Enhancing Performance of Serverless Architectures in Multi-Cloud Environments

References

[1] Baldini, I., Castro, P., Chang, K., Cheng, P., Fink, S., Ishakian, V., ... & Suter, P. (2017). Serverless computing: Current trends and open problems. In A. Singh & I. Chana (Eds.), Research advances in cloud computing (pp. 1–20). Springer. https://doi.org/10.1007/978-981-10-5026-8_1

[2] Eivy, A., & Weinman, J. (2018). Be wary of the economics of “serverless” cloud computing. IEEE Cloud Computing, 5(5), 6–12. https://doi.org/10.1109/MCC.2018.053711661

[3] Pulivarthy, P. (2024). Harnessing serverless computing for agile cloud application development. FMDB Transactions on Sustainable Computing Systems, 2(4), 201–210.

[4] Pulivarthy, P. (2024). Research on Oracle database performance optimization in IT-based university educational management system. FMDB Transactions on Sustainable Computing Systems, 2(2), 84–95.

[5] Pulivarthy, P. (2024). Semiconductor industry innovations: Database management in the era of wafer manufacturing. FMDB Transactions on Sustainable Intelligent Networks, 1(1), 15–26.

[6] Pulivarthy, P. (2024). Optimizing large scale distributed data systems using intelligent load balancing algorithms. AVE Trends in Intelligent Computing Systems, 1(4), 219–230.

[7] Pulivarthy, P. (2022). Performance tuning: AI analyse historical performance data, identify patterns, and predict future resource needs. International Journal of Innovative Advances in Software Engineering, 8, 139–155.

[8] Jonas, E., Schleier-Smith, J., Sreekanti, V., Tsai, C., Khandelwal, A., Pu, Q., ... & Stoica, I. (2019). Cloud programming simplified: A Berkeley view on serverless computing. University of California, Berkeley Technical Report No. UCB/EECS-2019-3. https://doi.org/10.48550/arXiv.1902.03383

[9] Spillner, J., Mateus, P., & Gomes, D. (2019). FAASDOM: A benchmark suite for serverless computing. In Proceedings of the 12th ACM International Conference on Utility and Cloud Computing (pp. 73–80). https://doi.org/10.1145/3368235.3368869

[10] Villamizar, M., Castro, H., Salamanca, L., Verano, M., Casallas, R., Gil, S., ... & Garcés, E. (2019). Evaluating the monolithic and the microservice architecture pattern to deploy web applications in the cloud. In Proceedings of the 10th Computing Colombian Conference (10CCC) (pp. 583–590). https://doi.org/10.1109/ColumbianCC.2015.7333476

[11] Pulivarthy, P., & Bhatia, A. B. (2025). Designing empathetic interfaces enhancing user experience through emotion. In S. Tikadar, H. Liu, P. Bhattacharya, & S. Bhattacharya (Eds.), Humanizing technology with emotional intelligence (pp. 47–64). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7011-7.ch004

[12] Puvvada, R. K. (2025). Enterprise revenue analytics and reporting in SAP S/4HANA Cloud. European Journal of Science, Innovation and Technology, 5(3), 25–40.

[13] Puvvada, R. K. (2025). Industry-specific applications of SAP S/4HANA finance: A comprehensive review. International Journal of Information Technology and Management Information Systems, 16(2), 770–782.

[14] Puvvada, R. K. (2025). SAP S/4HANA cloud: Driving digital transformation across industries. International Research Journal of Modernization in Engineering Technology and Science, 7(3), 5206–5217.

[15] Puvvada, R. K. (2025). The impact of SAP S/4HANA finance on modern business processes: A comprehensive analysis. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(2), 817–825.

[16] Ishakian, V., Muthusamy, V., & Slominski, A. (2018). Serving deep learning models in a serverless platform. In IEEE International Conference on Cloud Engineering (IC2E) (pp. 257–262). https://doi.org/10.1109/IC2E.2018.00052

[17] Xu, J., Zhou, P., & Buyya, R. (2020). Making serverless computing more efficient for deep learning model inference at the edge of cloud. ACM Transactions on Internet Technology, 20(2), 1–23. https://doi.org/10.1145/3372134

[18] Adzic, G., & Chatley, R. (2017). Serverless computing: Economic and architectural impact. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (pp. 884–889). https://doi.org/10.1145/3106237.3117767

[19] Natarajan, R., Lokesh, G. H., Flammini, F., Premkumar, A., Venkatesan, V. K., & Gupta, S. K. (2023). A novel framework on security and energy enhancement based on Internet of Medical Things for Healthcare 5.0. Infrastructures, 8(2), 22. https://doi.org/10.3390/infrastructures8020022

[20] Kumar, V. S., Alemran, A., Karras, D. A., Gupta, S. K., Dixit, C. K., & Haralayya, B. (2022). Natural language processing using graph neural network for text classification. In 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES) (pp. 1–5). https://doi.org/10.1109/ICKECS56523.2022.10060655

