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Cost Optimised Heuristic Algorithm (COHA) for Scientific Workflow Scheduling in IaaS Cloud Environment | IEEE Conference Publication | IEEE Xplore

Cost Optimised Heuristic Algorithm (COHA) for Scientific Workflow Scheduling in IaaS Cloud Environment


Abstract:

Cloud computing, a multipurpose and high-performance internet-based computing, can model and transform a large range of application requirements into a set of workflow ta...Show More

Abstract:

Cloud computing, a multipurpose and high-performance internet-based computing, can model and transform a large range of application requirements into a set of workflow tasks. It allows users to represent their computational needs conveniently for data retrieval, reformatting, and analysis. However, workflow applications are big data applications and often take long hours to finish executing due to their nature and data size. In this paper, we study the cost optimised scheduling algorithms in cloud and proposed a novel task splitting algorithm named Cost optimised Heuristic Algorithm (COHA) for the cloud scheduler to optimise the execution cost. In this algorithm, the large tasks are split into sub-tasks to reduce their execution time. The design purpose is to enable all tasks to adequately meet their deadlines. We have carefully tested the performance of the COHA with a list of workflow inputs. The simulation results have convincingly demonstrated that COHA can effectively perform VM allocation and deployment, and well handle randomly arrived tasks. It can efficiently reduce execution costs while also allowing all tasks to properly finish before their deadlines. Overall, the improvements in our algorithm have remarkably reduced the execution cost by 32.5% for Sipht, 3.9% for Montage, and 1.2% for CyberShake workflows when compared to the state of art work.
Date of Conference: 25-27 May 2020
Date Added to IEEE Xplore: 23 June 2020
ISBN Information:
Conference Location: Baltimore, MD, USA
References is not available for this document.

I. Introduction

Cloud computing is a multi-tenant internet-based platform that provides digital service delivery by various cloud service providers on-demand base which is subject to quality of service (QoS) constraints [1], [2], [3]. We have seen a rapid growth in cloud technology development in recent years. This is due to its vast benefits including 1) cost reduction in upfront capital expenditure on hardware, software, hosting and deployment; 2) availability; 3) location independence; 4) flexibility and 5) market agility [4]. Cloud provides: (i) softwares which are available via a third-party over the Internet referred as software as a service (SaaS), (ii) services such as storage, networking, and virtualization known as infrastructure as a service (IaaS) and (iii) hardware and software tools available over the internet which is commonly referred to as platform as a Service (PaaS) [5].

