Innovative Architectures and Algorithms for Optimized Resource Management in Cloud Computing
Keywords:
Cloud computing, resource management, optimization algorithms, energy efficiency, dynamic scheduling, heuristic methodsAbstract
Cloud computing has revolutionized the IT landscape by enabling scalable and flexible resource management, yet its adoption faces significant challenges due to inefficiencies in resource allocation, energy consumption, and load balancing. This paper explores innovative architectures and algorithms for optimized resource management in cloud computing, including adaptive scheduling, heuristic-based allocation, and energy-aware algorithms. A detailed review of existing solutions provides insights into their limitations and potential improvements. The study also proposes a hybrid architecture integrating machine learning models and dynamic optimization techniques to achieve superior performance in terms of cost, energy efficiency, and reliability. By combining theoretical frameworks with practical implementations, this research contributes to the development of more efficient, sustainable, and scalable cloud computing infrastructures.
References
1. Zhang, Qi, Lu Cheng, and Raouf Boutaba. "Cloud computing: state-of-the-art and research challenges." Journal of Internet Services and Applications, vol. 1, no. 1, 2010, pp. 7–18.
2. Beloglazov, Anton, Jemal Abawajy, and Rajkumar Buyya. "Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing." Future Generation Computer Systems, vol. 28, no. 5, 2012, pp. 755–768.
3. Verma, Anshul, Sandeep Kaushik, and Ramesh Subramaniam. "Dynamic resource allocation techniques for efficient workload management in cloud environments." IEEE Transactions on Cloud Computing, vol. 2, no. 3, 2014, pp. 333–347.
4. Chaisiri, Sivadon, Bu-Sung Lee, and Dusit Niyato. "Optimization of resource provisioning cost in cloud computing." IEEE Transactions on Services Computing, vol. 5, no. 2, 2012, pp. 164–177.
5. Li, Kejiang, et al. "Cloud task scheduling based on load balancing ant colony optimization." Proceedings of the 2011 Sixth Annual ChinaGrid Conference, 2011, pp. 3–9.
6. Buyya, Rajkumar, Chee Shin Yeo, and Srikumar Venugopal. "Market-oriented cloud computing: Vision, hype, and reality for delivering IT services as computing utilities." Proceedings of the 2008 10th IEEE International Conference on High Performance Computing and Communications, 2008, pp. 5–13.
7. Calheiros, Rodrigo N., et al. "CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms." Software: Practice and Experience, vol. 41, no. 1, 2011, pp. 23–50.
8. Zaman, Salman, and Daniel Grosu. "A combinatorial auction-based mechanism for dynamic VM provisioning and allocation in clouds." IEEE Transactions on Cloud Computing, vol. 1, no. 2, 2013, pp. 129–141.
9. Wu, Zhifeng, Saurabh Kumar Garg, and Rajkumar Buyya. "SLA-based resource allocation for software as a service provider (SaaS) in cloud computing environments." Proceedings of the 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2013, pp. 195–204.
10. Goudarzi, Mohammad, Mohammad Ghasemi, and Massoud Pedram. "SLA-based optimization of power and migration cost in cloud computing." Proceedings of the 2013 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2013, pp. 593–598.
Published
Issue
Section
License
Copyright (c) 2024 Lalu D. Damanhuri (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
