The Role of AI and Machine Learning in Optimizing Cloud Resource Allocation
DOI:
https://doi.org/10.47709/ijmdsa.v1i2.2190Keywords:
Keywords: Cloud Computing, Resource Allocation, Artificial Intelligence, Automation, Machine Learning, Evolutionary Algorithms, Deep Reinforcement Learning, Comparative Study.Abstract
Resource allocation inside the cloud infrastructure is an essential requirement for better performance and cost-effectiveness. With workloads growing so much more complex and dynamic, we now need automated solutions that allow us to move past tedious, manual resource management. AI provides a unique paradigm for optimizing resource allocation in such complex and dynamic environments. In this paper, we have compared various AI techniques such as machine learning, evolutionary algorithms and deep reinforcement learning for the problem of resource allocation in cloud infrastructure. We analyze their effectiveness for dynamic resource provisioning to satisfy performance objectives while reducing operational expenditure. We offer an in-depth performance comparison using real-world datasets and simulations to showcase the merits and drawbacks of each. Implications: We believe that our findings can offer guidelines to cloud providers, researchers, and practitioners who are interested in making proactive improvements on managing cloud infrastructures using intelligent resource allocation techniques.
References
Bianchini, R., Fontoura, M., Cortez, E., Bonde, A., Muzio, A., Constantin, A., Moscibroda, T., Magalhães, G., Bablani, G., & Russinovich, M. (2020, January 22). Toward ML-centric cloud platforms. Association for Computing Machinery, 63(2), 50-59. https://doi.org/10.1145/3364684
Buyya, G Z T. (2021, May 9). Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions. https://arxiv.org/pdf/2105.04086v1.pdf
Gejibo, S., Grasso, D., Mancini, F., & Mughal, K A. (2013, September 2). Secure cloud storage for remote mobile data collection
Sale, O S., Ghazali, O., & Al-Maatouk, Q. (2019, January 1). Graduation Certificate Verification Model: A Preliminary Study. Science and Information Organization, 10(7)
Sreenivas, F Q K A B R B B S. (2023, April 17). Reclaimer: A Reinforcement Learning Approach to Dynamic Resource Allocation for Cloud Microservices. https://arxiv.org/abs/2304.07941
Sudeep, Q S M D B C P. (2023, August 24). SHIELD: Sustainable Hybrid Evolutionary Learning Framework for Carbon, Wastewater, and Energy-Aware Data Center Management. https://arxiv.org/abs/2308.13086
Vadde, B. C., & Munagandla, V. B. (2023). Security-First DevOps: Integrating AI for Real-Time Threat Detection in CI/CD Pipelines. International Journal of Advanced Engineering Technologies and Innovations, 1(03), 423-433.
Vadde, B. C., & Munagandla, V. B. (2023). Integrating AI-Driven Continuous Testing in DevOps for Enhanced Software Quality. Revista de Inteligencia Artificial en Medicina, 14(1), 505-513.
Munagandla, V. B., Dandyala, S. S. V., Vadde, B. C., & Dandyala, S. S. M. (2023). Enhancing Data Quality and Governance Through Cloud Data Integration. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 14(1), 480-496.
Dalal, A., & Mahjabeen, F. (2012). Cloud Storage Security: Balancing Privacy and Security in the US, Canada, EU, and Asia. Revista de Inteligencia Artificial en Medicina, 3(1), 19-27.
Muhammad, S., Meerjat, F., Meerjat, A., Naz, S., & Dalal, A. (2023). Strengthening Mobile Platform Cybersecurity in the United States: Strategies and Innovations. Revista de Inteligencia Artificial en Medicina, 14(1), 84-112.
Muhammad, S., Meerjat, F., Meerjat, A., Dalal, A., & Abdul, S. (2023). Enhancing Cybersecurity Measures for Blockchain: Securing Transactions in Decentralized Systems. Unique Endeavor in Business & Social Sciences, 2(1), 120-141.
Dalal, A., & Mahjabeen, F. (2013). Strengthening SAP and ERP Security for US and European Enterprises: Addressing Emerging Threats in Critical Systems. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 4(1), 1-17.
A. Yella and A. Kondam, "The Role of AI in Enhancing Decision-Making Processes in Healthcare," Journal of Innovative Technologies, vol. 6, no. 1, 2023.
A. Yella and A. Kondam, "Big Data Integration and Interoperability: Overcoming Barriers to Comprehensive Insights," Advances in Computer Sciences, vol. 5, no. 1, 2022.
R. Vallabhaneni, S. A. Vaddadi, A. Maroju, and S. Dontu, "An Intrusion Detection System (Ids) Schemes for Cybersecurity in Software Defined Networks," ed, 2023
A. Kondam and A. Yella, "Artificial Intelligence and the Future of Autonomous Systems," Innovative Computer Sciences Journal, vol. 9, no. 1, 2023.
