RVEA-based multi-objective workflow scheduling in cloud environments

Cloud computing is a major heterogeneous distributed system that can obtain the required resources for the different needs of customers through the network. With the advancement of technology, cloud workflow scheduling has become a widely studied area aiming to utilise cloud resources efficiently. In general, the workflow scheduling problem in a cloud environment is parallel, dependent, and complex. So far, there are many algorithms in the field of workflow resource scheduling in the cloud environment. However, most of these algorithms only consider makespan or cost, and research on multiple targets is still relatively scarce. Considering the characteristics of tasks and users, this paper constructs a workflow scheduling model targeting makespan, cost, and load in the cloud environment. To better address the multi-objective cloud workflow scheduling model, a reference vector-guided evolutionary algorithm (RVEA) is used in this paper. The results show that the algorithm can effectively improve the performance of the proposed model and obtain a suitable workflow scheduling policy compared with existing multi-objective evolutionary algorithms.