Variable neighborhood search based multiobjective ACO-list scheduling for cloud workflows (original) (raw)

References

  1. Deelman E, Singh G, Su M-H, Blythe J, Gil Y, Kesselman C, Mehta G, Vahi K, Berriman GB, Good J et al (2005) Pegasus: a framework for mapping complex scientific workflows onto distributed systems. Sci Program 13(3):219–237
    Google Scholar
  2. Rodriguez MA, Buyya R (2017) A taxonomy and survey on scheduling algorithms for scientific workflows in IAAS cloud computing environments. Concurren Comput Pract Exp 29(8):4041
    Article Google Scholar
  3. Mell P, Grance T et al (2011) The nist definition of cloud computing. Special Publication, National Institute of Science and Technology
    Book Google Scholar
  4. Masdari M, ValiKardan S, Shahi Z, Azar SI (2016) Towards workflow scheduling in cloud computing: a comprehensive analysis. J Netw Appl 66:64–82
    Article Google Scholar
  5. Wang Y, Zuo X (2021) An effective cloud workflow scheduling approach combining PSA and idle time slot-aware rules. IEEE/CAA J Autom Sinica 8(5):1079–1094
    Article Google Scholar
  6. Xu Y, Li K, Hu J, Li K (2014) A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Inf Sci 270:255–287
    Article MathSciNet MATH Google Scholar
  7. Zhan Z-H, Liu X-F, Gong Y-J, Zhang J, Chung HS-H, Li Y (2015) Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Comput Surv 47(4):1–33
    Article Google Scholar
  8. Zhu Z, Zhang G, Li M, Liu X (2016) Evolutionary multi-objective workflow scheduling in cloud. IEEE Trans Parallel Distrib Syst 27(5):1344–1357
    Article Google Scholar
  9. Verma A, Kaushal S (2017) A hybrid multi-objective particle swarm optimization for scientific workflow scheduling. Parallel Comput 62:1–19
    Article MathSciNet Google Scholar
  10. Chen Z-G, Zhan Z-H, Lin Y, Gong Y-J, Gu T-L, Zhao F, Yuan H-Q, Chen X, Li Q, Zhang J (2019) Multiobjective cloud workflow scheduling: a multiple populations ant colony system approach. IEEE Trans Cybern 49(8):2912–2926. https://doi.org/10.1109/TCYB.2018.2832640
    Article Google Scholar
  11. Alba E (2005) Parallel metaheuristics: a new class of algorithms. Wiley, Hoboken
    Book MATH Google Scholar
  12. Hansen P, Mladenović N, Brimberg J, Pérez JAM (2019) Variable neighborhood search. In: Handbook of Metaheuristics, Springer, Cham, pp 57–97
  13. Angus D, Woodward C (2009) Multiple objective ant colony optimisation. Swarm Intell 3(1):69–85
    Article Google Scholar
  14. Pinedo ML (2016) Scheduling: theory, algorithms, and systems. Springer, Cham
    Book MATH Google Scholar
  15. Yu J, Buyya R, Ramamohanarao K (2008) In: Xhafa, F., Abraham, A. (eds.) Workflow scheduling algorithms for grid computing, pp. 173–214. Springer, Berlin. https://doi.org/10.1007/978-3-540-69277-5_7
  16. Durillo JJ, Prodan R (2014) Multi-objective workflow scheduling in Amazon EC2. Cluster Comput 17(2):169–189
    Article Google Scholar
  17. Han P, Du C, Chen J, Ling F, Du X (2021) Cost and makespan scheduling of workflows in clouds using list multiobjective optimization technique. J Syst Archit 112:101837
    Article Google Scholar
  18. Zhou X, Zhang G, Sun J, Zhou J, Wei T, Hu S (2019) Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based heft. Future Gener Comput Syst 93:278–289
    Article Google Scholar
  19. Ismayilov G, Topcuoglu HR (2020) Neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing. Future Gener Comput Syst 102:307–322
    Article Google Scholar
  20. Wu Q, Zhou M, Zhu Q, Xia Y, Wen J (2019) Moels: Multiobjective evolutionary list scheduling for cloud workflows. IEEE Trans Autom Sci Eng 17(1):166–176
    Article Google Scholar
  21. Zhang M, Li H, Liu L, Buyya R (2018) An adaptive multi-objective evolutionary algorithm for constrained workflow scheduling in clouds. Distrib Parallel Databases 36(2):339–368
    Article Google Scholar
  22. Yao G, Ding Y, Jin Y, Hao K (2017) Endocrine-based coevolutionary multi-swarm for multi-objective workflow scheduling in a cloud system. Soft Comput 21(15):4309–4322
    Article Google Scholar
