Security Enhanced Edge Computing Task Scheduling Method Based on Blockchain and Task Cache

Abstract

Aiming at edge computing nodes’ limited computing and storage capacity, a two-layer task scheduling model based on blockchain and task cache was proposed. The high-similarity task results were cached in the edge cache pool, and the blockchain-assisted task caching model was combined to enhance system security. The genetic evolution algorithm was used to solve the minimum cost that the optimal scheduling model can obtain. The genetic algorithm’s initialization and mutation operations were adjusted to improve the convergence rate. Compared with algorithms without cache pooling and blockchain, the proposed joint blockchain and task caching task scheduling model reduced the cost by 9.4% and 14.3%, respectively. As the capacity space of the cache pool increased, the system cost gradually decreased. Compared with the capacity space of 3GB, the system cost of 10Gbit capacity space was reduced by 10.6%. The system cost decreased as the computing power of edge nodes increased. Compared with edge nodes with a computing frequency of 8GHz, the nodes cost at 18GHz was reduced by 36.4%. Therefore, the proposed edge computing task scheduling model ensures the security of task scheduling based on reducing delay and control costs, providing a foundation for modern industrial task scheduling.