Resource Allocation for Generative AI Workloads: Advanced Cloud Resource Management Strategies for Optimized Model Performance

References

[1] Amodei, D., & Hernandez, D. (2018). **AI and compute**. OpenAI. Retrieved from https://openai.com/blog/ai-and-compute/

[2] Balasubramanian, R., Ghose, A., Mani, S., & Nayak, S. (2019). **Improving cloud efficiency through dynamic resource allocation and management**. *Journal of Cloud Computing*, *8*(1), 1-19.

[3] Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). **Language models are few-shot learners**. *arXiv preprint arXiv:2005.14165*.

[4] Dean, J., & Ghemawat, S. (2008). **MapReduce: Simplified data processing on large clusters**. *Communications of the ACM*, *51*(1), 107-113.

[5] Gaur, N., & Jain, D. (2020). **Performance and resource management for cloud computing using machine learning: A survey**. *Computing*, *102*(1), 1-28.

[6] He, K., Zhang, X., Ren, S., & Sun, J. (2016). **Deep residual learning for image recognition**. In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (pp. 770-778).

[7] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). **ImageNet classification with deep convolutional neural networks**. In *Advances in neural information processing systems* (pp. 1097-1105).

[8] Li, D., & Malik, M. (2020). **Scaling laws for neural language models**. *arXiv preprint arXiv:2001.08361*.

[9] Nair, V., & Hinton, G. E. (2010). **Rectified linear units improve restricted Boltzmann machines**. In *Proceedings of the 27th International Conference on Machine Learning* (pp. 807-814).

[10] Vaidya, A., Shah, A., & Kotecha, K. (2018). **A comprehensive study of cloud computing security and compliance**. *International Journal of Computer Applications*, *179*(28), 8-13.

[11] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). **Attention is all you need**. *arXiv preprint arXiv:1706.03762*.

[12] Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2010). **Spark: Cluster computing with working sets**. In *Proceedings of the 2nd USENIX conference on Hot topics in cloud computing* (pp. 10-10).

[13] Murthy, N. P. (2020). 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, 7(2), 359–369. https://doi.org/10.30574/wjarr.2020.07.2.0261

[14] Thakur, D. (2020, July 5). 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

[15] Mehra, A. (2020). Title of the article. International Research Journal of Modernization in Engineering Technology and Science, 2(9), pages. https://www.irjmets.com/uploadedfiles/paper/volume_2/issue_9_september_2020/4109/final/fin_irjmets1723651335.pdf

[16] Krishna, K. (2020). Towards Autonomous AI: Unifying Reinforcement Learning, Generative Models, and Explainable AI for Next-Generation Systems. Journal of Emerging Technologies and Innovative Research, 7(4), 60–61. https://www.jetir.org/papers/JETIR2004643.pdf

[17] Krishna, K. (2021, August 17). Leveraging AI for Autonomous Resource Management in Cloud Environments: A Deep Reinforcement Learning Approach - IRE Journals. IRE Journals. https://www.irejournals.com/paper-details/1702825

[18] Optimizing Distributed Query Processing in Heterogeneous Multi-Cloud Environments: A Framework for Dynamic Data Sharding and Fault-Tolerant Replication. (2021). International Research Journal of Modernization in Engineering Technology and Science. https://doi.org/10.56726/irjmets5524

[19] Thakur, D. (2021). Federated Learning and Privacy-Preserving AI: Challenges and Solutions in Distributed Machine Learning. International Journal of All Research Education and Scientific Methods (IJARESM), 9(6), 3763–3764. https://www.ijaresm.com/uploaded_files/document_file/Dheerender_Thakurx03n.pdf

[20] Krishna, K., & Thakur, D. (2021). Automated Machine Learning (AutoML) for Real-Time Data Streams: Challenges and Innovations in Online Learning Algorithms. In Journal of Emerging Technologies and Innovative Research (JETIR) (Vol. 8, Issue 12). http://www.jetir.org/papers/JETIR2112595.pdf

[21] Mehra, N. A. (2021). Uncertainty quantification in deep neural networks: Techniques and applications in autonomous decision-making systems. World Journal of Advanced Research and Reviews, 11(3), 482–490. https://doi.org/10.30574/wjarr.2021.11.3.0421

[22] Murthy, P. (2021, November 2). AI-Powered Predictive Scaling in Cloud Computing: Enhancing Efficiency through Real-Time Workload Forecasting - IRE Journals. IRE Journals. https://www.irejournals.com/paper-details/1702943

[23] Murthy, P., & Mehra, A. (2021). Exploring Neuromorphic Computing for Ultra-Low Latency Transaction Processing in Edge Database Architectures. Journal of Emerging Technologies and Innovative Research, 8(1), 25–26. https://www.jetir.org/papers/JETIR2101347.pdf

[24] Murthy, P. (2022). Title of the article. International Journal of Scientific Research and Engineering Development (IJSRED), 5(6). http://www.ijsred.com/volume5-issue6-part16.html

