figure is a schematic flow chart for determining the balance value of the scheduling conflict in some embodiments of the present application, where the determination of the balance value of the scheduling conflict can be implemented by adopting the following steps:
in step 1041, for each computing force resource node, extracting an extensible mean value of the computing task in the computing force resource node from the elastic computing force;
in step 1042, determining a conflict value between the computing power distribution and the privacy protection in the computing power resource nodes through the privacy protection cost of the computing power resource nodes to the privacy calculation task and the extensible average value, so as to obtain the conflict value between the computing power distribution and the privacy protection in each computing power resource node;
in step 1043, a balance value for the scheduling conflict in the blockchain is determined based on all of the conflict values.
When the method is specifically implemented, firstly, for each computing power resource node, the extensible value of the computing task in the computing power resource node is extracted from the elastic computing power, namely, all the computing tasks being executed in the computing power resource node are obtained, the average value of the extensible values of all the computing tasks can be used as the extensible average value of the computing tasks in the computing power resource node, then, the conflict value between computing power distribution and privacy protection in the computing power resource node is determined through the privacy protection cost of the computing power resource node to the privacy computing tasks and the extensible average value, and further, the conflict value between computing power distribution and privacy protection in the computing power resource node is obtained, namely, the inverse of the product of the extensible average value and the privacy protection cost can be used as the conflict value between computing power distribution and privacy protection in the computing power resource node, and finally, the balance value of scheduling conflict in a block chain can be obtained according to all the conflict values, namely, the variance of the conflict value in the block chain can be used as the scheduling balance value in the block chain.
In the application, the balance value represents the balance degree of conflict between the task privacy protection and the computing power resource allocation, the extensible average value represents the average extensible capability of the computing power resource node when processing the task, and the conflict value represents the mismatch degree between the task privacy protection requirement and the computing power resource allocation.
In some embodiments, the balanced allocation of the privacy calculation tasks to each computing resource node in the blockchain based on the balance value of the scheduling conflict can be achieved by acquiring a preset balance threshold from a control center of the blockchain, and unloading the task in the resource block with the highest computing power use in each computing resource node when the balance value of the scheduling conflict is lower than the preset balance threshold, and allocating the task to the resource block with the lowest computing power use in each computing resource node until the balance value of the scheduling conflict is greater than or equal to the preset balance threshold.
In addition, in some embodiments, the present application provides a blockchain-based privacy computing task scheduling system, referring to fig. 4, which is a schematic structural diagram of the blockchain-based privacy computing task scheduling system according to some embodiments of the present application, where the blockchain-based privacy computing task scheduling system includes a monitoring module 201, a processing module 202, and an executing module 203, which are respectively described as follows:
The monitoring module 201 is mainly used for monitoring task progress information of the privacy calculation task in the blockchain in real time when the blockchain executes the privacy calculation task;
The processing module 202 is configured to extract an execution state of a privacy calculation task in a blockchain from the task progress information, and further determine an elastic computing power of a scheduling resource in the blockchain for privacy protection according to the execution state and a task unloading policy of computing power resource scheduling in the blockchain;
it should be noted that, the processing module 202 is further configured to divide the computing power resources in the blockchain into a plurality of computing power resource nodes by using a hierarchical structure, further determine a resource sharing relationship between the computing power resource nodes, and determine privacy protection costs of the computing power resource nodes on the privacy calculation task according to the resource sharing relationship and privacy protection features in the blockchain;
And the execution module 203 is mainly used for evaluating the conflict relation between the computing power distribution and the privacy protection in the blockchain through each privacy protection cost and the elastic computing power to obtain the balance value of the scheduling conflict in the blockchain, and carrying out the balanced distribution of the privacy computing task on each computing power resource node in the blockchain based on the balance value of the scheduling conflict.
The above details the examples of the blockchain-based privacy calculation task scheduling method and system provided by the embodiments of the present application, and it can be understood that, in order to implement the above functions, the corresponding devices include corresponding hardware structures and/or software modules that perform the respective functions. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In some embodiments, the present application also provides a computer device including a memory for storing a computer program and a processor for calling and running the computer program from the memory, so that the computer device performs the blockchain-based privacy calculation task scheduling method described above.
