Research article
● Open access
Cloud Computing for Artificial Intelligence: A Comprehensive Review of Infrastructure, Performance Optimization, and Future Directions
Abstract
The rapid advancement of artificial intelligence (AI), particularly deep learning, has generated unprecedented demands for scalable computational infrastructure. Cloud computing has emerged as a critical enabler of modern AI systems by providing elastic scalability, high-performance computing resources, and cost-efficient deployment models. This study presents a comprehensive review and experimental evaluation of the role of cloud computing in supporting scalable and efficient AI workloads. A systematic literature review (2000–2025) was conducted alongside a comparative experimental analysis of three leading cloud platforms—Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure—using standardized machine learning models and datasets. Performance metrics including training time, accuracy, resource utilization, scalability, and cost efficiency were analyzed using one-way ANOVA and post-hoc testing.
Results indicate significant differences in training efficiency, with Google Cloud demonstrating the lowest mean training time, followed by AWS and Azure, while model accuracy remained statistically equivalent across platforms. GPU utilization and cost efficiency varied, with preemptible/spot instances reducing costs by up to 70%. Scalability testing showed near-linear performance gains up to 16 GPUs, though Azure exhibited higher variability. Security and compliance capabilities were robust across all platforms.
The findings confirm that while model performance is platform-independent, meaningful differences exist in operational efficiency and cost structure. Strategic cloud selection should therefore be guided by workload characteristics, cost considerations, and ecosystem integration rather than accuracy outcomes alone. As AI models continue to scale, the cloud-AI symbiosis will remain foundational to future intelligent systems.
Keywords
Cloud computing; artificial intelligence; machine learning; deep learning; infrastructure-as-a-service (IaaS); platform-as-a-service (PaaS); software-as-a-service (SaaS); GPU computing;
References
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Sparks, E. R., et al. (2013). MLlib: Machine learning in Apache Spark. Journal of Machine Learning Research, 17(1), 1235-1241.
Takabi, H., et al. (2010). Security and privacy challenges in cloud computing environments. IEEE Security & Privacy, 8(6), 24-31.
Thusoo, A., et al. (2009). Hive: A warehousing solution over a map-reduce framework. Proceedings of the VLDB Endowment, 2(2), 1626-1629.
Varia, J. (2010). Migrating your existing applications to the AWS cloud. Amazon Web Services.
Wang, S., et al. (2017). A comparison of machine learning as a service platforms. IEEE 10th International Conference on Cloud Computing, 1-8.
You, Y., et al. (2018). Large batch training of convolutional networks. arXiv preprint arXiv:1708.03888.
Zaharia, M., et al. (2010). Spark: Cluster computing with working sets. 2nd USENIX Workshop on Hot Topics in Cloud Computing, 1-7.
Zhang, L., Chen, W., & Li, X. (2019). Cloud computing and its role in big data analytics. International Journal of Cloud Computing and Services Science, 8(3), 101-113.
Zhao, J., Guo, Y., & Huang, Q. (2016). A review on cloud computing and its applications to artificial intelligence. International Journal of Computer Applications, 135(2), 19-25.
Armbrust, M., et al. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.
AWS. (2020). AWS Trainium: High-performance machine learning training chip. AWS re:Invent 2020.
Bansal, V., & Goyal, P. (2019). Security and privacy issues in cloud computing: A survey. Journal of Cloud Computing: Advances, Systems and Applications, 8(1), 5-15.
Brown, T. B., et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
Buyya, R., et al. (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.
Chaisiri, S., et al. (2012). Optimal virtual machine placement across multiple cloud providers. IEEE Transactions on Cloud Computing, 1(1), 1-14.
Chen, D., & Zhao, H. (2012). Data security and privacy protection issues in cloud computing. International Conference on Computer Science and Electronics Engineering, 1, 647-651.
Chowdhery, A., et al. (2022). PaLM: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311.
Dean, J., & Ghemawat, S. (2004). MapReduce: Simplified data processing on large clusters. 6th USENIX Symposium on Operating Systems Design and Implementation, 137-150.
Dean, J., et al. (2012). Large scale distributed deep networks. Advances in Neural Information Processing Systems, 25, 1223-1231.
Foster, I., Kesselman, C., & Tuecke, S. (2001). The anatomy of the grid: Enabling scalable virtual organizations. International Journal of High-Performance Computing Applications, 15(3), 200-222.
Gandhi, A., et al. (2014). Auto-scaling web applications in clouds: A taxonomy and survey. ACM Computing Surveys, 47(1), 1-33.
Goyal, P., et al. (2017). Accurate, large minibatch SGD: Training ImageNet in 1 hour. arXiv preprint arXiv:1706.02677.
Gunasekaran, A., & Subramanian, N. (2013). Cloud computing applications for health care systems: Case research. Information Systems Frontiers, 15(5), 587-597.
Hazelhurst, S. (2008). Scientific computing using virtual high-performance computing: A case study using the Amazon Elastic Compute Cloud. South African Computer Journal, 41, 15-25.
