Fibonacci Series based Virtual Machine Selection for Load Balancing in Cloud Computing

  • Authors

    • Japman Kaur Dhaliwal
    • Mohd Naseem
    • Aadil Ahamad Lawaye
    • Ehtesham Husain Abbasi
    2018-07-20
    https://doi.org/10.14419/ijet.v7i3.12.17634
  • Cloud Computing, Load Balancing, Round Robin, Fibonacci sequence, Virtual Machine.

  • Abstract

    The rapid advancement of the internet has given birth to many technologies. Cloud computing is one of the most emerging technology which aim to process large scale data by using the computational capabilities of shared resources. It gives support to the distributed parallel processing. Using cloud computing, we can process data by paying according to its uses which eliminates the requirement of device by individual users. As cloud computing grows, more users get attracted towards it. However, providing an efficient execution time and load distribution is a major challenging issue in the distributed systems. In our approach, weighted round robin algorithm is used and benefits of Fibonacci sequence is combined which results in better execution time than static round robin. Relevant virtual machines are chosen and jobs are assigned to them. Also, number of resources being utilized concurrently is reduced, which leads to resource saving thereby reducing the cost. There is no need to deploy new resources as resources such as virtual machines are already available.

     

     

  • References

    1. [1] P. Mell and T. Grance, “The NIST Definition of Cloud Computingâ€, National Institute of Standards and Technology, Sept. 2011.

      [2] Qaisar, E. J. “Introduction to cloud computing for developers: Key concepts, the players and their offerings.†In Information Technology Professional Conference (TCF Pro IT), 2012 IEEE TCF (pp. 1-6). IEEE, 2013.

      [3] Jadeja, Y., & Modi, K. “Cloud computing-concepts, architecture and challengesâ€. In Computing, Electronics and Electrical Technologies (ICCEET), 2012 International Conference on (pp. 877-880). IEEE, 2012

      [4] Avram M., “Advantages and challenges of adopting cloud computing from an enterprise perspectiveâ€, in Elsevier proc. of 7th International Conference Interdisciplinary in Engineering (INTER-ENG), pp. 529-534, 2013.

      [5] Seemakuthi S., “A Review on Various Scheduling Algorithmsâ€, International Journal of Scientific & Engineering Research, Vol. 6, No. 12, Pp. 769-779, 2015.

      [6] Sommer M., “Predictive Load Balancing in Cloud Computing Environments Based on Ensemble Forecastingâ€, Autonomic Computing (ICAC), IEEE International Conference, 2016.

      [7] Belalem, G., Tayeb, F., & Zaoui, W. (2010). Approaches to improve the resources management in the simulator CloudSim. Information Computing and Applications, 189-196.

      [8] Aslam S., “Load balancing algorithms in cloud computing: A survey of modern techniquesâ€, Software Engineering Conference (NSEC), 2015 National, February 2016

      [9] Kashyap, Dharmesh, and Jaydeep Viradiya. "A survey of various load balancing algorithms in cloud computing." Int. J. Sci. Technol. Res 3, no. 11 (2014): 115-119.

      [10] Dhakad K., “Performance Analysis Of Round Robin Scheduling Using Adaptive Approach Based On Smart Time Slice And Comparison With SRRâ€, International Journal of Advances in Engineering & Technology, Vol. 3, No. 2, Pp. 333-339, May 2012.

      [11] Saranya D., “Load Balancing Algorithms in Cloud Computing: A Reviewâ€, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 5, No. 7, Pp. 1107-1111, 2015.

      [12] Datta L., “Efficient Round Robin Scheduling Algorithm with Dynamic Time Sliceâ€, I.J. Education and Management Engineering, Vol. 2, Pp. 10-19, 2015

      [13] Kanagaraj, Gowtham, Naveen Shanmugasundaram, and Sathish Prakash. "Adaptive Load Balancing Algorithm Using Service Queue." Jurnal. ICCSIT (2012).

      [14] Zhao, Y., Chen, L., Li, Y., & Tian, W “Efficient task scheduling for Many Task Computing with resource attribute selection. China Communications, vol. 11(no. 12), pp. 125-140, 2014.

      [15] S. G. Damanal and G. R. Reddy, “Optimal Load Balancing in Cloud Computing by Efficient Utilization of Virtual Machinesâ€, in IEEE proc. of 6th International Conference on Communication Systems and Networks (COMSNETS), Jan. 2014.

      [16] Zuo, L., Shu, L., Dong, S., Zhu, C., & Hara, T. “A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computingâ€. IEEE Access, vol. 3, pp. 26872699, 2015.

      [17] Teymoori, P., Sohraby, K., & Kim, K.â€A fair and efficient resource allocation scheme for multi-server distributed systems and networksâ€. IEEE Transactions on Mobile Computing, vol. 15, no. 9, pp. 2137-2150, 2016

      [18] Ariharan, V., & Manakattu, S. S. (2015, March). Neighbour Aware Random Sampling (NARS) algorithm for load balancing in Cloud computing. In Electrical, Computer and Communication Technologies (ICECCT), 2015 IEEE International Conference on (pp. 1-5). IEEE.

      [19] Calheiros, R. N., Ranjan, R., De Rose, C. A., & Buyya, R. (2009). Cloudsim: A novel framework for modeling and simulation of cloud computing infrastructures and services. arXiv preprint arXiv:0903.2525.

      [20] Naseem M, Kumar C. Congestion-aware Fibonacci sequence based multipath load balancing routing protocol for MANETs. Wireless Personal Communications 2015; 84(4):2955–2974.

      [21] Naseem, Mohd, and Chiranjeev Kumar. "QSLB: queue size based single path load balancing routing protocol for MANETs." International Journal of Ad Hoc and Ubiquitous Computing 24.1-2 (2017): 90-100.

      [22] Naseem, Mohd, and Chiranjeev Kumar. "Queueâ€based multiple path load balancing routing protocol for MANETs." International Journal of Communication Systems 30, no. 6 (2017).

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  • How to Cite

    Kaur Dhaliwal, J., Naseem, M., Ahamad Lawaye, A., & Husain Abbasi, E. (2018). Fibonacci Series based Virtual Machine Selection for Load Balancing in Cloud Computing. International Journal of Engineering & Technology, 7(3.12), 1071-1077. https://doi.org/10.14419/ijet.v7i3.12.17634

    Received date: 2018-08-16

    Accepted date: 2018-08-16

    Published date: 2018-07-20