Efficient, Economical and Energy-saving Multi-Workflow Scheduling in Hybrid Cloud

The code of paper 'Efficient, Economical and Energy-saving Multi-Workflow Scheduling in Hybrid Cloud'. 
Permanent link to reproducible Capsule in Code Ocean platform: https://doi.org/10.24433/CO.5717787.v1
Zaixing Sun, Hejiao Huang, Zhikai Li, Chonglin Gu, Ruitao Xie, Bin Qian (2023) The code of paper 'Efficient, Economical and Energy-saving Multi-Workflow Scheduling in Hybrid Cloud'. [Source Code]. https://doi.org/10.24433/CO.5717787.v1
Sun, Z., Huang, H., Li, Z., Gu, C., Xie, R., & Qian, B. (2023). Efficient, economical and energy-saving multi-workflow scheduling in hybrid cloud. Expert Systems with Applications, 228(October 2023), Article 120401. https://doi.org/10.1016/j.eswa.2023.120401

This file provides the Python source code of compared approaches in experiments in the paper. 
The corresponding executable files are in 2019_IJPR_MACO, 2022_IS_GA, 2022_ASC_GMPSO, 2017_MSSA_EFT, 2022_SZX_MSSA and 2022_SZX_Three respectively.

This paper studies the problem of Tri-objective Privacy-aware Multi-Workflow Scheduling in Hybrid Cloud. The main contributions of this paper are presented as follows: (1) We establish a hybrid-cloud-based privacy-aware multi-workflow scheduling model, which simultaneously considers minimizing workflow-oriented total tardiness, private-cloud-oriented total energy consumption and public-cloud-oriented total monetary cost. (2) By dissecting various factors involved during scheduling, we creatively propose HSA9Fs, which dynamically selects the workflows and tasks to be scheduled and the VMs to be executed. Our scheme is non-greedy and can schedule tasks compactly by comprehensively considering 9 factors. (3) To explore Pareto solutions for trading off the tri-objective considered, we exploit MSSA to search Pareto solution globally to achieve the joint optimization of efficiency, economy and energy-savingly and IGA to deeply search in the neighborhoods of individual to improve the quality of the solution. (4) We conduct extensive Medium-Small-Scale and Large-Scale simulation experiments based on five well-known real-world workflow applications to investigate the diversity, convergence and efficiency of the proposed algorithms. The results show that both HSA9Fs and MSIA outperform state-of-the-art scheduling algorithms in several multi-objective performance metrics.