A review of cloud based applications and efficient resource utilization and allocation
DOI:
https://doi.org/10.29070/amncf555Keywords:
Cloud Computing, Benefits Machine Learning, Resource Allocation, VirtualizationAbstract
Cloud-based applications have revolutionized computing by offering scalable, flexible, and cost-effective solutions across diverse industries. These applications rely on efficient resource utilization and allocation to ensure optimal performance, minimize costs, and enhance user satisfaction. This review explores the evolution, architecture, and benefits of cloud-based applications, emphasizing the challenges associated with resource management. Key strategies for improving resource allocation, including virtualization, load balancing, task scheduling, and dynamic resource provisioning, are analyzed. Additionally, emerging technologies such as artificial intelligence, machine learning, and edge computing are examined for their role in optimizing resource allocation. Through a comprehensive assessment of existing studies and methodologies, this review highlights the importance of innovative solutions for addressing inefficiencies, managing workloads, and reducing latency.
Downloads
References
1. Al-Tous H. and Barhumi I. (2016) ‘Resource allocation for multiple-sources singlerelay cooperative communication OFDMA systems’, IEEE Transactions on Mobile Computing, Vo15, No. 4, pp. 964-981.
2. B. Lawson, E. Smirni, Power-aware resource allocation in high-end systems via online simulation, in Proceedings of the19th Annual International Conference on Supercomputing, Cambridge, USA 2005.
3. Chen S. Wu J. and Lu Z. (2012) ‘A cloud computing resource scheduling policy based on genetic algorithm with multiple fitness’, In Computer and Information Technology (CIT), 2012 IEEE 12th International Conference on, pp. 177-184, IEEE, 2012
4. Dabbagh, M, Hamdaoui, B, Guizani, M & Rayes, A 2015, ‘Toward energy-efficient cloud computing: Prediction, consolidation, and overcommitment', IEEE- Network, vol. 29, no. 2, pp. 56-61.
5. Hitesh A. Ravani, Hitesh A. Bheda, Vrunda J. Patel, “Genetic Algorithm Based Resource Scheduling Technique in Cloud Computing”, International Journal of Advanced Research in Computer Science and Management Studies, Volume 1, Issue 7, pp. 168-174, 2013.
6. J. E. Haddad, M. Manouvrier, G. Ramirez, and M. Rukoz, QoS-driven selection of web services for transactional composition, InProc. 6th Int’l Conf. Web Services (ICWS’08), 2008, 653–660
7. L. Shao, J. Zhang, Y. Wei, J. Zhao, B. Xie, and H. Mei. Personalized QoS prediction for web services via collaborative filtering, InProc. 5th Int’l Conf. Web Services (ICWS’07), 2007, 439–446.
8. Kapil Bakshi, Cisco Cloud Computing - Data Center Strategy, Architecture, and Solutions, Point of View White Paper for U.S. Public Sector 1st Edition Cisco Systems, Inc., 2009, 1-16
9. Ma Y.B. Jang S.H. and Lee J.S. (2011) ‘Ontology-based resource management for cloud computing’, In Asian Conference on Intelligent Information and Database Systems, pp. 343-352, Springer, Berlin, Heidelberg, 2011.
10. Sagar M.S. Singh B. and Ahmad W. (2013) ‘Study on cloud computing resource allocation strategies’, International Journal of Advance Research and Innovation, Vol. 1, No. 3, pp. 107-114.
11. Reddy C. and Suchithra R. (2016) ‘Virtual Machine Migration in Cloud Data Centers for Resource management’, International Journal of Engineering and Computer Science, Vol. 5, no 09, pp.18029-18034
12. Vignesh V. Sendhil Kumar K.S. and Jaisankar N. (2013) ‘Resource management and scheduling in cloud environment’, International journal of scientific and research publications Vol. 3, No. 6, pp. 1
13. Wang S.C. Yan K.Q. Liao W.P. and Wang S.S (2010) ‘Towards a load balancing in a three-level cloud computing network’, In Computer Science and information technology (ICCSIT), 2010 3rd IEEE International Conference on, Vol. 1, pp. 108- 113, IEEE, 2010.
14. Xie, R.; Jia, X.; Yang, K.; Zhang, B. Energy saving virtual machine allocation in cloud computing. Distributed Computing Systems Workshops (ICDCSW), 2013 IEEE 33rd International Conference on: IEEE; 2013. p. 132-137.
15. Yuan D. Yang Y. Liu X. and Chen J. (2010) ‘A data placement strategy in scientific cloud workflows’, Future Generation Computer Systems Vol. 26, No. 8, pp. 1200-1214.
16. Z. Zheng and M. R. Lyu, A distributed replication strategy evaluation and selection framework for fault tolerant web services, InProc. 6th Int’l Conf. Web Services (ICWS’08), 2008, 145–152.