Bio-inspired Based Task Scheduling in Cloud Computing

  • Marwa Gamal
  • Rawya RizkEmail author
  • Hani Mahdi
  • Basem Elhady
Part of the Studies in Computational Intelligence book series (SCI, volume 801)


Cloud computing meets numerous challenges at increasing number of users because the demand of resources sharing and usage are increased rapidly. Therefore, load balancing between resources is an important field for scheduling tasks to achieve better performance. In this chapter, a Hybrid Artificial Bee and Ant Colony optimization (H_BAC) load balancing algorithm is proposed. It depends on joining the important behavior of Ant Colony Optimization (ACO) such as discovering good solutions rapidly and Artificial Bee Colony (ABC) algorithm such as collective interaction of bees and sharing information by waggle dancing. The performance of the proposed algorithm is compared with ACO, ABC, and an existing hybrid algorithm. The simulation results show that H_BAC improves execution time, response time, makespan, resource utilization and standard deviation. This improvement reaches about 40% in the execution time and response time and 30% in the makespan over the other algorithms.


Ant colony optimization Artificial bee colony Bio-inspired systems Cloud computing Load balancing 


  1. 1.
    Endo, P.T., Rodrigues, M., Gonçalves, G.E., Kelner, J., Sadok, D.H., Curescu, C.: High availability in clouds: systematic review and research challenges. J. Cloud Comput. 5(1), 5–16 (2016)CrossRefGoogle Scholar
  2. 2.
    Xu, X., Hu, H., Hu, N., Ying, W.: Cloud task and virtual machine allocation strategy in cloud computing environment. In: Network Computing and Information Security (NCIS). Communications in Computer and Information Science, Berlin, vol. 345, pp. 113–120 (2012)Google Scholar
  3. 3.
    Saber, W., Rizk, R., Moussa, W., Ghonem, A.: LBSR: load balance over slow resources. In: 1st International Conference on Computer Applications & Information Security (ICCAIS), Riyadh, Saudi Arabia (2018)Google Scholar
  4. 4.
    Patil, A., Gala, H., Kapoor, J.: Dynamic load balancing in cloud computing using swarm intelligence algorithms. Int. J. Comput. Appl. 130(15), 15–21 (2015)Google Scholar
  5. 5.
    Hassanien, A.E., Alamry, E.: Swarm Intelligence: Principles, Advances, and Applications. CRC–Taylor & Francis Group (2015). ISBN 9781498741064 - CAT# K26721Google Scholar
  6. 6.
    Ghomi, E., Rahmani, A., Qader, N.: Load-balancing algorithms in cloud computing: a survey. J. Netw. Comput. Appl. 88(15), 50–71 (2017)CrossRefGoogle Scholar
  7. 7.
    Pasha, N., Agarwal, A., Rastogi, R.: Round robin approach for VM load balancing algorithm in cloud computing environment. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4(5), 34–39 (2014)Google Scholar
  8. 8.
    Wang, W., Casale, G.: Evaluating weighted round robin load balancing for cloud web services. In: International Conference on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Timisoara, Romania, pp. 393–400 (2014)Google Scholar
  9. 9.
    Patel, G., Mehta, R., Bhoi, U.: Enhanced load balanced min-min algorithm for static meta task scheduling in cloud computing. Procedia Comput. Sci. 57, 545–553 (2015)CrossRefGoogle Scholar
  10. 10.
    Devipriya, S., Ramesh, C.: Improved max-min heuristic model fortask scheduling in cloud. In: International conference on Green Computing, Communication and Conservation of Energy (ICGCE), India, pp. 883–888 (2013)Google Scholar
  11. 11.
    Wang, S.C., Yan, K.Q., Liao, W.P., Wang, S.S.: Towards a load balancing in a three-level cloud computing network. In: International Conference on Computer Science and Information Technology (ICCSIT), China, vol. 1, pp. 108–113 (2010)Google Scholar
  12. 12.
    Patel, D., Rajawat, A.: Efficient throttled load balancing algorithm in cloud environment. Int. J. Mod. Trends Eng. Res. 2(3), 464–480 (2015)Google Scholar
  13. 13.
    Domanal, S.G., Reddy, G.R.M.: Load balancing in cloud computing using modified throttled algorithm. In: International Conference on Cloud Computing in Emerging Markets (CCEM), India (2013)Google Scholar
  14. 14.
    Moharana, S.S., Ramesh, R.D., Powar, D.: Analysis of load balancers in cloud computing. Int. J. Comput. Sci. Eng. 2(2), 101–108 (2013)Google Scholar
  15. 15.
