CN115065468A - PBFT consensus optimization method based on grouping reputation value - Google Patents
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Abstract
Aiming at the problems that the most PBFT consensus algorithm is adopted in a alliance chain, when the number of nodes is increased, the algorithm time delay is prolonged, the throughput is reduced, and the main node is selected randomly, a PBFT consensus optimization method based on a grouping credit value is provided. Grouping nodes of the whole network, conveniently performing corresponding screening on the selection of the nodes and controlling the number of the nodes participating in the consensus algorithm; each node group converts the voting value of the figure set point-to-point into a specific value of a fuzzy set as a node reputation value, and selects the node with the highest reputation value in each group as a representative node to participate in consensus; randomly selecting a main node by using a verifiable random function in the consensus node, and increasing the unpredictability of the main node; finally, in order to reduce the communication frequency between nodes, the consensus process is simplified. Experimental tests show that the GV-PBFT algorithm can effectively reduce consensus time delay and improve throughput.
Description
Technical Field
The invention belongs to the field of block chain consensus mechanisms, and particularly relates to a PBFT consensus optimization method based on a grouping reputation value.
Background
The consensus algorithm plays a very important role in the blockchain system, and guarantees the correctness and consistency of data on the nodes. At present, the consensus algorithms are also many, and the workload certification (POW), the rights and interests certification (POS) and the copying and fault tolerance (raft) are common consensus algorithms, but all have certain problems, and the POW has the problems of power consumption and resource waste; the POS has the problems of centralization and insufficient consensus efficiency; raft has the problem of poor performance in high concurrency scenarios. One of the most commonly used consensus algorithms in the federation chain is the practical Byzantine Fault tolerant Algorithm (PBFT), which has a very high transaction throughput. However, the algorithm itself has some problems, when the number of nodes increases, the PBFT needs to perform point-to-point communication transfer, the performance thereof is sharply reduced, and secondly, the PBFT depends on the master node, but the master node is randomly selected, and a malicious node is easily selected as the master node, thereby also affecting the system efficiency.
In this respect, researchers at home and abroad also research and improve some existing consensus algorithms. Chenzihao et al propose to reduce the number of communication times of consensus by performing hierarchy division on nodes through a clustering algorithm, performing in-group consensus first and then performing out-group consensus. Wuyuseng et al propose to perform consensus in groups through an improved Raft consensus algorithm, and then perform PBFT consensus outside the groups according to the results of the consensus in the groups by group leaders. Zheng X et al proposes classifying nodes by using C4.5, selecting nodes with high trust level as a primary consensus group, and other nodes as secondary consensus groups, performing PBFT consensus on the secondary consensus group, and then performing a secondary PBFT consensus on the primary consensus group, and simultaneously introducing a score voting mechanism to determine that a leader node performs consensus. LI et al propose to use random functions in the nodes to select some nodes to participate in consensus, thereby reducing PBFT communication times and improving algorithm efficiency. However, in the process of evaluating the reputation value of the node in the above research, only the positive influence on the node is considered, but the negative influence on the node is not considered, and the judgment is not accurate enough.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a PBFT consensus optimization method based on a packet reputation value.
A PBFT consensus optimization method based on a grouping reputation value comprises the following specific steps:
step 1: grouping nodes of the whole network, conveniently performing corresponding screening on the selection of the nodes and controlling the number of the nodes participating in the consensus algorithm;
step 1.1: all nodes in the network are stored in a list, and the nodes are divided into a plurality of node groups according to the address identification of the nodes on the list, and the number of the nodes in each group has an upper limit which is set as L;
step 1.2: when a new node a appears in the network, it needs to send its own public key P to the periphery, and then node a enters a wait state,
step 1.3: after receiving the request information of a, other nodes b in the network firstly confirm that the node number of the group in which the node b is positioned does not reach the upper limit L, and then put the information of receiving the addition of a into the group into an event and transmit the information to the surrounding nodes to indicate that the node is added into the group;
step 1.4: after receiving the joining acceptance information, the node a confirms to join the group, and generates the receipt information again to represent that the node a accepts successfully;
step 1.5: after receiving the information, the node b broadcasts the information to nodes in the whole network, updates the small group node list, synchronizes the latest list to the node a, and then synchronizes all data information of the whole network;
