AI-Powered Workload Prediction in Distributed Cloud Databases

References

[1] Xu, M., Song, C., Wu, H., Gill, S. S., Ye, K., & Xu, C. (2022). EsDNN: Deep neural network based multivariate workload prediction approach in cloud environment. arXiv preprint arXiv:2203.02684. link.springer.comarxiv.org

[2] Xue, S., Qu, C., Shi, X., Liao, C., Zhu, S., Tan, X., Ma, L., Wang, S., Wang, S., Hu, Y., Lei, L., Zheng, Y., & Li, J. (2022). A meta reinforcement learning approach for predictive autoscaling in the cloud. arXiv preprint arXiv:2205.15795. arxiv.org

[3] J. Mangaiyarkkarasi, J. Shanthalakshmi Revathy, Shashi Kant Gupta, Shilpa Mehta; Blockchain-Powered IoT Innovations in Healthcare, Blockchain-Enabled Internet of Things Applications in Healthcare: Current Practices and Future Directions (2025) 1: 23. https://doi.org/10.2174/9789815305210125010005

[4] Rahul Joshi, Shashi Kant Gupta, Rajesh Natarajan, Krishna Pandey, Suman Kumari; Blockchain-Powered Monitoring of Healthcare Credentials through Blockchain-Based Technology, Blockchain-Enabled Internet of Things Applications in Healthcare: Current Practices and Future Directions (2025) 1: 170. https://doi.org/10.2174/9789815305210125010011

[5] P. Deepan, R. Vidy, N. Arul, S. Dhiravidaselvi, Shashi Kant Gupta; Revolutionizing Hen Care in Smart Poultry Farming: The Impact of AI-Driven Sensors on Optimizing Avian Health, Blockchain-Enabled Internet of Things Applications in Healthcare: Current Practices and Future Directions (2025) 1: 200. https://doi.org/10.2174/9789815305210125010012

[6] Pathak, A., Anbu, A.D., Jamil, A.B.A. et al. Evaluation of energy consumption data for business consumers. Environ Dev Sustain (2025). https://doi.org/10.1007/s10668-024-05960-0

[7] Manjushree Nayak, Asish Panigrahi, Ashish Kumar Dass, Brojo Kishore Mishra, Shashi Kant Gupta. “Blockchain in Industry 4.0 and Industry 5.0, A Paradigm Shift towards Decentralized Efficiency and Autonomous Ecosystems”, Book: Computational Intelligence in Industry 4.0 and 5.0 Applications, Edition 1st Edition, First Published 2025, Imprint Auerbach Publications, Pages 36, eBook ISBN 9781003581963; DOI: https://doi.org/10.1201/9781003581963-7

[8] Pathak, A., Anbu, A.D., Jamil, A.B.A. et al. Evaluation of energy consumption data for business consumers. Environ Dev Sustain (2025). https://doi.org/10.1007/s10668-024-05960-0

[9] Panyaram, S. & Kotte, K. R. (2025). Leveraging AI and Data Analytics for Sustainable Robotic Process Automation (RPA) in Media: Driving Innovation in Green Field Business Process. In S. Kulkarni, M. Valeri, & P. William (Eds.), Driving Business Success Through Eco-Friendly Strategies (pp. 249-262).

[10] Kotte, K. R. & Panyaram, S. (2025). Supply Chain 4.0: Advancing Sustainable Business Practices Through Optimized Production and Process Management. In S. Kulkarni, M. Valeri, & P. William (Eds.), Driving Business Success Through Eco-Friendly Strategies (pp. 303-320).

[11] Panyaram, S. (2024). Automation and Robotics: Key Trends in Smart Warehouse Ecosystems. International Numeric Journal of Machine Learning and Robots, 8(8), 1-13.

[12] Panyaram, S. (2023). Digital Transformation of EV Battery Cell Manufacturing Leveraging AI for Supply Chain and Logistics Optimization. vol, 18(1), 78-87.

[13] Panyaram, S. (2023). Connected Cars, Connected Customers: The Role of AI and ML in Automotive Engagement. International Transactions in Artificial Intelligence, 7(7), 1-15.