[21] Sakthivel, M., Gupta, S. K., Karras, D. A., Khang, A., Dixit, C. K., & Haralayya, B. (2022). Solving vehicle routing problem for intelligent systems using Delaunay triangulation. In 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES) (pp. 1–5). https://doi.org/10.1109/ICKECS56523.2022.10060807

[22] Tahilyani, S., Saxena, S., Karras, D. A., Gupta, S. K., Dixit, C. K., & Haralayya, B. (2022). Deployment of autonomous vehicles in agricultural and using Voronoi partitioning. In 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES) (pp. 1–5). https://doi.org/10.1109/ICKECS56523.2022.10060773

[23] Kumar, V. S., Alemran, A., Gupta, S. K., Hazela, B., Dixit, C. K., & Haralayya, B. (2022). Extraction of SIFT features for identifying disaster hit areas using machine learning techniques. In 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES) (pp. 1–5). https://doi.org/10.1109/ICKECS56523.2022.10060037

[24] Kumar, V. S., Sakthivel, M., Karras, D. A., Gupta, S. K., Gangadharan, S. M. P., & Haralayya, B. (2022). Drone surveillance in flood affected areas using firefly algorithm. In 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES) (pp. 1–5). https://doi.org/10.1109/ICKECS56523.2022.10060857

[25] Li, K., & Buyya, R. (2020). Performance-aware provisioning and auto-scaling for serverless applications in cloud computing environments. Journal of Systems and Software, 168, 110643. https://doi.org/10.1016/j.jss.2020.110643

[26] Castro, P., Ishakian, V., Muthusamy, V., & Slominski, A. (2019). The rise of serverless computing. Communications of the ACM, 62(12), 44–54. https://doi.org/10.1145/3368456

[27] Spillner, J., & Mateus, P. (2020). Towards serverless multi-cloud benchmarking. Procedia Computer Science, 176, 3212–3221. https://doi.org/10.1016/j.procs.2020.09.134

[28] Lloyd, W., Ramesh, S., Chinthalapati, S., Ly, L., & Pallickara, S. (2018). Serverless computing: An investigation of factors influencing microservice performance. In IEEE International Conference on Cloud Engineering (IC2E) (pp. 159–169). https://doi.org/10.1109/IC2E.2018.00039

[29] McGrath, G., & Brenner, P. (2017). Serverless computing: Design, implementation, and performance. In IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW) (pp. 405–410). https://doi.org/10.1109/ICDCSW.2017.59

[30] Sbarski, P., & Kroonenburg, S. (2022). Serverless architectures on AWS. Manning Publications.

[31] Somani, P., Vohra, S. K., Chowdhury, S., & Gupta, S. K. (2022). Implementation of a blockchain-based smart shopping system for automated bill generation using smart carts with cryptographic algorithms. In Blockchain for smart systems (pp. xx–xx). CRC Press. https://doi.org/10.1201/9781003269281-11

[32] Mewada, S., Chakravarthi, D. S., Sultanuddin, S. J., & Gupta, S. K. (2022). Design and implementation of a smart healthcare system using blockchain technology with a dragonfly optimization-based Blowfish encryption algorithm. In Blockchain for smart systems (pp. xx–xx). CRC Press. https://doi.org/10.1201/9781003269281-10

[33] Gupta, S. K., & Hayath, T. M. (2022). Lack of IT infrastructure for ICT based education as an emerging issue in online education. TTAICTE, 1(3), 19–24. https://doi.org/10.36647/TTAICTE/01.03.A004

[34] Varghese, B., & Buyya, R. (2018). Next generation cloud computing: New trends and research directions. Future Generation Computer Systems, 79, 849–861. https://doi.org/10.1016/j.future.2017.09.020

[35] Nadareishvili, I., Mitra, R., McLarty, M., & Amundsen, M. (2021). Microservice architecture: Aligning principles, practices, and culture. O’Reilly Media.

[36] Gupta, S. K., & Ferdous Alam, A. S. A. (2022). Concept of e-business standardization and its overall process. TJAEE, 1(3), 1–8.

[37] Kumar, A. K., Alemran, A., Karras, D. A., Gupta, S. K., Dixit, C. K., & Haralayya, B. (2023). An enhanced genetic algorithm for solving trajectory planning of autonomous robots. In 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1–6). https://doi.org/10.1109/ICICACS57338.2023.10099994

[38] Gupta, S. K., Kumar, V. S., Khang, A., Hazela, B., N. T., & Haralayya, B. (2023). Detection of lung tumor using an efficient quadratic discriminant analysis model. In 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC) (pp. 1–6). https://doi.org/10.1109/ICRTEC56977.2023.10111903

[39] Gupta, S. K., Alemran, A., Singh, P., Khang, A., Dixit, C. K., & Haralayya, B. (2023). Image segmentation on Gabor filtered images using projective transformation. In 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC) (pp. 1–6). https://doi.org/10.1109/ICRTEC56977.2023.10111885

[40] Gupta, S. K., Saxena, S., Khang, A., Hazela, B., Dixit, C. K., & Haralayya, B. (2023). Detection of number plate in vehicles using deep learning based image labeler model. In 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC) (pp. 1–6). https://doi.org/10.1109/ICRTEC56977.2023.10111862

[41] Gupta, S. K., Ahmad, W., Karras, D. A., Khang, A., Dixit, C. K., & Haralayya, B. (2023). Solving roulette wheel selection method using swarm intelligence for trajectory planning of intelligent systems. In 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC) (pp. 1–5). https://doi.org/10.1109/ICRTEC56977.2023.10111861

[42] Gupta, S. K., & Ferdous Alam, A. S. A. (2022). Concept of e-business standardization and its overall process. TJAEE, 1(3), 1–8.

