Serverless Computing with .NET: A Performance Analysis of Azure Functions vs. AWS Lambda

References

[1] Akkus, I. E., Chen, R., Rimac, I., Satzke, M. S. K., Beck, A., Aditya, P., & Hilt, V. (2020). SAND: Towards high-performance serverless computing. In Proceedings of the 2018 USENIX Annual Technical Conference, USENIX ATC 2018 (pp. 923–935). USENIX Association.

[2] Buyya, R., Srirama, S. N., Casale, G., Calheiros, R., Simmhan, Y., Varghese, B., … Shen, H. (2019). A manifesto for future generation cloud computing: Research directions for the next decade. ACM Computing Surveys, 51(5). https://doi.org/10.1145/3241737

[3] Benedict, S. (2013). Performance issues and performance analysis tools for HPC cloud applications: A survey. Computing, 95(2), 89–108. https://doi.org/10.1007/s00607-012-0213-0

[4] Baldini, I., Castro, P., Chang, K., Cheng, P., Fink, S., Ishakian, V., … Suter, P. (2017). Serverless computing: Current trends and open problems. In Research Advances in Cloud Computing (pp. 1–20). Springer Singapore. https://doi.org/10.1007/978-981-10-5026-8_1

[5] Dancheva, T., Alonso, U., & Barton, M. (2024). Cloud benchmarking and performance analysis of an HPC application in Amazon EC2. Cluster Computing, 27(2), 2273–2290. https://doi.org/10.1007/s10586-023-04060-4

[6] Duan, Q. (2017). Cloud service performance evaluation: status, challenges, and opportunities – a survey from the system modeling perspective. Digital Communications and Networks, 3(2), 101–111. https://doi.org/10.1016/j.dcan.2016.12.002

[7] Giménez-Alventosa, V., Moltó, G., & Caballer, M. (2019). A framework and a performance assessment for serverless MapReduce on AWS Lambda. Future Generation Computer Systems, 97, 259–274. https://doi.org/10.1016/j.future.2019.02.057

[8] Lin, W. T., Krintz, C., Wolski, R., Zhang, M., Cai, X., Li, T., & Xu, W. (2018). Tracking causal order in AWS lambda applications. In Proceedings - 2018 IEEE International Conference on Cloud Engineering, IC2E 2018 (pp. 50–60). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/IC2E.2018.00027

[9] Lim, S. B., Woo, J., & Li, G. (2020). Performance analysis of container-based networking Solutions for high-performance computing cloud. International Journal of Electrical and Computer Engineering, 10(2), 1507–1514. https://doi.org/10.11591/ijece.v10i2.pp1507-1514

[10] Machireddy, Jeshwanth, Harnessing AI and Data Analytics for Smarter Healthcare Solutions (January 14, 2023). International Journal of Science and Research Archive, 2023, 08(02), 785-798 , Available at SSRN: http://dx.doi.org/10.2139/ssrn.5159750

[11] Mohammed, F., Alzahrani, A. I., Alfarraj, O., & Ibrahim, O. (2017). Cloud Computing Fitness for E-Government Implementation: Importance-Performance Analysis. IEEE Access, 6, 1236–1248. https://doi.org/10.1109/ACCESS.2017.2778093

[12] McGrath, G., & Brenner, P. R. (2017). Serverless Computing: Design, Implementation, and Performance. In Proceedings - IEEE 37th International Conference on Distributed Computing Systems Workshops, ICDCSW 2017 (pp. 405–410). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICDCSW.2017.36

[13] Mavridis, I., & Karatza, H. (2017). Performance evaluation of cloud-based log file analysis with Apache Hadoop and Apache Spark. Journal of Systems and Software, 125, 133–151. https://doi.org/10.1016/j.jss.2016.11.037

[14] Machireddy, Jeshwanth, Automation in Healthcare Claims Processing: Enhancing Efficiency and Accuracy (April 16, 2023). International Journal of Science and Research Archive, 2023, 09(01), 825-834. http://dx.doi.org/10.2139/ssrn.5159747

[15] Mishra, A. (2022). Microsoft Azure for Java developers: Deploying Java applications through Azure WebApp, Azure Kubernetes Service, Azure Functions, and Azure Spring Cloud. Microsoft Azure for Java Developers: Deploying Java Applications through Azure WebApp, Azure Kubernetes Service, Azure Functions, and Azure Spring Cloud (pp. 1–356). Apress Media LLC. https://doi.org/10.1007/978-1-4842-8251-9

[16] Mishra, A. (2019). AWS Lambda. In Machine Learning in the AWS Cloud (pp. 237–255). Wiley. https://doi.org/10.1002/9781119556749.ch12

