Multi-Objective Task Scheduling in the Cloud Computing based on the Patrice Swarm Optimization

Full Text (PDF, 612KB), PP.61-66

Views: 0 Downloads: 0

Author(s)

Farnaz Sharifi Milani 1 Ahmad Habibizad Navin 1,*

1. Department of Computer, East Azarbaijan Science and Research Branch, Islamic Azad University, Tabriz, Iran

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2015.05.09

Received: 3 Sep. 2014 / Revised: 3 Dec. 2014 / Accepted: 19 Jan. 2015 / Published: 8 Apr. 2015

Index Terms

Cloud Computing, Scheduling, PSO, Network

Abstract

Cloud computing is the latest emerging trend in distributed computing, where shared resources are provided to end-users in an on demand fashion that brings many advantages, including data ubiquity, flexibility of access, high availability of resources, and flexibility. In this type of systems many challenges are existed that the task scheduling problem is one of them. The task scheduling problem in Cloud computing is an NP-hard problem. Therefore, many heuristics have been proposed, from low level execution of tasks in multiple processors to high level execution of tasks. In this paper, we propose a new algorithm based on PSO to schedule the tasks in the Cloud. The results demonstrated that the proposed algorithm has a better operation in terms of task execution time, waiting time and missed tasks in comparison of First Come First Served (FCFS), Shortest Process Next (SPN) and Highest Response Ratio Next (HRRN).

Cite This Paper

Farnaz Sharifi Milani, Ahmad Habibizad Navin, "Multi-Objective Task Scheduling in the Cloud Computing based on the Patrice Swarm Optimization", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.5, pp.61-66, 2015. DOI:10.5815/ijitcs.2015.05.09

Reference

[1]Oliveira, T., M. Thomas, and M. Espadanal, Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information & Management, 2014. 51(5): p. 497-510.

[2]Jafari Navimipour, N., et al., Expert Cloud: a Cloud-based framework to share the knowledge and skills of human resources Computer in Human Behaviour, 2014.

[3]Wang, S.-S. and S.-C. Wang, The consensus problem with dual failure nodes in a cloud computing environment. Information Sciences, 2014. 279(0): p. 213-228.

[4]Manvi, S.S. and G. Krishna Shyam, Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey. Journal of Network and Computer Applications, 2014. 41(0): p. 424-440.

[5]Liu, W., et al., Adaptive resource discovery in mobile cloud computing. Computer Communications, 2014. 50(0): p. 119-129.

[6]Chong, H.-Y., J.S. Wong, and X. Wang, An explanatory case study on cloud computing applications in the built environment. Automation in Construction, 2014. 44(0): p. 152-162.

[7]Gurkok, C., Chapter 4 - Securing Cloud Computing Systems, in Network and System Security (Second Edition), J.R. Vacca, Editor. 2014, Syngress: Boston. p. 83-126.

[8]Nandhini, A. and B. Saravana Balaji, Energy-Efficient PSO and Latency Based Group Discovery Algorithm in Cloud Scheduling. International Journal of Information Technology and Computer Science, 2014 6(10).

[9]Jou, M. and J. Wang, Observations of achievement and motivation in using cloud computing driven CAD: Comparison of college students with high school and vocational high school backgrounds. Computers in Human Behavior, 2013. 29(2): p. 364-369.

[10]Jafari Navimipour, N., et al., Behavioral modelling and automated verification of a Cloud-based framework to share the knowledge and skills of human resources. Computer in Industry, 2014.

[11]Jafari Navimipour, N. and L. Mohammad Khanli. The LGR method for task scheduling in computational grid. in Advanced Computer Theory and Engineering, 2008. ICACTE'08. International Conference on. 2008. IEEE.

[12]Habibizad Navin, A., et al., Expert Grid: New Type of Grid to Manage the Human Resources and Study the Effectiveness of its Task Scheduler. Arabian Journal for Science and Engineering, 2014.

[13]Jafari Navimipour, N., et al., Resource discovery mechanisms in grid systems: A survey. Journal of Network and Computer Applications, 2014. 41: p. 389-410.

[14]Laili, Y., et al., A Ranking Chaos Algorithm for dual scheduling of cloud service and computing resource in private cloud. Computers in Industry, 2013. 64(4): p. 448-463. 

[15]Tao, F., et al., CLPS-GA: A case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling. Applied Soft Computing, 2014. 19(0): p. 264-279.

[16]Wu, Z., et al., A market-oriented hierarchical scheduling strategy in cloud workflow systems. The Journal of Supercomputing, 2013. 63(1): p. 256-293.

[17]Navimipour, N.J., et al., Job scheduling in the Expert Cloud based on genetic algorithms. Kybernetes, 2014. 43(8): p. 12-12.

[18]Montazeri, A., B. Akbari, and M. Ghanbari, An incentive scheduling mechanism for peer-to-peer video streaming. Peer-to-Peer Networking and Applications, 2012. 5(3): p. 257-278.

