WORKFLOW SCHEDULING IN COVID 19 LOCKDOWN CRISIS USING ENERGY AWARE MAKESPAN MINIMIZATION MECHANISM IN ARTIFICIAL INTELLIGENCE CLOUD COMPUTING
DOI:
https://doi.org/10.25212/lfu.qzj.9.1.51Keywords:
Energy, Consumption, Make span, task execution, EAMMAbstract
Artificial Intelligence Cloud computing provides the flexible and on-demand business environment based on the resource sharing phenomena to make the service easily available for public utility. Workflow scheduling has been one of the prominent cloud computing applications; workflow comprises repeated business activity pattern which needs to execute in accordance with sequential checklist scheduling and it requires efficient QoS such as energy consumption, task execution time. In this work we address the problem of makes pan minimization and energy consumption, We have developed an efficient workflow mechanism named EAMM (Energy Aware Makes pan Minimization) to achieve the better performance in workflow scheduling. At first EAMM mechanism designs the problem of processing delay and execution time as a joint problem and solves through the same algorithm. here we focus on minimizing the make span and energy consumption in VM scheduling this is achieved through reducing the execution time on given local processor through designed algorithm. EAMM is evaluated by considering the dataset of scientific workflow based Montage and through the comparative analysis it is observed that EAMM simply outperforms the existing model in terms of total execution time and energy consumption
Downloads
References
E. M. Mocanu, M. Florea, M. I. Andreica and N. Ţăpuş, "Cloud Computing—Task scheduling based on genetic algorithms," 2012 IEEE International Systems Conference SysCon 2012, Vancouver, BC, 2012, pp. 1-6, doi: 10.1109/SysCon.2012.6189509.
G. Junwei, S. Shuo and F. Yiqiu, "Cloud resource scheduling algorithm based on improved LDW particle swarm optimization algorithm," 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, 2017, pp. 669-674.
M. Shelar, S. Sane, V. Kharat and R. Jadhav, "Autonomic and energy-aware resource allocation for efficient management of cloud data centre," 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore, India, 2017, pp. 1-8.
A. Malatpure, F. Qadri and J. Haskin, "Experience Report: Testing Private Cloud Reliability Using a Public Cloud Validation SaaS," 2017 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), Toulouse, 2017, pp. 56-56.
Chung-Yiwu, H. Y. Yu, J. C. Huang and J. J. Chen, "A hierarchical reliability-driven scheduling for cloud video transcoding," 2015 International Conference on Machine Learning and Cybernetics (ICMLC), Guangzhou, 2015, pp. 456-461.
Pourbahrami, S., Balafar, M. A., Khanli, L. M., & Kakarash, Z. A. (2020). A survey of neighborhood construction algorithms for clustering and classifying data points. Computer Science Review, 38, 100315.
X. J. Xu, C. B. Xiao, G. Z. Tian and T. Sun, "Hybrid Scheduling Deadline-Constrained Multi-DAGs Based on Reverse HEFT," 2016 International Conference on Information System and Artificial Intelligence (ISAI), Hong Kong, 2016, pp. 196-202.
J. Singh, S. Betha, B. Mangipudi, and N. Auluck, “Contention aware energy efficient scheduling on heterogeneous multiprocessors,” IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 5, pp. 1251–1264, May 2015.
A. Zhou, S. Wang, B. Cheng, and Z. Zheng, “Cloud service reliability enhancement via virtual machine placement optimization,” IEEE Trans. Serv. Comput., pp. 1–1, Jan. 2016.
Z. Cai, X. Li, and J. N. D. Gupta, “Heuristics for provisioning services to workflows in xaas clouds,” IEEE Trans. Serv. Comput., vol. 9, no. 2, pp. 250–263, Mar.-Apr. 2016.
Z. Tang, L. Qi, Z. Cheng, K. Li, S. U. Khan, and K. Li, “An energy-efficient task scheduling algorithm in dvfs-enabled cloud environment,” J Grid Comput., vol. 14, no. 1, pp. 55–74, Mar. 2016
H. Chen, J. Wen, W. Pedrycz and G. Wu, "Big Data Processing Workflows Oriented Real-Time Scheduling Algorithm using Task-Duplication in Geo-Distributed Clouds," in IEEE Transactions on Big Data, vol. 6, no. 1, pp. 131-144, 1 March 2020, doi: 10.1109/TBDATA.2018.2874469.