Select All
1.
G. Patel, R. Mehta and U. Bhoi, "Enhanced load balanced min-min algorithm for static meta task scheduling in cloud computing", Procedia Computer Science, vol. 57, pp. 545-553, 2015.
2.
S. Bitam, "Bees life algorithm for job scheduling in cloud computing", Proceedings of The Third International Conference on Communications and Information Technology, pp. 186-191, 2012.
3.
A. Fox, R. Griffith, A. Joseph, R. Katz, A. Konwinski, G. Lee, et al., "Above the clouds: A berkeley view of cloud computing", Dept. Electrical Eng. and Comput. Sciences University of California Berkeley Rep. UCB/EECS, vol. 28, no. 13, pp. 2009, 2009.
4.
L. InfoTech, "What is cloud computing", IBM Journal of Research and Development, vol. 60, no. 4, pp. 41-44, 2012.
5.
L. Savu, "Cloud computing: Deployment models delivery models risks and research challenges", 2011 International Conference on Computer and Management (CAMAN), pp. 1-4, 2011.
6.
M. Masdari, S. ValiKardan, Z. Shahi and S. I. Azar, "Towards workflow scheduling in cloud computing: a comprehensive analysis", Journal of Network and Computer Applications, vol. 66, pp. 64-82, 2016.
7.
X. Zhou, G. Zhang, J. Sun, J. Zhou, T. Wei and S. Hu, "Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based heft", Future Generation Computer Systems, vol. 93, pp. 278-289, 2019.
8.
M. A. Rodriguez and R. Buyya, "Budget-driven scheduling of scientific workflows in iaas clouds with fine-grained billing periods", ACM Transactions on Autonomous and Adaptive Systems (TAAS), vol. 12, no. 2, pp. 1-22, 2017.
9.
J. Sahni and D. P. Vidyarthi, "A cost-effective deadline-constrained dynamic scheduling algorithm for scientific workflows in a cloud environment", IEEE Transactions on Cloud Computing, vol. 6, no. 1, pp. 2-18, 2015.
10.
N. Anwar and H. Deng, "Elastic scheduling of scientific workflows under deadline constraints in cloud computing environments", Future Internet, vol. 10, no. 1, pp. 5, 2018.
11.
A. A. Nasr, N. A. El-Bahnasawy, G. Attiya and A. El-Sayed, "Costeffective algorithm for workflow scheduling in cloud computing under deadline constraint", Arabian Journal for Science and Engineering, vol. 44, no. 4, pp. 3765-3780, 2019.
12.
A. M. Manasrah and Ba Ali, H, "Workflow scheduling using hybrid gapso algorithm in cloud computing", Wireless Communications and Mobile Computing, vol. 2018, 2018.
13.
J. Liu, E. Pacitti, P. Valduriez and M. Mattoso, "Parallelization of scientific workflows in the cloud".
14.
G. Juve, A. Chervenak, E. Deelman, S. Bharathi, G. Mehta and K. Vahi, "Characterizing and profiling scientific workflows", Future Generation Computer Systems, vol. 29, no. 3, pp. 682-692, 2013.
15.
M. A. R. Sossa, Resource provisioning and scheduling algorithms for scientific workflows in cloud computing environments, 2016.
16.
Z. Li, J. Ge, H. Hu, W. Song, H. Hu and B. Luo, "Cost and energy aware scheduling algorithm for scientific workflows with deadline constraint in clouds", IEEE Transactions on Services Computing, vol. 11, no. 4, pp. 713-726, 2015.
17.
Y. Chawla and M. Bhonsle, "A study on scheduling methods in cloud computing", International Journal of Emerging Trends Technology in Computer Science (IJETTCS), vol. 1, no. 3, pp. 12-17, 2012.
18.
M. R. Garey, "Computers and intractability: A guide to the theory of np-completeness", Revista Da Escola De Enfermagem Da USP, vol. 44, no. 2, pp. 340, 1979.
19.
A. Awad, N. El-Hefnawy and H. Abdel kader, "Enhanced particle swarm optimization for task scheduling in cloud computing environments", Procedia Computer Science, vol. 65, pp. 920-929, 2015.
20.
B. P. Rimal and M. Maier, "Workflow scheduling in multi-tenant cloud computing environments", IEEE Transactions on parallel and distributed systems, vol. 28, no. 1, pp. 290-304, 2016.
21.
R. A. Haidri, C. P. Katti and P. C. Saxena, "Cost effective deadline aware scheduling strategy for workflow applications on virtual machines in cloud computing", Journal of King Saud University-Computer and Information Sciences, 2017.
22.
S. Elsherbiny, E. Eldaydamony, M. Alrahmawy and A. E. Reyad, "An extended intelligent water drops algorithm for workflow scheduling in cloud computing environment", Egyptian informatics journal, vol. 19, no. 1, pp. 33-55, 2018.
23.
M. Kalra and S. Singh, "A review of metaheuristic scheduling techniques in cloud computing", Egyptian informatics journal, vol. 16, no. 3, pp. 275-295, 2015.
24.
Y. Jiang, Z. Huang and D. H. Tsang, "Towards max-min fair resource allocation for stream big data analytics in shared clouds", IEEE Transactions on Big Data, vol. 4, no. 1, pp. 130-137, 2016.
25.
H. M. Fard, R. Prodan, J. J. D. Barrionuevo and T. Fahringer, "A multi-objective approach for workflow scheduling in heterogeneous environments", 2012 12th IEEE/ACM International Symposium on Cluster Cloud and Grid Computing (ccgrid 2012), pp. 300-309, 2012.
26.
W. Zheng, Y. Qin, E. Bugingo, D. Zhang and J. Chen, "Cost optimization for deadline-aware scheduling of big-data processing jobs on clouds", Future Generation Computer Systems, vol. 82, pp. 244-255, 2018.
27.
M. A. Alworafi, A. Dhari, S. A. El-Booz, A. A. Nasr, A. Arpitha and S. Mallappa, "An enhanced task scheduling in cloud computing based on hybrid approach", Data Analytics and Learning, pp. 11-25, 2019.
28.
E. Deelman, K. Vahi, G. Juve, M. Rynge, S. Callaghan, P. J. Maechling, R. Mayani, W. Chen, R. F. Da Silva, M. Livny et al., "Pegasus a workflow management system for science automation", Future Generation Computer Systems, vol. 46, pp. 17-35, 2015.
29.
J. Cao, L. Wen and X. Liu, Process-Aware Systems: First International Workshop PAS 2014, vol. 495, October 17, 2014, 2015.
30.
W. Chen and E. Deelman, "Workflowsim: A toolkit for simulating scientific workflows in distributed environments", 2012 IEEE 8th International Conference on E-Science, pp. 1-8, 2012.

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References

References is not available for this document.