Skip to main content

A Survey of PSO-Based Scheduling Algorithms in Cloud Computing

Abstract

Cloud computing provides effective mechanisms for distributing the computing tasks to the virtual resources. To provide cost-effective executions and achieve objectives such as load balancing, availability and reliability in the cloud environment, appropriate task and workflow scheduling solutions are needed. Various metaheuristic algorithms are applied to deal with the problem of scheduling, which is an NP-hard problem. This paper presents an in-depth analysis of the Particle Swarm Optimization (PSO)-based task and workflow scheduling schemes proposed for the cloud environment in the literature. Moreover, it provides a classification of the proposed scheduling schemes based on the type of the PSO algorithms which have been applied in these schemes and illuminates their objectives, properties and limitations. Finally, the critical future research directions are outlined.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

References

  1. 1.

    Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J. Internet Serv. Appl. 1, 7–18 (2010)

    Article  Google Scholar 

  2. 2.

    Zeng, L., Veeravalli, B., Zomaya, A.Y.: An integrated task computation and data management scheduling strategy for workflow applications in cloud environments. J. Netw. Comput. Appl. 50, 39–48 (2015)

    Article  Google Scholar 

  3. 3.

    Pandey S., Wu L., Guru S.M., Buyya R.: A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments, In: Advanced Information Networking and Applications (AINA), 2010 24th IEEE International Conference on, 2010, pp. 400–407

  4. 4.

    Masdari, M., Nabavi, S.S., Ahmadi, V.: An overview of virtual machine placement schemes in cloud computing. J. Netw. Comput. Appl. 66, 106–127 (2016)

  5. 5.

    Masdari, M., ValiKardan, S., Shahi, Z., Azar, S.I.: Towards workflow scheduling in cloud computing: a comprehensive analysis. J. Netw. Comput. Appl. 66, 64–82 (2016)

  6. 6.

    Bessai K., Youcef S., Oulamara A., Godart C., Nurcan S.: Bi-criteria workflow tasks allocation and scheduling in Cloud computing environments, In: Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on, 2012, pp. 638–645

  7. 7.

    Dillon T., Wu C., Chang E.: Cloud computing: issues and challenges, In: Advanced Information Networking and Applications (AINA), 2010 24th IEEE International Conference on, 2010, pp. 27–33

  8. 8.

    Foster I, Zhao Y, Raicu I., Lu S.: Cloud computing and grid computing 360° compared, In: Grid Computing Environments Workshop, 2008. GCE’08, 2008, pp. 1–10

  9. 9.

    Manvi, S.S., Shyam, G.K.: Resource management for Infrastructure as a Service (IaaS) in cloud computing: a survey. J. Netw. Comput. Appl. 41, 424–440 (2014)

    Article  Google Scholar 

  10. 10.

    Malhotra, L., Agarwal, D., Jaiswal, A.: Virtualization in cloud computing. J. Inform. Tech. Softw. Eng. 4, 2 (2014)

    Google Scholar 

  11. 11.

    Kong, X., Lin, C., Jiang, Y., Yan, W., Chu, X.: Efficient dynamic task scheduling in virtualized data centers with fuzzy prediction. J. Netw. Comput. Appl. 34, 1068–1077 (2011)

    Article  Google Scholar 

  12. 12.

    Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25, 599–616 (2009)

    Article  Google Scholar 

  13. 13.

    Ahmad, R.W., Gani, A., Hamid, S.H.A., Shiraz, M., Yousafzai, A., Xia, F.: A survey on virtual machine migration and server consolidation frameworks for cloud data centers. J. Netw. Comput. Appl. 52, 11–25 (2015)

    Article  Google Scholar 

  14. 14.

    Arya L.K., Verma A.: Workflow scheduling algorithms in cloud environment-A survey, In: Engineering and Computational Sciences (RAECS), 2014 Recent Advances in, 2014, pp. 1–4

  15. 15.

    Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., et al.: A view of cloud computing. Commun. ACM 53, 50–58 (2010)

    Article  Google Scholar 

  16. 16.

    Ramezani F., Lu J., Hussain F.: Task scheduling optimization in cloud computing applying multi-objective particle swarm optimization, in Service-Oriented Computing, pp. 237–251. Springer (2013)

  17. 17.

    Nallakumar R., Sruthi Priya K.S.: A survey on scheduling and the attributes of task scheduling in the cloud. Int. J. Adv. Res. Comput. Commun. Eng. 3, 8167–8171 (2014)

  18. 18.

    Wang L., Ai L.: Task scheduling policy based on ant colony optimization in cloud computing environment, In: LISS 2012, pp. 953–957. Springer (2013)

  19. 19.

    Savitha, P., Reddy, J.G.: A review work on task scheduling in cloud computing using genetic algorithm. Int. J. Sci. Technol. Res. 2, 241–245 (2013)

    Google Scholar 

  20. 20.

    Garg, S.K., Toosi, A.N., Gopalaiyengar, S.K., Buyya, R.: SLA-based virtual machine management for heterogeneous workloads in a cloud datacenter. J. Netw. Comput. Appl. 45, 108–120 (2014)

    Article  Google Scholar 

  21. 21.

    Zhong H., Tao K., Zhang X.: An approach to optimized resource scheduling algorithm for open-source cloud systems, In: ChinaGrid Conference (ChinaGrid), 2010 Fifth Annual, 2010, pp. 124–129

  22. 22.

    Bala A., Chana I.: A survey of various workflow scheduling algorithms in cloud environment, In: 2nd National Conference on Information and Communication Technology (NCICT) (2011)

  23. 23.

    Liu L., Zhang M., Lin Y., Qin L.: A survey on workflow management and scheduling in cloud computing, In Cluster, Cloud and Grid Computing (CCGrid), 2014 14th IEEE/ACM International Symposium on, 2014, pp. 837–846

  24. 24.

    Shimpy E., Sidhu M.J.: Different scheduling algorithms in different cloud environment, Algorithms 3, 8003–8006 (2014)

  25. 25.

    Jafarpour B., Meybodi M., Shiry S.: A hybrid method for optimization (discrete PSO + CLA), In: Intelligent and Advanced Systems, 2007. ICIAS 2007. International Conference on, 2007, pp. 55–60

  26. 26.

    Gendreau, M., Potvin, J.-Y.: Metaheuristics in combinatorial optimization. Ann. Oper. Res. 140, 189–213 (2005)

    MathSciNet  Article  MATH  Google Scholar 

  27. 27.

    Tsai, C.-W., Rodrigues, J.J.: Metaheuristic scheduling for cloud: A survey. IEEE Syst. J. 8, 279–291 (2014)

    Article  Google Scholar 

  28. 28.

    Shelokar, P., Siarry, P., Jayaraman, V.K., Kulkarni, B.D.: Particle swarm and ant colony algorithms hybridized for improved continuous optimization. Appl. Math. Comput. 188, 129–142 (2007)

    MathSciNet  MATH  Google Scholar 

  29. 29.

    Huang J., Wu K., Leong L. K., Ma S., Moh M.: A tunable workflow scheduling algorithm based on particle swarm optimization for cloud computing. Criterion 12, 14 (2013)

  30. 30.

    Eberhart R.C., Shi Y.: Particle swarm optimization: developments, applications and resources, In: Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, 2001, pp. 81–86

  31. 31.

    Wen X., Huang M., Shi J.: Study on Resources Scheduling Based on ACO Allgorithm and PSO Algorithm in Cloud Computing, In: Distributed Computing and Applications to Business, Engineering & Science (DCABES), 2012 11th International Symposium on, 2012, pp. 219–222

  32. 32.

    Zhang H., Li P., Zhou Z., Yu X.: A PSO-Based Hierarchical Resource Scheduling Strategy on Cloud Computing, in Trustworthy Computing and Services, pp. 325–332. Springer (2013)

  33. 33.

