Multi-objective Optimization for Cloud Task Scheduling Based on the ANP Model

LI Kunlun; WANG Jun

doi:10.1049/cje.2017.07.019

Volume 26 Issue 5

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Article Navigation > Chinese Journal of Electronics > 2017 > 26(5): 889-898

LI Kunlun and WANG Jun, “Multi-objective Optimization for Cloud Task Scheduling Based on the ANP Model,” Chinese Journal of Electronics, vol. 26, no. 5, pp. 889-898, 2017, doi: 10.1049/cje.2017.07.019

Citation:

LI Kunlun and WANG Jun, “Multi-objective Optimization for Cloud Task Scheduling Based on the ANP Model,” Chinese Journal of Electronics, vol. 26, no. 5, pp. 889-898, 2017, doi: 10.1049/cje.2017.07.019

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Multi-objective Optimization for Cloud Task Scheduling Based on the ANP Model

doi: 10.1049/cje.2017.07.019

LI Kunlun,
WANG Jun

Electronic Information Engeering College, Hebei University, Baoding 071002, China

Funds: This work is supported by the National Science and Technology Support Program (No.2013BAK07B00), the Natural Science Foundation of Heibei Province of China (No.F2013201170), and the Educational Commission of Hebei Province of China (No.ZD2014008).

Received Date: 2015-07-23
Rev Recd Date: 2015-12-06
Publish Date: 2017-09-10

Abstract

Abstract

We propose a multi-objective optimization algorithm for cloud task scheduling based on the Analytic network process (ANP) model to solve the problems in cloud task scheduling, such as the deficiencies of mathematical description, limited optimization abilities of the traditional multi-objective optimization algorithm and the selection of the Pareto optimal solutions. Firstly, we present the mathematical description of cloud task scheduling using matrix theory. Then, the improved Nondominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) multiobjective evolutionary algorithm whose optimization ability is improved by Gene expression programming (GEP) algorithm has been introduced into the cloud task scheduling field to search the Pareto set among multi-objects. Finally, ANP model has been combined with the improved NSGA-Ⅱ to solve the selection problems of Pareto solutions. Comparing with the multi-objective optimization algorithm based on the weighted polynomial, the proposed algorithm can optimize multiple goals at the same time, and can avoid the additional iterations due to the change of users preferences effectively. The simulation results indicate that the proposed algorithm is effective.
- Cloud computing,
- Multi-objective task scheduling,
- NSGA-II algorithm,
- ANP model,
- GEP algorithm

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References

MELL P, GRANCE T, "The NIST definition of cloud computing", National Institute of Standards and Technology, available at http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf, 2011.

LUO Jun-zhou, JIN Jia-hui, SONG Ai-bo, DONG Fang, "Cloud computing:architecture and key technologies", Journal on communications, Vol.32, No.7, pp.3-21, 2011.

Teena Mathew, K. Chandra Sekaran, John Jose, "Study and analysis of various task scheduling algorithms in the cloud computing environment", International Conference on Advances in Computing, Communications and Informatics (ICACCI), New Delhi, IEEE, pp.658-664, 2014.

Cândida Ferreira, "Gene expression programming:A new adaptive algorithm for solving problems", Complex Systems, Vol.13, No.2, pp.87-129, 2001.

Chen Chuan, "A new live virtual machine migration strategy", International symposium on information technology in medicine and education, Hokodate, Hokkaido, Japan, IEEE, pp.173-176, 2012.

WANG Yang, ZHENG Peng, LI Dong, ZHANG Hua-liang, YU Hai-bin, "A performance modeling of decentralized cloud computing based on multiple M/M/m/n+M queuing systems", Acta Electronica Sinica, Vol.42, No.10, pp.2055-2059, 2014.

Qi Zang, Zhani M.F, Boutaba R, "Dynamic heterogeneity-Aware resource provisioning in the cloud", the Cloud Computing IEEE Transactions, Vol.2, No.1, pp.14-28, 2014.

