The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tasks. However, executing scientific workflows in IaaS cloud environments poses significant challenges due to conflicting objectives, such as minimizing execution time (makespan) and reducing resource utilization costs. This study responds to the increasing need for efficient and adaptable optimization solutions in dynamic and complex environments, which are critical for meeting the evolving demands of modern users and applications. This study presents an innovative multi-objective approach for scheduling scientific workflows in IaaS cloud environments. The proposed algorithm, MOS-MWMC, aims to minimize total execution time (makespan) and resource utilization costs by leveraging key features of virtual machine instances, such as a high number of cores and fast local SSD storage. By integrating realistic simulations based on the WRENCH framework, the method effectively dimensions the cloud infrastructure and optimizes resource usage. Experimental results highlight the superiority of MOS-MWMC compared to benchmark algorithms HEFT and Max-Min. The Pareto fronts obtained for the CyberShake, Epigenomics, and Montage workflows demonstrate closer proximity to the optimal front, confirming the algorithm’s ability to balance conflicting objectives. This study contributes to optimizing scientific workflows in complex environments by providing solutions tailored to specific user needs while minimizing costs and execution times.
References
[1]
Taylor, I.J. Deelman, E. Gannon, D.B. and Shields, M. (2007) Workflows for e-Science. Springer. https://doi.org/10.1007/978-1-84628-757-2
[2]
Deelman, E., Vahi, K., Juve, G., Rynge, M., Callaghan, S., Maechling, P.J., et al. (2015) Pegasus, a Workflow Management System for Science Automation. Future Generation Computer Systems, 46, 17-35. https://doi.org/10.1016/j.future.2014.10.008
des PVC de disque persistant. https://cloud.google.com/products/compute
[5]
\"Services de Cloud Computing. Microsoft Azure. https://azure.microsoft.com/fr-fr/
[6]
Casanova, H., Tanaka, R., Koch, W. and Ferreira da Silva, R. (2021) Teaching Parallel and Distributed Computing Concepts in Simulation with Wrench. Journal of Parallel and Distributed Computing, 156, 53-63. https://doi.org/10.1016/j.jpdc.2021.05.009
[7]
Shahid, M., Ashraf, Z., Alam, M., Ahmad, F. and Imran, M. (2021) A Multi-Objective Workflow Allocation Strategyin IaaS Cloud Environment. 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, 19-20 February 2021, 308-313. https://doi.org/10.1109/icccis51004.2021.9397081
[8]
Zhang, H., Wu, Y. and Sun, Z. (2021) EHEFT-R: Multi-Objective Task Scheduling Scheme in Cloud Computing. Complex & Intelligent Systems, 8, 4475-4482. https://doi.org/10.1007/s40747-021-00479-7
[9]
Hussain, M., Luo, M., Hussain, A., Javed, M.H., Abbas, Z. and Wei, L. (2023) Deadline-Constrained Cost-Aware Workflow Scheduling in Hybrid Cloud. Simulation Modelling Practice and Theory, 129, Article 102819. https://doi.org/10.1016/j.simpat.2023.102819
[10]
Mangalampalli, S., Hashmi, S.S., Gupta, A., Karri, G.R., Rajkumar, K.V., Chakrabarti, T., et al. (2024) Multi Objective Prioritized Workflow Scheduling Using Deep Reinforcement Based Learning in Cloud Computing. IEEE Access, 12, 5373-5392. https://doi.org/10.1109/access.2024.3350741
[11]
Malti, A.N., Hakem, M. and Benmammar, B. (2023) A New Hybrid Multi-Objective Optimization Algorithm for Task Scheduling in Cloud Systems. Cluster Computing, 27, 2525-2548. https://doi.org/10.1007/s10586-023-04099-3
[12]