[14] Navaneetha Krishnan Rajagopal, Mankeshva Saini, Rosario Huerta-Soto, Rosa Vílchez-Vásquez, J. N. V. R. Swarup Kumar, Shashi Kant Gupta, Sasikumar Perumal, "Human Resource Demand Prediction and Configuration Model Based on Grey Wolf Optimization and Recurrent Neural Network", Computational Intelligence and Neuroscience, vol. 2022, Article ID 5613407, 11 pages, 2022. https://doi.org/10.1155/2022/5613407

[15] Qiu, H., Mao, W., Wang, C., Franke, H., Youssef, A., Kalbarczyk, Z., Başar, T., & Iyer, R. K. (2023). AWARE: Automate workload autoscaling with reinforcement learning in production cloud systems. In Proceedings of the 2023 USENIX Annual Technical Conference (pp. 387–402). USENIX Association. usenix.org

[16] Arbat, S., Jayakumar, V. K., Lee, J., Wang, W., & Kim, I. K. (2022). Wasserstein adversarial transformer for cloud workload prediction. arXiv preprint arXiv:2203.06501. link.springer.com+2arxiv.org+2arxiv.org+2

[17] Saxena, D., & Singh, A. K. (2021). Workload forecasting and resource management models based on machine learning for cloud computing environments. arXiv preprint arXiv: 2106.15112.arxiv.org+1link.springer.com+1

[18] Navaneetha Krishnan Rajagopal, Naila Iqbal Qureshi, S. Durga, Edwin Hernan Ramirez Asis, Rosario Mercedes Huerta Soto, Shashi Kant Gupta, S. Deepak, "Future of Business Culture: An Artificial Intelligence-Driven Digital Framework for Organization Decision-Making Process", Complexity, vol. 2022, Article ID 7796507, 14 pages, 2022. https://doi.org/10.1155/2022/7796507

[19] Eshrag Refaee, Shabana Parveen, Khan Mohamed Jarina Begum, Fatima Parveen, M. Chithik Raja, Shashi Kant Gupta, Santhosh Krishnan, "Secure and Scalable Healthcare Data Transmission in IoT Based on Optimized Routing Protocols for Mobile Computing Applications", Wireless Communications and Mobile Computing, vol. 2022, Article ID 5665408, 12 pages, 2022. https://doi.org/10.1155/2022/5665408

[20] Rajesh Kumar Kaushal, Rajat Bhardwaj, Naveen Kumar, Abeer A. Aljohani, Shashi Kant Gupta, Prabhdeep Singh, Nitin Purohit, "Using Mobile Computing to Provide a Smart and Secure Internet of Things (IoT) Framework for Medical Applications", Wireless Communications and Mobile Computing, vol. 2022, Article ID 8741357, 13 pages, 2022. https://doi.org/10.1155/2022/8741357

[21] Bramah Hazela et al 2022 ECS Trans. 107 2651 https://doi.org/10.1149/10701.2651ecst

[22] Ashish Kumar Pandey et al 2022 ECS Trans. 1072681https://doi.org/10.1149/10701.2681ect

[23] Deepika Saxena & Ashutosh K. Singh. (2021). Workload Forecasting and Resource Management Models Based on Machine Learning for Cloud Computing Environments. arXiv. arxiv.org

[24] Gao, J., Wang, H., & Shen, H. (2020). Machine learning based workload prediction in cloud computing. In 29th International Conference on Computer Communications and Networks (ICCCN) (pp. 9). link.springer.com+2link.springer.com+2dl.acm.org+2

[25] Kumar, J., Singh, A. K., & Buyya, R. (2021). Selfdirected learning-based workload forecasting model for cloud resource management. Information Sciences, 543, 345–366. link.springer.com+1dl.acm.org+1

[26] Shaheen, N., Raza, B., Shahid, A. R., & Malik, A. K. (2021). Autonomic workload performance modeling for largescale databases and data warehouses through deep belief network with data augmentation. IEEE Access, 9, 97603–97620. link.springer.com

[27] Nguyen, C., Klein, C., & Elmroth, E. (2019). Multivariate LSTMbased locationaware workload prediction for edge data centers. In 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (pp. 341–350). link.springer.com

[28] G. S. Jayesh et al 2022 ECS Trans. 107 2715 https://doi.org/10.1149/10701.2715ecst

[29] Shashi Kant Gupta et al 2022 ECS Trans. 107 2927 https://doi.org/10.1149/10701.2927ecst

[30] S. Saxena, D. Yagyasen, C. N. Saranya, R. S. K. Boddu, A. K. Sharma and S. K. Gupta, "Hybrid Cloud Computing for Data Security System," 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), 2021, pp. 1-8, doi: 10.1109/ICAECA52838.2021.9675493.

