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An Adaptive Firefly Algorithm for Load Balancing in Cloud Computing

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Proceedings of Sixth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 546))

Abstract

Over the past few years, cloud computing has become a popular paradigm that provides computing over the internet. There are umpteen factors that a cloud ecosystem need such as reliability, flexibility, dynamic load balancing etc. With the internet facility, resources are provided dynamically to the end users in an on-demand fashion. Users could be billions in number accessing the cloud. Their need for services have been increasing at an alarming rate. To enhance the performance of the system, resources should be used efficiently. Cloud computing needs to identify different issues and challenges. One of the main issues in cloud computing is Load balancing, in which workload is distributed dynamically to all the nodes. Load balancing not only optimize the resource use, maximize throughput, minimize processing time of datacenters and response time of user base, but also helps in evading the overloading of any single resource. This paper proposes an Adaptive firefly algorithm (ADF) for solving the load balancing problem in cloud computing by performing virtual machine scheduling over datacenters. The results have been compared with Ant Colony Optimization (ACO) algorithm used for load balancing.

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References

  1. Kalra, M., Singh, S.: A review of metaheuristic scheduling techniques in cloud computing. Egypt. Inf. J. 16, 1–20 (2015)

    Article  Google Scholar 

  2. Florence, A.P., Shanthi, V.: A load balancing model using firefly algorithm in cloud computing. J. Comput. Sci. 10(7), 1156 (2014)

    Article  Google Scholar 

  3. Dam, S., Mandal, G., Dasgupta, K., Dutta, P.: An ant colony based load balancing strategy in cloud computing. In: Kumar Kundu, M., Mohapatra, D.P., Konar, A., Chakraborty, A. (eds.) Advanced Computing, Networking and Informatics- Volume 2. SIST, vol. 28, pp. 403–413. Springer, Heidelberg (2014). doi:10.1007/978-3-319-07350-7_45

    Chapter  Google Scholar 

  4. Mohammadi, S., et al.: An adaptive modified firefly optimisation algorithm based on Hong’s point estimate method to optimal operation management in a microgrid with consideration of uncertainties. Energy 51, 339–348 (2013)

    Article  Google Scholar 

  5. Ahmed, T., Singh, Y.: Analytic study of load balancing techniques using tool cloud analyst. Int. J. Eng. Res. Appl. 2, 1027–1030 (2012)

    Google Scholar 

  6. Wickremasinghe, B.: CloudAnalyst: A CloudSim-based tool for modelling and analysis of large scale cloud computing environments. MEDC Proj. Rep. 22(6), 433–659 (2009)

    Google Scholar 

  7. Dasgupta, K., et al.: A genetic algorithm (GA) based load balancing strategy for cloud computing. Procedia Technol. 10, 340–347 (2013)

    Article  Google Scholar 

  8. Mesbahi, M., Rahmani, A.M.: Load balancing in cloud computing: a state of the art survey. Int. J. Mod. Educ. Comput. Sci. 8(3), 64 (2016)

    Article  Google Scholar 

  9. Gao, R., Juebo, W.: Dynamic load balancing strategy for cloud computing with ant colony optimization. Future Int. 7(4), 465–483 (2015)

    Article  Google Scholar 

  10. Tan, G., Zheng, W., Du, Y., Xin, D.: A cloud resource scheduling strategy based on ant colony optimization algorithm. In: Control, Mechatronics and Automation Technology: Proceedings of the International Conference on Control, Mechatronics and Automation Technology (ICCMAT 2014), Beijing, China, 24–25 July 2014, vol. 6, p. 189. CRC Press (2015)

    Google Scholar 

  11. Wen, W.-T., Wang, C.-D., Wu, D.-S., Xie, Y.-Y.: An ACO-based scheduling strategy on load balancing in cloud computing environment. In: Ninth International Conference on Frontier of Computer Science and Technology (FCST) (2015)

    Google Scholar 

  12. Wu, X., et al.: A task scheduling algorithm based on QoS-driven in cloud computing. Procedia Comput. Sci. 17, 1162–1169 (2013)

    Article  Google Scholar 

  13. Singh, S., Chana, I.: QRSF: QoS-aware resource scheduling framework in cloud computing. J. Supercomputing 71(1), 241–292 (2015)

    Article  Google Scholar 

  14. Zuo, L., Shu, L., Dong, S., Zhu, C., Hara, T.: A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. IEEE Access 3, 2687–2699 (2015)

    Article  Google Scholar 

  15. Zhan, Z.-H., et al.: Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Computing Surveys (CSUR) 47(4), 63 (2015)

    Article  Google Scholar 

  16. Manvi, S.S., Shyam, G.K.: Resource management for Infrastructure as a Service (IaaS) in cloud computing: a survey. J. Netw. Comput. Appl. 41, 424–440 (2014)

    Article  Google Scholar 

  17. Liu, Z., Wang, X.: A PSO-based algorithm for load balancing in virtual machines of cloud computing environment. In: Tan, Y., Shi, Y., Ji, Z. (eds.) ICSI 2012. LNCS, vol. 7331, pp. 142–147. Springer, Heidelberg (2012). doi:10.1007/978-3-642-30976-2_17

    Chapter  Google Scholar 

  18. Fister, I., Yang, X.-S., Brest, J.: A comprehensive review of firefly algorithms. Swarm Evol. Comput. 13, 34–46 (2013)

    Article  Google Scholar 

  19. Cho, K.-M., et al.: A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing. Neural Comput. Appl. 26(6), 1297–1309 (2015)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, India

    Gundipika Kaur & Kiranbir Kaur

Authors
  1. Gundipika Kaur
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  2. Kiranbir Kaur
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Correspondence to Gundipika Kaur .

Editor information

Editors and Affiliations

  1. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

  2. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  3. Department of Mathematics, National Institute of Technology, Silchar, Silchar, Assam, India

    Kedar Nath Das

  4. School of Mathematics, Thapar Institute of Engineering and Technology University, Patiala, Punjab, India

    Arvind Kumar Lal

  5. School of Mathematics, Thapar University Patiala, Patiala, Punjab, India

    Harish Garg

  6. Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya K. Nagar

  7. Department of Applied Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

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Kaur, G., Kaur, K. (2017). An Adaptive Firefly Algorithm for Load Balancing in Cloud Computing. In: Deep, K., et al. Proceedings of Sixth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 546. Springer, Singapore. https://doi.org/10.1007/978-981-10-3322-3_7

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  • DOI: https://doi.org/10.1007/978-981-10-3322-3_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3321-6

  • Online ISBN: 978-981-10-3322-3

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