and 2, step: each node group converts the voting value of the figure set point-to-point into a specific value of a fuzzy set as a node reputation value, and selects the node with the highest reputation value in each group as a representative node to participate in consensus, namely a consensus node;
the concrete value for converting the Vague set into the fuzzy set is used as the node reputation value, and the concrete formula is as follows:
wherein, [ t ] P (x),1-f P (x)]Fuzzy values that generally represent x; the concept of the Vague set is explained using a voting model, which means that when the number of votes supporting a vote is redundant with the number of votes not supporting a vote, the person who cast the right vote may prefer the party that is supported, and vice versa; λ is a constant, λ>0 is usually 1;
and step 3: randomly selecting a main node by using a verifiable random function in the consensus node, and increasing the unpredictability of the main node;
selecting a main node in the consensus node according to a verifiable random function, which comprises the following specific steps:
step 3.1: each common identification node takes a private key Sk and a message X as input to generate a random number y and a Proof function Proof;
step 3.2: judging whether the consensus node is a main node or not, if the consensus node meets the conditions of the following formula, returning to YES to become the main node;
when one node is verified to be the master node, other common nodes are slave nodes;
and 4, step 4: carrying out consensus among the consensus nodes through a GV-PBFT algorithm to realize global consensus;
the GV-PBFT algorithm is obtained by calculating a credit value based on a PBFT algorithm, randomly selecting a representative node with high credit as a main node, increasing the probability that the main node is an honest node, changing three-stage algorithm optimization into two stages and canceling a submission stage;
step 4.1: the client c sends a REQUEST message < REQUEST, o, t, c, X > to the consensus node; wherein o represents the specific operation of the request, t represents the timestamp of the request, and X is a random value selected by the client;
step 4.2: when the consensus node verifies that the consensus node is the master node, a sequence number n is distributed for the request in a view v, and a PRE-prefix message < < PRE-PREPARE, v, n, d > < Verify < Pk, y, Proof >, m > is broadcasted to other slave nodes, wherein v is a view number, m is a client request message, d is a summary of a message m, Pk is a public key of the master node, y is a random number generated by the master node, and Proof is a Proof function generated by the master node;
step 4.3: the slave node receives the message sent by the master node, and verifies the identity of the master node and the validity of the message; each slave node sends a prefix message < prefix, v, n, d, i > to other common nodes except the slave node after the message verification is passed, wherein i is a node number;
step 4.4: each consensus node verifies the received message, and sends a response message to the client when receiving 2f effective messages sent by different consensus nodes except the consensus node; the client receives the REPLY message < REPLY, v, t, c, i, r > with the same f +1, and the consensus is completed.
The invention has the beneficial technical effects that:
the method introduces the specific value of the Vague set converted into the fuzzy set as the basis of selecting the representative node as the node reputation value, thereby better reducing the probability of selecting the malicious node as the consensus node. And a verifiable random function is introduced to select the main node, so that the unpredictability of the main node is increased, and the system safety is improved. The consensus process of the original PBFT algorithm is simplified, the three-stage algorithm optimization is changed into two stages, and the submission stage is cancelled, so that the communication frequency among the nodes is reduced, and the algorithm performance is improved.
Drawings
FIG. 1 is a flow chart of a PBFT consensus optimization method based on packet reputation values.
FIG. 2 is a flowchart of the algorithm of GV-PBFT in the PBFT consensus optimization method based on the packet reputation value.
FIG. 3 is a flow chart of the PBFT algorithm of the present invention.
Detailed Description
The invention is further explained below with reference to the figures and examples;
the invention provides a PBFT consensus optimization algorithm based on a grouping reputation value, aiming at the problems of time prolonging, high throughput and random master node selection of the original PBFT consensus algorithm in a alliance chain. The algorithm firstly dynamically groups the nodes, selects representative nodes as the reputation value of each node according to the score of the figure set in the group, and performs global consensus on the representative nodes, so that the three situations that the figure set is integrated into the vote are more realistic, the nodes can be more accurately evaluated, and the participation of abnormal nodes is reduced. And a verifiable random function is used in the representative node to randomly determine the main node, so that the main node is unpredictable and the system security is protected. Since the reputation evaluation increases the probability that the master node is an honest node, the consensus process can be simplified into two stages to reduce the number of communications.
