The Performance Balancer

Another upcoming feature that will augment Circular's architecture is the Performance Balancer (PB), a neural network based component that will collect data on the different nodes and subnets and generate single-node and single-chain settings that will be propagated to the nodes to ensure optimal architectural performances.

In a decentralized network, nodes vary greatly in terms of their CPU/GPU and connectivity capabilities. The aim of the PB is to always maintain optimal and steady flows of transactions across the entire architecture.

We have already implemented an initial prototype, but we are eagerly awaiting the availability of real data to train the model and assess its performance in real-world scenarios.

Neural networks are known to be highly effective in managing systems with a large number of parameters, providing exceptional resilience and rapid adaptability in response to new unpredicted situations.

However, it's essential to note that the quality and quantity of data used to train the model will play a significant role in determining its effectiveness. Therefore, it's critical to obtain as much traffic data as possible to train and fine-tune the model, resulting in the best possible configurations for the network nodes and ultimately enhancing the overall performance of the network.