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IEEE Trans Cybern

Title:		Multiobjective Cloud Workflow Scheduling: A Multiple Populations Ant Colony System Approach
Author(s):		Chen ZG; Zhan ZH; Lin Y; Gong YJ; Gu TL; Zhao F; Yuan HQ; Chen X; Li Q; Zhang J;
Address:
Journal Title:		IEEE Trans Cybern
Year:		2019
Volume:		20180518
Issue:		8
Page Number:		2912 - 2926
DOI:		10.1109/TCYB.2018.2832640
ISSN/ISBN:		2168-2275 (Electronic) 2168-2267 (Linking)
Abstract:		"Cloud workflow scheduling is significantly challenging due to not only the large scale of workflow but also the elasticity and heterogeneity of cloud resources. Moreover, the pricing model of clouds makes the execution time and execution cost two critical issues in the scheduling. This paper models the cloud workflow scheduling as a multiobjective optimization problem that optimizes both execution time and execution cost. A novel multiobjective ant colony system based on a co-evolutionary multiple populations for multiple objectives framework is proposed, which adopts two colonies to deal with these two objectives, respectively. Moreover, the proposed approach incorporates with the following three novel designs to efficiently deal with the multiobjective challenges: 1) a new pheromone update rule based on a set of nondominated solutions from a global archive to guide each colony to search its optimization objective sufficiently; 2) a complementary heuristic strategy to avoid a colony only focusing on its corresponding single optimization objective, cooperating with the pheromone update rule to balance the search of both objectives; and 3) an elite study strategy to improve the solution quality of the global archive to help further approach the global Pareto front. Experimental simulations are conducted on five types of real-world scientific workflows and consider the properties of Amazon EC2 cloud platform. The experimental results show that the proposed algorithm performs better than both some state-of-the-art multiobjective optimization approaches and the constrained optimization approaches"
Keywords:
Notes:		"PubMed-not-MEDLINEChen, Zong-Gan Zhan, Zhi-Hui Lin, Ying Gong, Yue-Jiao Gu, Tian-Long Zhao, Feng Yuan, Hua-Qiang Chen, Xiaofeng Li, Qing Zhang, Jun eng 2018/07/12 IEEE Trans Cybern. 2019 Aug; 49(8):2912-2926. doi: 10.1109/TCYB.2018.2832640. Epub 2018 May 18"