Y. Kong, M. Zhang, and D. Ye, “A belief propagation-based method for task allocation in open and dynamic cloud environments,” Knowl.-Based Syst., vol. 115, pp. 123–132, Jan. 2017.
Ehab Nabiel Alkhanak, Sai Peck Lee, and Saif Ur Rehman Khan. 2015. Cost-aware challenges for workflow scheduling approaches in cloud computing environments: Taxonomy and opportunities. Fut. Gen. Comp. Syst. 50 (2015), 3–21.
Toktam Ghafarian and Bahman Javadi. 2015. Cloud-aware data intensive workflow scheduling on volunteer computing systems. Fut. Gen. Comp. Syst. 51 (2015), 87–97.
Weihong Chen, Guoqi Xie, Renfa Li, Yang Bai, Chunnian Fan, and Keqin Li. 2017. Efficient task scheduling for budget constrained parallel applications on heterogeneous cloud computing systems. Fut. Gen. Comp. Syst. 74 (2017), 1–11.
Artem M. Chirkin, Adam S. Z. Belloum, Sergey V. Kovalchuk, Marc X. Makkes, Mikhail A. Melnik, Alexander A.Visheratin, and Denis A. Nasonov. 2017. Execution time estimation for workflow scheduling. Fut. Gen. Comp. Syst.75 (2017), 376–387.
Vishakha Singh, Indrajeet Gupta, and Prasanta K. Jana. 2018. A novel cost-efficient approach for deadline constrained workflow scheduling by dynamic provisioning of resources. Fut. Gen. Comp. Syst. 79 (2018), 95–110.
Young Choon Lee, Hyuck Han, Albert Y. Zomaya, and Mazin Yousif. 2015. Resource-efficient workflow scheduling
a. in clouds. Knowl.-Based Syst. 80 (2015), 153–162.
Xin Ye, Sihao Liu, Yanli Yin, and Yaochu Jin. 2017. User-oriented many-objective cloud workflow scheduling based on an improved knee point driven evolutionary algorithm. Knowl.-Based Syst. 135 (2017), 113–124.
Raza Abbas Haidri, Chittaranjan Padmanabh Katti, and Prem Chandra Saxena. 2017. Cost effective deadline aware scheduling strategy for workflow applications on virtual machines in cloud computing. J. King Saud Univ. Comput. Inf. Sci. (2017). DOI:https://doi.org/10.1016/j.jksuci.2017.10.009.
Klavdiya Bochenina, Nikolay Butakov, and Alexander Boukhanovsky. 2016. Static scheduling of multiple workflows with soft deadlines in non-dedicated heterogeneous environments. Fut. Gen. Comp. Syst. 55 (2016), 51–61.
H. R. Faragardi, M. R. Saleh Sedghpour, S. Fazliahmadi, T. Fahringer and N. Rasouli, "GRP-HEFT: A Budget-Constrained Resource Provisioning Scheme for Workflow Scheduling in IaaS Clouds," in IEEE Transactions on Parallel and Distributed Systems, vol. 31, no. 6, pp. 1239-1254, 1 June 2020, doi: 10.1109/TPDS.2019.2961098.
Kakarash, Z. A., Karim, S. H. T., Ahmed, N. F., & Omar, G. A. (2021). New Topology Control base on Ant Colony Algorithm in Optimization of Wireless Sensor Network. Passer Journal of Basic and Applied Sciences, 3(2), 123-129.
Pourbahrami, S., Balafar, M. A., Khanli, L. M., & Kakarash, Z. A. (2020). A survey of neighborhood construction algorithms for clustering and classifying data points. Computer Science Review, 38, 100315
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Qalaai Zanist Journal allows the author to retain the copyright in their articles. Articles are instead made available under a Creative Commons license to allow others to freely access, copy and use research provided the author is correctly attributed.
Creative Commons is a licensing scheme that allows authors to license their work so that others may re-use it without having to contact them for permission