    Shi Y., Eberhart R.: A modified particle swarm optimizer, In: Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence, The 1998 IEEE International Conference on, 1998, pp. 69–73

  34. 34.

    Reyes-Sierra, M., Coello, C.C.: Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int. J. Comput. Intel Res 2, 287–308 (2006)

    MathSciNet  Google Scholar 

  35. 35.

    Du, F., Evans, G.W.: A bi-objective reverse logistics network analysis for post-sale service. Comput. Oper. Res. 35, 2617–2634 (2008)

    Article  MATH  Google Scholar 

  36. 36.

    Liang J.J., Qin A.K., Suganthan P.N., Baskar S.: Particle swarm optimization algorithms with novel learning strategies, In: Systems, Man and Cybernetics, 2004 IEEE International Conference on, 2004, pp. 3659–3664

  37. 37.

    Ismail, A., Jeng, D.: SEANN: a self-evolving neural network based on PSO and JPSO algorithms. J. Hybrid Technol. 1, 17–29 (2013)

    Google Scholar 

  38. 38.

    Lin C., Lu S: Scheduling scientific workflows elastically for cloud computing, in Cloud Computing (CLOUD), 2011 IEEE International Conference on, 2011, pp. 746–747

  39. 39.

    Tripathy, L., Patra, R.R.: Scheduling in cloud computing. Int. J. Cloud Comput. Serv. Archit. (IJCCSA) 4, 21–27 (2014)

    Google Scholar 

  40. 40.

    Li W., Tordsson J., Elmroth E: Modeling for dynamic cloud scheduling via migration of virtual machines, In: Cloud Computing Technology and Science (CloudCom), 2011 IEEE Third International Conference on, 2011, pp. 163–171

  41. 41.

    Nagadevi, S., Satyapriya, K., Malathy, D.: A survey on economic cloud schedulers for optimized task scheduling. Int. J. Advanced Eng. Technol. 5, 58–62 (2013)

    Google Scholar 

  42. 42.

    Patel, R., Mer, H.: A survey of various qos-based task scheduling algorithm in cloud computing environment. Int. J. Sci. Technol. Res. 2, 109–112 (2013)

    Google Scholar 

  43. 43.

    Teng, F.: Ressource Allocation and Scheduling Models for Cloud Computing. Châtenay-Malabry, Ecole centrale de Paris (2011)

    Google Scholar 

  44. 44.

    Buyya R., Yeo C.S., Venugopal S.: Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities, In: High Performance Computing and Communications, 2008. HPCC’08. 10th IEEE International Conference on, 2008, pp. 5–13

  45. 45.

    Buyya, R., Abramson, D., Venugopal, S.: The grid economy. Proc. IEEE 93, 698–714 (2005)

    Article  Google Scholar 

  46. 46.

    Annette, J.R., Banu, W.A., Shriram, S.: A taxonomy and survey of scheduling algorithms in cloud: based on task dependency. Int. J. Comput. Appl. 82, 20–26 (2013)

    Google Scholar 

  47. 47.

    Singh, R., Petriya, P.K.: Workflow Scheduling in Cloud Computing. Int. J. Comput. Appl. 61, 38–40 (2013)

    Google Scholar 

  48. 48.

    Yassa S., Chelouah R., Kadima H., Granado B.: Multi-objective approach for energy-aware workflow scheduling in cloud computing environments. Sci. World J. 2013, 350934 (2013). doi:10.1155/2013/350934

  49. 49.

    Chen W.-N., Zhang J.: A set-based discrete PSO for cloud workflow scheduling with user-defined QoS constraints, In: Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on, 2012, pp. 773–778

  50. 50.

    Bharathi S., Chervenak A., Deelman E., Mehta G., Su M.-H., Vahi K.: Characterization of scientific workflows, In: Workflows in Support of Large-Scale Science, 2008. WORKS 2008. Third Workshop on, 2008, pp. 1–10

  51. 51.

