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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62250
Volume:
10 |
4 |
April 2026
Pages:
1-12
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Master Computer Application
Pune vidyarthi griha's college of science and Commerce
Department of Master Computer Application
Pune vidyarthi griha's college of science and Commerce
Department of Master Computer Application
Pune vidyarthi griha's college of science and Commerce
Serverless architecture has emerged as a transformative paradigm in cloud computing, providing a cost-effective, scalable, and high-performance solution for modern cloud-native applications. This study presents a comprehensive comparative analysis of serverless computing across three fundamental dimensions: performance, scalability, and cost efficiency. The research critically evaluates the services offered by major cloud providers, including AWS Lambda, Azure Functions, and Google Cloud Functions, to identify differences in their operational characteristics, pricing models, and real-world applicability. The paper begins with an overview of serverless computing, outlining its core principles, advantages, and inherent limitations. Furthermore, it examines key performance metrics such as latency, cold start behavior, and concurrency management to assess the suitability of serverless platforms for diverse application workloads. Scalability is analyzed through the evaluation of auto-scaling mechanisms under varying traffic conditions, demonstrating the ability of these platforms to handle dynamic demand efficiently. Visual representations, including tables and graphs, are utilized to present comparative data effectively and support informed decision-making. The paper concludes by discussing best practices for adopting serverless architectures, along with strategies for optimizing performance and minimizing costs. It also explores emerging trends, including cold start optimization and the integration of serverless computing with edge technologies. The findings of this research aim to assist organizations in effectively leveraging serverless architectures to achieve enhanced operational efficiency within cloud-native environments.
KEYWORDS
The future of serverless computing is shaped by advancements in cloud technologies, evolving application requirements, and the need for more efficient, scalable, and cost-effective solutions. Here is an in-depth exploration of emerging trends and innovations that are defining the next phase of serverless computing: 1. Advances in Cold Start Optimization Problem: Cold starts—initial latency when a function is invoked after a period of inactivity—remain a significant drawback of serverless computing. They affect real-time applications like APIs, chatbots, and low-latency systems. Emerging Solutions: • Pre-warming mechanisms: Cloud providers are enhancing pre-warming to keep serverless functions ready for execution. • Intelligent provisioning: AI-driven approaches predict traffic spikes and preemptively provision resources. • Edge preloading: Functions are preloaded closer to the edge to reduce latency. Example Innovations: • AWS Lambda SnapStart improves Java function cold starts by pre-initializing execution environments. • Providers are exploring WebAssembly for faster initialization and execution. 2. Hybrid Models: Serverless and Containers Trend: Increasingly, organizations are combining serverless architectures with containerized workloads to balance flexibility and performance. Use Cases: • Long-running processes that exceed serverless timeouts. • Applications requiring finer control over runtime environments.
[1] Fan, C. F., Jindal, A., & Gerndt, M. (2020). Microservices vs Serverless: A Performance Comparison on a Cloud-native Web Application. CLOSER 2020. Retrieved from https://www.scitepress.org [2] Kaviani, N., Kalinin, D., & Maximilien, M. (2019). Towards Serverless as Commodity: A Case of Knative. Proceedings of the 2019 ACM International Workshop on Serverless Computing. Retrieved from https://dl.acm.org [3] McGrath, G., & Brenner, P. R. (2017). Serverless Computing: Design, Implementation, and Performance. 2017 IEEE 37th International Conference on Distributed Computing Systems(ICDCS). Retrieved from https://ieeexplore.ieee.org [4] Pelle, I., Czentye, J., & Dóka, J. (2019). Towards Latency-Sensitive Cloud-Native Applications: A Performance Study on AWS. IEEE International Conference on Cloud Computing Technology and Science.Retrieved from https://ieeexplore.ieee.org [5] Mahmoudi,N., & Khazaei,H.(2020). Performance Modeling of Serverless Computing Platforms. IEEE Transactions on Cloud Computing.Retrieved from https://ieeexplore.ieee.org [6] Bonam, V. S. M., Vangoor, V. K. R., & Alluri, V. R. R. (2018). Serverless Computing for DevOps: Practical Use Cases and Performance Analysis. Advances and Broad Applications in Computing. Retrieved from https://dlabi.org [7] Kritikos, K., & Skrzypek, P. (2018). A Review of Serverless Frameworks. IEEE/ACM International Conference on Utility and Cloud Computing.Retrieved from https://ieeexplore.ieee.org [8] Mohanty, S. K., & Premsankar, G. (2018). An Evaluation of Open Source Serverless Computing Frameworks. International Conference on Cloud Computing and Services Science. Retrieved from https://research.aalto.fi [9] Lynn, T., Rosati, P., Lejeune, A., & Emeakaroha, A. (2017). A Preliminary Review of Enterprise Serverless Cloud Computing (Function-as-aService) Platforms. 2017 IEEE International Conference on Cloud Computing Technology and Science. Retrieved from
Publication Date
5/2/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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