Soft Computing

, Volume 21, Issue 15, pp 4309–4322 | Cite as

Endocrine-based coevolutionary multi-swarm for multi-objective workflow scheduling in a cloud system

  • Guangshun Yao
  • Yongsheng Ding
  • Yaochu Jin
  • Kuangrong Hao
Methodologies and Application


The workflow scheduling with multiple objectives is a well-known NP-complete problem, and even more complex and challenging when the workflow is executed in cloud computing system. In this study, an endocrine-based coevolutionary multi-swarm for multi-objective optimization algorithm (ECMSMOO) is proposed to satisfy multiple scheduling conflicting objectives, such as the total execution time (makespan), cost, and energy consumption. To avoid the influence of elastic available resources, a manager server is adopted to collect the available resources for scheduling. In ECMSMOO, multi-swarms are adopted and each swarm employs improved multi-objective particle swarm optimization to find out non-dominated solutions with one objective. To avoid falling into local optima which is common in traditional heuristic algorithms, an endocrine-inspired mechanism is embedded in the particles’ evolution process. Furthermore, a competition and cooperation technique among swarms is designed in the ECMSMOO. All these strategies effectively improve the performance of ECMSMOO. We compare the quality of the proposed method with other algorithms for multi-objective task scheduling by hybrid and parallel workflow jobs. The results highlight the better performance of the proposed approach than that of the compared algorithms.


Multi-objective workflow scheduling Multi-swarm Particle swarm optimization Endocrine regulation mechanism Cloud computing system 



