Abstract
In a distributed resource environment, container technology highly facilitates the deployment and execution of scientific workflow tasks. However, existing scientific workflow scheduling studies barely consider the multi-channel programming of computational resources, which makes it hard to simultaneously achieve effective container sharing and optimize task parallelism and resource utilization. In this paper, we propose a segmented workflow scheduling strategy based on container technology in multi-vCPU devices environment. It reduces the solution space size of the heuristic algorithm through a segmented scheduling approach. And it uses an adaptive discrete particle swarm optimization algorithm with genetic operators (ADPSOGA) to optimize the average completion time of each workflow under the constraint of device rental cost. In addition, we propose a dynamic scaling scheme between containers and devices to reuse containers and solve the problems related to resource contention when tasks are parallel in a device. Experimental results indicate that ADPSOGA outperforms other similar heuristics algorithms, and the segmented scheduling approach significantly improves the optimization-seeking efficiency of the algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-Scale Science, pp. 1–10. https://doi.org/10.1109/WORKS.2008.4723958
Dua, R., Raja, A.R., Kakadia, D.: Virtualization vs containerization to support paas. In: 2014 IEEE International Conference on Cloud Engineering, pp. 610–614. https://doi.org/10.1109/IC2E.2014.41
Gao, Y., Zhang, S., Zhou, J.: A hybrid algorithm for multi-objective scientific workflow scheduling in IAAS cloud. IEEE Access 7, 125783–125795 (2019). https://doi.org/10.1109/ACCESS.2019.2939294
Liu, L., Zhang, M., Buyya, R., Fan, Q.: Deadline-constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing. Concurr. Comput. Pract. Exp. 29(5), e3942 (2017). https://doi.org/10.1002/cpe.3942
Nardelli, M., Hochreiner, C., Schulte, S.: Elastic provisioning of virtual machines for container deployment. In: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion, pp. 5–10. Association for Computing Machinery (2017). https://doi.org/10.1145/3053600.3053602
Pahl, C.: Containerization and the paas cloud. IEEE Cloud Comput. 2(3), 24–31 (2015). https://doi.org/10.1109/MCC.2015.51
Pang, S., Li, W., He, H., Shan, Z., Wang, X.: An eda-ga hybrid algorithm for multi-objective task scheduling in cloud computing. IEEE Access 7, 146379–146389 (2019). https://doi.org/10.1109/ACCESS.2019.2946216
Rajasekar, P., Palanichamy, Y.: Scheduling multiple scientific workflows using containers on IaaS cloud. J. Ambient Intell. Human. Comput. 12(7), 7621–7636 (2020). https://doi.org/10.1007/s12652-020-02483-0
Rodriguez, M.A., Buyya, R.: Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans. Cloud Comput. 2(2), 222–235 (2014). https://doi.org/10.1109/TCC.2014.2314655
Taghinezhad-Niar, A., Pashazadeh, S., Taheri, J.: Workflow scheduling of scientific workflows under simultaneous deadline and budget constraints. Cluster Comput. 24(4), 3449–3467 (2021). https://doi.org/10.1007/s10586-021-03314-3
Tan, B., Ma, H., Mei, Y.: A group genetic algorithm for resource allocation in container-based clouds. In: Paquete, L., Zarges, C. (eds.) EvoCOP 2020. LNCS, vol. 12102, pp. 180–196. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-43680-3_12
Topcuoglu, H., Hariri, S., Min-You, W.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002). https://doi.org/10.1109/71.993206
Wu, Z., Ni, Z., Gu, L., Liu, X.: A revised discrete particle swarm optimization for cloud workflow scheduling. In: 2010 International Conference on Computational Intelligence and Security, pp. 184–188 (2010). https://doi.org/10.1109/CIS.2010.46
Acknowledgements
This work is partly supported by the Natural Science Foundation of China under Grant No. 62072108, the University-Industry Cooperation of Fujian Province under Grant No. 2022H6024.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xiang, P., Lin, B., Yu, H., Liu, D. (2023). Container-Driven Scheduling Strategy for Scientific Workflows in Multi-vCPU Environments. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2022. Communications in Computer and Information Science, vol 1681. Springer, Singapore. https://doi.org/10.1007/978-981-99-2356-4_26
Download citation
DOI: https://doi.org/10.1007/978-981-99-2356-4_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-2355-7
Online ISBN: 978-981-99-2356-4
eBook Packages: Computer ScienceComputer Science (R0)