    Patel P., Ranabahu A.H., Sheth A.P.: Service level agreement in cloud computing (2009)

  52. 52.

    Buyya R., Ranjan R., Calheiros R.N.: Intercloud: Utility-oriented federation of cloud computing environments for scaling of application services, In: Algorithms and architectures for parallel processing, pp. 13–31. Springer (2010)

  53. 53.

    Jang, S.H., Kim, T.Y., Kim, J.K., Lee, J.S.: The study of genetic algorithm-based task scheduling for cloud computing. Int. J. Control Autom. 5, 157–162 (2012)

    Google Scholar 

  54. 54.

    Kliazovich D., Arzo S.T., Granelli F., Bouvry P., Khan S.U.: e-STAB: energy-efficient scheduling for cloud computing applications with traffic load balancing, In: Green Computing and Communications (GreenCom), 2013 IEEE and Internet of Things (iThings/CPSCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing, 2013, pp. 7–13

  55. 55.

    Buyya R., Beloglazov A., Abawajy J.: Energy-efficient management of data center resources for cloud computing: A vision, architectural elements, and open challenges, arXiv preprint arXiv:1006.0308, (2010)

  56. 56.

    Guo, L., Zhao, S., Shen, S., Jiang, C.: Task scheduling optimization in cloud computing based on heuristic algorithm. J. Netw. 7, 547–553 (2012)

    Google Scholar 

  57. 57.

    Zhang G., Zuo X.: Deadline constrained task scheduling based on standard-PSO in a hybrid cloud, In: Advances in Swarm Intelligence, pp. 200–209. Springer (2013)

  58. 58.

    Qin, X., Yang, Z., Li, W., Yang, Y.: Optimized task scheduling and resource allocation in cloud computing using PSO based fitness function. Inf. Technol. J. 12, 7090–7095 (2013)

    Article  Google Scholar 

  59. 59.

    Wu Z., Ni Z., Gu L., Liu X.: A revised discrete particle swarm optimization for cloud workflow scheduling, In: Computational Intelligence and Security (CIS), 2010 International Conference on, 2010, pp. 184–188

  60. 60.

    Jianfang, C., Junjie, C., Qingshan, Z.: An optimized scheduling algorithm on a cloud workflow using a discrete particle swarm. Cybern. Inf. Technol. 14, 25–39 (2014)

    MathSciNet  Google Scholar 

  61. 61.

    Demir, E., Bektaş, T., Laporte, G.: The bi-objective pollution-routing problem. Eur. J. Oper. Res. 232, 464–478 (2014)

    MathSciNet  Article  MATH  Google Scholar 

  62. 62.

    Netjinda, N., Achalakul, T., Sirinaovakul, B.: Cloud provisioning for workflow application with deadline using discrete PSO. ECTI Trans. Comput. Inf. Technol. 7, 43–51 (2013)

    Google Scholar 

  63. 63.

    Wang Y., Wang J., Wang C., Song X.: Research on resource scheduling of cloud based on improved particle swarm optimization algorithm, In Advances in Brain Inspired Cognitive Systems, pp. 118–125. Springer (2013)

  64. 64.

    Beegom A.A., Rajasree M.: A particle swarm optimization based pareto optimal task scheduling in cloud computing, In: Advances in Swarm Intelligence, pp. 79–86. Springer (2014)

  65. 65.

    Verma A., Kaushal S.: Bi-Criteria Priority based Particle Swarm Optimization workflow scheduling algorithm for cloud, In: Engineering and Computational Sciences (RAECS), 2014 Recent Advances in, 2014, pp. 1–6

  66. 66.

    Zhan, S., Huo, H.: Improved PSO-based task scheduling algorithm in cloud computing. J. Inf. Comput. Sci. 9, 3821–3829 (2012)

    Google Scholar 

  67. 67.

