Skip to main content
Log in

Chaotic social spider algorithm for load balance aware task scheduling in cloud computing

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In recent years, the revolution of cloud computing has taken the IT business to greater heights with the rapid sharing of vast web resources over the internet. Proficient task scheduling and balanced task distribution is still exists as a major challenging issue in cloud computing system due to dynamic heterogeneous nature of resources and tasks. It is a NP-hard problem where the scheduler needs to find the best optimal virtual machines with minimum makespan and proper resource utilization. The major part of this problem is to design an efficient intelligent searching pattern to schedule the tasks in best virtual available machines. In this paper we propose a meta heuristic algorithm called chaotic social spider algorithm inspired by social spider to tackle the problem of task scheduling in various heterogeneous virtual machines. This paper focus on minimizing overall makespan with effective load balancing by modelling the swarm intelligence of social spider with chaotic inertia weight based random selection. The proposed algorithm prevents the local convergence and explores the global intelligent searching in finding the best optimized virtual machine for the user task among the set of virtual machines with minimum makespan and balanced resource utilization. We have made the simulation and performance evaluation using cloudsim toolkit and compared the results with other swarm intelligent based algorithms such as GA, PSO and ABC. The evaluation results show that there is a major improvement in minimizing the makespan with balanced task distribution.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Aceto, G., Botta, A., de Donato, W., Pescapè, A.: Cloud monitoring: a survey. Int. J. Comput. Netw. 57(9), 2093–2115 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Agarwal, M., Srivastava, G.M.S.: An efficient approach to genetic algorithm for task scheduling in cloud computing environment. Int. J. Inf. Technol. Comput. Sci. 10, 74–79 (2012)

    Google Scholar 

  4. Bölöni, L., Turgut, D.: Value of information based scheduling of cloud computing resources. Fut. Gener. Comput. Syst. 71, 212–220 (2017)

    Article  Google Scholar 

  5. Tawfeek, M.A., El-Sisi, A., Keshk, A.E., Torkey, F.A.: Cloud task scheduling based on ant colony optimization. In: 2013 8th International Conference on Computer Engineering & Systems (ICCES), pp. 64–69

  6. Tsai, J.-T., Fang, J.-C., Chou, J.-H.: Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm. Comput. Oper. Res. 40(12), 3045–3055 (2013)

    Article  MATH  Google Scholar 

  7. Abdullahi, M., Ngadi, M.A.: Symbiotic organism search optimization based task scheduling in cloud computing environment. Fut. Gener. Comput. Syst. 56, 640–650 (2016)

    Article  Google Scholar 

  8. Akbar, M.F., Munir, E.U., Rafique, M.M., Malik, Z., Khan, S.U., Yang, L.T.: List-based task scheduling for cloud computing. In: 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 652–659 (2016)

  9. Kalra, M., Singh, S.: A review of metaheuristic scheduling techniques in cloud computing. Egypt. Inf. J. 16(3), 275–295 (2015)

    Article  Google Scholar 

  10. Pacini, E., Mateos, C., Garino, C.G.: Balancing throughput and response time in online scientific clouds via ant colony optimization. Int. J. Adv. Eng. Softw. 84, 31–47 (2015)

    Article  Google Scholar 

  11. Karthikeyan, P., Chandrasekaran, M.: Dynamic programming inspired virtual machine instances allocation in cloud computing. J. Comput. Theor. Nanosci. 14, 551–560 (2017)

    Article  Google Scholar 

  12. Uetz, G.W.: Foraging strategies of spiders. Trends Ecol. Evol. 7(5), 155–159 (1992)

    Article  Google Scholar 

  13. Kumari, V., Kalra, M., Singh, S.: Independent task scheduling in cloud environment using Big Bang-Big Crunch approach. In: 2nd International Conference on Recent Advances in Engineering & Computational Sciences (RAECS) (2015)

  14. Vidhya, M., Sadhasivam, N.: Parallel particle swarm optimization for task scheduling in cloud computing. Int. J. Innov. Res. Sci. Eng. Technol. 4(6), 136–140 (2015)

    Google Scholar 

  15. Pradhan, P., Behera, P.K., Ray, B.N.B.: Modified round robin algorithm for resource allocation in cloud computing. In: International Conference on Computational Modeling and Security (CMS 2016), Procedia Computer Science, vol. 85, pp. 878–890 (2016)

