HTSCC A Hybrid Task Scheduling Algorithm in Cloud Computing Environment

Rasha Ali Al-Arasi; Anwar Saif

doi:10.24297/ijct.v17i2.7584

Authors

Rasha Ali Al-Arasi Sana‘a University, Department of Computer Science
Anwar Saif Sana‘a University, Department of Information Systems, Sana‘a, Yemen

DOI:

https://doi.org/10.24297/ijct.v17i2.7584

Keywords:

Cloud computing, Task scheduling, Genetic algorithm (GA), Particle swarm optimization (PSO), Makespan, Resource utilization

Abstract

Nowadays, cloud computing makes it possible for users to use the computing resources like application, software, and hardware, etc., on pay as use model via the internet. One of the core and challenging issue in cloud computing is the task scheduling. Task scheduling problem is an NP-hard problem and is responsible for mapping the tasks to resources in a way to spread the load evenly. The appropriate mapping between resources and tasks reduces makespan and maximizes resource utilization. In this paper, we present and implement an independent task scheduling algorithm that assigns the users' tasks to multiple computing resources. The proposed algorithm is a hybrid algorithm for task scheduling in cloud computing based on a genetic algorithm (GA) and particle swarm optimization (PSO). The algorithm is implemented and simulated using CloudSim simulator. The simulation results show that our proposed algorithm outperforms the GA and PSO algorithms by decreasing the makespan and increasing the resource utilization.

Downloads

Download data is not yet available.

References

Senyo, P.K., E. Addae, and R. Boateng, Cloud computing research: A review of research themes, frameworks, methods and future research directions. International Journal of Information Management, 2018. 38(1): p. 128-139.

Agarwal, M. and G.M.S. Srivastava, A Cuckoo Search Algorithm-Based Task Scheduling in Cloud Computing, in Advances in Computer and Computational Sciences. 2018, Springer. p. 293-299.

Al-maamari, A. and F.A. Omara, Task scheduling using hybrid algorithm in cloud computing environments. Journal of Computer Engineering (IOSR-JCE), 2015. 17(3): p. 96-106.

Singh, P., M. Dutta, and N. Aggarwal, A review of task scheduling based on meta-heuristics approach in cloud computing. Knowledge and Information Systems, 2017: p. 1-51.

Yang, X.-S., et al., Swarm intelligence and bio-inspired computation: theory and applications. 2013: Newnes.

Chhabra, A., Hybrid PSACGA Algorithm for Job Scheduling to Minimize Makespan in Heterogeneous Grids, in Industry Interactive Innovations in Science, Engineering and Technology. 2018, Springer. p. 107-120.

Meng, Q., L. Zhang, and Y. Fan, A Hybrid Particle Swarm Optimization Algorithm for Solving Job Shop Scheduling Problems, in Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. 2016, Springer. p. 71-78.

Kumar, A.S. and M. Venkatesan, Task scheduling in a cloud computing environment using HGPSO algorithm. Cluster Computing, 2018: p. 1-7.

Al-maamari, A. and F.A. Omara, Task scheduling using PSO algorithm in cloud computing environments. International Journal of Grid and Distributed Computing, 2015. 8(5): p. 245-256.

Shojafar, M., et al., FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method. Cluster Computing, 2015. 18(2): p. 829-844.

Zhu, K., et al. Hybrid genetic algorithm for cloud computing applications. in Services Computing Conference (APSCC), 2011 IEEE Asia-Pacific. 2011. IEEE.

Udomkasemsub, O., L. Xiaorong, and T. Achalakul. A multiple-objective workflow scheduling framework for cloud data analytics. in Computer Science and Software Engineering (JCSSE), 2012 International Joint Conference on. 2012. IEEE.

Joseph, C.T., K. Chandrasekaran, and R. Cyriac, A novel family genetic approach for virtual machine allocation. Procedia Computer Science, 2015. 46: p. 558-565.

Aron, R., I. Chana, and A. Abraham, A hyper-heuristic approach for resource provisioning-based scheduling in grid environment. The Journal of Supercomputing, 2015. 71(4): p. 1427-1450.

Ebadifard, F. and S.M. Babamir, A PSO?based task scheduling algorithm improved using a load?balancing technique for the cloud computing environment. Concurrency and Computation: Practice and Experience, 2018. 30(12): p. e4368.

Dordaie, N. and N.J. Navimipour, A hybrid particle swarm optimization and hill climbing algorithm for task scheduling in the cloud environments. ICT Express, 2017.

Hamad, S.A. and F.A. Omara, Genetic-based task scheduling algorithm in cloud computing environment. International Journal of Advanced computer Science and Applications, 2016. 7(4): p. 550-556.

Jaeyalakshmi, M. and P. Kumar, Task Scheduling Using Meta-Heuristic Optimization Techniques in Cloud Environment. International Journal Of Engineering And Computer Science, 2016. 5(11).

Babukartik, R. and P. Dhavachelvan, Hybrid Algorithm using the advantage of ACO and Cuckoo Search for Job Scheduling. International Journal of Information Technology Convergence and Services, 2012. 2(4): p. 25.

Muthuram, R. and G. Kousalya, GAF–Genetic Algorithm based Framework for Cloud Resource Scheduling.

Tarek, Z., M. Zakria, and F.A. Omara, Pso optimization algorithm for task scheduling on the cloud computing environment. International Journal of Computers and Technology, 2014. 13(9).

Kennedy, J. and R. Eberhart, Particle swarm optimization 1995 IEEE International Conference on Neural Networks Proceedings. 1942, Vols.

Poli, R., An analysis of publications on particle swarm optimization applications. Essex, UK: Department of Computer Science, University of Essex, 2007.

Imran, M., R. Hashim, and N.E.A. Khalid, An overview of particle swarm optimization variants. Procedia Engineering, 2013. 53: p. 491-496.

Kao, Y.-T. and E. Zahara, A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Applied Soft Computing, 2008. 8(2): p. 849-857.

Saini, N., Review of Selection Methods in Genetic Algorithms. International Journal Of Engineering And Computer Science, 2017. 6(12): p. 22261-22263.

Bansal, J.C., Particle Swarm Optimization, in Evolutionary and Swarm Intelligence Algorithms. 2019, Springer. p. 11-23.

Calheiros, R.N., et al., CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience, 2011. 41(1): p. 23-50.