This work was supported in part by the Key Project of the National Nature Science Foundation of China (No. 61134009), the National Nature Science Foundation of China (Nos. 61473077, 61473078), Cooperative research funds of the National Natural Science Funds Overseas and Hong Kong and Macao scholars (No. 61428302), Program for Changjiang Scholars from the Ministry of Education, Specialized Research Fund for Shanghai Leading Talents, Project of the Shanghai Committee of Science and Technology (No. 13JC1407500), Innovation Program of Shanghai Municipal Education Commission (No. 14ZZ067), the Fundamental Research Funds for the Central Universities (15D110423), Natural Science Foundation of Anhui Province (No. 1508085MF123), and Key Project of Anhui University Science Research (No. KJ2015A190).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Abramson D, Buyya R, Giddy J (2002) A computational economy for grid computing and its implementation in the Nimrod-G resource broker [J]. Future Gener Comput Syst 18(8):1061–1074CrossRefzbMATHGoogle Scholar
  2. Berman F, Wolski R, Casanova H et al (2003) Adaptive computing on the grid using AppLeS [J]. IEEE Trans Parallel Distrib Syst 14(4):369–382CrossRefGoogle Scholar
  3. Braun TD, Siegel HJ, Beck N et al (2001) A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems [J]. J Parallel Distrib Comput 61(6):810–837CrossRefGoogle Scholar
  4. Brooks DM, Bose P, Schuster SE et al (2000) Power-aware microarchitecture: design and modeling challenges for next-generation microprocessors [J]. IEEE Micro 20(6):26–44CrossRefGoogle Scholar
  5. Buyya R, Yeo CS, Venugopal S et al (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility [J]. Future Gener Comput Syst 25(6):599–616CrossRefGoogle Scholar
  6. Chen CL, Huang SY, Tzeng YR et al (2014) A revised discrete particle swarm optimization algorithm for permutation flow-shop scheduling problem [J]. Soft Comput 18(11):2271–2282CrossRefGoogle Scholar
  7. Chen W, Deelman E (2012) Workflowsim: a toolkit for simulating scientific workflows in distributed environments [C]. In: 2012 IEEE 8th international conference on e-science (e-science), pp 1–8Google Scholar
  8. Cheng J, Zhang G, Li Z et al (2012) Multi-objective ant colony optimization based on decomposition for bi-objective traveling salesman problems [J]. Soft Comput 16(4):597–614CrossRefzbMATHGoogle Scholar
  9. Chen WN, Zhang J (2009) An ant colony optimization approach to a grid workflow scheduling problem with various QoS requirements [J]. IEEE Trans Syst Man Cybern Part C Appl Rev 39(1):29–43CrossRefGoogle Scholar
  10. Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization [J]. IEEE Trans Evol Comput 8(3):256–279CrossRefGoogle Scholar
  11. Coello CCA (2006) Evolutionary multi-objective optimization: a historical view of the field [J]. IEEE Comput Intell Mag 1(1):28–36MathSciNetCrossRefGoogle Scholar
  12. Deb K, Pratap A, Agarwal S et al (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II [J]. IEEE Trans Evol Comput 6(2):182–197CrossRefGoogle Scholar
  13. Deelman E, Vahi K, Juve G et al (2015) Pegasus, a workflow management system for science automation [J]. Future Gener Comput Syst 46:17–35CrossRefGoogle Scholar
  14. Durillo JJ, Nae V, Prodan R (2014) Multi-objective energy-efficient workflow scheduling using list-based heuristics [J]. Future Gener Comput Syst 36:221–236CrossRefGoogle Scholar
  15. Durillo JJ, Prodan R (2014) Multi-objective workflow scheduling in Amazon EC2 [J]. Clust Comput 17(2):169–189CrossRefGoogle Scholar
  16. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory [C]. In: The 6th international symposium on micro machine and human science, pp 39–43Google Scholar
  17. Fard HM, Prodan R, Fahringer T (2014) Multi-objective list scheduling of workflow applications in distributed computing infrastructures [J]. J Parallel Distrib Comput 74(3):2152–2165CrossRefzbMATHGoogle Scholar
  18. Frey J, Tannenbaum T, Livny M et al (2002) Condor-G: a computation management agent for multi-institutional grids [J]. Clust Comput 5(3):237–246CrossRefGoogle Scholar
  19. Gao L, Hailu A (2010) Comprehensive learning particle swarm optimizer for constrained mixed-variable optimization problems [J]. Int J Comput Intell Syst 3(6):832–842CrossRefGoogle Scholar
  20. Garg SK, Buyya R, Siegel HJ (2009) Scheduling parallel applications on utility grids: time and cost trade-off management [C]. In: Proceedings of the thirty-second Australasian conference on computer science, vol 91. Australian Computer Society Inc, pp 151–160Google Scholar
  21. Gómez J, Gil C, Baños R et al (2013) A Pareto-based multi-objective evolutionary algorithm for automatic rule generation in network intrusion detection systems [J]. Soft Comput 17(2):255–263CrossRefGoogle Scholar
  22. Hu Y-F, Ding Y-S, Hao K-R et al (2014) An immune orthogonal learning particle swarm optimization algorithm for routing recovery of wireless sensor networks with mobile sink [J]. Int J Syst Sci 45(3):337–350CrossRefzbMATHGoogle Scholar
  23. Hu Y-F, Ding Y-S, Ren L-H et al (2015) An endocrine cooperative particle swarm optimization algorithm for routing recovery of wireless sensor networks with multiple mobile sinks [J]. Inf Sci 300:100–113CrossRefGoogle Scholar
  24. James K, Russell E (1995) Particle swarm optimization [C]. Proc IEEE Int Conf Neural Netw 1995:1942–1948Google Scholar
  25. Juve G, Chervenak A, Deelman E et al (2013) Characterizing and profiling scientific workflows [J]. Future Gener Comput Syst 29(3):682–692CrossRefGoogle Scholar
  26. Liu D, Tan KC, Goh CK et al (2007) A multiobjective memetic algorithm based on particle swarm optimization [J]. IEEE Trans Syst Man Cybern Part B Cybern 37(1):42–50CrossRefGoogle Scholar
  27. Subrata R, Zomaya AY, Landfeldt B (2008) A cooperative game framework for QoS guided job allocation schemes in grids [J]. IEEE Trans Comput 57(10):1413–1422MathSciNetCrossRefGoogle Scholar
  28. Tao F, Feng Y, Zhang L et al (2014) CLPS-GA: a case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling [J]. Appl Soft Comput 19:264–279CrossRefGoogle Scholar
  29. Teng S, Hay LL, Peng CE (2007) Multi-objective ordinal optimization for simulation optimization problems [J]. Automatica 43(11):1884–1895MathSciNetCrossRefzbMATHGoogle Scholar
  30. Topcuoglu H, Hariri S, Wu M (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing [J]. IEEE Trans Parallel Distrib Syst 13(3):260–274CrossRefGoogle Scholar
  31. Viswanathan S, Veeravalli B, Robertazzi TG (2007) Resource-aware distributed scheduling strategies for large-scale computational cluster/grid systems [J]. IEEE Trans Parallel Distrib Syst 18(10):1450–1461CrossRefGoogle Scholar
  32. Wieczorek M, Hoheisel A, Prodan R (2009) Towards a general model of the multi-criteria workflow scheduling on the grid [J]. Future Gener Comput Syst 25(3):237–256CrossRefGoogle Scholar
  33. Yassa S, Chelouah R, Kadima H et al (2013) Multi-objective approach for energy-aware workflow scheduling in Cloud computing environments [J]. Sci World J Article ID 350934:1–13Google Scholar
  34. Yazdani D, Nasiri B, Sepas-Moghaddam A et al (2013) A novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization [J]. Appl Soft Comput 13(4):2144–2158CrossRefGoogle Scholar
  35. Zhan ZH, Li J, Cao J et al (2013) Multiple populations for multiple objectives: a coevolutionary technique for solving multiobjective optimization problems [J]. IEEE Trans Cybern 43(2):445–463CrossRefGoogle Scholar
  36. Zhang Y, Gong D, Ding Z (2011) Handling multi-objective optimization problems with a multi-swarm cooperative particle swarm optimizer [J]. Expert Syst Appl 38(11):13933–13941Google Scholar
  37. Zhang F, Cao J, Li K et al (2014) Multi-objective scheduling of many tasks in cloud platforms [J]. Future Gener Comput Syst 37:309–320CrossRefGoogle Scholar
  38. Zheng W, Sakellariou R (2013) Budget-deadline constrained workflow planning for admission control [J]. J Grid Comput 11(4):633–651CrossRefGoogle Scholar
  39. Zitzler E, Thiele L, Laumanns M et al (2003) Performance assessment of multiobjective optimizers: an analysis and review [J]. IEEE Trans Evol Comput 7(2):117–132CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Guangshun Yao
    • 1
    • 2
  • Yongsheng Ding
    • 1
  • Yaochu Jin
    • 1
    • 3
  • Kuangrong Hao
    • 1
  1. 1.Engineering Research Center of Digitized Textile and Apparel Technology, Ministry of Education, College of Information Science and TechnologyDonghua UniversityShanghaiPeople’s Republic of China
  2. 2.College of Computer and Information EngineeringChuzhou UniversityChuzhouPeople’s Republic of China
  3. 3.Department of ComputingUniversity of SurreyGuildfordUK

Personalised recommendations