Volume 9 • Issue 2 • PP: 54-67 • 2025
An Intelligent Framework for Flavor Recommendation and Cost Optimization in Hybrid Cloud Autoscaling
View PDF Abstract
This research presents a flavor recommendation framework that intends to be used in hybrid clouds to address resource provisioning and cost issues. A cloud “flavor” is an instance type that assigns values for CPU, memory, storage, and networking. Today the flavor selection process is manual, and no dynamic technique is used, therefore, the process is inefficient because some flavors are underutilized. The proposed framework also provides flavor recommendations for autopiloted dynamic capacity provisioning using predictor analysis of workload and cost proportional to different CSPs. It uses an RNN_LSTM-based Proactive Predictive Engine (PPE) to quantitatively estimate future resource requirements and a Recommendation Engine consisting of the scoring and flavor engines. This framework receives the application’s actual and predicted consumption of CPU and memory, cost fluctuations, and CSPs’ options and then the selection of various flavors is performed in the runtime. Metrics are gathered, stored, and analyzed in real-time through Telegraf, InfluxDB, and Apache Libcloud for current resource allocation. Experimental results of the system on AWS and OpenStack show the benefit of using the proposed framework, which reduced the number of EBS and VMs by 19% and the cost saving by up to 17% compared with traditional and reactive approaches. This solution turns static resource allocation into a real-time predictive accuracy of how resources are best utilized as well as the expense at the hybrid cloud environment.
Keywords
References
[1] S. K. Sarma, "Metaheuristic based auto-scaling for microservices in cloud environment: a new container-aware application scheduling," International Journal of Pervasive Computing and Communications, vol. 19, no. 1, pp. 74-76, 2023.
[2] U. Gupta et al., "Deeprecsys: A system for optimizing end-to-end at-scale neural recommendation inference," in 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA), pp. 982–995, 2020.
[3] G. Kaur and A. Bala, "Prediction-based task scheduling approach for floodplain application in cloud environment," Computing, vol. 103, no. 5, pp. 895–916, May 2021.
[4] A. Smith and B. Johnson, "A survey of cloud computing architectures for big data applications," Journal of Cloud Computing: Advances, Systems and Applications, vol. 11, no. 1, pp. 1-15, 2022.
[5] L. Chen et al., "Enhancing security in cloud computing through advanced encryption techniques," International Journal of Information Security, vol. 20, no. 3, pp. 225-239, 2021.
[6] M. R. Patel et al., "IoT-based smart healthcare system: A review," Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 4, pp. 4287-4299, 2021.
[7] A. Y. Krishna et al., "Machine learning techniques for predicting financial market trends," Financial Engineering and Risk Management, vol. 10, no. 2, pp. 112-130, 2023.
[8] J. Doe and R. Smith, "Blockchain technology in supply chain management: A review," Journal of Supply Chain Management, vol. 15, no. 2, pp. 45-60, 2022.
[9] H. Lee et al., "A survey on machine learning for big data analytics," Big Data Research, vol. 25, pp. 100-110, 2023.
[10] T. Nguyen et al., "Edge computing for IoT applications: A comprehensive survey," IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3456-3475, 2021.
[11] A. Kumar and S. Mehta, "Data mining techniques for healthcare: A survey," Journal of Healthcare Engineering, vol. 2022, p. 123456, 2022.
[12] R. Gupta et al., "Deep learning for image classification: A survey," Journal of Image Processing, vol. 30, no. 1, pp. 1-20, 2023.
[13] K. Vengatesan et al., "AI-based fraud detection in banking systems," Journal of Financial Technology, vol. 5, no. 1, pp. 15-25, 2024.
[14] M. Alkhalaileh et al., "Data-intensive application scheduling on Mobile Edge Cloud Computing," Journal of Network and Computer Applications, vol. 167, p. 102735, Oct. 2020.
[15] J. Brown et al., "Smart grid technology: A review of the state-of-the-art," IEEE Transactions on Smart Grid, vol. 13, no. 2, pp. 123-135, 2022.
[16] A. B. Gadicha and V. B. Gadicha, "Implicit authentication approach by generating strong password through visual key cryptography," International Journal of Information Security, vol. 29, no. 1, pp. 5-16, 2021.
[17] H. Ahmed, "A survey on the use of AI in financial technology," Journal of Financial Technology and Innovation, vol. 2, no. 1, pp. 17-30, 2023.
[18] Y. Huang et al., "Task scheduling with optimized transmission time in collaborative cloud-edge learning," in 2018 27th International Conference on Computer Communication and Networks (ICCCN), pp. 1–9, 2021.
[19] A. B. Smith et al., "Cloud computing for IoT applications: A review," Journal of Cloud Computing: Advances, Systems and Applications, vol. 12, no. 1, pp. 1-20, 2023.
[20] R. Alubady et al., "Blockchain-based e-Medical Record and Data Security Service Management based on IoMT resource," Journal of Intelligent Systems and Internet of Things, vol. 8, no. 2, pp. 86-100, 2023.
[21] M. Altaee et al., "A Multi-level Fusion System for Intelligent Capture and Assessment of Student Activity in Physical Training based on Machine Learning," Journal of Intelligent Systems and Internet of Things, vol. 9, no. 1, pp. 08-23, 2023.
[22] A. Boukerche et al., "Sustainable Offloading in Mobile Cloud Computing: Algorithmic Design and Implementation," ACM Computing Surveys, vol. 52, no. 1, pp. 1–37, Feb. 2019.
[23] T. Zhang and Y. Wang, "A review of machine learning techniques for predictive maintenance," Journal of Manufacturing Systems, vol. 60, pp. 101-115, 2023.
[24] N. Patel et al., "Smart agriculture using IoT: A review," Journal of Agricultural Informatics, vol. 12, no. 1, pp. 1-10, 2024.
Cite This Article
Choose your preferred format