[17] Odun-Ayo, I., Williams, T. A., Odusami, M., & Yahaya, J. (2021). A systematic mapping study of performance analysis and modelling of cloud systems and applications. International Journal of Electrical and Computer Engineering, 11(2), 1839–1848. https://doi.org/10.11591/ijece.v11i2.pp1839-1848

[18] Pérez, A., Moltó, G., Caballer, M., & Calatrava, A. (2018). Serverless computing for container-based architectures. Future Generation Computer Systems, 83, 50–59. https://doi.org/10.1016/j.future.2018.01.022

[19] Peng, Y., & Wu, I. C. (2021). A cloud-based monitoring system for performance analysis in IoT industry. Journal of Supercomputing, 77(8), 9266–9289. https://doi.org/10.1007/s11227-021-03640-8

[20] Sasmal, S. (2024). Exploring Usage of AWS Lambda in Data Processing. International Journal of Information Technology and Computer Engineering, (42), 35–42. https://doi.org/10.55529/ijitc.42.35.42

[21] Sawhney, R., & Chanumolu, K. (2023). Beginning Azure Functions. Beginning Azure Functions. Apress. https://doi.org/10.1007/978-1-4842-9203-7

[22] Sawhney, R., & Chanumolu, K. (2023). Introduction to Azure Functions. In Beginning Azure Functions (pp. 1–12). Apress. https://doi.org/10.1007/978-1-4842-9203-7_1

[23] Saif, S., & Wazir, S. (2018). Performance Analysis of Big Data and Cloud Computing Techniques: A Survey. In Procedia Computer Science (Vol. 132, pp. 118–127). Elsevier B.V. https://doi.org/10.1016/j.procs.2018.05.172

[24] Savazzi, S., Nicoli, M., & Rampa, V. (2020). Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks. IEEE Internet of Things Journal, 7(5), 4641–4654. https://doi.org/10.1109/JIOT.2020.2964162

[25] Villamizar, M., Garcés, O., Ochoa, L., Castro, H., Salamanca, L., Verano, M., … Lang, M. (2017). Cost comparison of running web applications in the cloud using monolithic, microservice, and AWS Lambda architectures. Service Oriented Computing and Applications, 11(2), 233–247. https://doi.org/10.1007/s11761-017-0208-y

[26] Wang, L., Li, M., Zhang, Y., Ristenpart, T., & Swift, M. (2020). Peeking behind the curtains of serverless platforms. In Proceedings of the 2018 USENIX Annual Technical Conference, USENIX ATC 2018 (pp. 133–145). USENIX Association.

[27] Yussupov, V., Soldani, J., Breitenbücher, U., Brogi, A., & Leymann, F. (2021). FaaSten your decisions: A classification framework and technology review of function-as-a-Service platforms. Journal of Systems and Software, 175. https://doi.org/10.1016/j.jss.2021.110906

[28] Zagan, E., & Danubianu, M. (2023). Data Lake Architecture for Storing and Transforming Web Server Access Log Files. IEEE Access, 11, 40916–40929. https://doi.org/10.1109/ACCESS.2023.3270368

[29] Zaman, F., Khan, A., Res, M. O.-Int. J. Sci. Technol., & 2021, U. (2021). Performance evaluation Of Amazon’s, Google’s, and Microsoft’s serverless functions: A comparative study. Researchgate.Net. Retrieved from https://sci-hub.ren/https://www.researchgate.net/profile/Fahim-Uz-Zaman/publication/351096586_Performance_Evaluation_Of_Amazon’s_Google’s_And_Microsoft’s_Serverless_Functions_A_Comparative_Study/links/608666e12fb9097c0c0cf423/Performance-Evaluation-Of-Ama

@article{1707656,
      author = {Sohan Singh Chinthalapudi},
      title = {Serverless Computing with .NET: A Performance Analysis of Azure Functions vs. AWS Lambda},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {9},
      pages = {468-481},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707656.pdf},
      abstract = {Serverless computing has revolutionized cloud-based application deployment by offering scalable, cost-effective, and highly available execution environments. Among the leading platforms for serverless computing, Microsoft Azure Functions and Amazon Web Services (AWS) Lambda are widely used, particularly for .NET applications. This study presents a comparative performance analysis of these two services, evaluating key metrics such as execution time, cold start latency, scalability, and cost efficiency. Using controlled experiments with identical .NET workloads, we analyze how each platform optimizes resource allocation and execution under varying loads. The research highlights the strengths and limitations of Azure Functions and AWS Lambda, providing insights into their suitability for different application scenarios. The findings will aid developers and organizations in making informed decisions when choosing a serverless provider for their .NET-based applications.},
      keywords = {Serverless Computing, .NET Performance, Azure Functions, AWS Lambda, Cloud Performance Analysis},
      month = {March},
  }