Advertisement

A Survey on Metaheuristic Approaches and Its Evaluation for Load Balancing in Cloud Computing

  • Deepak GargEmail author
  • Pardeep Kumar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 955)

Abstract

In daily life there exist many problems whose objective are to either maximize or minimize some value with following some constraints (like load balancing in cloud with aim to maximizing QoS, Travelling salesman problem with aim to minimize total length of trip). These types of problems are optimization problems. Out of these problems there exist many problems which comes under NP-Hard category. To get nearby optimal solution of these problems in polynomial time the metaheuristics approaches are used. Metaheuristics are nature inspired algorithms which provides optimal solution by utilizing combination of exploration and exploitation. This paper provides a survey of Metaheuristic approaches (consisting of need, applications, characteristics, general classification and fourteen approaches under it). Compared all approaches corresponding to key parameters, mechanism. On the basis of literature survey and comparison, cuckoo search has been considered better due to global search via levy flight and generality (because of single parameter setting in cuckoo search). Implemented Randomized algorithm, Genetic Algorithm and Cuckoo Search to solve Load Balancing problem in Cloud Computing with aim to minimize makespan time and proved through results that cuckoo search is better. These experimental results were obtained using CloudSim 3.0.3 toolkit by extending few base classes.

Keywords

Cloud computing Cuckoo search Genetic algorithms Load balancing Metaheuristic algorithms 

References

  1. 1.
    Boussaid, I., Lepagnot, J., Patrick S.: A survey on optimization metaheuristics. In: Web of Science, Elsevier Information Science, vol. 237(5), pp. 82–117 (2013)Google Scholar
  2. 2.
    Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Op. Res. 13(5), 533–549 (1986)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Holland, J.H.: Adaption in Natural and Artificial Systems. The University of Michigan Press, Ann Harbor, MI (1975)zbMATHGoogle Scholar
  4. 4.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system- optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  6. 6.
    Moscato, P., Norman, M.G.: A memetic approach for the traveling salesman problem implementation of a computational ecology for combinatorial optimization on message-passing systems. In: International Conference on Parallel Computing and Transputer Application, pp. 86–177 (1992)Google Scholar
  7. 7.
    Kennedy, J., Eberhart R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  8. 8.
    Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. In: IEEE Control Systems Magazine, pp. 52–67 (2006)Google Scholar
  9. 9.
    Eusuff, M., Lansey, K., Pasha, F.: Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng. Optim. 38(2), 129–154 (2006)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8, 687–697 (2007)CrossRefGoogle Scholar
  11. 11.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization- artificial bee colony (ABC) algorithm. J. Glob. Optim. 39, 459–471 (2007)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Yang, X.S., He, X.: Firefly algorithm- recent advances and applications. Int. J. Swarm Intell. 1(1), 36–50 (2013)CrossRefGoogle Scholar
  13. 13.
    Yang, X.S.: Firefly algorithm, stochastic test functions and design optimization. Int. J. Bio-Inspir. Comput. 2(2), 78–84 (2010)CrossRefGoogle Scholar
  14. 14.
    Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)CrossRefGoogle Scholar
  15. 15.
    Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: IEEE Conference Publication World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 210–214 (2009)Google Scholar
  16. 16.
    Yang, X.S., Deb, S.: Engineering optimisation by cuckoo search. Int. J. Math. Modell. Numer. Optim. 1(4), 330–343 (2010)zbMATHGoogle Scholar
  17. 17.
    Yang, X.S., Deb, S.: Cuckoo search- recent advances and applications. Neural Comput. Appl. 24(1), 169–174 (2014)CrossRefGoogle Scholar
  18. 18.
    Yang, X.S.: Bat algorithm- literature review and applications. Int. J. Bio-Inspir. Comput. 5(3), 141–149 (2013)CrossRefGoogle Scholar
  19. 19.
    Yang, X.S., Karamanoglu M.: Multi-objective flower algorithm for optimization. In: International Conference on Computational Science, Elsevier Science, pp. 861–868 (2013)Google Scholar
  20. 20.
    Yang, X.S.: Flower pollination algorithm for global optimization, unconventional computation and natural computation. Lect. Notes Comput. Sci. 44(5), 240–249 (2012)Google Scholar
  21. 21.
    Yang, X.S., Deb, S., Fong, S., Xingshi, H., Zhao, Y.: From swarm intelligence to metaheuristics- nature-inspired optimization algorithms. IEEE Comput. Soc. 49(9), 52–59 (2016)CrossRefGoogle Scholar
  22. 22.
    Wang, F., Yang, X.S., Yang, S.M.: Markov model and convergence analysis based on cuckoo search algorithm. Comput. Eng. 38(11), 180–185 (2012)Google Scholar
  23. 23.
    Gandomi, A.H., Yang, X.S., Alavi, A.H.: Cuckoo search algorithm- a metaheuristic approach to solve structural optimization problems. Eng. Comput. 29(1), 17–35 (2013)CrossRefGoogle Scholar
  24. 24.
    Gandomi, A.H., Yang, X.S., Talatahari, S., Deb, S.: Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization. Comput. Math Appl. 63(1), 191–200 (2012)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Srivastava, P.R., Chis, M., Deb, S., Yang, X.S.: An efficient optimization algorithm for structural software testing. Int. J. Artif. Intell. 9(12), 68–77 (2012)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and ApplicationKurukshetra UniversityKurukshetraIndia

Personalised recommendations