AN ENERGY EFFICIENT AND SELF ADAPTIVE RESOURCE ALLOCATION FRAMEWORK USING MODIFIED CLONAL SELECTION ALGORITHM FOR CLOUD BASED SOFTWARE SERVICES

Authors

  • Dr Balamurugan E University of Africa, Toru-Orua, Nigeria Author
  • Md. Shahidul Hasan Research Scholar, Texila American University, Guyana Author
  • Mohammad Shawkat Akbar Almamun Research Scholar, Texila American University, Guyana Author
  • Sangeetha K University of Africa, Toru-Orua, Nigeria Author

DOI:

https://doi.org/10.61841/2wm1g637

Keywords:

IT industries, Virtual Machine, Modified Clonal Selection Algorithm (MCSA)

Abstract

Cloud computing is a prominent model for computation, which lays greater impact on IT industries and the way in which software applications are developed and deployed. The workload in cloud computing changes dynamically and thereby introduces many challenges in on-demand resource provisioning and allocation. Resource allocation techniques are not widely available. Those that exist also have high energy consumption as well as the ineffectiveness of allocation. Frequent virtual machine switches in virtual machine allocation lead to a tradeoff between QoS and consumption of energy. Hence there is a need to find a solution that provides a quality of service and low energy consumption for use in cloud services. The proposed work suggests a self-adaptive framework for allocation of resources composed of feedback loops to meet the QoS requirements as well as less energy consumption in cloud computing services. The self-adaptive resource allocation framework comprises three stages, namely the QoS prediction model, the improved cuckoo search algorithm (ICSA)-based runtime decision algorithm, and the Energy Efficient Model (EEM). 

In the first stage, the QoS prediction model works over historical data of a system that aids in improving the accuracy rate of QoS prediction. Second, an energy-efficient model based on the Modified Clonal Selection Algorithm (MCSA) has been suggested for reducing the energy consumption. The third stage is a runtime decision-making algorithm that works according to the ICSA and helps to find proper operations for allocating resources in an online real context. Experiments are conducted, and the results convey that the proposed work could decrease the number of times hosts are switched on/off. This technique, when compared to existing methods, helps to save power, provides cost-effectiveness, and improves the QoS for cloud computing environments. 

Downloads

Download data is not yet available.

References

1. Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P. P., Kolodziej, J., Balaji, P.,... & Khan, S.U. (2016). A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing, 98(7), 751-774.

2. Shyamala, K. & Rani, T. S. (2015). An analysis on efficient resource allocation mechanisms in cloud computing. Indian Journal of Science and Technology, 8(9), 814.

3. Buyya, R., Beloglazov, A., & Abawajy, J. (2010). Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. arXiv preprint arXiv:1006.0308.

4. Mohan, N. R., & Raj, E. B. (2012, November). Resource Allocation Techniques in Cloud Computing—Research Challenges for Applications. In 2012, fourth international conference on computational intelligence and communication networks (pp. 556–561). IEEE.

5. Banerjee, A., Agrawal, P., & Iyengar, N. C. S. (2013). Energy efficiency model for cloud computing. International Journal of Energy, Information, and Communications, 4(6), 29-42.

6. Li, H., Zhu, G., Cui, C., Tang, H., Dou, Y., & He, C. (2016). Energy-efficient migration and consolidation

algorithm of virtual machines in data centers for cloud computing. Computing, 98(3), 303-317.

7. Parikh, S. M. (2013, November). A survey on cloud computing resource allocation techniques. In 2013 Nirma University International Conference on Engineering (NUiCONE) (pp. 1-5). IEEE.

8. Yang, Z., Liu, M., Xiu, J., & Liu, C. (2012). Study on cloud resource allocation strategy based on particle swarm ant colony optimization algorithm. In 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems (Vol. 1, pp. 488-491). IEEE.

9. Shiny, J. J., & Vignesh, S. (2017, January). A comprehensive review on QoS measures for resource allocation in a cloud environment. In 2016 Eighth International Conference on Advanced Computing (ICoAC) (pp. 157-164). IEEE.

10. Li, Y. K. (2014). QoS-aware dynamic virtual resource management in the cloud. In Applied Mechanics and Materials (Vol. 556, pp. 5809-5812). Trans Tech Publications Ltd.

