To achieve high throughput in the POW based blockchain systems, researchers proposed a series of methods, and DAG is one of the most active and promising fields. We designed and implemented the StreamNet, aiming to engineer a scalable and endurable DAG system. When attaching a new block in the DAG, only two tips are selected. One is the parent tip whose definition is the same as in Conflux[1]; another is using Markov Chain Monte Carlo (MCMC) technique by which the definition is the same as IOTA [2]. We infer a pivotal chain along the path of each epoch in the graph, and a total order of the graph could be calculated without a centralized authority. To scale up, we leveraged the graph streaming property; high transaction validation speed will be achieved even if the DAG is growing. To scale out, we designed the direct signal gossip protocol to help disseminate block updates in the network, such that messages can be passed in the network more efficiently. We implemented our system based on IOTA's reference code (IRI) and ran comprehensive experiments over the different sizes of clusters of multiple network topologies.
@article{arxiv.1908.06405,
title = {StreamNet: A DAG System with Streaming Graph Computing},
author = {Zhaoming Yin and Anbang Ruan and Ming Wei and Huafeng Li and Kai Yuan and Junqing Wang and Yahui Wang and Ming Ni and Andrew Martin},
journal= {arXiv preprint arXiv:1908.06405},
year = {2020}
}