[25] Krishna, K., & Murthy, P. (2022). AI-ENHANCED EDGE COMPUTING: BRIDGING THE GAP BETWEEN CLOUD AND EDGE WITH DISTRIBUTED INTELLIGENCE. TIJER - INTERNATIONAL RESEARCH JOURNAL, 9(2). https://tijer.org/tijer/papers/TIJER2202006.pdf

[26] Krishna, K. (2022, August 1). Optimizing query performance in distributed NoSQL databases through adaptive indexing and data partitioning techniques. International Journal of Creative Research Thoughts (IJCRT). https://ijcrt.org/viewfulltext.php?&p_id=IJCRT2208596

[27] Thakur, D. (2022, June 1). AI-Powered Cloud Automation: Enhancing Auto-Scaling Mechanisms through Predictive Analytics and Machine Learning. IJCRT. Retrieved from https://ijcrt.org/viewfulltext.php?&p_id=IJCRT22A6978

[28] Murthy, P., & Thakur, D. (2022). Cross-Layer Optimization Techniques for Enhancing Consistency and Performance in Distributed NoSQL Database. International Journal of Enhanced Research in Management & Computer Applications, 35. https://erpublications.com/uploaded_files/download/pranav-murthy-dheerender-thakur_fISZy.pdf

[29] Mehra, A. (2024, August 1). HYBRID AI MODELS: INTEGRATING SYMBOLIC REASONING WITH DEEP LEARNING FOR COMPLEX DECISION-MAKING. https://www.jetir.org/view?paper=JETIR2408685

[30] Kanungo, S., Kumar, A., & Zagade, R. (2022). OPTIMIZING ENERGY CONSUMPTION FOR IOT IN DISTRIBUTED COMPUTING. Journal of Emerging Technologies and Innovative Research, Volume 9(Issue 6). https://www.jetir.org/papers/JETIR2206A70.pdf

[31] Kanungo, S. (2024, April 16). Edge-to-Cloud Intelligence: Enhancing IoT Devices with Machine Learning and Cloud Computing - IRE Journals. IRE Journals. https://www.irejournals.com/index.php/paper-details/1701284

[32] Kanungo, S. (2020). Decoding AI: Transparent Models forUnderstandable Decision-Making. propulsiontechjournal.com. https://doi.org/10.52783/tjjpt.v41.i4.5637

[33] Nasr Esfahani, M. (2023). Breaking language barriers: How multilingualism can address gender disparities in US STEM fields. International Journal of All Research Education and Scientific Methods, 11(08), 2090-2100. https://doi.org/10.56025/IJARESM.2024.1108232090

[34] Favour: Hossain, M., & Madasani, R. C. (2023, October). Improving the Long-Term Durability of Polymers Used in Biomedical Applications. In ASME International Mechanical Engineering Congress and Exposition (Vol. 87615, p. V004T04A020). American Society of Mechanical Engineers.

[35] Madasani, R. C., & Reddy, K. M. (2014). Investigation Analysis on the performance improvement of a vapor compression refrigeration system. Applied Mechanics and Materials, 592, 1638-1641.

[36] Oyeniyi, J. Combating Fingerprint Spoofing Attacks through Photographic Sources.

[37] Bhadani, U. (2020). Hybrid Cloud: The New Generation of Indian Education Society.

[38] Bhadani, U. A Detailed Survey of Radio Frequency Identification (RFID) Technology: Current Trends and Future Directions.

[39] Bhadani, U. (2022). Comprehensive Survey of Threats, Cyberattacks, and Enhanced Countermeasures in RFID Technology. International Journal of Innovative Research in Science, Engineering and Technology, 11(2).

@article{1704589,
      author = {Pranav Murthy, Aditya Mehra, Lalit Mishra},
      title = {Resource Allocation for Generative AI Workloads: Advanced Cloud Resource Management Strategies for Optimized Model Performance},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {12},
      pages = {1428-1437},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/17045891.pdf},
      abstract = {This article continues from the previous work describing the next-generation turnkey solution to enhance generative AI models and their performance resolution in the cloud, which covers the strategies in the use of resources. It describes various types of scaling and slices, including auto-scaled and spot instances, for various unpredictable workloads. Other topics in the article are right-sized and load-balanced optimization of resources that improve and further the performance and cost. In cost containment measures, cost allocation tags and budget alerting are explained to make cost tracking without hindering the delivery of services. Real-time metrics as the means of performance control and the application of the customized dashboards and their application in ensuring the proper performance of artificial intelligence are also explained. In addition, the article covers information such as caching and partitioning of data and model optimization, as well as options like pruning and Quantization. The propagation of sound, logically evolved 'hybrid cloud' strategies for the on-premises and the cloud are deemed to keep the ratio in balance with the requirements of the business, as well as the security and compliance to guarantee that data is adequately protected. Below is the analysis of these strategies and the lesson any organization seeking to get the best out of generative AI in the cloud can learn.},
      keywords = {Generative AI, Cloud Computing, Dynamic Resource Allocation, Auto-scaling, Spot Instances, Resource Optimization, Right-Sizing, Load Balancing, Cost Management},
      month = {June},
  }