doi: 10.4304/jsw.9.2.443-450
An Improved Algorithm Based on NSGA-II for Cloud PDTs Scheduling
- 1 Nanjing University of Information Science & Technology, School of Computer and Software, Nanjing, China
- 2 Nanjing University of Information Science & Technology, Jiangsu Engineering Center of Network Monitoring, Nanjing, China
Abstract
Partly dependent tasks (PDTs) scheduling withmulti-objective optimization in cloud computing is anNP-hard problem. Taking the quality of service (QoS)requirements of users that use cloud computing into account,we set the cost and time requirements of handling the PDTsas the multiple objectives and present an improvedalgorithm based on the non-dominated sorting geneticalgorithm-II (NSGA-II) to find the Pareto optimal set of thePDTs scheduling. In this paper, the similar task ordercrossover (STOX) operator is applied to make the evolutionmore efficient while the shift mutation operator is applied inthe process of evolution to avoid the premature convergence.In addition, we present a new method named self-adaptingcrowding distance (SCD) operator, which can improve thediversity of individuals in the Pareto-optimal front. Thesimulation results and analysis show that the proposedalgorithm performs better than NSGA-II in maintaining thediversity and the distribution of the Pareto-optimalsolutions in the cloud PDTs scheduling.
Keywords
- PDTs
- multi-objective optimization
- clou
How to Cite
Shengjun Xue, Fei Liu, and Xiaolong Xu, "An Improved Algorithm Based on NSGA-II for Cloud PDTs Scheduling," Journal of Software, vol. 9, no. 2, pp. 443-450, 2014. https://doi.org/ 10.4304/jsw.9.2.443-450
Copyright & License
Copyright © 2014 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).