  23. Anwar N, Deng H (2018) A hybrid metaheuristic for multi-objective scientific workflow scheduling in a cloud environment. Appl Sci 8(4):538
    Article Google Scholar
  24. Topcuoglu H, Hariri S, Wu M-Y (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst 13(3):260–274
    Article Google Scholar
  25. Wen Y, Xu H, Yang J (2011) A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system. Inf Sci 181(3):567–581
    Article Google Scholar
  26. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197
    Article Google Scholar
  27. Rodriguez MA, Buyya R (2014) Deadline based resource provisioningand scheduling algorithm for scientific workflows on clouds. IEEE Trans Cloud Comput 2(2):222–235
    Article Google Scholar
  28. Liu L, Zhang M, Buyya R, Fan Q (2017) Deadline-constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing. Concurr Comput-Pract Exp 29(5):3942
    Article Google Scholar
  29. Abrishami S, Naghibzadeh M, Epema DH (2013) Deadline-constrained workflow scheduling algorithms for infrastructure as a service clouds. Future Gener Comput Syst 29(1):158–169
    Article Google Scholar
  30. Wu Q, Ishikawa F, Zhu Q, Xia Y, Wen J (2017) Deadline-constrained cost optimization approaches for workflow scheduling in clouds. IEEE Trans Parallel Distrib Syst 28(12):3401–3412
    Article Google Scholar
  31. Chen Z-G, Du K-J, Zhan Z-H, Zhang J (2015) Deadline constrained cloud computing resources scheduling for cost optimization based on dynamic objective genetic algorithm. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 708–714. https://doi.org/10.1109/CEC.2015.7256960
  32. Zuo X, Zhang G, Tan W (2013) Self-adaptive learning pso-based deadline constrained task scheduling for hybrid IAAS cloud. IEEE Trans Autom Sci Eng 11(2):564–573
    Article Google Scholar
  33. Jia Y-H, Chen W-N, Yuan H, Gu T, Zhang H, Gao Y, Zhang J (2021) An intelligent cloud workflow scheduling system with time estimation and adaptive ant colony optimization. IEEE Trans Syst Man Cybern Syst 51(1):634–649. https://doi.org/10.1109/TSMC.2018.2881018
    Article Google Scholar
  34. Arabnejad H, Barbosa JG (2014) A budget constrained scheduling algorithm for workflow applications. J Grid Comput 12(4):665–679
    Article Google Scholar
  35. Faragardi HR, Sedghpour MRS, Fazliahmadi S, Fahringer T, Rasouli N (2019) Grp-heft: a budget-constrained resource provisioning scheme for workflow scheduling in IAAS clouds. IEEE Trans Parallel Distrib Sys 31(6):1239–1254
    Article Google Scholar
  36. Zheng W, Sakellariou R (2013) Budget-deadline constrained workflow planning for admission control. J Grid Comput 11(4):633–651
    Article Google Scholar
  37. Li J, Su S, Cheng X, Huang Q, Zhang Z (2011) Cost-conscious scheduling for large graph processing in the cloud. In: 2011 IEEE International Conference on High Performance Computing and Communications, pp 808–813. https://doi.org/10.1109/HPCC.2011.147
  38. Su S, Li J, Huang Q, Huang X, Shuang K, Wang J (2013) Cost-efficient task scheduling for executing large programs in the cloud. Parallel Comput 39(4–5):177–188
    Article Google Scholar
  39. Choudhary A, Gupta I, Singh V, Jana PK (2018) A GSA based hybrid algorithm for bi-objective workflow scheduling in cloud computing. Future Gener Comput Syst 83:14–26
    Article Google Scholar
  40. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66
    Article Google Scholar
  41. Herbst NR, Kounev S, Reussner R (2013) Elasticity in cloud computing: What it is, and what it is not. In: Proceedings of the 10th International Conference on Autonomic Computing (ICAC 2013), pp 23–27
  42. Juve G, Chervenak A, Deelman E, Bharathi S, Mehta G, Vahi K (2013) Characterizing and profiling scientific workflows. Future Gener Comput Syst 29(3):682–692
    Article Google Scholar
  43. Amazon Elastic Compute Cloud (2021). https://aws.amazon.com/cn/ec2/
  44. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans Evol Comput 3(4):257–271
    Article Google Scholar
  45. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18
    Article Google Scholar

Download references