The Hybrid Algorithm for Load Balancing and Efficient Management of Resources in Cloud Computing

Authors

  • Shabir Ahmad Teli  M.Tech (Scholar), CSE Depatment Desh Bhagat University, Mandi Gobindgarh, Punjab, India
  • Manoj Kumar Srivastava  CSE Department, Desh Bhagat University, Mandi Gobindgarh, Punjab, India

Keywords:

Cloud computing, load balancing, simulation, CloudSim

Abstract

The field of parallel and distributed computing has considerably transformed in cloud computing and load balancer techniques play an important role in the next generation of cloud computing for the storage and access to the applications. Load balancing is a strategy for distributing workload among numerous computers or other resources through network links in order to optimise use of resources, maximum performance and minimise reaction time, and prevent overload. Load balance can be achieved by using resource efficiently to meet end user demands, and helps servers less computing, volunteer computing, software defining computing, etc. The recent development of technology is a key issue in cloud computing for the control of resources or load balancing. This report describes the conceptualization for the efficient management of resources, enhances the stability of web services and presents numerous approaches for load balance. Round Robin algorithms (SLBA), dynamic algorithms for load balancing (DLBA) and dynamic nature inspired algorithms for load balancing (NDLBA). DLBA and NDLBA are more efficient than SLBA according to experimental results. This article presents the future guidelines for cloud computing.