The invention provides a PBFT consensus optimization method based on a grouping credit value, which solves the problems of increased number of nodes, low consensus throughput, long time delay and random selection of a main node of an original PBFT consensus mechanism. As shown in the attached figure 1, the method comprises the following specific steps:
step 1: grouping nodes of the whole network, conveniently performing corresponding screening on the selection of the nodes and controlling the number of the nodes participating in the consensus algorithm;
step 1.1: all nodes in the network are stored in a list, the nodes are divided into a plurality of node groups according to the address identification of the nodes on the list, and the number of the nodes in each group has an upper limit which is set as L;
step 1.2: when a new node a appears in the network, it needs to send its own public key P to the periphery, and then node a enters a wait state,
step 1.3: after receiving the request information of a, other nodes b in the network firstly confirm that the number of the nodes of the group in which the nodes are positioned does not reach the upper limit L, then put the information of accepting the addition of a into the group into an event, and transmit the information to the surrounding nodes to indicate that the nodes are added into the group;
step 1.4: after receiving the joining acceptance information, the node a confirms to join the group, and generates the receipt information again to represent that the node a accepts successfully;
step 1.5: after receiving the information, the node b broadcasts the information to nodes in the whole network, updates the small group node list, synchronizes the latest list to the node a, and then synchronizes all data information of the whole network;
step 2: each node group converts the voting value of the figure set point-to-point into a specific value of a fuzzy set as a node reputation value, and selects the node with the highest reputation value in each group as a representative node to participate in consensus, namely a consensus node;
the concrete value for converting the Vague set into the fuzzy set is used as the node reputation value, and the concrete formula is as follows:
wherein, [ t ] P (x),1-f P (x)]Fuzzy values that generally represent x; the concept of the Vague set is explained using a voting model, which means that when the number of votes supporting a vote is redundant with the number of votes not supporting a vote, the person who cast the right vote may prefer the party that is supported, and vice versa; λ is a constant, λ>0 is usually 1;
if, for example, 10 people vote, suppose P is a set of Vague in U, x is U and value of Vague is [0.5, 0.9 ]]So t p (x)＝0.5，f p (x) 1-0.9-0.1, the value of Vague is then [0.5, 0.9 ═ 0.1]The number of people who throw the support ticket is 5, the number of people who throw the non-support ticket is 1, and 4 people abandon the right;
and step 3: randomly selecting a main node by using a verifiable random function in the consensus node, and increasing the unpredictability of the main node;
selecting a main node in the consensus node according to a verifiable random function, which comprises the following specific steps:
step 3.1: each common identification node takes a private key Sk and a message X as input to generate a random number y and a Proof function Proof;
step 3.2: judging whether the consensus node is a main node or not, if the consensus node meets the conditions of the following formula, returning to YES, and becoming the main node;
when one node is verified to be the master node, other common nodes are slave nodes;
and 4, step 4: performing consensus among the consensus nodes through a GV-PBFT algorithm to realize global consensus; as shown in fig. 2;
the GV-PBFT algorithm is obtained by calculating a credit value based on a PBFT algorithm, randomly selecting a representative node with high credit as a main node, increasing the probability that the main node is an honest node, changing three-stage algorithm optimization into two stages and canceling a submission stage;
step 4.1: the client c sends a REQUEST message < REQUEST, o, t, c, X > to the consensus node; wherein o represents a specific operation of the request, t represents a timestamp of the request, and X is a random value selected by the client;
and 4.2: when the consensus node verifies that the consensus node is the master node, a sequence number n is distributed for the request in a view v, and a PRE-prefix message < < PRE-PREPARE, v, n, d > < Verify < Pk, y, Proof >, m > is broadcasted to other slave nodes, wherein v is a view number, m is a client request message, d is a summary of a message m, Pk is a public key of the master node, y is a random number generated by the master node, and Proof is a Proof function generated by the master node;
step 4.3: the slave node receives the message sent by the master node, and verifies the identity of the master node and the validity of the message; each slave node sends a prefix message < prefix, v, n, d, i > to other common nodes except the slave node after the message verification is passed, wherein i is a node number;
step 4.4: each consensus node verifies the received message, and sends a response message to the client when receiving 2f effective messages sent by different consensus nodes except the consensus node; the client receives the REPLY message < REPLY, v, t, c, i, r > with the same f +1, and the consensus is completed.
As shown in FIG. 3, it can be seen that the three phase process of PBFT includes pre-preparation, preparation and commit. The PBFT algorithm achieves consensus and comprises the following specific steps:
in the pre-preparation phase, the client first sends a request to the master node. The master node checks and processes the client's request and then broadcasts a prepare message to the slave nodes. Each slave node receives and verifies the validity of the prepare message. Once the message is verified to be correct, the slave node will accept the request and begin the preparation phase.
In the preparation phase, each slave node transmits a message to other nodes, and the slave nodes also accept the messages sent by other slave nodes and check the validity of the messages. The preparation phase ends when a slave node obtains a valid preparation message of 2f from a different slave node, where f is the number of malicious nodes.
In the submission stage, each node broadcasts a submission message to other nodes for verification, and once the node in the preparation stage receives a message number which is equal to or greater than 2f +1 and includes other nodes, the node sends a reply message to the client. When the client receives the reply message and the client receives f +1 of the same reply message, the consensus is reached.
The original PBFT consensus algorithm comprises three stages of pre-preparation, preparation and submission, wherein the submission stage is used for ensuring that a node with enough credibility completes the verification of a proposal. When the master node is an honest node, the two phases can achieve consensus without the commit phase. And the GV-PBFT algorithm randomly selects a representative node with high reputation as a main node through reputation value calculation, and increases the probability that the main node is an honest node, so that the GV-PBFT algorithm changes three-stage algorithm optimization into two stages and cancels a submission stage. The GV-PBFT is improved in throughput and time delay compared with the original PBFT. The consensus process of the original PBFT algorithm is simplified, the three-stage algorithm optimization is changed into two stages, and the submission stage is cancelled, so that the communication frequency among the nodes is reduced, and the algorithm performance is improved.
