Browse > Article
http://dx.doi.org/10.3837/tiis.2018.08.001

Improved Hybrid Symbiotic Organism Search Task-Scheduling Algorithm for Cloud Computing  

Choe, SongIl (College of Information Science, Kim Il Sung University)
Li, Bo (College of Management and Economics, Tianjin University)
Ri, IlNam (College of Information Science, Kim Il Sung University)
Paek, ChangSu (Department of Information Science, HuiChon Industry University)
Rim, JuSong (Department of Control Science, University of Science)
Yun, SuBom (Department of Information Science, HuiChon Industry University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.12, no.8, 2018 , pp. 3516-3541 More about this Journal
Abstract
Task scheduling is one of the most challenging aspects of cloud computing nowadays, and it plays an important role in improving overall performance in, and services from, the cloud, such as response time, cost, makespan, and throughput. A recent cloud task-scheduling algorithm based on the symbiotic organisms search (SOS) algorithm not only has fewer specific parameters, but also incurs time complexity. SOS is a newly developed metaheuristic optimization technique for solving numerical optimization problems. In this paper, the basic SOS algorithm is reduced, and chaotic local search (CLS) is integrated into the reduced SOS to improve the convergence rate. Simulated annealing (SA) is also added to help the SOS algorithm avoid being trapped in a local minimum. The performance of the proposed SA-CLS-SOS algorithm is evaluated by extensive simulation using the Matlab framework, and is compared with SOS, SA-SOS, and CLS-SOS algorithms. Simulation results show that the improved hybrid SOS performs better than SOS, SA-SOS, and CLS-SOS in terms of convergence speed and makespan.
Keywords
cloud computing; cloud task scheduling; symbiotic organisms search; simulated annealing; chaotic local search;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Abdullahi Mohammed and Ngadi Md Asri, "Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment," PLoS One, 11(6), e0158229, Jun, 2016.   DOI
2 M Abdullahi, MA Ngadi and SI Dishing, "Chaotic Symbiotic Organisms Search for Task Scheduling Optimization on Cloud Computing Environment," in Proc. of Ict International Student Project Conference on. IEEE, pp. 1-4, May, 2017.
3 Subhodip Saha and V. Mukherjee, "A novel chaos-integrated symbiotic organisms search algorithm for global optimization," Soft Computing, 4, pp. 1-20, April, 2017.
4 Yang D, Li G and Cheng G, "On the efficiency of chaos optimization algorithms for global optimization," Chaos Solitons Fract, 34(4), pp. 1366-1375, November, 2007.   DOI
5 Liu B, Wang L, Jin YH and Huang D, "Improved particle swarm optimization combined with chaos," Chaos Solitons Fract, 25(5), pp. 1261-1271, September, 2005.   DOI
6 Xiang T, Liao X and Wong K, "An improved particle swarm optimization algorithm combined with piecewise linear chaotic map," Appl Math Comput, 190(2), pp. 1637-1645, July, 2007.   DOI
7 MY Cheng and D Prayogo, "Symbiotic organisms search: a new metaheuristic optimization algorithm," Comput Struct, 139, pp. 98-112, July, 2014.   DOI
8 Q Cai, D Shan, W Zhao, "Resource scheduling in cloud computer based on improved particle swarm optimization algorithm," J. Liaoning Tech. Univ. (Natural Science), January, 2016.
9 XiaoLi He, Yu Song and Ralf Volker Binsack, "The Intelligent Task Scheduling Algorithm in Cloud Computing," International Journal of Grid and Distributed Computing, 9(4), pp. 313-324, April, 2016.
10 LI Li-Fen, YL Zhu and JY Zhang, "A cloud model based multiple ant colony algorithm for the routing optimization of WSN with a long-chain structure," Comput. Eng. Sci, 32(11), pp. 10-14, November, 2010. http://en.cnki.com.cn/Article_en/CJFDTOTAL-JSJK201011002.htm
11 F Nzanywayingoma and Y Yang, " Effective Task Scheduling and Dynamic Resource Optimi zation based on Heuristic Algorithms in Cloud Computing Environment," KSII Transactions on Internet & Information Systems, 11(12), pp. 5780-5802, December, 2017.
12 S. Balamurugan, Dr.P.Visalakshi, "Strategies for Solving the NP-Hard Workflow Scheduling Problems in Cloud Computing Environments," Australian Journalof Basic and Applied Sciences, 8(16), pp. 345-355, October, 2014. http://ajbasweb.com/old/ajbas/2014/October/345-355.pdf
13 SM Abdulhamid, MS Abd Latiff, G Abdul-Salaam, SH Hussain Madni, "Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm," Plos One, 11(7), pp. 1-18, July 12, 2016.
14 T Mathew, KC Sekaran, J Jose, "Study and analysis of various task scheduling algorithms in the cloud computing environment," ICACCI, pp. 658-664, December 2014.
15 Awad A.I. et al, "Enhanced particle swarm optimization for task scheduling in cloud computing environments," Procedia Comput. Sci, 65, pp. 920-929, December, 2015.   DOI
16 Z Wu, X Liu, Z Ni and Y Yang, "A market-oriented hierarchical scheduling strategy in cloud workflow systems," Journal of Supercomputing, 63(1), pp. 256-293, January, 2013.   DOI
17 K Kurowski and A Oleksiak, "Hierarchical scheduling strategies for parallel tasks and advance reservations in grids," Journal of Scheduling, 16 (4), pp. 349-368, August, 2013.   DOI
18 N Sadhasivam, R Balamurugan and M Pandi, "Cancer Diagnosis Epigenomics Scientific Workflow Scheduling in the CloudComputing Environment Using an Improved PSO Algorithm," Asian Pacific journal of cancer prevention : APJCP, 19(1), pp. 243-246, January, 2018.