References

  1. Buyya, R., Ranjan, R., Calheiros, R. N. (2009) „Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: Challenges and opportunities.‟ International Conference on High Performance Computing & Simulation’, HPCS ’09., pp.1-11.
  2. Y. Mansouri, A. N. Toosi, R. Buyya, Data Storage Management in Cloud Environments: Taxonomy, Survey, and Future Directions, ACM Computing Surveys (2017) 1-51.
  3. D. M. Shila, W. Shen, Y. Cheng, X. Tian, a. X. S. Shen, AMCloud: Toward a Secure Autonomic Mobile Ad Hoc Cloud Computing System, IEEE Wireless Communications 24 (2) (2017) 74-81.
  4. D. Petcu, G. Macariu, S. Panica, C. Crciun, Portable Cloud Applications: From Theory to Practice, Future Generation Computer Systems 29 (6) (2013) 1417-1430.
  5. Gill, Sukhpal Singh, and Rajkumar Buyya. "SECURE: Self-Protection Approach in Cloud Resource Management." IEEE Cloud Computing 5, no. 1 (2018): 60-72.
  6. Haiying Shen, “RIAL: Resource Intensity Aware Load Balancing in Clouds”, Volume: PP, Issue: 99, 2017, DOI 10.1109/TCC.2017.2737628, IEEE Transactions on Cloud Computing.
  7. A. Sharma, S. Verma, "A Survey Report on Load Balancing Algorithm in Grid Computing Environment", Int. J. Adv. Engg. Res. Studies/IV/II/Jan.-March 128, pp:132 -138, 2015.
  8. R. M. Singh, S. Paul, A. Kumar, "Task scheduling in cloud computing: Review." International Journal of Computer Science and Information Technologies 5.6, pp: 7940-7944, 2014.
  9. S. Nagadevi, K. Satyapriya, Dr.D.Malathy, “A Survey on Economic Cloud Schedulers for Optimized Task Scheduling”, International Journal of Advanced Engineering Technology, 2013.
  10. W. Huai, Z. Qian, X. Li, G. Luo, and S. Lu, “Energy Aware Task Scheduling in Data Centers, Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications”, volume: 4, number: 2, pp. 18-38, 2013.
  11. D. Kliazovich, S. T. Arzo, F. Granelli, P. Bouvry, S. U. Khan, “e-STAB: Energy-Efficient Scheduling for Cloud Computing Applications with Traffic Load Balancing”, IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing e-STAB, 2013.
  12. S. H. Jang, T. Y. Kim, J. K. Kim , J. S. L. School, “The Study of Genetic Algorithm-based Task Scheduling for Cloud Computing”, International Journal of Control and Automation Vol. 5, No. 4, December, 2012
  13. S. Sudhir, “Survey on Scheduling Issues in Cloud Computing”, Procedia Engineering, Elsevier, 2012.
  14. A. mantri, S. Nandi, G. Kumar, S. Kumar, High Performance Architecture and Grid Computing Computing, International Conference HPAGC 2011, Chandigarh, India, July, Proceedings, 2011.
  15. Ashkan Paya, Dan C Marinescu, "Energy-aware Load Balancing and Application Scaling for the Cloud Ecosystem", IEEE Transactions on Cloud Computing, Vol: 5, Issue: 1, Jan.-March 1, 2017, pp. 1-14.
  16. Calheiros R. N., Ranjan, R., Rose, C. A. F. D., Buyya, R. (2009) „CloudSim: a novel framework for modeling and simulation of cloud computing infrastructures and services‟ Computing Research Repository, vol. Abs/0903.2525.
  17. Sharma S, Singh S, Sharma M. Performance analysis of load balancing algorithms. World Academy of Science, Engineering and Technology. 2008 Feb 21; 38(3):269-72.
  18. Hou L, Zhao S, Xiong X, Zheng K, Chatzimisios P, Hossain MS, Xiang W. Internet of things cloud: Architecture and implementation. IEEE Communications Magazine. 2016 Dec; 54(12):32-9.
  19. X. Xu, X. Zhao, A Framework for Privacy-Aware Computing on Hybrid Clouds with Mixed-Sensitivity Data, in: 17th IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Symposium on Cyberspace Safety and Security, and 12th IEEE International Conference on Embedded Software and Systems, 2015, pp. 1344-1349.
  20. A. N. Toosi, R. O. Sinnott, R. Buyya, Resource Provisioning for Data-intensive Applications with Deadline Constraints on Hybrid Clouds Using Aneka, Future Generation Computer Systems
  21. Li, Z., Yan, C., Yu, L., & Yu, X. (2018). “Energy-aware and multi-resource overload probability constraint-based virtual machine dynamic consolidation method.” Future Generation Computer Systems, 80, 139-156.
  22. Li, Z., Yan, C., Yu, X., & Yu, N. (2017). “Bayesian network-based virtual machines consolidation method.” Future Generation Computer Systems, 69, 75-87.
  23. Ranjbari, M., & Torkestani, J. A. (2018). “A learning automata-based algorithm for energy and SLA efficient consolidation of virtual machines in cloud data centers.” Journal of Parallel and Distributed Computing, 113, 55-62.
  24. Mahdhi, T., & Mezni, H. (2018). “A prediction-Based VM consolidation approach in IaaS Cloud Data Centers.” Journal of Systems and Software, 146, 263-285.
  25. Abdelsamea, A., El-Moursy, A. A., Hemayed, E. E., & Eldeeb, H. (2017). “Virtual machine consolidation enhancement using hybrid regression algorithms.” Egyptian Informatics Journal, 18(3), 161-170.
  26. Haiying Shen, “RIAL: Resource Intensity Aware Load Balancing in Clouds”, Volume: PP, Issue: 99, 2017, DOI 10.1109/TCC.2017.2737628, IEEE Transactions on Cloud Computing.
  27. Wang SC, Yan KQ, Liao WP, Wang SS. Towards a load balancing in a three-level cloud computing network. In 2010 3rd International Conference on Computer Science and Information Technology 2010 Jul 9 (Vol. 1, pp. 108-113). IEEE
  28. R. Trapero, J. Modic, M. Stopar, A. Taha, N. Suri, A Novel Approach to Manage Cloud Security SLA Incidents, Future Generation Computer Systems 72 (2017) 193 - 205.
  29. T. A. Nguyen, D. S. Kim, J. S. Park, Availability Modeling and Analysis of a Data Center for Disaster Tolerance, Future Generation Computer Systems 56 (2016) 27 -50.
  30. X. Yuan, G. Min, L. T. Yang, Y. Ding, Q. Fang, A Game Theory-based Dynamic Resource Allocation Strategy in Geo-distributed Datacenter Clouds, Future Generation Computer Systems 76 (2017) , 63 - 72.
  31. A. Hameed, A. Khoshkbarforoushha, R. Ranjan, P. P. Jayaraman, J. Kolodziej, P. Balaji, S. Zeadally, Q. M.   Malluhi, N. Tziritas, A. Vishnu, S. U. Khan, A. Zomaya, A Survey and Taxonomy on Energy E cient Resource Allocation Techniques for Cloud Computing Systems, Computing 98 (7) (2016) 751-774.
  32. J. Lopez, R. Rios, F. Bao, G. Wang, Evolving Privacy: From Sensors to the Internet of Things, Future Generation Computer Systems 75 (2017) 46 -57.
  33. Blesson Varghese and Rajkumar Buyya, Next Generation Cloud Computing: New Trends and Research Directions," Future Generation Computer Systems, ISSN: 0167-739X, Elsevier Press, Amsterdam, The Netherlands, 2017 (in press).

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2021-08-30

Issue

Volume 7, Issue 4, July-August-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

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

How to Cite

[1]
Shabir Ahmad Teli, Manoj Kumar Srivastava, " The Hybrid Algorithm for Load Balancing and Efficient Management of Resources in Cloud Computing" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 4, pp.341-349, July-August-2021.