Intelligent Auto-Scaling in AWS: Machine Learning Approaches for Predictive Resource Allocation
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The deployment of intelligent auto-scaling solutions across the cloud environment simultaneously decreases the operational spend as well as distribute resources effectively. The research investigates the deployment of predictive auto-scaling with machine learning in Amazon Web Services (AWS) to improve system scalability as well as management efficiency and economical resource usage. The proposed system implements advanced ML algorithms to reach 92% prediction accuracy thus it minimizes scaling latency and optimizes resource utilization. Analysis reveals that ML-based approaches exceed threshold-based methods because they provide superior response times as well as reduced costs and maximum system availability. Performance evaluations with cost analysis reveal that predictive resource allocation has great future potential for cloud infrastructure management. The discovery demonstrates how ML-based auto-scaling creates a perfect solution for modern cloud challenges by uniting cost-saving measures with high scalability and efficiency benefitsIndependent Researcher
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1. Aslanpour, M. S., Gill, S. S., & Toosi, A. N. (2020). Performance Evaluation Metrics for Cloud, Fog and Edge Computing: A Review, Taxonomy, Benchmarks and Standards for Future Research. Internet of Things, 12, 100273. https://doi.org/10.1016/j.iot.2020.100273
2. Naveen Kodakandla, “Optimizing Kubernetes for Edge Computing: Challenges and Innovative Solutions,” IRE Journals, vol. 4, no. 10, pp. 210–221, Apr. 2021, Available: https://www.researchgate.net/profile/Naveen-Kodakandla/publication/386877301
3. Barakabitze, A. A., Ahmad, A., Hines, A., & Mijumbi, R. (2019). 5G Network Slicing using SDN and NFV: A Survey of Taxonomy, Architectures and Future Challenges. Computer Networks, 167, 106984. https://doi.org/10.1016/j.comnet.2019.106984
4. Bhardwaj, A. (2021). Distributed denial of service attacks in cloud: State-of-the-art of scientific and commercial solutions. Computer Science Review, 39, 100332. https://doi.org/10.1016/j.cosrev.2020.100332
5. Costa, R. L. de C., Moreira, J., Pintor, P., dos Santos, V., & Lifschitz, S. (2021). Data-driven Performance Tuning for Big Data Analytics Platforms. Big Data Research, 100206. https://doi.org/10.1016/j.bdr.2021.100206
6. Naveen Kodakandla, “Serverless Architectures: A Comparative Study of Performance, Scalability, and Cost in Cloud-native Applications,” IRE Journals, vol. 5, no. 2, pp. 136–150, Aug. 2021, Available: https://www.researchgate.net/profile/Naveen-Kodakandla/publication/386876894
7. Huang, D., & Wu, H. (2018). Mobile Cloud Computing Taxonomy. 5–29. https://doi.org/10.1016/b978-0-12-809641-3.00002-8
8. Jauro, F., Chiroma, H., Gital, A. Y., Almutairi, M., Abdulhamid, S. M., & Abawajy, J. H. (2020). Deep learning architectures in emerging cloud computing architectures: Recent development, challenges and next research trend. Applied Soft Computing, 96, 106582. https://doi.org/10.1016/j.asoc.2020.106582
9. Lohachab, A., Lohachab, A., & Jangra, A. (2020). A comprehensive survey of prominent cryptographic aspects for securing communication in post-quantum IoT networks. Internet of Things, 9, 100174. https://doi.org/10.1016/j.iot.2020.100174
10. Marinescu, D. C. (2016). Computer Clouds. Elsevier EBooks, 113–145. https://doi.org/10.1016/b978-0-12-804041-6.00004-9
11. Marinescu, D. C. (2018). Cloud Resource Management and Scheduling. Elsevier EBooks, 321–363. https://doi.org/10.1016/b978-0-12-812810-7.00012-1
12. Molligan, J., Stapp, R., Patel, M., London, J., Goswami, C., Evans, J., & Peiper, S. (2017). Pathology Informatics Summit 2017. Journal of Pathology Informatics, 8(1), 26–26. https://doi.org/10.1016/s2153-3539(22)00430-8
13. Peddi, S. V. B., Kuhad, P., Yassine, A., Pouladzadeh, P., Shirmohammadi, S., & Shirehjini, A. A. N. (2017). An intelligent cloud-based data processing broker for mobile e-health multimedia applications. Future Generation Computer Systems, 66, 71–86. https://doi.org/10.1016/j.future.2016.03.019