Abualigah, L. and Diabat, A. (2020) A Novel Hybrid Antlion Optimization Algorithm for Multi-Objective Task Scheduling Problems in Cloud Computing Environments. Cluster Computing, 24, 205-223. https://doi.org/10.1007/s10586-020-03075-5
[13]
Kruekaew, B. and Kimpan, W. (2022) Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm with Reinforcement Learning. IEEE Access, 10, 17803-17818. https://doi.org/10.1109/access.2022.3149955
[14]
Doostali, S., Babamir, S.M. and Eini, M. (2021) CP-PGWO: Multi-Objective Workflow Scheduling for Cloud Computing Using Critical Path. Cluster Computing, 24, 3607-3627. https://doi.org/10.1007/s10586-021-03351-y
[15]
Shukla, P. and Pandey, S. (2023) DE-GWO: A Multi-Objective Workflow Scheduling Algorithm for Heterogeneous Fog-Cloud Environment. Arabian Journal for Science and Engineering, 49, 4419-4444. https://doi.org/10.1007/s13369-023-08425-0
[16]
Zeedan, M., Attiya, G. and El-Fishawy, N. (2022) Enhanced Hybrid Multi-Objective Workflow Scheduling Approach Based Artificial Bee Colony in Cloud Computing. Computing, 105, 217-247. https://doi.org/10.1007/s00607-022-01116-y
[17]
Mohammadzadeh, A. and Masdari, M. (2021) Scientific Workflow Scheduling in Multi-Cloud Computing Using a Hybrid Multi-Objective Optimization Algorithm. Journal of Ambient Intelligence and Humanized Computing, 14, 3509-3529. https://doi.org/10.1007/s12652-021-03482-5
[18]
Calzarossa, M.C., Vedova, M.L.D., Massari, L., Nebbione, G. and Tessera, D. (2021) Multi-Objective Optimization of Deadline and Budget-Aware Workflow Scheduling in Uncertain Clouds. IEEE Access, 9, 89891-89905. https://doi.org/10.1109/access.2021.3091310
[19]
Konjaang, J.K. and Xu, L. (2021) Multi-Objective Workflow Optimization Strategy (MOWOS) for Cloud Computing. Journal of Cloud Computing, 10, Article No. 11. https://doi.org/10.1186/s13677-020-00219-1
[20]
Qin, S., Pi, D., Shao, Z., Xu, Y. and Chen, Y. (2023) Reliability-Aware Multi-Objective Memetic Algorithm for Workflow Scheduling Problem in Multi-Cloud System. IEEE Transactions on Parallel and Distributed Systems, 34, 1343-1361. https://doi.org/10.1109/tpds.2023.3245089
[21]
Belgacem, A. and Beghdad-Bey, K. (2021) Multi-Objective Workflow Scheduling in Cloud Computing: Trade-off between Makespan and Cost. Cluster Computing, 25, 579-595. https://doi.org/10.1007/s10586-021-03432-y
[22]
Rizvi, N., Ramesh, D., Wang, L. and Basava, A. (2023) A Workflow Scheduling Approach with Modified Fuzzy Adaptive Genetic Algorithm in IaaS Clouds. IEEE Transactions on Services Computing, 16, 872-885. https://doi.org/10.1109/tsc.2022.3174112
[23]
Kakkottakath Valappil Thekkepuryil, J., Suseelan, D.P. and Keerikkattil, P.M. (2021) An Effective Meta-Heuristic Based Multi-Objective Hybrid Optimization Method for Workflow Scheduling in Cloud Computing Environment. Cluster Computing, 24, 2367-2384. https://doi.org/10.1007/s10586-021-03269-5
[24]
Cai, X., Li, M., Zhang, Y., Zhao, T., Zhang, W. and Chen, J. (2024) Multitasking Bi-Level Evolutionary Algorithm for Data-Intensive Scientific Workflows on Clouds. Expert Systems with Applications, 238, Article 121833. https://doi.org/10.1016/j.eswa.2023.121833
[25]
Automate, Recover, and Debug Scientific Computations. Pegasus WMS. https://pegasus.isi.edu/
[26]
Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M. and Vahi, K. (2008) Characterization of Scientific Workflows. 2008 Third Workshop on Workflows in Support of Large-Scale Science, Austin, 17 November 2008, 1-10. https://doi.org/10.1109/works.2008.4723958
[27]
Gerasoulis, A. and Yang, T. (1992) A Comparison of Clustering Heuristics for Scheduling Directed Acyclic Graphs on Multiprocessors. Journal of Parallel and Distributed Computing, 16, 276-291. https://doi.org/10.1016/0743-7315(92)90012-c
