Abstract
In local computing environment, when we are dealing scientific computation using scientific workflow scheduling environment under deadline constraint, QoS is one of the most challenging tasks for any system used in scientific computing systems. Because when we are focusing on minimizing the workflow execution cost as well as time, we should not forget to consider the user-defined quality of service requirements while minimizing the workflow execution of cost and time. Therefore, to reduce the cost and time, we used cloud environment. Since cloud computing environment is in elastic nature, in which availability of resources is readily available as when and then required, its utilization is another challenge while using cloud computing environment. Therefore, in this paper, we use intelligence ant colony optimization (ACO), in which underutilized VMs allocation is initialized by Pareto distribution. ACO is used to converge the decision of virtual machine (VM) migration by its convergence to minima of cost and time. In our experiments, we have set up a local simulator in stand-alone system; for that workflow simulator, 1.0 has been used, where we have used Java eclipse for executing our program to calculate total execution time (TET) and total execution cost (TEC). In which, we have run our simulator ten times for each scientific workflow application and then average is taken for each workflow application to compare the output in which we found that ACO shows significant performance component when compare to existing genetic algorithm.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
L. Liu, M. Zhang, R. Buyya. “Deadline‐constrained co evolutionary genetic algorithm for scientific workflow scheduling in cloud computing.” Concurrency and Computation: Practice and Experience (2016).
L. Liu, M. Zhang, Y. Lin, L. Qin, “A survey on workflow management and scheduling in cloud computing.” Cluster, Cloud and Grid Computing (CCGrid), 2014 14th IEEE/ACM International Symposium on. IEEE, 2014.
S. Pandey, L. Wu, S. M. Guru, R. Buyya, “A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments.” Advanced information networking and applications (AINA), 2010 24th IEEE international conference on. IEEE, 2010.
Z. Zhu, G. Zhang, M. Li and X. Liu, “Evolutionary Multi-Objective Workflow Scheduling in Cloud,” in IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 5, pp. 1344–1357, May 1 2016.
S. Shukla, A. K. Gupta, S. Saxena and S. Kumar “An Evolutionary Study of Multi-Objective Workflow Scheduling in Cloud Computing.” International Journal of Computer Applications (0975–8887) Volume (2016).
G. J. Rathanam, and A. Rajaram. “Trust-Based Meta-Heuristics Workflow Scheduling in Cloud Service Environment.” Circuits and Systems 7.04: 520 (2016).
E. N. Alkhanak, E. Nabiel, S. P. Lee, and S. U. R. Khan. “Cost-aware challenges for workflow scheduling approaches in cloud computing environments: Taxonomy and opportunities.” Future Generation Computer Systems 50, 3–21 (2015).
A. Deldari, Arash, M. Naghibzadeh, and S. Abrishami. “CCA: a deadline-constrained workflow scheduling algorithm for multicore resources on the cloud.” The Journal of Supercomputing (2016): 1–26.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lal, A., Rama Krishna, C. (2018). Critical Path-Based Ant Colony Optimization for Scientific Workflow Scheduling in Cloud Computing Under Deadline Constraint. In: Perez, G., Tiwari, S., Trivedi, M., Mishra, K. (eds) Ambient Communications and Computer Systems. Advances in Intelligent Systems and Computing, vol 696. Springer, Singapore. https://doi.org/10.1007/978-981-10-7386-1_39
Download citation
DOI: https://doi.org/10.1007/978-981-10-7386-1_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7385-4
Online ISBN: 978-981-10-7386-1
eBook Packages: EngineeringEngineering (R0)