Hwang, I., & Pedram, M. (2018). Machine learning-based prediction and optimization for cloud computing. IEEE Transactions on Cloud Computing, 6(4), 1023-1036.
Iosup, A., et al. (2011). Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Transactions on Parallel and Distributed Systems, 22(6), 931-945.
Isard, M., et al. (2007). Dryad: Distributed data-parallel programs from sequential building blocks. ACM SIGOPS Operating Systems Review, 41(3), 59-72.
Jayaraman, A., Arul, S., & Vijayakumar, V. (2021). Cloud computing technologies for artificial intelligence applications. International Journal of Cloud Computing and Services Science, 9(1), 1-15.
Jouppi, N. P., et al. (2017). In-datacenter performance analysis of a tensor processing unit. *ACM/IEEE 44th Annual International Symposium on Computer Architecture*, 1-12.
Khanna, A., Rajput, U., & Garg, A. (2020). A survey on cloud-based AI applications: Technologies and trends. Future Generation Computer Systems, 108, 383-396.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Leitner, P., & Cito, J. (2016). Patterns in the chaos: A study of performance variation and predictability in public IaaS clouds. ACM Transactions on Internet Technology, 16(3), 1-23.
Li, A., et al. (2010). CloudCmp: Comparing public cloud providers. 10th ACM SIGCOMM Conference on Internet Measurement, 1-14.
Low, Y., et al. (2012). Distributed GraphLab: A framework for machine learning and data mining in the cloud. Proceedings of the VLDB Endowment, 5(8), 716-727.
Mao, M., & Humphrey, M. (2012). Auto-scaling to minimize cost and meet application deadlines in cloud workflows. International Conference for High Performance Computing, Networking, Storage and Analysis, 1-12.
Marinos, A., & Briscoe, G. (2010). Community cloud computing. Cloud Computing: Principles, Systems and Applications, 472-484.
McMahan, B., et al. (2017). Communication-efficient learning of deep networks from decentralized data. 20th International Conference on Artificial Intelligence and Statistics, 1273-1282.
Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. National Institute of Standards and Technology, U.S. Department of Commerce.
Nickolls, J., & Dally, W. J. (2010). The GPU computing era. IEEE Micro, 30(2), 56-69.
NVIDIA. (2020). NVIDIA A100 Tensor Core GPU architecture. NVIDIA Whitepaper.
Owens, J. D., et al. (2008). A survey of general-purpose computation on graphics hardware. Computer Graphics Forum, 26(1), 80-113.
Papernot, N., et al. (2018). Scalable private learning with PATE. International Conference on Learning Representations.
Paszke, A., et al. (2019). PyTorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, 32, 8026-8037.
Pearson, S. (2013). Privacy, security and trust in cloud computing. Privacy and Security for Cloud Computing, 3-42.
Rajbhandari, S., et al. (2020). ZeRO: Memory optimizations toward training trillion parameter models. International Conference for High Performance Computing, Networking, Storage and Analysis, 1-16.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117.
Sergeev, A., & Del Balso, M. (2018). Horovod: Fast and easy distributed deep learning in TensorFlow. arXiv preprint arXiv:1802.05799.
Sharma, P., et al. (2016). Kingsman: Cost-aware configuration of cloud applications. International Conference on Service-Oriented Computing, 1-16.
Shoeybi, M., et al. (2019). Megatron-LM: Training multi-billion parameter language models using model parallelism. arXiv preprint arXiv:1909.08053.
Si, Z., Liu, Y., & Yang, J. (2017). Efficient resource management in cloud computing: A survey. Journal of Computing and Information Technology, 25(2), 85-99.
Sparks, E. R., et al. (2013). MLlib: Machine learning in Apache Spark. Journal of Machine Learning Research, 17(1), 1235-1241.
Takabi, H., et al. (2010). Security and privacy challenges in cloud computing environments. IEEE Security & Privacy, 8(6), 24-31.
Thusoo, A., et al. (2009). Hive: A warehousing solution over a map-reduce framework. Proceedings of the VLDB Endowment, 2(2), 1626-1629.
Varia, J. (2010). Migrating your existing applications to the AWS cloud. Amazon Web Services.
Wang, S., et al. (2017). A comparison of machine learning as a service platforms. IEEE 10th International Conference on Cloud Computing, 1-8.
You, Y., et al. (2018). Large batch training of convolutional networks. arXiv preprint arXiv:1708.03888.
Zaharia, M., et al. (2010). Spark: Cluster computing with working sets. 2nd USENIX Workshop on Hot Topics in Cloud Computing, 1-7.
Zhang, L., Chen, W., & Li, X. (2019). Cloud computing and its role in big data analytics. International Journal of Cloud Computing and Services Science, 8(3), 101-113.
Zhao, J., Guo, Y., & Huang, Q. (2016). A review on cloud computing and its applications to artificial intelligence. International Journal of Computer Applications, 135(2), 19-25.