Advertisement

Improved Bee Swarm Optimization Algorithm for Load Scheduling in Cloud Computing Environment

  • Divya ChaudharyEmail author
  • Bijendra Kumar
  • Sakshi Sakshi
  • Rahul Khanna
Conference paper
  • 1.1k Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 799)

Abstract

The cloud acts as a model that contains an aggregation of resources and data that needs to be shared among users. The scheduling of the load acts as a major challenge to fulfill the requests of the several users. Till now several algorithms have been proposed for fulfilling the purpose of load scheduling in cloud. The latest works are based on swarm-intelligence techniques. However, one such swarm-intelligence technique Bee Swarm Optimization (BSO) has not been exploited for serving this purpose. In this paper, an improvised version of BSO, the Improved Bee Swarm Optimization in Cloud (IBSO-C) has been proposed with the objective of efficient and cost-effective scheduling in cloud. It uses the swarm of particles as bees for scheduling and updated total cost evaluation function. The proposed algorithm is validated and tested by analysis on large set of iterations. The comparison of results with existing techniques has proven, the proposed IBSO-C to be a more cost-effective algorithm.

Keywords

Cloud computing Load scheduling Swarm intelligence  PSO BSO 

References

  1. 1.
    Wang, S.C., et al.: Towards a load balancing in a three-level cloud computing network. In: 3rd IEEE International Conference Computer Science and Information Technology (ICCSIT), vol. 1 pp. 108–113 (2010)Google Scholar
  2. 2.
    Easwarakumar, D.M.K.: A double min min algorithm for task metascheduler on hypercubic P2P grid systems. Int. J. Comput. Sci. Issues 7(4), 8–18 (2010)Google Scholar
  3. 3.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  4. 4.
    Li, G., Niua, P., Xiao, X.: Development and investigation of efficient artificial bee colony for numerical function optimization. Appl. Soft Comput. 12, 320–332 (2012)CrossRefGoogle Scholar
  5. 5.
    Cui, X., Potok, T.E., Palathingal, P.: Document clustering using particle swarm optimization. In: 2005 Proceedings of Swarm Intelligence Symposium, SIS 2005. IEEE (2005)Google Scholar
  6. 6.
    Karaboga, D., Akay, B.: A survey: algorithms simulating bee swarm intelligence. Artif. Intell. Rev. 31, 61–85 (2009)CrossRefGoogle Scholar
  7. 7.
    Yang, X.-S.: Engineering optimizations via nature-inspired virtual bee algorithms. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 317–323. Springer, Heidelberg (2005).  https://doi.org/10.1007/11499305_33CrossRefGoogle Scholar
  8. 8.
    Wedde, H.R., Farooq, M.: The wisdom of the hive applied to mobile ad-hoc networks. In: 2005 Proceedings of Swarm Intelligence Symposium, SIS 2005, pp. 341–348. IEEE (2005)Google Scholar
  9. 9.
    Pham, D.T., Ghanbarzadeh, A., Koc, E, Otri, S., Rahim, S., Zaidi, M.: The bees algorithm. Technical report, Manufacturing Engineering Centre, Cardiff University, UK (2005)Google Scholar
  10. 10.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report. Computer Engineering Department, Engineering Faculty, Erciyes University (2005)Google Scholar
  11. 11.
    Banharnsakun, A., Achalakul, T., Sirinaovakul, B.: The best-so-far selection in artificial bee colony algorithm. Appl. Soft Comput. 11, 2888–2901 (2011)CrossRefGoogle Scholar
  12. 12.
    Secui, D.C.: A new modified artificial bee colony algorithm for the economic dispatch problem. Energy Convers. Manag. 89, 43–62 (2015)CrossRefGoogle Scholar
  13. 13.
    Zhu, G., Kwong, S.: Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl. Math. Comput. 217, 3166–3173 (2010)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Akbari, R., Mohammadi, A., Ziarati, K.: A powerful bee swarm optimization algorithm. IEEE (2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Divya Chaudhary
    • 1
    Email author
  • Bijendra Kumar
    • 1
  • Sakshi Sakshi
    • 1
  • Rahul Khanna
    • 1
  1. 1.Department of Computer EngineeringNetaji Subhas Institute of TechnologyDwarkaIndia

Personalised recommendations