[31] S. K. Gupta, B. Pattnaik, V. Agrawal, R. S. K. Boddu, A. Srivastava and B. Hazela, "Malware Detection Using Genetic Cascaded Support Vector Machine Classifier in Internet of Things," 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA), 2022, pp. 1-6, doi: 10.1109/ICCSEA54677.2022.9936404.

[32] Natarajan, R.; Lokesh, G.H.; Flammini, F.; Premkumar, A.; Venkatesan, V.K.; Gupta, S.K. A Novel Framework on Security and Energy Enhancement Based on Internet of Medical Things for Healthcare 5.0. Infrastructures 2023,8,22. https://doi.org/10.3390/infrastructures8020022

[33] Z. Cortez, A. Bonde, A. Muzio, M. Russinovich, M. Fontoura, & R. Bianchini. (2017). Resource central: Understanding and predicting workloads for improved resource management in large cloud platforms. In Proceedings of the 26th Symposium on Operating Systems Principles (pp. 153–167). link.springer.com

[34] Nguyen, H. M., Kalra, G., & Kim, D. (2019). Host load prediction in cloud computing using long shortterm memory encoder–decoder. Journal of Supercomputing, 75(11), 7592–7605. link.springer.com+1dl.acm.org+1

[35] Gupta, A., & Bhadauria, H. S. (2023). Workload prediction for SLA performance in cloud environment: ESANN approach. Interactive Data Technologies. dl.acm.org+7journals.sagepub.com+7link.springer.com+7

[36] Dr. Shashi Kant Gupta, Dr. A. S. A. Ferdous Alam, “Concept of E Business Standardization and its Overall Process” TJAEE 2022 August; 1(3): 1–8. Published online 2022 August

[37] A. Kishore Kumar, A. Alemran, D. A. Karras, S. Kant Gupta, C. Kumar Dixit and B. Haralayya, "An Enhanced Genetic Algorithm for Solving Trajectory Planning of Autonomous Robots," 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), Raichur, India, 2023, pp. 1-6, doi: 10.1109/ICICACS57338.2023.10099994

[38] S. K. Gupta, V. S. Kumar, A. Khang, B. Hazela, N. T and B. Haralayya, "Detection of Lung Tumor using an efficient Quadratic Discriminant Analysis Model," 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC), Mysore, India, 2023, pp. 1-6, doi: 10.1109/ICRTEC56977.2023.10111903.

[39] S. K. Gupta, A. Alemran, P. Singh, A. Khang, C. K. Dixit and B. Haralayya, "Image Segmentation on Gabor Filtered images using Projective Transformation," 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC), Mysore, India, 2023, pp.1-6, doi: 10.1109/ICRTEC56977.2023.10111885.

[40] S. K. Gupta, S. Saxena, A. Khang, B. Hazela, C. K. Dixit and B. Haralayya, "Detection of Number Plate in Vehicles using Deep Learning based Image Labeler Model," 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC), Mysore, India, 2023, pp. 1-6, doi: 10.1109/ICRTEC56977.2023.10111862.

[41] S. K. Gupta, W. Ahmad, D. A. Karras, A. Khang, C. K. Dixit and B. Haralayya, "Solving Roulette Wheel Selection Method using Swarm Intelligence for Trajectory Planning of Intelligent Systems," 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC), Mysore, India, 2023, pp. 1-5, doi: 10.1109/ICRTEC56977.2023.10111861.

[42] Bera, R., Kanellopoulos, K., Nori, A., Shahroodi, T., Subramoney, S., & Mutlu, O. (2021). Pythia: A customizable hardware prefetching framework using online reinforcement learning. In Proceedings of the 54th IEEE/ACM International Symposium on Microarchitecture (pp. 1121–1137). mdpi.com

[43] Liu, C., Liu, C., Shang, Y., Chen, S., Cheng, B., & Chen, J. (2017). An adaptive prediction approach based on workload pattern discrimination in the cloud. Journal of Network and Computer Applications, 80, 35–44. link.springer.com

[44] Dr. Shashi Kant Gupta, Dr. A. S. A. Ferdous Alam, “Concept of E Business Standardization and its Overall Process” TJAEE 2022 August; 1(3): 1–8. Published online 2022 August

[45] A. Kishore Kumar, A. Alemran, D. A. Karras, S. Kant Gupta, C. Kumar Dixit and B. Haralayya, "An Enhanced Genetic Algorithm for Solving Trajectory Planning of Autonomous Robots," 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), Raichur, India, 2023, pp. 1-6, doi: 10.1109/ICICACS57338.2023.10099994

[46] S. K. Gupta, V. S. Kumar, A. Khang, B. Hazela, N. T and B. Haralayya, "Detection of Lung Tumor using an efficient Quadratic Discriminant Analysis Model," 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC), Mysore, India, 2023, pp. 1-6, doi: 10.1109/ICRTEC56977.2023.10111903.