14. Ravi, K., Khandelwal, Y., Krishna, B. S., & Ravi, V. (2018). Analytics in/for cloud-an interdependence: A review. Journal of Network and Computer Applications, 102, 17–37. https://doi.org/10.1016/j.jnca.2017.11.006
15. Ray, P. P., & Kumar, N. (2021). SDN/NFV architectures for edge-cloud oriented IoT: A systematic review. Computer Communications, 169, 129–153. https://doi.org/10.1016/j.comcom.2021.01.018
16. Salhab, N., Langar, R., & Rahim, R. (2021). 5G network slices resource orchestration using Machine Learning techniques. Computer Networks, 188, 107829. https://doi.org/10.1016/j.comnet.2021.107829
17. Singh, A. K., Firoz, N., Tripathi, A., Singh, K. K., Choudhary, P., & Prem Chand Vashist. (2020). Internet of Things: from hype to reality. Elsevier EBooks, 191–230. https://doi.org/10.1016/b978-0-12-821326-1.00007-3
18. Syed, H. J., Gani, A., Ahmad, R. W., Khan, M. K., & Ahmed, A. I. A. (2017). Cloud monitoring: A review, taxonomy, and open research issues. Journal of Network and Computer Applications, 98, 11–26. https://doi.org/10.1016/j.jnca.2017.08.021
19. Tahaei, H., Afifi, F., Asemi, A., Zaki, F., & Anuar, N. B. (2020). The rise of traffic classification in IoT networks: A survey. Journal of Network and Computer Applications, 154, 102538. https://doi.org/10.1016/j.jnca.2020.102538
20. Taherizadeh, S., Jones, A. C., Taylor, I., Zhao, Z., & Stankovski, V. (2018). Monitoring self-adaptive applications within edge computing frameworks: A state-of-the-art review. Journal of Systems and Software, 136, 19–38. https://doi.org/10.1016/j.jss.2017.10.033
21. Tavana, M., Hajipour, V., & Oveisi, S. (2020). IoT-based Enterprise Resource Planning: Challenges, Open Issues, Applications, Architecture, and Future Research Directions. Internet of Things, 11(1), 100262. https://doi.org/10.1016/j.iot.2020.100262
22. Aslanpour, M. S., Gill, S. S., & Toosi, A. N. (2020). Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research. Internet of Things, 12, 100273. https://doi.org/10.1016/j.iot.2020.100273
23. Barakabitze, A. A., Ahmad, A., Mijumbi, R., & Hines, A. (2019). 5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges. Computer Networks, 167, 106984. https://doi.org/10.1016/j.comnet.2019.106984
24. Tong, W., Hussain, A., Bo, W. X., & Maharjan, S. (2019). Artificial Intelligence for Vehicle-to-Everything: a survey. IEEE Access, 7, 10823–10843. https://doi.org/10.1109/access.2019.2891073
25. Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020). Convergence of Edge Computing and Deep Learning: A Comprehensive survey. IEEE Communications Surveys & Tutorials, 22(2), 869–904. https://doi.org/10.1109/comst.2020.2970550
26. Zhu, G., Liu, D., Du, Y., You, C., Zhang, J., & Huang, K. (2020). Toward an intelligent edge: wireless communication meets machine learning. IEEE Communications Magazine, 58(1), 19–25. https://doi.org/10.1109/mcom.001.1900103
27. M. Stoyanova, Y. Nikoloudakis, S. Panagiotakis, E. Pallis, and E. K. Markakis, “A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches and Open Issues,” IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 1–1, Jan. 2020, doi: https://doi.org/10.1109/comst.2019.2962586
28. M. Stoyanova, Y. Nikoloudakis, S. Panagiotakis, E. Pallis, and E. K. Markakis, “A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches and Open Issues,” IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 1–1, Jan. 2020, doi: https://doi.org/10.1109/comst.2019.2962586
29. F. Liu, G. Tang, Y. Li, Z. Cai, X. Zhang, and T. Zhou, “A Survey on Edge Computing Systems and Tools,” Proceedings of the IEEE, vol. 107, no. 8, pp. 1537–1562, Aug. 2019, doi: https://doi.org/10.1109/jproc.2019.2920341
30. A. A. Barakabitze, A. Ahmad, A. Hines, and R. Mijumbi, “5G Network Slicing using SDN and NFV: A Survey of Taxonomy, Architectures and Future Challenges,” Computer Networks, vol. 167, p. 106984, Nov. 2019, doi: https://doi.org/10.1016/j.comnet.2019.106984
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