Optimizing QoS and Energy using ESSA for Budget-constrained Workflows in the Cloud
Keywords:
Optimisation, QoS, cloud computing, mathematical model, makespan, budget, and ESSA.Abstract
Cloud computing has acquired colossal interest. Here resources are observed as services; hence employment of it in an organized manner is performed in two integral parts, i.e., asset provisioning as well as process organizing. Workflows are big data applications in the distributed community. Cloud infrastructures' flexibility makes them an appropriate choice for scientific workflow computations. An effective task scheduling technique must determine the order of tasks in the workflow for processing so as to satisfy the needs of the client. Most of the procedures concentrate on upgrading resource usage and fulfilling the QoS. Furthermore, these workflow computations require more computing power and the energy consumed to meet those requests are neglected. This fact persuaded us to present an Enhanced Salp Swarm Algorithm (ESSA) for scheduling scientific workflows with energy consciousness. On evaluating the potentiality, the proposed ESSA scheduling technique outperformed three other approaches in the simulation of synthetic scientific job trials. The solutions portray reduced execution time and energy usage by raising total resource consumption and employment.
Downloads
References
Meena, F., Kumar, M., Vardhan, M., Jain, S.: A systematic review on practical workflow scheduling algorithms in cloud under deadline constraint, International Journal of Circuit Theory and Applications, 9(41), 1160-1170 (2016).
Arabnejad, V., Bubendorfer, K., and Ng, B.: Budget and Deadline Aware e-Science Workflow Scheduling in Clouds, IEEE Trans. Parallel Distrib. Syst., 30(1), 29–44 (2019).
Rambabu Medara, Ravi Shankar Singh, Amit.: Energy-aware workflow task scheduling in clouds with virtual machine consolidation using discrete water wave optimization, Simulation Modelling Practice and Theory, 110 (2021). https://doi.org/10.1016/j.simpat.2021.102323
Poola, D., Garg, S. k., Buyya, R., Yang, Y., and Ramamohanarao, K.: Robust Scheduling of Scientific Workflows with Deadline and Budget Constraints in Clouds, IEEE 28th International Conference on Advanced Information Networking and Applications. CONFERENCE 2014, pp. 858–865 (2014). doi: 10.1109/AINA.2014.105
Ma, l., Lu, y., Zhang, F., and Sun, S.: Dynamic Task Scheduling in Cloud Computing Based on Greedy Strategy. Trustworthy Computing and Services, 156–162 (2013).
Fakhfakh, F., Kacem, H.H., Kacem, A.H.: Workflow scheduling in cloud computing: a survey, IEEE 18th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations, CONFERENCE 2014, pp. 372–378 (2014).
Liu, L., Zhang, M., Lin, Y., Qin, L.: A survey on workflow management and scheduling in cloud computing, 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 837–846, IEEE (2014).
Singh, L., Singh, S.: A Survey of Workflow Scheduling Algorithms and Research Issues. International Journal of Computer Applications. 74. https://doi.org/10.5120/12961-0069.
Jennings, B., Stadler, R.: Resource Management in Clouds: Survey and Research Challenges, J. Netw. Syst. Manag., 23(3), 567–619 (2015). doi: 10.1007/s10922-014-9307-7.
Fakhfakh, F., Kacem, H. H., Kacem, A. H.: Workflow Scheduling in Cloud Computing: A Survey, IEEE 18th International Enterprise Distributed Object Computing Conference, Workshops and Demonstrations, CONFERENCE 2014, pp. 372–378 (2014). doi: 10.1109/EDOCW.2014.61
Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: stateof-the-art and research challenges, J. Internet Serv. Appl., 1(1), 7–18 (2010). doi: 10.1007/s13174-010-0007-6
Khanghahi, N., Ravanmehr, R.: Cloud Computing Performance Evaluation: Issues and Challenges, Int. J. Cloud Comput. Serv. Archit., 3(5), 29–41 (2013). doi: 10.5121/ijccsa.2013.3503.
C. K. Sripavithra, V. B. Kirubanand: A review on recent scheduling algorithms in the cloud environment. AIP Conf. Proc. 28 November 2023; 2909 (1): 030007. https://doi.org/10.1063/5.0183242.
Zhou,X., Zhang, G., Sun, J., Zhou,J., Wei,T., Hu, S.: Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based heft, Future Gener. Comput. Syst. 93, 278–289 (2019).
Wu, H., Chen, X., Song, X., Zhang, C., Guo, H.: Scheduling large-scale scientific workflow on virtual machines with different numbers of vCPUs. J. Supercomput. 77, 1 (Jan 2021), 679–710. https://doi.org/10.1007/s11227-020-03273-3.