[19]Rius, J., F. Cores, and F. Solsona, Cooperative scheduling mechanism for large-scale peer-to-peer computing systems. Journal of Network and Computer Applications, 2013. 36(6): p. 1620-1631.

[20]Jafari Navimipour, N. and F. Sharifi Milani, A comprehensive study of the resource discovery techniques in Peer-to-Peer networks. Peer-to-Peer Networking and Applications, 2015: p. 1-19.

[21]Jafari Navimipour, N. and F. Sharifi Milani, Task Scheduling in the Cloud Computing based on the Cuckoo Search Algorithm. International Journal of Modeling and Optimization, 2014.

[22]Zhang, F., et al., Multi-objective scheduling of many tasks in cloud platforms. Future Generation Computer Systems, (0).

[23]Gutierrez-Garcia, J.O. and K.M. Sim, A family of heuristics for agent-based elastic Cloud bag-of-tasks concurrent scheduling. Future Generation Computer Systems, 2013. 29(7): p. 1682-1699.

[24]Garg, S.K., et al., Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers. Journal of Parallel and Distributed Computing, 2011. 71(6): p. 732-749.

[25]Mezmaz, M., et al., A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. Journal of Parallel and Distributed Computing, 2011. 71(11): p. 1497-1508.

[26]Li, J., et al., Online optimization for scheduling preemptable tasks on IaaS cloud systems. Journal of Parallel and Distributed Computing, 2012. 72(5): p. 666-677.

[27]Abrishami, S. and M. Naghibzadeh, Deadline-constrained workflow scheduling in software as a service Cloud. Scientia Iranica, 2012. 19(3): p. 680-689.

[28]Lu, K., et al. Resources collaborative scheduling model based on trust mechanism in cloud. in Trust, Security and Privacy in Computing and Communications (TrustCom), 2012 IEEE 11th International Conference on. 2012. IEEE.

[29]Wang, W., et al., Cloud-DLS: Dynamic trusted scheduling for Cloud computing. Expert Systems with Applications, 2012. 39(3): p. 2321-2329.

[30]Dogan, A. and F. Ozguner, Matching and scheduling algorithms for minimizing execution time and failure probability of applications in heterogeneous computing. Parallel and Distributed Systems, IEEE Transactions on, 2002. 13(3): p. 308-323.

[31]Van den Bossche, R., K. Vanmechelen, and J. Broeckhove, Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds. Future Generation Computer Systems, 2013. 29(4): p. 973-985.

[32]Wang, Y., et al., Resource scheduling of cloud with QoS constraints, in Advances in Neural Networks–ISNN 2013. 2013, Springer. p. 351-358.

[33]Frîncu, M.E., Scheduling highly available applications on cloud environments. Future Generation Computer Systems, 2014. 32: p. 138-153.

[34]Liu, Z., et al., Resource preprocessing and optimal task scheduling in cloud computing environments. Concurrency and Computation: Practice and Experience, 2014.

[35]Jung, D., et al., A Workflow Scheduling Technique for Task Distribution in Spot Instance-Based Cloud, in Ubiquitous Information Technologies and Applications, Y.-S. Jeong, et al., Editors. 2014, Springer Berlin Heidelberg. p. 409-416.

[36]Thulasiraman, K. and M. Swamy, Acyclic Directed Graphs. Graphs: Theory and Algorithms, 1992.

[37]Baioletti, M., G. Busanello, and B. Vantaggi, Acyclic directed graphs representing independence models. International Journal of Approximate Reasoning, 2011. 52(1): p. 2-18.

[38]Rebollo-Ruiz, I. and M. Graña, An empirical evaluation of Gravitational Swarm Intelligence for graph coloring algorithm. Neurocomputing, 2014. 132(0): p. 79-84.

[39]Saka, M.P., E. Doğan, and I. Aydogdu, 2 - Analysis of Swarm Intelligence–Based Algorithms for Constrained Optimization, in Swarm Intelligence and Bio-Inspired Computation, X.-S. Yang, et al., Editors. 2013, Elsevier: Oxford. p. 25-48.

[40]Wu, G., et al., Superior solution guided particle swarm optimization combined with local search techniques. Expert Systems with Applications, 2014. 41(16): p. 7536-7548.

[41]Kennedy, J. and R. Eberhart. Particle swarm optimization. in Proceedings of IEEE international conference on neural networks. 1995. Perth, Australia.

[42]Alam, S., et al., Research on particle swarm optimization based clustering: A systematic review of literature and techniques. Swarm and Evolutionary Computation, 2014. 17(0): p. 1-13.

[43]Rada-Vilela, J., M. Johnston, and M. Zhang, Population statistics for particle swarm optimization: Resampling methods in noisy optimization problems. Swarm and Evolutionary Computation, 2014. 17(0): p. 37-59.

[44]Shi, Y. and R. Eberhart. A modified particle swarm optimizer. in Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on. 1998. IEEE.