In some embodiments, reference is made to fig. 5, in which a dashed line indicates that the unit or the module is optional, which is a schematic diagram of a computer device implementing a blockchain-based privacy calculation task scheduling method according to an embodiment of the present application. The blockchain-based privacy computing task scheduling method described in the above embodiment may be implemented by a computer device shown in fig. 5, where the computer device includes at least one processor 301, a memory 302, and at least one communication unit 305, and the computer device may be a terminal device or a server or a chip.
Processor 301 may be a general purpose processor or a special purpose processor. For example, the processor 301 may be a central processing unit (central processing unit, CPU) which may be used to control, execute and process data of a software program for a computer device, which may further comprise a communication unit 305 for enabling input (reception) and output (transmission) of signals.
For example, the computer device may be a chip, the communication unit 305 may be an input and/or output circuit of the chip, or the communication unit 305 may be a communication interface of the chip, which may be an integral part of a terminal device or a network device or other devices.
For another example, the computer device may be a terminal device or a server, the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.
The computer device may include one or more memories 302 having a program 304 stored thereon, the program 304 being executable by the processor 301 to generate instructions 303 such that the processor 301 performs the methods described in the method embodiments described above in accordance with the instructions 303. Optionally, data (e.g., a goal audit model) may also be stored in memory 302. Alternatively, the processor 301 may also read data stored in the memory 302, which may be stored at the same memory address as the program 304, or which may be stored at a different memory address than the program 304.
The processor 301 and the memory 302 may be provided separately or may be integrated together, for example, on a System On Chip (SOC) of the terminal device.
It should be understood that the steps of the above-described method embodiments may be accomplished by logic circuitry in hardware or instructions in software in the processor 301, and the processor 301 may be a CPU, digital signal processor (DIGITAL SIGNAL processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field programmable gate array (field programmable GATE ARRAY, FPGA), or other programmable logic device, such as discrete gates, transistor logic, or discrete hardware components.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
For example, in some embodiments, the present application also provides a computer-readable storage medium having instructions or code stored therein that, when executed on a computer, cause the computer to perform the blockchain-based privacy calculation task scheduling method described above.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. The privacy calculation task scheduling method based on the blockchain is characterized by comprising the following steps of:
When the block chain executes the privacy calculation task, task progress information of the privacy calculation task in the block chain is monitored in real time;
Extracting an execution state of a privacy calculation task in a blockchain from the task progress information, and further determining the elastic computing power of scheduling resources in the blockchain on privacy protection according to the execution state and a task unloading strategy of computing power resource scheduling in the blockchain, wherein the elastic computing power represents task computing power which can be scheduled by a computing power resource node under the condition of not reducing performance when the computing task is processed;
Dividing the computing power resources in the block chain into a plurality of computing power resource nodes by using a hierarchical structure, further determining a resource sharing relation among the computing power resource nodes, and determining privacy protection cost of the computing power resource nodes on a privacy calculation task according to the resource sharing relation and privacy protection characteristics in the block chain, wherein the privacy protection cost represents computing power required by the computing power resource nodes for carrying out privacy protection on the privacy calculation task;
Evaluating conflict relation between computing power distribution and privacy protection in the blockchain through each privacy protection cost and the elastic computing power to obtain balance values of scheduling conflicts in the blockchain, and carrying out balanced distribution of privacy computing tasks on each computing power resource node in the blockchain based on the balance values of the scheduling conflicts, wherein the balance values of the scheduling conflicts represent balance degrees of task privacy protection and computing power resource distribution conflicts;
The determining the resource sharing relation among the computing power resource nodes specifically comprises the following steps:
for each computing power resource node, acquiring all shared blocks between the computing power resource node and other computing power resource nodes;
Determining a topological association diagram of the computing power resource nodes through all the shared blocks, and further obtaining the topological association diagram of each computing power resource node;
generating resource sharing relations among all computing power resource nodes according to all the topological association diagrams;
The determining the privacy protection cost of each computing power resource node to the privacy computing task according to the resource sharing relationship and the privacy protection characteristics in the blockchain specifically comprises the following steps:
For each computing power resource node, extracting the resource sharing characteristics of the computing power resource node from the resource sharing relation;
extracting the trust degree of the computing resource node on the privacy calculation task from privacy protection features in the blockchain, wherein the privacy protection features refer to a set of privacy protection means of each computing resource node in the blockchain, and the protection degree of the computing resource node using a privacy protection technology in the privacy protection features is used as the trust degree of the computing resource node on the privacy calculation task;
and constructing a multivariate regression model, combining and correlating the resource sharing characteristics with the trust degree, and calculating the privacy protection cost of the computing power resource node on the privacy calculation task.
