Advertisement

Multi-Objective Task Scheduling Using Hybrid Genetic-Ant Colony Optimization Algorithm in Cloud Environment

  • A. M. Senthil KumarEmail author
  • M. Venkatesan
Article
  • 3 Downloads

Abstract

Task allocation within the cloud computing environment is a nondeterministic polynomial time class problem that is laborious to get the best solution. It is an important issue in the cloud computing setting. The usage of cloud based applications and cloud users are increasing tremendously. In order to handle the massive cloud user’s requests, effective multi-objective Hybrid Genetic Algorithm–Ant Colony Optimization (HGA–ACO) based task allocation technique is proposed in this paper. Utility based scheduler identifies the task order and suitable resources to be scheduled. The proposed HGA–ACO considers the utility based scheduler output and finds the best task allocation method based on response time, completion time and throughput. The HGA–ACO algorithm combines Genetic and Ant Colony Optimization algorithms together. Genetic algorithm (GA) initializes the effective pheromone for ant colony optimization (ACO). ACO is used to enhance the GA solutions for crossover operation of GA. The experimental results show that the proposed framework has better performance in task allocation and ensuring quality of service parameters.

Keywords

Task allocation Cloud computing Utility based scheduler Genetic algorithm Ant colony optimization QoS parameters 

Notes

References

  1. 1.
    Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 25(6), 599–616.CrossRefGoogle Scholar
  2. 2.
    Qiyi, H., & Tinglei, H. (2010). An optimistic job scheduling strategy based on QoS for cloud computing. In IEEE international conference on intelligent computing and integrated systems (ICISS) (pp. 673–675).Google Scholar
  3. 3.
    Pan, B. L., Wang, Y. P., Li, H. X., & Qian, J. (2014). Task scheduling and resource allocation of cloud computing based on QoS. Advanced Materials Research, 915, 1382–1385.CrossRefGoogle Scholar
  4. 4.
    MadniI, S. H. H., LatiffI, M. S. A., CoulibalyI, Y., & AbdulhamidI, S. M. (2016). Resource scheduling for infrastructure as a service (IaaS) in cloud computing: Challenges and opportunities. Journal of Network and Computer Applications, 68, 173–200.CrossRefGoogle Scholar
  5. 5.
    Pacini, E., Mateos, C., & Garino, C. G. (2015). Balancing throughput and response time in online scientific clouds via Ant colony optimization. Advances in Engineering, 84(1), 31–47.Google Scholar
  6. 6.
    Panda, S. K., & Jana, P. K. (2015). A multi-objective task scheduling algorithm for heterogeneous multi-cloud environment. In International conference on electronic design, computer networks and automated verification (EDCAV) (pp. 82–87).Google Scholar
  7. 7.
    Arianyan, E., Maleki, D., Yari, A., & Ariayan, I. (2012). Efficient resource allocation in cloud data centers through genetic algorithm. In 6th International symposium on telecommunications (pp. 566–570).Google Scholar
  8. 8.
    Ramezani, F., Lu, J., & Hussain, F. (2013). Task scheduling optimization in cloud computing applying multi-objective particle swarm optimization. In Service-oriented computing. ICSOC 2013 (pp. 237–251).Google Scholar
  9. 9.
    Wang, T., Liu, Z., Chen, Y., Xu, Y., & Dai, X. (2014). Load balancing task scheduling based on genetic algorithm in cloud computing. In Dependable, autonomic and secure computing (DASC) (pp. 146–152).Google Scholar
  10. 10.
    Xue, S., Li, M., Xu, X., Chen, J., & Xue, S. (2014). An ACO-LB algorithm for task scheduling in the cloud environment. Journal of Software, 9, 466–473.Google Scholar
  11. 11.
    Fan, Z., Shen, H., Wu, Y., & Li, Y. (2013) Simulated-annealing load balancing for resource allocation in cloud environments. In International conference on parallel and distributed computing applications and technologies (PDCAT) (pp. 1–6).Google Scholar
  12. 12.
    Wang, T., Liu, Z., Chen, Y., Xu, Y., & Dai, X. (2014). Load balancing task scheduling based on genetic algorithm in cloud computing. In International conference on dependable, autonomic and secure computing (DASC) (pp. 146–152).Google Scholar
  13. 13.
    Kaur, Shaminder. (2012). An efficient approach to genetic algorithm for task scheduling in cloud computing environment. International Journal of Information Technology and Computer Science, 10, 74–79.CrossRefGoogle Scholar
  14. 14.
    Wang, L., & Ai, L. (2012). Task scheduling policy based on ant colony optimization in cloud computing environment. In International conference on logistics, informatics and service science (LISS2012) (pp. 953–957).Google Scholar
  15. 15.
    Ping, G., Chunbo, X., Yi, C., Jing, L., & Yanqing, L. (2014). Adaptive ant colony optimization algorithm. In International conference on mechatronics and control (ICMC) (pp. 95–98).Google Scholar
  16. 16.
    Dai, Y., Lou, Y., & Lu, X. (2015) A task scheduling algorithm based on genetic algorithm and ant colony optimization algorithm with multi-QoS constraints in cloud computing. In 7th international conference on intelligent human-machine systems and cybernetics (IHMSC) (pp. 428–431).Google Scholar
  17. 17.
    Liu, C. Y., Zou, C.-M., Wu, P. (2014). A task scheduling algorithm based on genetic algorithm and ant colony optimization in cloud computing. In 13th international symposium on distributed computing and applications to business, engineering and science (pp. 68–72).Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of CSEKoneru Lakshmaiah Education FoundationVaddeswaram, GunturIndia
  2. 2.KSR Institute for Engineering and TechnologyTiruchengodeIndia

Personalised recommendations