    Sharma, E.S., Kaur, G.: Optimized utilization of resources using improved particle swarm optimization based task scheduling algorithms in cloud computing. Int. J. Emerg. Technol. Adv. Eng. 4, 110–115 (2014)

    Google Scholar 

  68. 68.

    Visalakshi, P., Sivanandam, S.: Dynamic task scheduling with load balancing using hybrid particle swarm optimization. Int. J. Open Problems Compt. Math. 2, 475–488 (2009)

    Google Scholar 

  69. 69.

    Krishnasamy K., Gomathi B.: Task scheduling algorithm based on hybrid particle swarm optimization in cloud computing environment, J. Theor. Appl. Inf. Technol. 55, 33–38 (2013)

    Google Scholar 

  70. 70.

    Xue, S.-J., Wu, W.: Scheduling workflow in cloud computing based on hybrid particle swarm algorithm. TELKOMNIKA Indones. J. Electr. Eng. 10, 1560–1566 (2012)

    Google Scholar 

  71. 71.

    JieHui, J.U., WeiZheng, B.A.O., ZhongYou, W.A.N.G., Ya, W.A.N.G., WenJuan, L.I.: Research for the task scheduling algorithm optimization based on hybrid PSO and ACO for cloud computing. Int. J. Grid Distrib. Comput. 7(87–96), 2014 (2014)

    Google Scholar 

  72. 72.

    Xiaoguang Y., Tingbin C., Qisong Z.: Research on cloud computing schedule based on improved hybrid PSO, In: Computer Science and Network Technology (ICCSNT), 2013 3rd International Conference on, 2013, pp. 388–391

  73. 73.

    Zuo, X., Zhang, G., Tan, W.: Self-adaptive learning PSO-based deadline constrained task scheduling for hybrid IaaS cloud. IEEE Trans. Autom. Sci. Eng. 11, 564–573 (2014)

    Article  Google Scholar 

  74. 74.

    Chitra S., Madhusudhanan B., Sakthidharan G., Saravanan P.: Local minima jump PSO for workflow scheduling in cloud computing environments, In: Advanced in Computer Science and its Applications, pp. 1225–1234. Springer (2014)

  75. 75.

    Zahraa Tarek M.Z., Omara F.A.: Pso optimization algorithm for task scheduling on the cloud computing environment, Int. J. Comput. Technol. 13 (2014)

  76. 76.

    Zhao, G.: Cost-Aware Scheduling Algorithm Based on PSO in Cloud Computing Environment. International Journal of Grid & Distributed Computing 7, 33–42 (2014)

    Article  Google Scholar 

  77. 77.

    Pragaladan R., Maheswari R.: Improve workflow scheduling technique for novel particle swarm optimization in cloud environment. Int. J. Eng. Res. Gen. Sci. 2, 5 (2014)

  78. 78.

    Solmaz Abdi S.A.M., Sharifian S.: Task scheduling using modified PSO algorithm in cloud computing environment, In: International Conference on Machine Learning, Electrical and Mechanical Engineering (ICMLEME’2014), Dubai, 2014

  79. 79.

    Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41, 23–50 (2011)

    Article  Google Scholar 

  80. 80.

    Calheiros R.N., Ranjan R, De Rose C.A, Buyya R.: Cloudsim: A novel framework for modeling and simulation of cloud computing infrastructures and services, arXiv preprint arXiv:0903.2525, (2009)

  81. 81.

    Cloud A.E.C.: Amazon web services, Retrieved November, vol. 9, (2011)

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Mohammad Masdari.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Masdari, M., Salehi, F., Jalali, M. et al. A Survey of PSO-Based Scheduling Algorithms in Cloud Computing. J Netw Syst Manage 25, 122–158 (2017). https://doi.org/10.1007/s10922-016-9385-9

Download citation

Keywords

  • Task
  • Workflow
  • Meta-heuristic
  • PSO
  • Makespan
  • Cost
  • Load balancing
  • SLA