2. The method of claim 1, wherein extracting the execution state of the privacy computation task in the blockchain from the task progress information specifically comprises:
Acquiring all the executing computing tasks in the block chain;
for each calculation task, extracting the task completion progress and the task completion speed of the calculation task from the task progress information;
Determining the execution characteristics of the computing tasks according to the task completion progress and the task completion speed, and further obtaining the execution characteristics of each computing task;
and extracting the execution state of the privacy calculation task in the blockchain from all the execution characteristics.
3. The method of claim 1, wherein determining the resiliency of the scheduled resources in the blockchain to privacy protection by the execution state and a task offload policy of the scheduling of the computing resources in the blockchain specifically comprises:
Acquiring a task unloading strategy of computing power resource scheduling in a block chain;
Extracting an execution state value of each calculation task from the execution state for each calculation task;
Performing expansibility evaluation on the execution state value through the task unloading strategy to obtain an expandable value of the computing power in the computing task, further obtaining the expandable value of the computing power in each computing task, initializing a computing power evaluation model based on a neural network when the computing power is specifically realized, taking the task unloading strategy as a computing power release strategy of the computing task in the computing power evaluation model, taking the execution state value as an output tag in the computing power evaluation model, using the computing power evaluation model to evaluate the expandable computing power in the computing task, taking an evaluation result of the computing power evaluation model as the expandable value of the computing power in the computing task, and further obtaining the expandable value of the computing power in each computing task;
and determining the elasticity computing power of the scheduling resources in the blockchain on privacy protection according to all the extensible values.
4. The method of claim 1, wherein using the hierarchy to divide the computational resources in the blockchain into a plurality of computational resource nodes specifically comprises:
acquiring all resource blocks of a block chain, and further determining the computational power characteristics of each resource block;
carrying out calculation classification on all calculation features through a layered structure to obtain a plurality of calculation grades;
All resource blocks are divided into a plurality of computing power resource nodes according to each computing power level.
5. The method of claim 1, wherein the privacy computing task is a distributed computing task based on homomorphic privacy encryption.
6. A blockchain-based privacy computing task scheduling system that employs the method of any of claims 1-5 for blockchain-based privacy computing task scheduling, the system comprising:
the monitoring module is used for monitoring task progress information of the privacy calculation task in the blockchain in real time when the blockchain executes the privacy calculation task;
The processing module is used for extracting the execution state of the privacy calculation task in the blockchain from the task progress information, and further determining the elastic computing power of the scheduling resource in the blockchain on privacy protection through the execution state and a task unloading strategy of computing power resource scheduling in the blockchain;
The processing module is further used for dividing the computing power resources in the blockchain into a plurality of computing power resource nodes by using the hierarchical structure, further determining resource sharing relations among the computing power resource nodes, and determining privacy protection cost of the computing power resource nodes to the privacy calculation task according to the resource sharing relations and privacy protection characteristics in the blockchain;
and the execution module is used for evaluating the conflict relation between the computing power distribution and the privacy protection in the block chain through each privacy protection cost and the elastic computing power to obtain a balance value of the scheduling conflict in the block chain, and carrying out balanced distribution of the privacy computing task on each computing power resource node in the block chain based on the balance value of the scheduling conflict.
7. A computer device comprising a memory for storing a computer program and a processor for calling and running the computer program from the memory, such that the computer device performs the blockchain-based privacy calculation task scheduling method of any of claims 1 to 5.
8. A computer readable storage medium having instructions or code stored therein which, when executed on a computer, cause the computer to perform the blockchain-based privacy calculation task scheduling method of any of claims 1 to 5.
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