    Mandal, B., Dutta, P., Mandal, J., Dam, S., Dasgupta, K.: A genetic algorithm (GA) based load balancing strategy for cloud computing. Procedia Technol. 10, 340–347 (2013)CrossRefGoogle Scholar
  16. 16.
    Singh, S., Kalra, M.: A review of metaheuristic scheduling techniques in cloud computing. Egypt. Inform. J. 16(3), 275–295 (2015)Google Scholar
  17. 17.
    Singh, G., Kaur, A.: Bio inspired algorithms: an efficient approach for resource scheduling in cloud computing. Int. J. Comput. Appl. 116(10), 16–21 (2015)Google Scholar
  18. 18.
    Balusamy, B., Sridhar, J., Dhamodaran, D., Krishna, P.V.: Bio-inspired algorithms for cloud computing: a review. Int. J. Innov. Comput. Appl. 6, 182–202 (2015)CrossRefGoogle Scholar
  19. 19.
    Kansal, N., Chana, I.: Energy-aware virtual machine migration for cloud computing—a firefly optimization approach. J. Grid Comput. 14, 327–345 (2016)CrossRefGoogle Scholar
  20. 20.
    Thilagavathi, D., Thanamani, A.S.: Scheduling in high performance computing environment using firefly algorithm and intelligent water drop algorithm. Int. J. Eng. Trends Technol. 14(1), 8–12 (2014)CrossRefGoogle Scholar
  21. 21.
    Singh, R.: Cuckoo genetic optimization algorithm for efficient job scheduling with load balance in grid computing. Int. J. Comput. Netw. Inf. Secur. 8(8), 59–66 (2016)Google Scholar
  22. 22.
    Mandal, T., Acharyya, S.: Optimal task scheduling in cloud computing environment: meta heuristic approaches. In: International Conference on Electrical Information and Communication Technology (EICT), IEEE, Khulna, Bangladesh, pp. 24–28 (2015)Google Scholar
  23. 23.
    Yakhchi, M., Ghafari, S., Yakhchi, S., Fazeli, M., Patooghi, A.: Proposing a load balancing method based on cuckoo optimization algorithm for energy Management in cloud computing infrastructures. In: International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO), IEEE, Turkey, pp. 1–5 (2015)Google Scholar
  24. 24.
    Nishant, K., Sharma, P., Krishna, V., Gupta, C., Singh, K.P., Nitin, Rastogi, R.: Load balancing of nodes in cloud using ant colony optimization. In: International Conference of Computer Modelling and Simulation (UKSim), Cambridge, pp. 3–8 (2012)Google Scholar
  25. 25.
    Wen, W.T., Wang, C.D., Wu, D.S., Xie, Y.Y.: An ACO-based scheduling strategy on load balancing in cloud computing environment. In: International Conference on Frontier of Computer Science and Technology (FCST), China, pp. 364–369 (2015)Google Scholar
  26. 26.
    Dam, S., Mandal, G., Dasgupta, K., Dutta, P.: An ant colony based load balancing strategy in cloud computing. Adv. Comput. Netw. Inform. 2, 403–413 (2014)Google Scholar
  27. 27.
    Babua, L.D.D., Krishnab, P.V.: Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl. Soft Comput. 13(5), 2292–2303 (2013)CrossRefGoogle Scholar
  28. 28.
    Rathore, M., Rai, S., Saluja, N.: Load balancing of virtual machine using honey bee galvanizing algorithm in cloud. Int. J. Comput. Sci. Inf. Technol. 6(4), 4128–4132 (2015)Google Scholar
  29. 29.
    Hashem, W., Nashaat, H., Rizk, R.: Honey bee based load balancing in cloud computing. KSII Trans. Internet Inf. Syst. (TIIS) 11(12), 5694–5711 (2017)Google Scholar
  30. 30.
    Singh, S., Vivek, T.: Implementation of a hybrid load balancing algorithm for cloud computing. Int. J. Adv. Technol. Eng. Sci. 3(1), 73–81 (2015)Google Scholar
  31. 31.
    Madivi, R., Kamath, S.: An hybrid bio-inspired task scheduling algorithm. In: Proceedings of the 5th International Conference on Computing Communication and Networking Technologies (ICCCNT), China, pp. 1–7 (2014)Google Scholar
  32. 32.
    Gamal, M., Rizk, R., Mahdi, H., El-Hady, B.: Bio-inspired load balancing algorithm in cloud computing. In: International Conference on Advanced Intelligent Systems and Informatics (AISI), Egypt, pp. 579–589 (2017)Google Scholar
  33. 33.
    Calheiros, R., Ranjan, R., Beloglazov, A., Rose, C., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract Exp. 41(1), 23–50 (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Marwa Gamal
    • 1
  • Rawya Rizk
    • 2
    Email author
  • Hani Mahdi
    • 3
  • Basem Elhady
    • 1
  1. 1.Electrical Engineering DepartmentSuez Canal UniversityIsmailiaEgypt
  2. 2.Electrical Engineering DepartmentPort Said UniversityPort SaidEgypt
  3. 3.Computers and Systems Engineering DepartmentAin Shams UniversityCairoEgypt

Personalised recommendations