Topcuoglu, H., Hariri, S., Wu, M. Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing, IEEE Trans. Parallel Distrib. Syst. 13, 260–274 (2002).
Safari, M., Khorsand, R.: Energy-aware scheduling algorithm for time-constrained workflow tasks in dvfs-enabled cloud environment, Simul. Model. Pract. Theory, 87, 311–326 (2018).
Fernandez-Cerero, D., Jakobik, A., Fernandez-Montes, A., Kolodziej, J.: Game-score: Game-based energy-aware cloud scheduler and simulator for computational clouds, Simul. Model. Pract. Theory 93, 3–20 (2019).
Li, C., Tang, J., Ma, T., Yang, X., Luo, Y.: Load balance based workflow job scheduling algorithm in distributed cloud, J. Netw. Comput. Appl. 152, 102518 (2020).
Abazari, F., Analoui, M., Takabi, H., Fu, S.: Mows: multi-objective workflow scheduling in cloud computing based on heuristic algorithm, Simul. Model. Pract. Theory, 93, 119–132 (2019).
Mohanapriya, N., Kousalya, G., Balakrishnan, P., Pethuru Raj, C.: Energy efficient workflow scheduling with virtual machine consolidation for green cloud computing, J. Intell. Fuzzy Systems, 34, 1561–1572 (2018).
Li, Z., Ge, J., Hu, H., Song, W., Hu, H., Luo, B.: Cost and energy aware scheduling algorithm for scientific workflows with deadline constraint in clouds, IEEE Trans. Serv. Comput. 11, 713–726 (2018). http://dx.doi.org/10.1109/TSC.2015.2466545
F. Yao, C. Pu, and Z. Zhang, Task Duplication-Based Scheduling Algorithm for Budget-Constrained Workflows in Cloud Computing, IEEE Access, 9, 37262–37272 (2021). doi: 10.1109/ACCESS.2021.3063456
Long, S., Dai, X., Pei, T., Cao, J., Sekiya, H., Choi, Y.-J.: Energy-efficient VM opening algorithms for real-time workflows in heterogeneous clouds, Neurocomputing, 483, 501–514 (2022). doi: https://doi.org/10.1016/j.neucom.2021.08.145
Ferdaus, M.H., Murshed, M., Calheiros, R.N., Buyya, R.: Virtual machine consolidation in cloud data centers using aco metaheuristic, In: European Conference on Parallel Processing, Springer, 306–317 (2014).
Medara, R., Singh, R.S., Kumar, U.S., Barfa, S.: Energy efficient virtual machine consolidation using water wave optimization, In: IEEE Congress on Evolutionary Computation (CEC), IEEE, 1–7 (2020).
C. K. Sripavithra and V. B. Kirubanand, ESSA Scheduling Algorithm for Optimizing Budget-Constrained Workflows. In: IEEE International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) (2022) https://doi.org/10.1109/ICECCME55909.2022.9988009.
Mirjalili, S., Amir, H. Gandomi, Mirjalili, S., Saremi, S., Faris, H., Mirjalili, S.: Salp Swarm Algorithm: A bioinspired optimizer for engineering design problems, Advances in Engineering Software 114, 163-191 (2017).
Abualigah, Laith, Shehab, M., Alshinwan, M., Alabool, H.: Salp swarm algorithm: a comprehensive survey, Neural Computing and Applications, 32(15), 11195-11215 (2020).
S. Rahnamayan, H. R. Tizhoosh and M. M. A. Salama. Quasi-oppositional Differential Evolution. In: IEEE Congress on Evolutionary Computation, Singapore, 2007, pp. 2229-2236, doi: 10.1109/CEC.2007.4424748.
Badr, S., El Mahalawy, A., Attiya, G., & Nasr, A. A. Task consolidation based power consumption minimization in cloud computing environment. Multimedia Tools and Applications, 82(14), 21385-21413 (2023). https://doi.org/10.1007/s11042-022-14009-1
Murad, S. A., Muzahid, A. J. M., Azmi, Z. R. M., Hoque, M. I., & Kowsher, M. A review on job scheduling technique in cloud computing and priority rule based intelligent framework. Journal of King Saud University-Computer and Information Sciences, 34(6), 2309-2331 (2022). Doi: 10.1016/j.jksuci.2022.03.027.
Ahmed, O. H., Lu, J., Ahmed, A. M., Rahmani, A. M., Hosseinzadeh, M., & Masdari, M. Scheduling of scientific workflows in multi-fog environments using Markov models and a hybrid salp swarm algorithm. IEEE Access, 8, 189404-189422 (2020). doi: 10.1109/ACCESS.2020.3031472
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