[43] Kumar, A. K., Alemran, A., Karras, D. A., Gupta, S. K., Dixit, C. K., & Haralayya, B. (2023). An enhanced genetic algorithm for solving trajectory planning of autonomous robots. In 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1–6). https://doi.org/10.1109/ICICACS57338.2023.10099994

[44] Gupta, S. K., Kumar, V. S., Khang, A., Hazela, B., N. T., & Haralayya, B. (2023). Detection of lung tumor using an efficient quadratic discriminant analysis model. In 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC) (pp. 1–6). https://doi.org/10.1109/ICRTEC56977.2023.10111903

[45] Gupta, S. K., Alemran, A., Singh, P., Khang, A., Dixit, C. K., & Haralayya, B. (2023). Image segmentation on Gabor filtered images using projective transformation. In 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC) (pp. 1–6). https://doi.org/10.1109/ICRTEC56977.2023.10111885

[46] Gupta, S. K., Saxena, S., Khang, A., Hazela, B., Dixit, C. K., & Haralayya, B. (2023). Detection of number plate in vehicles using deep learning based image labeler model. In 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC) (pp. 1–6). https://doi.org/10.1109/ICRTEC56977.2023.10111862

[47] Gupta, S. K., Ahmad, W., Karras, D. A., Khang, A., Dixit, C. K., & Haralayya, B. (2023). Solving roulette wheel selection method using swarm intelligence for trajectory planning of intelligent systems. In 2023 International Conference on Recent Trends in Electronics and Communication (ICRTEC) (pp. 1–5). https://doi.org/10.1109/ICRTEC56977.2023.10111861

[48] 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

[49] Mehta, S., Abougreen, A. N., & Gupta, S. K. (Eds.). (2024). Emerging materials, technologies, and solutions for energy harvesting. IGI Global. https://doi.org/10.4018/979-8-3693-2003-7

[50] Shukla, R., Choudhary, A. K., Kumar, V. S., Tyagi, P., Mutharasan, A., Kumar, S., & Gupta, S. K. (2024). Understanding integration issues in intelligent transportation systems with IoT platforms, cloud computing, and connected vehicles. Journal of Autonomous Intelligence, 7(4), 13. https://doi.org/10.32629/jai.v7i4.1043

@article{1709488,
      author = {Prabhdeep Singh},
      title = {Enhancing Performance of Serverless Architectures in Multi-Cloud Environments},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {12},
      pages = {1757-1765},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709488.pdf},
      abstract = {Serverless computing is a revolutionary model of cloud computing that provides developers with the capability to run and deploy code without worrying about infrastructure management. Its pay-as-you-go model, scalability, and speedy deployment have resulted in its increasing popularity as a go-to option for contemporary cloud-native applications. Yet, as firms embrace multi-cloud deployments to ensure vendor independence, resiliency, and taking advantage of the strengths of multiple cloud vendors, there is substantial performance difficulty when serverless systems are hosted on multiple cloud platforms. This study seeks to investigate and apply methods for optimizing the performance of serverless systems in multi-cloud environments. It examines how functions may be optimally allocated, scheduled, and tuned across various providers like AWS Lambda, Google Cloud Functions, and Azure Functions. The paper names important performance bottlenecks like cold starts, latency caused by inter-cloud communications, irregular load balancing, and restrictions in observability and monitoring. We propose a hybrid architecture that combines edge computing, container-based execution environments, and AI-orchestration to solve the above issues. With the help of simulation and case studies, the work illustrates how function pre-warming, caching, and adaptive scaling techniques effectively decrease execution latency and enhance throughput with cost-effectiveness. A performance evaluation framework is also presented to compare the proposed solution with traditional serverless models running in single-cloud and na?ve multi-cloud scenarios. This research adds to the existing literature on clouds by providing implementable, scalable, and provider-independent methods for serverless performance optimization in complex deployment scenarios. The results of this research are especially important for enterprises that develop highly available and robust systems where performance and agility are paramount. Future research can investigate adding quantum-safe security, green computing metrics, and decentralized registries of functions to further increase the robustness and efficiency of multi-cloud serverless platforms.},
      keywords = {Serverless Computing, Multi-Cloud Architecture, Performance Optimisation, Cold Start, Function Orchestration, Edge Computing, AI-Driven Scheduling, Cloud Scalability, Function-as-a-Service (FaaS), Cloud Latency, Cloud-Native Applications, Load Balancing, Inter-Cloud Communication, Cloud Monitoring, Hybrid Cloud},
      month = {June},
  }