Fusion-partitioning genetic task scheduling algorithm based on deterministic annealing technology in DAG blockchains

Abstract

The traditional blockchain structure cannot adapt to large-scale and real-time-application scenarios because of its inherently slow response. To solve this problem, a theoretical framework of DAG (directed acyclic graph) blockchain is proposed, transforming the chain processing of a traditional blockchain into parallel processing. On this basis, the non-independent task-scheduling problem in the DAG blockchain environment is studied, and a fusion-partitioning genetic task-scheduling algorithm based on deterministic annealing technology in DAG blockchains is proposed. The experimental results show that the algorithm can adapt to the heterogeneity, dynamism, and wide area of DAG blockchain nodes, and its scheduling performance is better than that of the traditional scheduling algorithm. While optimizing the task-completion time, the algorithm takes account of the load-balancing problem and effectively improves the response speed. It is a feasible method for solving the non-independent task-scheduling problem in the DAG blockchain environment.