Accelerating Tip Selection in Burst Message Arrivals for DAG-Based Blockchain Systems

Xun Xiao

Abstract: Blockchain systems (e.g., IOTA) use Directed Acyclic Graph (DAG) topology to organize its ledger records. A new message is added by attaching to the tips selected from the DAG. For tip selection, a stochastic approach is widely used, where a random walk is simulated until it ends at a tip of the DAG. Prior art intensively focused on the fairness and security issues of this random walk approach. However, its computational efficiency issue was largely neglected. This paper reports that under a burst message arrival condition, the random walk approach, even with parallelization, will suffer from severe delays. To solve this problem, this paper proposes a new approach inspired by Absorbing Markov chain (AMC) theory. Specifically, the new approach first periodically calculates a tip selection probability distribution (TSPD) of the DAG ledger. With this information, a processing node only needs to do sampling from the calculated TSPD, which significantly accelerates the tip selection process. Rigorous theoretical complexity analysis is provided; in addition, the approach is compared with both single- and multi-processing random walk schemes. Evaluation results confirm the key findings and demonstrate the benefits of the solution.