Architecting Predictive Analytics-Based Dynamic Scaling Solutions for Multi-Tenant Cloud Platforms
Keywords:
dynamic scaling, predictive analyticsAbstract
Scalability is needed for multi-tenant cloud workload fluctuations. Cloud platforms must distribute resources to tenants for optimum performance and cost as enterprises grow. This research uses sophisticated auto-scaling methods, predictive models, and shared infrastructure cost management to deliver predictive analytics-based dynamic scaling solutions for multi-tenant cloud environments. Under shifting demand, multi-tenant cloud expansion requires technological and economic cloud resource management.
This study estimates resource requirements using predictive analytics and cloud platform dynamic scaling. Previous consumption, tenant behavior, and workload may anticipate resource demands. Projection may improve resource allocation. Machine learning, time-series forecasting, and hybrid approaches estimate multi-tenant resource use.
References
M. Zaharia, A. Konwinski, A. D. Joseph, R. Katz, and I. Stoica, “Improving MapReduce performance in heterogeneous environments,” Proceedings of the 8th USENIX conference on Operating Systems Design and Implementation, 2008, pp. 29-42.
Y. Zheng, C. Xu, J. Zhang, and L. Yao, "Dynamic scaling for cloud computing resources based on predictive analytics," IEEE Transactions on Cloud Computing, vol. 8, no. 4, pp. 1058-1069, July-August 2020.
M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, and R. Katz, "Above the clouds: A Berkeley view of cloud computing," UC Berkeley Technical Report No. UCB/EECS-2009-28, 2009.
T. N. Gia, S. Misra, and M. N. Nair, “Resource allocation in multi-tenant cloud environments: Challenges and solutions,” Journal of Cloud Computing: Advances, Systems and Applications, vol. 8, no. 1, pp. 45-61, Feb. 2020.
K. A. Hummel, D. P. Andersen, and D. W. P. Bauer, "Predictive scaling of cloud resources in multi-tenant systems," Proceedings of the 10th IEEE/ACM International Conference on Utility and Cloud Computing, 2017, pp. 151-158.
N. K. Sharma and S. R. Krishnan, "Machine learning-based predictive analytics for cloud resource scaling," IEEE Transactions on Services Computing, vol. 13, no. 3, pp. 467-479, May-June 2020.
M. Liu, Z. Yu, and Z. Li, "An intelligent auto-scaling mechanism for cloud-based applications using machine learning algorithms," Proceedings of the IEEE 12th International Conference on Cloud Computing, 2019, pp. 94-102.
K. Nia, M. S. Jang, and R. K. Gupta, "Adaptive scaling of cloud resources with deep learning," IEEE Cloud Computing, vol. 7, no. 6, pp. 58-66, December 2020.
L. Yang, W. Li, and X. Zhang, "Data-driven resource optimization for cloud computing: A predictive approach," IEEE Access, vol. 8, pp. 38954-38968, 2020.
R. Jain and S. Pandey, "A hybrid framework for dynamic scaling in multi-tenant cloud environments using reinforcement learning," Proceedings of the 2020 IEEE Global Communications Conference, 2020, pp. 1-6.
S. K. Sharma, S. Ghosh, and R. K. Singhal, "Cost-efficient resource management for cloud computing environments," International Journal of Cloud Computing and Services Science, vol. 9, no. 2, pp. 155-168, March 2020.
J. White, T. Oates, and B. Williams, "The role of AI in predictive scaling for cloud resources," IEEE Transactions on Artificial Intelligence, vol. 5, no. 3, pp. 1-15, Sept. 2020.
M. K. Soni, V. K. Singh, and S. S. Ghosh, "Predictive resource management in cloud computing using time series analysis," IEEE Transactions on Cloud Computing, vol. 9, no. 7, pp. 2586-2597, July-August 2021.
S. Patil, G. P. Kumar, and V. D. Verma, "Scalable dynamic scaling models for multi-cloud and hybrid cloud environments," Proceedings of the 2019 IEEE International Conference on Cloud Computing Technology and Science, 2019, pp. 155-163.
S. Bhattacharya, S. Chatterjee, and S. Ghosh, "Resource scheduling and optimization in cloud computing using predictive analytics," Journal of Cloud Computing: Advances, Systems and Applications, vol. 7, no. 3, pp. 102-113, July 2019.
A. M. Nascimento, S. M. D. P. Barbosa, and H. S. A. Ribeiro, "Multi-tenancy and resource allocation in cloud environments," IEEE Cloud Computing, vol. 6, no. 5, pp. 78-87, October 2019.
X. Zhang, Z. Chen, and H. Song, "Resource pooling in cloud environments: A hybrid predictive approach for scaling workloads," Proceedings of the 2018 IEEE 4th International Conference on Cloud Computing and Big Data Analysis, 2018, pp. 276-283.
J. Huang and Y. Wu, "Fair resource allocation in cloud computing systems using predictive analytics," Proceedings of the 2019 IEEE International Symposium on Parallel and Distributed Computing, 2019, pp. 312-319.
C. A. Freitas, C. R. de Souza, and S. G. G. Silva, "Blockchain-based solutions for fairness in cloud resource allocation," Proceedings of the IEEE International Conference on Cloud Computing, 2020, pp. 345-350.
L. Li, J. Xie, and F. Chen, "Edge computing and dynamic scaling in distributed environments," Proceedings of the IEEE 8th International Conference on Edge Computing, 2020, pp. 158-165.
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