AI-Powered Predictive Scaling in Cloud Computing: Enhancing Efficiency through Real-Time Workload Forecasting

References

[1] Arora, A., & Bhattacharjee, S. (2020). Predictive scaling for cloud computing using machine learning techniques. Journal of Cloud Computing: Advances, Systems and Applications, 9(1), 1-15.

[2] Bai, Y., & Liu, J. (2019). An intelligent predictive scaling mechanism for cloud computing based on machine learning. Computers & Electrical Engineering, 74, 272-284.

[3] Cheng, W., & Zhang, X. (2018). Machine learning for predictive resource scaling in cloud computing environments. IEEE Transactions on Cloud Computing, 6(4), 1051-1063.

[4] Kumar, A., & Kumar, P. (2017). Enhancing cloud resource management using AI-based predictive scaling. Future Generation Computer Systems, 75, 196-208.

[5] Zhao, J., & Zheng, L. (2016). Adaptive predictive scaling of cloud resources based on historical data and machine learning. Journal of Computer Science and Technology, 31(2), 242-255.

[6] Krishna, K. (2020, April 1). Towards Autonomous AI: Unifying Reinforcement Learning, Generative Models, and Explainable AI for Next-Generation Systems. https://www.jetir.org/view?paper=JETIR2004643

[7] Mehra, A. D. (2020). UNIFYING ADVERSARIAL ROBUSTNESS AND INTERPRETABILITY IN DEEP NEURAL NETWORKS: A COMPREHENSIVE FRAMEWORK FOR EXPLAINABLE AND SECURE MACHINE LEARNING MODELS. International Research Journal of Modernization in Engineering Technology and Science, 02. https://www.irjmets.com/uploadedfiles/paper/volume_2/issue_9_september_2020/4109/final/fin_irjmets1723651335.pdf

[8] KUNUNGO, S., RAMABHOTLA, S., & BHOYAR, M. (2018). The Integration of Data Engineering and Cloud Computing in the Age of Machine Learning and Artificial Intelligence. In IRE Journals (Vol. 1, Issue 12, pp. 79–80). https://www.irejournals.com/formatedpaper/1700696.pdf

[9] Kanungo, s. k. (2020). Revolutionizing Data Processing: Advanced Cloud Computing and AI Synergy for IoT Innovation. International Research Journal of Modernization in Engineering Technology and Science, 2, 1032–1040. https://www.researchgate.net/profile/Satyanarayan-Kanungo/publication/380424963_REVOLUTIONIZING_DATA_PROCESSING_ADVANCED_CLOUD_COMPUTING_AND_AI_SYNERGY_FOR_IOT_INNOVATION/links/663babeb7091b94e930a3d76/REVOLUTIONIZING-DATA-PROCESSING-ADVANCED-CLOUD-COMPUTING-AND-AI-SYNERGY-FOR-IOT-INNOVATION.pdf

[10] Bhadani, Ujas. “Hybrid Cloud: The New Generation of Indian Education Society.” Sept. 2020.

[11] Abughoush, K., Parnianpour, Z., Holl, J., Ankenman, B., Khorzad, R., Perry, O., Barnard, A., Brenna, J., Zobel, R. J., Bader, E., Hillmann, M. L., Vargas, A., Lynch, D., Mayampurath, A., Lee, J., Richards, C. T., Peacock, N., Meurer, W. J., & Prabhakaran, S. (2021). Abstract P270: Simulating the Effects of Door-In-Door-Out Interventions. Stroke, 52(Suppl_1). https://doi.org/10.1161/str.52.suppl_1.p270

[12] A. Dave, N. Banerjee and C. Patel, "SRACARE: Secure Remote Attestation with Code Authentication and Resilience Engine," 2020 IEEE International Conference on Embedded Software and Systems (ICESS), Shanghai, China, 2020, pp. 1-8, doi: 10.1109/ICESS49830.2020.9301516.

[13] Dave, A., Wiseman, M., & Safford, D. (2021, January 16). SEDAT:Security Enhanced Device Attestation with TPM2.0. arXiv.org. https://arxiv.org/abs/2101.06362

[14] A. Dave, N. Banerjee and C. Patel, "CARE: Lightweight Attack Resilient Secure Boot Architecture with Onboard Recovery for RISC-V based SOC," 2021 22nd International Symposium on Quality Electronic Design (ISQED), Santa Clara, CA, USA, 2021, pp. 516-521, doi: 10.1109/ISQED51717.2021.9424322.

[15] KANUNGO, S. (2019b). Edge-to-Cloud Intelligence: Enhancing IoT Devices with Machine Learning and Cloud Computing. In IRE Journals (Vol. 2, Issue 12, pp. 238–239). https://www.irejournals.com/formatedpaper/17012841.pdf

[16] Thakur, D. (2024b, July 23). Optimizing Query Performance in Distributed Databases Using Machine Learning Techniques: A Comprehensive Analysis and Implementation - IRE Journals. IRE Journals. https://www.irejournals.com/paper-details/1702344

[17] Murthy, P. (2024). Optimizing cloud resource allocation using advanced AI techniques: A comparative study of reinforcement learning and genetic algorithms in multi-cloud environments. World Journal of Advanced Research and Reviews. https://doi.org/10.30574/wjarr.2020.07.2.0261

@article{1702943,
      author = {Pranav Murthy},
      title = {AI-Powered Predictive Scaling in Cloud Computing: Enhancing Efficiency through Real-Time Workload Forecasting},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {5},
      number = {4},
      pages = {143-152},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/17029432.pdf},
      abstract = {AI-powered predictive scaling in cloud computing leverages machine learning algorithms to anticipate future workload demands and optimize resource allocation accordingly. Unlike traditional scaling methods that react to changes in demand, predictive scaling proactively adjusts resources based on predictions derived from historical and real-time data. This approach offers significant benefits, including improved resource utilization, cost savings, and enhanced system performance. However, it also faces challenges such as data quality issues, algorithm limitations, and integration complexities. As AI and machine learning technologies continue to advance, predictive scaling is expected to evolve, integrating with emerging technologies and adapting to diverse cloud environments. This paper explores the mechanisms of predictive scaling, its benefits and challenges, and future trends in its development.},
      keywords = {AI-Powered Predictive Scaling, Cloud Computing, Machine Learning, Resource Management, Predictive Analytics, Cloud Resource Optimization, Real-Time Data Forecasting},
      month = {October},
  }