11. Kumar, N., & Saxena, S. (2015). A preference-based resource allocation in cloud computing systems.Procedia computer science, 57, 104-111.

12. Goudarzi, H., & Pedram, M. (2011, July). Multi-dimensional SLA-based resource allocation for multi-tier cloud computing systems. In 2011 IEEE 4th International Conference on Cloud Computing (pp. 324-331). IEEE.

13. Wang, H., Tianfield, H., & Mair, Q. (2014). Auction-based resource allocation in cloud computing. Multiagent and Grid Systems, 10(1), 51-66.

14. RamMohan, N. R., & Baburaj, E. (2014). Resource Allocation Using Interference-Aware Techniques in a Cloud Computing Environment. International Journal Of Digital Content Technology And Its Applications, 8(1), 35.

15. Xiong, A. P. & Xu, C. X. (2014). Energy-efficient multi-resource allocation of virtual machines based on PSO in a cloud data center. Mathematical Problems in Engineering, 2014.

16. Sharma, N. K., & Reddy, G. R. M. (2015, March). Novel energy-efficient virtual machine allocation at the data center using a genetic algorithm. In the 2015 3rd International Conference on Signal Processing, Communication, and Networking (ICSCN) (pp. 1-6). IEEE.

17. Wang, S., Liu, Z., Zheng, Z., Sun, Q., & Yang, F. (2013). Particle swarm optimization for energy-aware virtual machine placement optimization in virtualized data centers. In 2013 International Conference on Parallel and Distributed Systems (pp. 102-109). IEEE.

18. Liu, X. F., Zhan, Z. H., Du, K. J., & Chen, W. N. (2014, July). Energy-aware virtual machine placement scheduling in cloud computing based on an ant colony optimization approach. In Proceedings of the 2014 annual conference on genetic and evolutionary computation (pp. 41–48).

19. Joseph, C. T., Chandrasekaran, K., & Cyriac, R. (2015). A novel family genetic approach for virtual machine allocation. Procedia Computer Science, 46, 558-565.

20. Tang, M., & Pan, S. (2015). A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers. Neural processing letters, 41(2), 211-221.

21. Marphatia, A., Muhnot, A., Sachdeva, T., Shukla, E., & Kurup, L. (2013). Optimization of FCFS-based resource provisioning algorithms for cloud computing. IOSR Journal of Computer Engineering (IOSRJCE) (Mar.-Apr. 2013), 10(5), 1-5.

22. Srinivasa, K. G., Srinidhi, S., Kumar, K. S., Shenvi, V., Kaushik, U. S., & Mishra, K. (2014, February). Game theoretic resource allocation in cloud computing. In The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014) (pp. 36-42). IEEE.

23. Chen, X., Wang, H., Ma, Y., Zheng, X., & Guo, L. (2020). Self-adaptive resource allocation for cloud-based software services based on an iterative QoS prediction model. Future Generation Computer Systems, 105, 287-296.

24. Andrews, S., Tsochantaridis, I., & Hofmann, T. (2003). Support vector machines for multiple-instance learning. In Advances in neural information processing systems (pp. 577–584).

25. Canziani, A., Paszke, A., & Culurciello, E. (2016). An analysis of deep neural network models for practical applications. arXiv preprint arXiv:1605.07678.

26. De Castro, L. N., & Von Zuben, F. J. (2000, July). The clonal selection algorithm with engineering applications. In Proceedings of GECCO (Vol. 2000, pp. 36-39).

27. Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) (pp. 210-214). IEEE.

Downloads

Published

30.04.2020

Issue

Vol. 24 No. 2 (2020): 24 (2) 2020

Section

Articles

License

Copyright (c) 2020 AUTHOR

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

You are free to:

  1. Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
  2. Adapt — remix, transform, and build upon the material for any purpose, even commercially.
  3. The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  1. Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  2. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .

No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

How to Cite

E, B., Shahidul Hasan, M., Shawkat Akbar Almamun, M., & K, S. (2020). AN ENERGY EFFICIENT AND SELF ADAPTIVE RESOURCE ALLOCATION FRAMEWORK USING MODIFIED CLONAL SELECTION ALGORITHM FOR CLOUD BASED SOFTWARE SERVICES. International Journal of Psychosocial Rehabilitation, 24(2), 5182-5203. https://doi.org/10.61841/2wm1g637