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).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jadeja Y, Modi K (2012) Cloud computing-concepts, architecture and challenges. In: International conference on computing electronics and electrical technologies (ICCEET), pp 877–880
Dorigo M, Birattari M (2010) Ant colony optimization. Springer, US, pp 36–39
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 325–331
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 992–997
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 600–604
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. IEEE
Parida S, Panchal B (2018) Review paper on throttled load balancing algorithm in cloud computing environment
Aliyu AN, Souley B (2019) Performance analysis of a hybrid approach to enhance load balancing in a heterogeneous cloud environment
Hirsch P (2019) Task scheduling using improved weighted round robin techniques
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 522–526
Rathore J, Keswani B, Rathore V (2019) Analysis of load balancing algorithms using cloud analyst. In: Emerging trends in expert applications and security, pp 291–298
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Nakrani, T., Hiran, D., Sindhi, C., Sandhi, M. (2021). Genetic Algorithm Based Task Scheduling for Load Balancing in Cloud. In: Kotecha, K., Piuri, V., Shah, H., Patel, R. (eds) Data Science and Intelligent Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 52. Springer, Singapore. https://doi.org/10.1007/978-981-15-4474-3_32
Download citation
DOI: https://doi.org/10.1007/978-981-15-4474-3_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-4473-6
Online ISBN: 978-981-15-4474-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)