Abstract
The primary requirement of heterogeneous computing is minimization of task waiting time in order to well regulate services to the users with efficient resource utilization. In this paper, we propose a dynamic load balancing with advanced reservation (DLBAR) of resources that commits advanced reservation of resources to tasks to minimize load imbalance on nodes with optimum makespan. The objective of this work is to allocate and calculate load earlier in advance on each resource before task execution started to efficiently distribute load among available resources, and other parameters like makespan are computed for performance evaluation. In order to show the effectiveness of proposed model, an unbiased comparative performance analysis is carried out with other well-known load balancing heuristic approach available in the literature. The simulation study reveals the motivation of algorithm with the superior performance of the proposed algorithm on account of all considered parameters under study.
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
Xhafa, F., Abraham, A.: Computational models and heuristic methods for Grid scheduling problems, Vol. 4. Future generation computer systems. 1(2010) 608–621.
Shah, R., Veeravalli, B., Misra, M.: On the design of adaptive and decentralized load balancing algorithms with load estimation for computational grid environments, Vol. 18. IEEE Transactions on parallel and distributed systems, (2007) 1675–1686.
Braun, T. D., Siegel, H. J., Beck, N., Bölöni, L. L., Maheswaran, M., Reuther, A. I., Freund, R. F.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems, Vol. 61. Journal of Parallel and Distributed computing. (2001) 810–837.
Saxena, R., Kumar, A., Kumar, A., & Saxena, S.: AHSWDG: An Ant Based Heuristic Approach to Scheduling and Workload Distribution in Computational Grids. In Computational Intelligence & Communication Technology (CICT), 2015 IEEE International Conference. 2(2015) 569–574.
Ludwig, S. A., & Moallem, A.: Swarm intelligence approaches for grid load balancing, Vol. 3. Journal of Grid Computing. 3(2011) 279–301.
Hao, Y., Liu, G., & Wen, N. An enhanced load balancing mechanism based on deadline control on GridSim, Vol. 28. Future Generation Computer Systems, 4(2012) 657–665.
Rajavel, R.: De-centralized load balancing for the computational grid environment. Communication and Computational Intelligence (INCOCCI), 2010 International Conference, 10(2010) 419–424.
Rathore, N., & Chana, I. A sender initiate based hierarchical load balancing technique for grid using variable threshold value. In Signal Processing, Computing and Control (ISPCC), IEEE International Conference. (2013, September) 1–6.
Sulistio, A., Buyya, R.: A grid simulation infrastructure supporting advance reservation. 16th International Conference on Parallel and Distributed Computing and Systems (PDCS 2004). 11(2004) 9–11.
Kokilavani, T., Amalarethinam, D. G.: Load balanced min-min algorithm for static meta-task scheduling in grid computing, Vol. 20. International Journal of Computer Applications, 2(2011) 43–49.
Muthuvelu, N., Liu, J., Soe, N. L., Venugopal, S., Sulistio, A., Buyya, R.: A dynamic job grouping-based scheduling for deploying applications with fine-grained tasks on global grids. Proceedings of the 2005 Australasian workshop on Grid computing and e-research, Vol. 44. Australian Computer Society, Inc. (2005, January) 41–48.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sheikh, S., Nagaraju, A., Shahid, M. (2018). Dynamic Load Balancing with Advanced Reservation of Resources for Computational Grid. In: Pattnaik, P., Rautaray, S., Das, H., Nayak, J. (eds) Progress in Computing, Analytics and Networking. Advances in Intelligent Systems and Computing, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-7871-2_48
Download citation
DOI: https://doi.org/10.1007/978-981-10-7871-2_48
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7870-5
Online ISBN: 978-981-10-7871-2
eBook Packages: EngineeringEngineering (R0)