  16. Hamad, S.A., Omara, F.A.: Genetic-based task scheduling algorithm in cloud computing environment. Int. J. Adv. Comput. Sci. Appl. 7(4), 550–556 (2016)

    Google Scholar 

  17. Keshanchi, B., Souri, A., Navimipour, N.J.: An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J. Syst. Softw. 124, 1–21 (2017)

    Article  Google Scholar 

  18. Abdi, S., Motamedi, S.A., Sharifian, S.: Task scheduling using modified PSO algorithm in cloud computing environment. In: International Conference on Machine Learning, Electrical and Mechanical Engineering (ICMLEME’2014) Jan. 8–9, Dubai (UAE) (2014)

  19. Jeyakrishnan, V., Sengottuvelan, P.: A hybrid strategy for resource allocation and load balancing in virtualized data centers using BSO algorithms. Wirel. Pers. Commun. 94, 2363–2375 (2017)

    Article  Google Scholar 

  20. Dhinesh Babua, L.D., Venkata Krishna, P.: Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl. Soft Comput. 13, 2292–2303 (2013)

    Article  Google Scholar 

  21. Awada, A.I., El-Hefnawya, N.A., Abdel kaderb, H.M.: Enhanced particle swarm optimization for task scheduling in cloud computing environments. Procedia Comput. Sci. 65, 920–929 (2015)

  22. Mondal, B., Dasgupta, K., Dutta, P.: Load balancing in cloud computing using stochastic hill climbing-a soft computing approach. Procedia Technol. 4, 783–789 (2012)

    Article  Google Scholar 

  23. Zhan, Z.-H., Zhang, G.-Y., Gong, Y.-J., Zhang, J.: Load balance aware genetic algorithm for task scheduling in cloud computing. In: Simulated Evolution and Learning 10th International Conference, pp. 15–18 (2014)

  24. Guo-Ning, G., Ting-Lei, H.: Genetic simulated annealing algorithm for task scheduling based on cloud computing environment. In: Proceedings of International Conference on Intelligent Computing and Integrated Systems, pp. 60–63 (2010)

  25. Yu, J.Q., Li, V.O.: A social spider algorithm for global optimization. Int. J. Appl. Soft Comput. 30, 614–627 (2015)

    Article  Google Scholar 

  26. Martinez, G., Zeadally, S., Chao, H.-C.: Editorial: cloud computing service and architecture models. Inf. Sci. 258(10), 353–354 (2014)

    Article  Google Scholar 

  27. Ghom, E.J., Rahmani, A.M., Qader, N.N.: Load-balancing algorithms in cloud computing: a survey. J. Netw. Comput. Appl. 88, 50–71 (2017)

    Article  Google Scholar 

  28. Abdelmaboud, A., Jawawi, D.N., Ghani, I., Elsafi, A., Kitchenham, B.: Quality of service approaches in cloud computing: a systematic mapping study. J. Syst. Softw. 101, 159–179 (2015)

    Article  Google Scholar 

  29. Park, J.B., Jeong, Y.W., Shin, J.R., Lee, K.Y.: An improved particle swarm optimization for nonconvex economic: dispatch problems. IEEE Trans. Power Syst. 25(1), 156–166 (2010)

    Article  Google Scholar 

  30. Shengsong, L., Min, W., Zhijian, H.: Hybrid algorithm of chaos optimization and SLP for optimal power flow problems with multimodal characteristic. Proceedings of the institution of Electrical Engineers, Generation, Transmission and Distribution 150(5), 543–547 (2003)

    Article  Google Scholar 

  31. Arul Xavier, V.M., Annadurai, S.: HFKCS: hybrid fuzzy K-means++ with clonal selection algorithm for task scheduling and load balancing in cloud computing. Int. J. Appl. Eng. Res. 10(20), 20140–20156 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to V. M. Arul Xavier.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arul Xavier, V.M., Annadurai, S. Chaotic social spider algorithm for load balance aware task scheduling in cloud computing. Cluster Comput 22 (Suppl 1), 287–297 (2019). https://doi.org/10.1007/s10586-018-1823-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-1823-x

Keywords

Navigation