Arya L.K, Verma A, "Workflow scheduling algorithms in cloud environment-A survey", Recent Advances in Engineering and Computational Sciences, Chandigarh, IEEE, pp.1-4, 2014.

Wang Dengke, Li Zhong, "A task scheduling algorithm based on PSO and ACO for cloud computing", Computer Application and Software, Vol.30, No.1, pp.290-293, 2013.

LI Jian-feng, PENG Jian, "Task scheduling algorithm based on improved genetic algorithm in cloud computing environment", Journal of Computer Applications, Vol.31, No.1, pp.184-186, 2011.

ZHU Zongbin, DU Zhongjun, "Improved GA-based task scheduling algorithm in cloud computing", Computer Engineering and Applications, Vol.49, No.5, pp.77-80, 2013.

Xingquan Zuo, Guoxiang Zhang, Wei Tan, "Self-adaptive learning PSO-based deadline constrained task scheduling for hybrid IaaS Cloud", IEEE Transactions on Automation Science and Engineering, Vol.11, No.2, pp.564-573, 2014.

ZHANG Xiaodong, CUI Xiaoyan, ZHENG Shizhuo, "Heuristic task scheduling algorithm based on rational ant colony optimization", Chinese Journal of Electronics, Vol.23, No.2, pp.311-314, 2014.

JH Holland, "Classifier systems and genetic algorithms", Artificial Intelligence, Elsevier, pp.235-282, 1989.

Marco Dorigo, "Ant systerm:Optimization by a colony of cooperating agents", IEEE Transactions on Systems, Man, and Cybernetics-Part B:Cybernetics, Vol.26, No.1, pp.29-41, 1996.

Marco Dorigo, "Ant colony system:A cooperative learning approach to the traveling salesman problem", IEEE Transactions on Evolutionary Computation, Vol.1, No.1, pp.53-66, 1997.

LI Qiang, HAO Qin-Fen, XIAO Li-Min, LI Zhou-Jun, "Adaptive management and multi-objective optimization for virtual machine placement in cloud computing", Chinese Journal of Computers, Vol.34, No.12, pp.2253-2264, 2011.

Mingyue Feng, Xiao Wang, Yongjin Zhang, Jianshi Li, "Multiobjective particle swarm optimization for resource allocation in cloud computing", Cloud Computing and Intelligent Systems (CCIS), Hangzhou, IEEE, pp.1161-1165, 2012.

ZHANG Shi-wen, LI Zhi-yong, LIN Ya-ping, "A multi-objective memetic optimization algorithm based on ecological population preying-competition model", Acta Electronica Sinica, Vol.43, No.8, pp.1488-1498, 2015. (in Chinese)

ZNing Liu, Ziqian Dong, Roberto Rojas-Cessa, "Task scheduling and server provisioning for energy-efficient cloud computing data centers", Distributed Computing Systems Workshops (ICDCSW), Philadelphia, IEEE, pp.226-231, 2013.

Xiaomin Zhu, Laurence T. Yang, Huangke Chen, Ji Wang, Shu Yin, Xiaocheng Liu, "Real-time tasks oriented energy-aware scheduling in virtualized clouds", IEEE Transactions on Cloud Computing, Vol.2, No.2, pp.168-180, 2014.

MA Yan, GONG Bin, GUO Zhihong, CHEN Yunfeng, ZOU Lida, "Energy-aware scheduling of parallel application in hybrid computing system", Chinese Journal of Electronics, Vol.23, No.4, pp.688-694, 2014.

LIN Chuang, LI Yin, WAN Jian-Xiong, "Optimization approaches for QoS in computer networks:A survey", Chinese Journal of Computers, Vol.34, No.1, pp.1-14, 2011.

LIN Chuang, TIAN Yuan, YAO Min, "Green network and green evaluation:Mechanism, modeling and evaluation", Chinese Journal of Computers, Vol.34, No.4, pp.593-612, 2011.

Deb K, Pratap A, Agarwal S, et al., "A fast and elitist multiobjective genetic algorithm:NSGA-Ⅱ", IEEE Transactions on Evolutionary Computation, Vol.6, No.2, pp.182-197, 2002.

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