A many-objective optimized task allocation scheduling model in cloud computing

The characteristics of randomness, running style, and unpredictability of user requirements in the cloud environment, brings great challenges to task scheduling. Meanwhile, the scheduling efficiency of cloud task allocation is an important factor affecting cloud resource systems. Therefore, this paper takes into account the characteristics of tasks, systems and users, a many-objective task scheduling model was constructed in cloud computing. In order to better solve the proposed many-objective task scheduling model, a reference vector guided evolutionary algorithm based on angle-penalty distance of normal distribution (RVEA-NDAPD) is proposed, and compared with the existing standard many-objective evolutionary algorithms (MaOEAs). Simulation results show that the algorithm can effectively improve the performance of the proposed model in cloud computing and obtain a suitable task allocation strategy.