Advertisement

A Hybrid Task Scheduling Approach Based on Genetic Algorithm and Particle Swarm Optimization Technique in Cloud Environment

  • Bappaditya Jana
  • Jayanta Poray
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)

Abstract

As per current trends, cloud services are becoming more popular day by day. Because these services satisfy several demands for heterogeneous resources without using dedicated IT infrastructure. In cloud platform, user gets shared pool of resources through Internet irrespective of any geographical location. But cloud services need to handle a gigantic amount of request, as the number of users increases exponentially. In order to manage a pool of requests till now no effective scheduling mechanism is available in practice. So to minimize the time delay and optimize the overall complexity, suitable scheduling methodology is very much required. In our study, we have presented a novel scheme for scheduling algorithm after the enhancement of genetic algorithm and particle swarm optimization technique. We have proposed a methodology that can provide a better response time from cloud provider and minimize the waiting time for particular clients in cloud environment.

Keywords

Cloud computing Genetic algorithm Max–min scheduling Min execution time Scheduling algorithm Particle swarm optimization Hybrid task scheduling algorithm Time hybrid task scheduling 

References

  1. 1.
    Zhu, K., Song, H., Liu, L., Gao, J., Cheng, G.: Hybrid genetic algorithm for cloud computing applications. In: 2011 IEEE Asia-Pacific Services Computing Conference (APSCC), pp. 182–187. IEEE (2011)Google Scholar
  2. 2.
    Paul, M., Sanyal, G.: Survey and analysis of optimal scheduling strategies in cloud environment. In: Proceedings of the IEEE International Conference on Information and Communication Technologies, Georgia, USA, pp. 789–792 (2012)Google Scholar
  3. 3.
    Lv, Z., Halawani, A., Feng, S., Li, H., Rehman, S.U.: Multimodal hand and foot gesture interaction for handheld devices. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 11(1s), Article 10, 19 pages (2014)CrossRefGoogle Scholar
  4. 4.
    Tangang, Zhan, R., Shibo, Xindi: Comparatively analysis and simulation of load balancing scheduling algorithm based on cloud resource. J. Springer (2014)Google Scholar
  5. 5.
    Yin, H., Wu, H., Zhou, J.: An Improved Genetic Algorithm with Limited Iteration for Grid Scheduling. In: IEEE Sixth International Conference on Grid and Cooperative Comput ing, 2007. GCC 2007, Los Alamitos, pp. 221–227 (2007)Google Scholar
  6. 6.
    Guo, G., Ting-Iei, H., Shuai, G.: Genetic simulated annealing algorithm for task scheduling based on cloud computing environment. In: IEEE International Conference on Intelligent Computing and Integrated Systems (ICISS), 2010, Guilin, pp. 60–63 (2010)Google Scholar
  7. 7.
    Wang, J., Duan, Q., Jiang, Y., Zhu, X.: A new algorithm for grid independent task schedule genetic simulated annealing. In: IEEE World Automat ion Congress (WAC), 2010, Kobe, pp. 165–171 (2010)Google Scholar
  8. 8.
    Randles, M., et al.: Biased random walks on resource network graphs for load balancing. J. Springer (2009)CrossRefGoogle Scholar
  9. 9.
    Qiyi, H., Tinglei, H.: An optimistic job scheduling strategy based on QoS for cloud computing. In: Proceedings of the IEEE International Conference on Intelligent Computing and Integrated Systems, Guilin, China, pp. 673–675 (2010)Google Scholar
  10. 10.
    Wei, Z., Pierre, G., Chi, C.: CloudTPS: scalable transactions for web applications in the cloud. IEEE Trans. Serv. Comput. 5(4), 525–539 (2012)CrossRefGoogle Scholar
  11. 11.
    Dean, J., Ghemawats, S.: MapReduce simplified data processing on large clusters[C]. In: Proceedings of the 6th Symposium on Operating System Design and Implementation, pp. 137–150. ACM, New York (2004)Google Scholar
  12. 12.
    Cryptographic Key Management Issues & Challenges in Cloud Services Ramaswamy Chandramouli Michaela Iorga Santosh Chokhani, NISTIR 7956, http://dx.doi.org/10.6028/NIST.IR.7956
  13. 13.
    Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., et al.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)CrossRefGoogle Scholar
  14. 14.
    Beloglazov, A., Buyya, R.: Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Trans. Parallel Distrib. Syst. 24, 1366–1379 (2013)CrossRefGoogle Scholar
  15. 15.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer (1992)CrossRefGoogle Scholar
  16. 16.
    Goldberg, E.: The existential pleasures of genetic algorithms. In: Winter, G. (ed.) Genetic Algorithms in Engineering and Computer Science, pp. 23–31. Wiley, New York (1995)Google Scholar
  17. 17.
    Yusoh, M., Izzah, Z., Maolin, T.: Clustering composite SaaS components in cloud computing using a grouping genetic algorithm. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2012)Google Scholar
  18. 18.
    Buyya, R., Ranjan, R., Calheiros, N.: Modeling and Simulation of scalable cloud computing environments and the cloudsim toolkit: challenges and opportunities. In: Proceedings of the 7th High Performance Computing and Simulation Conference, Leipzig, Germany, pp. 1–11 (2009)Google Scholar
  19. 19.
    Mao, Y., Chen, X., Li, X.: Max-Min task scheduling algorithm for load balancing in cloud computing. J. Springer (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringTechno India UniversityKolkataIndia

Personalised recommendations