Social Spider Foraging Based Resource Placement Policies in Cloud Environment

  • Preeti AbrolEmail author
  • Savita Gupta
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 46)


Expansion in the cloud infrastructure leads to the challenge of resource placement. The existing resource placement techniques are not sufficiently effective. In this paper, the mathematical model of social spider cloud web algorithm is presented that targets the improvement in the utilization and focuses on the overall cloud performance. A new novel nature-inspired algorithm, social spider cloud web algorithm, helps in resource placement and load balancing of the cloud. It works on the foraging behavior of social spider and sorts the tasks and allocates the resources which leads to the efficient cloud performance.


Cloud computing Cloud architecture Resource placement module Social Spider Cloud Web Algorithm (SSCWA) 



We want to thank Mr. Sukhwinder Singh for his guidance.


  1. 1.
    P. Abrol, S. Gupta, K. Kaur, Social spider cloud web algorithm (SSCWA): a new meta-heuristic for avoiding premature convergence in cloud. Int. J. Innov. Res. Comput. Commun. Eng. 3(6), 5698–5704 (2015). ISSN (Online): 2320-9801, ISSN (Print): 2320-9798. .
  2. 2.
    P. Abrol, S. Gupta, K. Kaur, in Analysis of resource management and placement policies using a new nature inspired meta heuristic SSCWA avoiding premature convergence in cloud. International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT) (2016), pp. 127–132. ISSN 978-1-5090-0082-1/16/$31.00 ©2016 IEEEGoogle Scholar
  3. 3.
    L. Bater, in Incredible insects: answers to questions about miniature marvels. Vero Beach: Rourke Publishing LLC. Post Office Box 3328 (2007). ISBN 978-1-60044-348-0Google Scholar
  4. 4.
    C. Eric, K.S. Yip, in Cooperative capture of large prey solves scaling challenge faced by spider societies. Proceedings of the National Academy of Sciences of the United States of America (vol. 105, Issue 33, 2008), pp. 11818–11822Google Scholar
  5. 5.
    S. Levin, in Encyclopedia of biodiversity (Academic Press, Elsevier Inc, London, 2013a, 2013b, 2013c, 2013d, 2013e, 2013f). ISBN 978-0-12-384719-5Google Scholar
  6. 6.
    T.B. Lubin, in The Evolution of Sociality in Spiders, ed. by H.J. Brockmann. Advances in the Study of Behavior, (vol. 37, 2007), pp. 83–145Google Scholar
  7. 7.
    F.F. Campon, Group foraging in the colonial spider parawixia bistariata (Araneidae): effect of resource level and prey size. Animal Behav. Elsevier (2007),
  8. 8.
    A. Tchernykh, U. Schwiegelsohn, V. Alexandrov, E. Talbi, Towards understanding uncertainty in cloud computing resource provisioning. Procedia Comput. Sci. 51, 1772–1781 (2015).
  9. 9.
    C.E. Klein, E.H.V. Segundo, V.C. Mariani, L.D.S. Coelho, Modified social-spider optimization algorithm applied to electromagnetic optimization. IEEE Trans. Mag. 52(3), (2016)
  10. 10.
    E. Cuevas, M. Cienfuegos, D. Zaldivar, M. Perez-Cisneros, A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst. Appl. 40(16), 6374–6384 (2013)Google Scholar
  11. 11.
    J.Q. Yu James, O.K. Li Victor, A social spider algorithm for global optimization. J. Appl. Soft Comput 30(C), 614–627. Elsevier Science (2015)Google Scholar
  12. 12.
    E. Cuevas, M. Cienfuegos, R. Rojas, A. Padilla, in A Computational Intelligence Optimization Algorithm Based on the Behavior of the Social-Spider. Computational Intelligence Applications in Modeling and Control, Studies in Computational Intelligence (Springer, 2015) pp. 123–146.
  13. 13.
    C. Erik, C. Miguel, Z. Daniel, P.-C. Marco, A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst. Appl. 40, 6374–684 (2013) Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.STDCDACMohaliIndia
  2. 2.CSEUIETChandigarhIndia

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