Advertisement

Genetic Algorithm Based Task Scheduling for Load Balancing in Cloud

  • Tulsidas NakraniEmail author
  • Dilendra Hiran
  • Chetankumar Sindhi
  • MahammadIdrish Sandhi
Conference paper
  • 18 Downloads
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 52)

Abstract

Due to on-demand services for online resources like processing power, storage, software, infrastructure, etc., provided by cloud computing, it becomes incredibly popular today. So, intensity of Web data is increasing day by day. To balance the load of different nodes is the biggest challenge in this era. Load balancing method makes sure that no any node is over utilized or underutilized. It is considered to be an optimization problem. This paper proposes a genetic algorithm-based task scheduling for load balancing. The proposed strategy is simulated using cloud analyst. The results demonstrate that proposed method is better than existing algorithm like Round Robin (RR), Equally Spread Current Execution Load algorithm (ESCELEA) and Throttled algorithm (TA).

Keywords

Genetic algorithm Load balancing Cloud 

References

  1. 1.
    Jadeja Y, Modi K (2012) Cloud computing-concepts, architecture and challenges. In: International conference on computing electronics and electrical technologies (ICCEET), pp 877–880Google Scholar
  2. 2.
    Dorigo M, Birattari M (2010) Ant colony optimization. Springer, US, pp 36–39Google Scholar
  3. 3.
    Sun J, Feng B, Xu W (2004) Particle swarm optimization with particles having quantum behaviour. In: Proceedings of the 2004 congress on evolutionary computation. vol 1, pp 325–331Google Scholar
  4. 4.
    Benlalia Z, Beni-hssane A, Abouelmehdi K, Ezati A (2019) A new service broker algorithm optimizing the cost and response time for cloud computing. Procedia Comput Sci 992–997Google Scholar
  5. 5.
    Tyagi N, Rana A, Kansal V (2019) Creating elasticity with enhanced weighted optimization load balancing algorithm in cloud computing. In: Amity international conference on artificial intelligence, pp 600–604Google Scholar
  6. 6.
    Swarnakar S, Raza Z, Bhattacharya S, Banerjee C (2018) A novel improved hybrid model for load balancing in cloud environment. In: 2018 Fourth international conference on research in computational intelligence and communication networks (ICRCICN), pp 18–22. IEEEGoogle Scholar
  7. 7.
    Parida S, Panchal B (2018) Review paper on throttled load balancing algorithm in cloud computing environmentGoogle Scholar
  8. 8.
    Aliyu AN, Souley B (2019) Performance analysis of a hybrid approach to enhance load balancing in a heterogeneous cloud environmentGoogle Scholar
  9. 9.
    Hirsch P (2019) Task scheduling using improved weighted round robin techniquesGoogle Scholar
  10. 10.
    Alshammari D, Singer J, Storer T (2018) Performance evaluation of cloud computing simulation tools. In: 2018 IEEE 3rd international conference on cloud computing and big data analysis, pp 522–526Google Scholar
  11. 11.
    Rathore J, Keswani B, Rathore V (2019) Analysis of load balancing algorithms using cloud analyst. In: Emerging trends in expert applications and security, pp 291–298Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  • Tulsidas Nakrani
    • 1
    Email author
  • Dilendra Hiran
    • 1
  • Chetankumar Sindhi
    • 2
  • MahammadIdrish Sandhi
    • 3
  1. 1.Pacific UniversityUdaipurIndia
  2. 2.Nividous Software Solutions Pvt LtdAhmedabadIndia
  3. 3.Sankalchand Patel College of EngineeringVisnagarIndia

Personalised recommendations