Claims (5)
1. A PBFT consensus optimization method based on a grouping reputation value is characterized by comprising the following specific steps:
step 1: grouping nodes of the whole network, conveniently performing corresponding screening on the selection of the nodes and controlling the number of the nodes participating in the consensus algorithm;
step 2: each node group converts the voting value of the figure set point-to-point into a specific value of a fuzzy set as a node reputation value, and selects the node with the highest reputation value in each group as a representative node to participate in consensus, namely a consensus node;
and step 3: randomly selecting a main node by using a verifiable random function in the consensus node, and increasing the unpredictability of the main node;
and 4, step 4: carrying out consensus among the consensus nodes through a GV-PBFT algorithm to realize global consensus;
the GV-PBFT algorithm is obtained by calculating through a credit value based on a PBFT algorithm, randomly selecting a representative node with high credit as a main node, increasing the probability that the main node is an honest node, changing three-stage algorithm optimization into two stages and canceling a submission stage.
2. The PBFT consensus optimization method based on the packet reputation value according to claim 1, wherein step 1 specifically comprises:
step 1.1: all nodes in the network are stored in a list, the nodes are divided into a plurality of node groups according to the address identification of the nodes on the list, and the number of the nodes in each group has an upper limit which is set as L;
step 1.2: when a new node a appears in the network, it needs to send its own public key P to the periphery, and then node a enters a wait state,
step 1.3: after receiving the request information of a, other nodes b in the network firstly confirm that the node number of the group in which the node b is positioned does not reach the upper limit L, and then put the information of receiving the addition of a into the group into an event and transmit the information to the surrounding nodes to indicate that the node is added into the group;
step 1.4: after receiving the joining acceptance information, the node a confirms to join the group, and generates the receipt information again to represent that the node a accepts successfully;
step 1.5: after receiving the information, the node b broadcasts the information to the nodes in the whole network, updates the small group node list, synchronizes the latest list to the node a, and then synchronizes all data information in the whole network.
3. The PBFT consensus optimization method based on grouping reputation values according to claim 1, wherein step 2 converts the Vague set into specific values of the fuzzy set as node reputation values, and the specific formula is as follows:
wherein, [ t ] P (x),1-f P (x)]Fuzzy values that generally represent x; the concept of a Vague set is explained using a voting model, which means that when the number of votes supporting a vote is redundant with the number of votes not supporting a vote, the right to surrender isThe person of the ticket may prefer the party that is supported, and vice versa; λ is a constant, λ>0 is usually taken to be 1.
4. The PBFT consensus optimization method based on the packet reputation value according to claim 1, wherein step 3 selects a master node in the consensus node according to a verifiable random function, and comprises the following specific steps:
step 3.1: each common identification node takes a private key Sk and a message X as input to generate a random number y and a Proof function Proof;
step 3.2: judging whether the consensus node is a main node or not, if the consensus node meets the conditions of the following formula, returning to YES to become the main node;
when one node is verified to be the master node, the other consensus nodes are slave nodes.
5. The PBFT consensus optimization method based on the packet reputation value according to claim 1, wherein step 4 specifically comprises:
step 4.1: the client c sends a REQUEST message < REQUEST, o, t, c, X > to the consensus node; wherein o represents a specific operation of the request, t represents a timestamp of the request, and X is a random value selected by the client;
and 4.2: when the consensus node verifies that the consensus node is the master node, a sequence number n is distributed for the request in a view v, and a PRE-prefix message < < PRE-PREPARE, v, n, d > < Verify < Pk, y, Proof >, m > is broadcasted to other slave nodes, wherein v is a view number, m is a client request message, d is a summary of a message m, Pk is a public key of the master node, y is a random number generated by the master node, and Proof is a Proof function generated by the master node;
step 4.3: the slave node receives the message sent by the master node, and verifies the identity of the master node and the validity of the message; each slave node sends a prefix message < prefix, v, n, d, i > to other common nodes except the slave node after the message verification is passed, wherein i is a node number;
step 4.4: each consensus node verifies the received message, and sends a response message to the client when receiving 2f effective messages sent by different consensus nodes except the consensus node; the client receives the REPLY message < REPLY, v, t, c, i, r > with the same f +1, and the consensus is completed.
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| CN117408718A (en) * | 2023-12-14 | 2024-01-16 | 南京邮电大学 | PBFT optimization method for large-scale medicine traceability |
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