Research Area:  Cloud Computing
As a NP-hard problem, it is always baffling to figure out a scheduling strategy to arrange the interconnected tasks of a workflow on the infinite number of resources in the cloud environment so that the workflow can be addressed efficiently and robustly. This paper focuses on scheduling the workflows tasks on the cloud resources with less rental cost of resources while the whole schedule length (makespan) will not exceed the given deadline. As one of the most popular evolutionary algorithms, particle swarm optimization (PSO) has been successfully applied for the workflow scheduling problem. Inspired by the idea of multiple groups and the distributed parallel computing, we develop an enhanced PSO algorithm for the workflow scheduling problem in clouds. Besides, a pretreatment strategy is adopted to simplify the workflows structure. The experimental results demonstrate that our proposal has good performance on improving the algorithms searching ability and finding better solutions.
Author(s) Name:  Chang Lu; Dayu Feng; Jie Zhu; Haiping Huang
Conferrence name:  2021 IEEE International Conference on Progress in Informatics and Computing (PIC)
Publisher name:  IEEE
Paper Link:   https://ieeexplore.ieee.org/abstract/document/9687073