Skip to main content
SpringerLink
Log in

Swarm Optimization for Solving Load Balancing in Cloud Computing

  • Conference paper
  • First Online:
The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019) (AMLTA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 921))

Abstract

Cloud computing is the new paradigm of representing computing capabilities as a service. With its facility of resource sharing and being cost-effective, it exists in every domain of life, enhancing their functionality and adding new opportunities to it. Accordingly, the focus on solving its dilemmas like load balancing becomes more challenging and the research in swarm-based algorithms to find optimal results has been expanding. This paper discusses the use of two swarm algorithms including Ant-Lion optimizer (ALO) and Grey wolf optimizer (GWO) in task scheduling of the Cloud Computing environment. Additionally, compare the results with commonly known swarm algorithms: Particle Swarm Optimization (PSO) and Firefly Algorithm (FFA). The results show the ALO and GWO are a strong adversary to Particle Swarm Optimization (PSO), and better than Firefly (FFA) and they have potential in load balancing.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Aslanzadeh, S., Chaczko, Z.: Load balancing optimization in cloud computing: applying endocrine-particale swarm optimization. In: IEEE International Conference 2015 Electro/Information Technology (EIT), pp. 165–169. IEEE (2015)

    Google Scholar 

  2. Ramezani, F., Lu, J., Hussain, F.K.: Task-based system load balancing in cloud computing using particle swarm optimization. Int. J. Parallel Prog. 42(5), 739–754 (2014)

    Article  Google Scholar 

  3. Almezeini, N., Hafez, A.: Task Scheduling in Cloud Computing using Lion Optimization Algorithm. Algorithms 5, 7 (2017)

    Google Scholar 

  4. Gabi, D., Ismail, A.S., Zainal, A., Zakaria, Z.: Solving task scheduling problem in cloud computing environment using orthogonal taguchi-cat algorithm. Int. J. Electr. Comput. Eng. (IJECE) 7(3), 1489–1497 (2017)

    Article  Google Scholar 

  5. Pathak, P., Mahajan, K.: A pollination based optimization for load balancing task scheduling in cloud computing. Int. J. Adv. Res. Comput. Sci. 25(10) (2017)

    Google Scholar 

  6. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  7. Mishra, S.K., Sahoo, B., Parida, P.P.: Load balancing in cloud computing: a big picture. J. King Saud Univ.-Comput. Inf. Sci. (2018)

    Google Scholar 

  8. Alam, M., Khan, Z.A.: Issues and challenges of load balancing algorithm in cloud computing environment. Indian J. Sci. Technol. 10(25), 1–12 (2017)

    Article  Google Scholar 

  9. Kaur, S., Sharma, S.: load balancing in cloud computing with enhanced optimal cost scheduling algorithm. Imp. J. Interdisc. Res. 2(9), 1460–1466 (2016)

    Google Scholar 

  10. Patel, G., Mehta, R., Bhoi, U.: Enhanced load balanced min-min algorithm for static meta task scheduling in cloud computing. Procedia Comput. Sci. 57, 545–553 (2015)

    Article  Google Scholar 

  11. Susila, N., Chandramathi, S., Kishore, R.: A fuzzy-based firefly algorithm for dynamic load balancing in cloud computing environment. J. Emerg. Technol. Web Intell. 6(4), 35–40 (2014)

    Google Scholar 

  12. Kaur, J., Bhardwaj, V.: A novel approach of task scheduling for cloud computing using adaptive firefly. Int. J. Comput. Appl. 147(12), 9–13 (2016)

    Google Scholar 

  13. Al-maamari, A., Omara, F.A.: Task scheduling using hybrid algorithm in cloud computing environments. J. Comput. Eng. (IOSR-JCE) 17(3), 96–106 (2015)

    Google Scholar 

  14. Jena, R.K.: Multi objective task scheduling in cloud environment using nested PSO framework. Procedia Comput. Sci. 57, 1219–1227 (2015)

    Article  Google Scholar 

  15. Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)

    Article  Google Scholar 

  16. Mishra, S.K.: How has cloud computing affected the retail business. PCQuest, 5 October 2018. www.pcquest.com/cloud-computing-affected-retail-business/. Accessed 5 Oct 2018

  17. Ryan: The Industries Most Affected by the Evolution of Cloud Computing. UTG, www.utgsolutions.com/the-industries-most-affected-by-the-evolution-of-cloud-computing. Accessed 5 Oct 2018

  18. Cloud Computing – Allcenta Inc. http://allcenta.com/cloud-computing/. Accessed 5 Oct 2018

Download references

Author information

Authors and Affiliations

  1. Ain Shams University, Cairo, Egypt

    Aya A. Salah Farrag, Safia Abbas Mohamad & El Sayed M. El-Horbaty

Authors
  1. Aya A. Salah Farrag
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Safia Abbas Mohamad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. El Sayed M. El-Horbaty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aya A. Salah Farrag .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

  3. School of Computing, Science and Engineering, University of Salford, Salford, Greater Manchester, UK

    Tarek Gaber

  4. Department of Computer Science and Engineering, School of Computing and IT, Faculty of Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

    Roheet Bhatnagar

  5. Faculty of Computer and Information Science, Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Farrag, A.A.S., Mohamad, S.A., El-Horbaty, E.S.M. (2020). Swarm Optimization for Solving Load Balancing in Cloud Computing. In: Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and Computing, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-030-14118-9_11

Download citation

Publish with us

Policies and ethics

Search

Navigation