A new brain blockchain 4.0 of seele

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A New Brain Blockchain: An Introduction to the Seeline's Consent Neural Algorithm of a peer-to-peer Network consisting of multiple nodes, or computers, that communicate with each other to enable network functionality. Many technologies rely on peer-to-peer networks, including blockchain. One of the most difficult issues in peer-to-peer networks is consensus, or agreement between all nodes on the current network status. In blockchain, this is very important. Each node in a network can receive or report different values for a particular event, so the network as a whole may not be in agreement. This becomes very important, for example, when ordering transactions in a network. Consider the following 2 transactions recorded by nodes A, B, and C:

Due to potential problems like nodes or network issues that are not faithful, not all nodes are in the consensus! In the example above, node B believes that transaction 2 occurs before transaction 1, while nodes A and C believe that it is the other way around. As you might imagine, this becomes destructive when blockchain stores a large book of distributed transactions.

The current state of the Consensus Algorithm

So how do we overcome this? The current blockchain network uses many different algorithms to reach consensus. The most common algorithm is work proof, but many others have been developed. Unfortunately, the current blockchain consensus algorithm can not be measured, safe, and efficient.

Evidence of work is inefficient because of the high computational overhead required to solve the problem of creating cryptographic blocks. Currently, the Digiconomist reports that Bitcoin miners use about the same amount of electricity as the entire country of Chile. Proof of ownership has no security, which risks the possibility of a "nothing is at stake" attack, where pedestrians have an incentive to stake both sides of the fork. The proof of EOS share ownership (DPoS) also lacks security and introduces centralization into the consensus process by utilizing only a small number of decision-making delegates. Lastly, the practical Byzantine error tolerance (PBFT), which many consensus algorithms are based out of, has no scalability given the high cost of networking required as the network grows.

Nervous Consensus & EDA

Three problems of scalability, security, and efficiency are the current limitations for all decentralized network and blockchain technologies. Seele has analyzed the advantages and disadvantages of the current consensus algorithm, and proposes the Nervous Consensus Algorithm and differential ED (EDA) agreements to address this problem.

The Neural Concensus Algorithm is distributed using ε-differential agreements, a mathematical process that unifies the entire node network at a consistent value. This value can be either a specific transaction sequence, a block height, or any other value that a decentralized network needs to agree on. In this article, we will use transaction orders as our example.

The nerve consensus can run asynchronously and scale its performance linearly with the number of vertices in a network. Can handle 40 +% nodes fail in network and can be optimized for various use cases. We imagine that it can help solve the scalability, security, and efficiency issues that exist in the current consensus algorithm and help lay the groundwork for a new era of Internet of Value.

How it works

The purpose of EDA is to ensure that each node in the network reaches consensus at a certain value. Let's break the EDA into a series of steps. Before EDA starts, some parameters can be adjusted for optimal efficiency in various use cases:

s: percentage of nodes sampled by each node during each round.
ε: target convergence interval for network values. When all node values are in ε-differential, the network is considered to be in consensus.
r: number of voting rounds done by the system.
Once this is set, we're ready to get started!

The Jupyter Notebook Demo for the procedure below hosted on mybinder can be found here

I. Distribution of transactions within the network: When a user transacts, transactions are distributed across the network to all nodes. Each node generates a collection of interruptions from unconfirmed transactions that will be included in the blockchain. This situation can be modeled by the graph below, representing a network of 1,000 nodes:

Here, each x-value represents a different node. The colored dots above each x value represent different transactions, and the time recorded by each node for each different transaction is represented on the y-axis. Currently, this network is messy! Many nodes have different transaction order

II. Sampling node: Each node reaches to random% s from other nodes in the network for polling and records their values. Each node then uses the statistical function to create a composite value of the sample. For example, it may calculate "median transaction time" of the transaction time of the other 5 sampled nodes. Node then adopts this statistic as its own value.

I, I, I. Convergence: Several rounds of sampling occur. Because sampling is done randomly, each sample node has different node options in each round. Since each node adopts a calculated value from a sample taken in the previous round, through multiple rounds (at most 6 or 7), all nodes converge to the same value. Network is considered in consensus when the difference between all node values is less than preset ε.

The example below visualizes several rounds of sampling leading to convergent commands:

We can see that the value of ε grows lower over time:

Some Technical Details:

Determine whether the network is in consensus: because nodes do not know whether the entire network is in consensus (ie in differential-ε), they can only guess by a certain percentage of confidence based on their random sample that the network is in the consensus. If the sample node is very similar to a small deviation, then there is a higher probability that all nodes have been assembled.
Divergences: in unlikely scenarios (possibly due to strange parameters or unusual aggregation methods) that the network does not converge on one value, the side with the highest confidence percentage is selected.
Different settlement methods: consensus can be achieved through different means, for example after a set of numbers is defined or when all nodes are at least a few percent sure that the system is in ε-differential
Faulty nodes: for simplicity, the above demonstration does not include the wrong node. However, EDA has been tested to be a network-tolerant error with over 40% problematic or malicious nodes.
Parallel sorting: in the example above, each node sorts 10 transactions in parallel for each round. This amount may change between rounds or with different parameter configurations. Usually, at the beginning of the network, fewer values are sorted in parallel and are gradually added when the system is stable.

Conclusions and Further Reading

Neural Consensus is a scalable, safe, and efficient algorithm that enables distributed networks to reach consensus. This will help solve many of the problems that exist in the current consensus algorithm. Seele uses Nerve Consensus and EDA on its chain-chain, allowing rapid consensus across its Heterogeneous Forest Network.

Interested in trying out algorithm? Play around with the Jupyter Notebook demo hosted on mybinder. You can also read more about the Neural consensus algorithm in Seele white paper. The technical yellow paper on Nervous Consensus and EDA will be released soon. join this great project

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