A. Yella and A. Kondam, "From Data Lakes to Data Streams: Modern Approaches to Big Data Architecture," Innovative Computer Sciences Journal, vol. 8, no. 1, 2022.
S. Tavarageri, G. Goyal, S. Avancha, B. Kaul, and R. Upadrasta, "AI Powered Compiler Techniques for DL Code Optimization," arXiv preprint arXiv: 2104.05573, 2021.
A. Yella and A. Kondam, "Integrating AI with Big Data: Strategies for Optimizing Data-Driven Insights," Innovative Engineering Sciences Journal, vol. 9, no. 1, 2023.
Dalal, A., & Mahjabeen, F. (2012). Managing Bring Your Own Device (BYOD) Security: A Comparative Study in the US, Australia, and Asia. Revista de Inteligencia Artificial en Medicina, 3(1), 19-30.
Hayat, Yawar, Mehtab Tariq, Adil Hussain, Aftab Tariq, and Saad Rasool. "A Review of Biosensors and Artificial Intelligence in Healthcare and Their Clinical Significance." International Research Journal of Economics and Management Studies IRJEMS 3, no. 1.
Cuthrell, Kimberly Morton, and Nikolaos Tzenios. "Sleep Disturbances as a Manifestation of Neurodevelopmental Disorders." International Neuropsychiatric Disease Journal 20, no. 4 (2023): 36-47.
Justus, Oladele, Ghassan Salibi, and Nikolaos Tzenios. "Distribution of Preterm Births in Nigeria." Special Journal of the Medical Academy and other Life Sciences. 1, no. 8 (2023). [34]. PARAMASIVAM, THEBEN RAJ, Ghassan Salibi, and Nikolaos Tzenios. "NEGLIGENCE OF ASIANS ON DENGUE FEVER." Special Journal of the Medical Academy and other Life Sciences. 1, no. 8 (2023).
Tzenios, Nikolaos, Mary E. Tazanios, Omasyarifa Binti Jamal Poh, and Mohamed Chahine. "The effects of ketogenic diet on the immune system: A meta-analysis." (2022). [36]. Tzenios, Nikolaos, Mary Tazanios, and Mohamad Chahine. "Variable vs. Fixed Dosing of Monoclonal Antibodies in Oncology." (2022
Mohammed, Obaidur Rahman, D. V. Suresh, and Hamid M. Lankarani. "Evaluation of automotive hood and bumper performance with composite material by pedestrian impactor systems." In ASME International Mechanical Engineering Congress and Exposition, vol. 84522, p. V005T05A056. American Society of Mechanical Engineers, 2020.
Munagandla, V. B., Dandyala, S. S. V., Vadde, B. C., & Dandyala, S. S. M. (2023). Cloud-Based Real-Time Data Integration for Scalable Pooled Testing in Pandemic Response. Revista de Inteligencia Artificial en Medicina, 14(1), 485-504.
Dalal, A., & Mahjabeen, F. (2011). Strengthening Cybersecurity Infrastructure in the US and Canada: A Comparative Study of Threat Detection Models. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 2(1), 1-9.
.Mohammed, Obaidur Rahman, Shabbir Memon, and Hamid M. Lankarani. "Pedestrian collision responses using legform impactor subsystem and full-sized pedestrian model on different workbenches." In ASME International Mechanical Engineering Congress and Exposition, vol. 52187, p. V013T05A013. American Society of Mechanical Engineers, 2018.
Tanveer, H., Adam, M. A., Khan, M. A., Ali, M. A., & Shakoor, A. (2023). Analyzing the Performance and Efficiency of Machine Learning Algorithms, such as Deep Learning, Decision Trees, or Support Vector Machines, on Various Datasets and Applications. The Asian Bulletin of Big Data Management, 3(2
Mohammed, R. R. (2023). The Future of Outage Management: How Information Technology is Powering Innovation in the Energy Industry. The Future of Outage Management: How Information Technology is Powering Innovation in the Energy Industry, 1-45
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.
Marzolla, M. (2017). Cloud computing: principles and paradigms. John Wiley & Sons.
Sravan Kumar Pala. (2016). Credit Risk Modeling with Big Data Analytics: Regulatory Compliance and Data Analytics in Credit Risk Modeling. (2016). International Journal of Transcontinental Discoveries, ISSN: 3006-628X, 3(1), 33-39.
Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 25(6), 599-616.
Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media
Jatin Vaghela, Security Analysis and Implementation in Distributed Databases: A Review. (2019). International Journal of Transcontinental Discoveries, ISSN: 3006-628X, 6(1), 35-42. https://internationaljournals.org/index.php/ijtd/article /view/54
Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep Learning (Vol. 1). MIT press Cambridge.
Russell, S. J., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Pearson Education Limited.
Friedman, J., Hastie, T., & Tibshirani, R. (2009). The elements of statistical learning (Vol. 1). Springer series in statistics New York.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Bimalendu Pendy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
info@itscience.org, web: 