19 P Huang, H Peng, P Lin and X Li, "Static strategy and dynamic adjustment: An effective method for Grid task scheduling," Future Generation Computer Systems, 25(8), pp. 884-892, September, 2009.   DOI
20 Y Gao, H Guan, Z Qi, Y Hou and L Liu, "A multi-objective ant colony system algorithm for virtual machine placement in cloud computing," J. Comput. Syst. Sci, 79(8), pp. 1230-1242, December, 2013.   DOI
21 Raju, R et al, "Minimizing the makespan using hybrid algorithm for cloud computing," Adv. Comput. Conf, 7903, pp. 957-962, February, 2013.
22 Zhang Nan, Yang Xiaolong, Zhang Min and Long Keping, "A genetic algorithm-based task scheduling for cloud resource crowd-funding model," INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, 31(1), September, 2017.
23 M Cuppini, "A genetic algorithm for channel assignment problems," Eur. Trans. Telecommun, 5(2), pp. 285-294, March, 2010.   DOI
24 Y Xu, K Li, J Hu and K Li, "A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues," Inf. Sci, 270(6), pp. 255-287, June, 2014.   DOI
25 YS Jiang and WM Chen, "Task scheduling for grid computing systems using a genetic Algorithm," Journal of Supercomputing, 71(4), pp. 1357-1377, April, 2015.   DOI
26 Dasgupta and Kousik, "A genetic algorithm (GA) based load balancing strategy for cloud computing," Procedia Technol, December, 2013.
27 Manasrah Ahmad M and Ali Hanan Ba, "Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing," WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 3, pp. 1-16, January, 2018.
28 Lin Yang-Kuei and Chong Chin Soon, "Fast GA-based project scheduling for computing resources allocation in a cloudmanufacturing system," JOURNAL OF INTELLIGENT MANUFACTURING, 28(5), pp. 1189-1201, June, 2017.   DOI
29 Guan T.T. et al, "Application research of multi objective partice swarm optimization in logistics distribution," Nanchang University, Nanchang, 2012.
30 Abdullahi Mohammed, Ngadi Md Asri and Abdulhamid Shafi'i Muhammad, "Symbiotic Organism Search optimization based task scheduling in cloud computing environment," Future Generation Computer Systems, 56, pp. 640-650, August, 2015.
31 Vincent F.Y , Redi A.P. , Yang C.L , Ruskartina E and Santosa B, "Symbiotic organisms search and two solution representations for solving the capacitated vehicle routing problem," Applied Soft Computing, 52, pp. 657-672, October, 2016.
32 Tejani GG et al, "Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization," J.Comput Design Eng, 3(3), pp. 226-249, February, 2016.   DOI
33 Hwang Chii-Ruey, "Simulated annealing: theory and applications," Acta Applicandae Mathematicae, 37(1), pp. 108-111, 1987.
34 R. Maheswaran and S.G. Ponnambalam, "A meta-heuristic approach to single machine scheduling problems," The International Journal of Advanced Manufacturing Technology, 25(7-8), pp. 772-776, April, 2005.   DOI
35 Gan Na, Huang Yufeng and Lu Xiaomei, "Niching Particle Swarm Optimization Algorithm for Solving Task Scheduling in CloudComputing," AGRO FOOD INDUSTRY HI-TECH, 28(3), pp. 876-879, May, 2017. https://www.researchgate.net/publication/319091663_Niching_particle_swarm_optimization_algorithm_for_solving_task_scheduling_in_cloud_computing
36 Strobl Maximilian AR and Barker Daniel, "On Simulated Annealing Phase Transitionsin Phylogeny Reconstruction," Molecular Phylogenetics and Evolution, 101, pp. 46-55,May, 2016.   DOI
37 Absalom El-Shamir Ezugwu, Aderemi Adewumi and Marc Frincu, "Simulated annealing based symbiotic organisms search optimization algorithm for traveling salesman problem," Expert Systems With Applications, 77, pp. 189-210, February, 2017.   DOI
38 Casas I, Taheri J, Ranjan R and Zomaya AY, "PSO-DS: a scheduling engine for scientific workflow managers," JOURNAL OF SUPERCOMPUTING, 73(9), pp. 3924-3947, September, 2017.   DOI
39 Kalra Mala and Singh Sarbjeet, "A review of metaheuristic scheduling techniques in Cloud computing," Egyption Informatics Journal, vol. 16, no. 3, pp. 275-295, August, 2015.   DOI
40 Young-Choon Lee and Albert Zomaya, "A Novel State Transition Method for Metaheuristic-Based Scheduling in Heterogeneous Computing Systems," IEE Transactions on Parallel and Distributed Systems, 19(9), pp. 1215-1223, September, 2008.   DOI
41 U Jaiswal and S A Ggarwal, "Ant Colony Optimization," International Journal of Scientific & Engineering Research, 2(7), pp. 2229-5518, July, 2011. https://www.ijser.org/researchpaper/ant_colony_optimization.pdf
42 M edhat Tawfeek, Arabi Keshk, Ashraf EI-Sisi and Fawzy A. Torket, "Cloud Task Scheduling Based on Ant Colony Optimization," INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY, 12(2), pp. 64-69, November, 2013.
43 Gao Ying, Duan Jiajie and Shu Wanneng, "A Novel Ant Optimization Algorithm for Task Scheduling and Resource Allocation in Cloud Computing Environment," JOURNA OF INTERNET TECHNOLOGY, 16(7), pp. 1329-1338, January, 2015.