[47] S. K. Gupta, A. Alemran, P. Singh, A. Khang, C. K. Dixit and B. Haralayya, "Image Segmentation on Gabor Filtered images using Projective Transformation," 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC), Mysore, India, 2023, pp. 1-6, doi: 10.1109/ICRTEC56977.2023.10111885.

[48] S. K. Gupta, S. Saxena, A. Khang, B. Hazela, C. K. Dixit and B. Haralayya, "Detection of Number Plate in Vehicles using Deep Learning based Image Labeler Model," 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC), Mysore, India, 2023, pp. 1-6, doi: 10.1109/ICRTEC56977.2023.10111862.

[49] Gupta, S. K., Mehta, S., Tripathi, R. K., & Siddiqui, S. A. (2024). Optimization of Processing Sequence and Computation Mode in IoT for Mobile Edge Computing: A Comprehensive Analysis. In S. Mehta, A. Abougreen, & S. Gupta (Eds.), Emerging Materials, Technologies, and Solutions for Energy Harvesting (pp. 16-32). IGI Global. https://doi.org/10.4018/979-8-3693-2003-7.ch002

[50] Gupta, S. K., Mehta, S., Abougreen, A. N., & Singh, P. (2024). Antenna Identification and Power Allocation in Multicell Massive MIMO Downstream: Energy Conservation Under User Sum-Rate Constraint. In S. Mehta, A. Abougreen, & S. Gupta (Eds.), Emerging Materials, Technologies, and Solutions for Energy Harvesting (pp. 1-15). IGI Global. https://doi.org/10.4018/979-8-3693-2003-7.ch001

@article{1709487,
      author = {Prof. Dr. Parin Somani},
      title = {AI-Powered Workload Prediction in Distributed Cloud Databases},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {12},
      pages = {1749-1756},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709487.pdf},
      abstract = {With the age of cloud-native applications, data-intensive operations in multi-cloud environments have become more complicated to manage. With growing enterprises, unforeseen spikes in workload can dramatically hit the performance of the database, resulting in latency, downtime, and inefficiencies in costs. Conventional workload management practices, which are largely based on static provisioning of resources or rule-based monitoring, fail to capture the dynamic aspects of contemporary workloads. To address this challenge, this study investigates the application of Artificial Intelligence (AI) for proactive workload prediction and performance optimization in distributed cloud database systems.The study investigates the application of machine learning (ML) methods to cloud-native databases for predicting patterns of workload trends from historical usage patterns, query logs, and resource utilization metrics. In particular, LSTM, Random Forest, and linear regression models were used to examine workload variations and predict future peaks. Predictions enable dynamic scaling of resources and routing of queries to maximize throughput and reduce latency.A test environment was established with a hybrid cloud configuration of distributed nodes and datasets replicating academic and commercial-level applications. System performance prior to and after the incorporation of AI-based predictions was contrasted in the study. The findings indicate a significant improvement in resource allocation precision, a 25% decrease in query response time, and a 30% reduction in peak-hour system downtime. Additionally, the AI models reached prediction accuracies above 85%, demonstrating their viability for application into database management systems.This work contributes to the new research area of intelligent database systems through a practicable approach to real-time forecasting of workloads in distributed systems. This work further emphasizes the significance of AI towards making cloud services more resilient, scalable, and cost-efficient. The research ends with an overview of future research areas and challenges in implementation, such as real-time integration of feedback, adaptive learning models, and AI-driven data sharding methodologies.},
      keywords = {Artificial Intelligence for Cloud Computing, Workload Forecasting, Distributed Databases, Machine Learning, Cloud Resource Optimization, Database Performance, LSTM, Real-Time Analytics, Cloud Scalability, Intelligent Data Systems},
      month = {June},
  }