Authors : Karthik Venkatesan; Siddharth

Volume/Issue : Volume 9 - 2024, Issue 11 - November

Google Scholar : https://tinyurl.com/e4xzucud

Scribd : https://tinyurl.com/53877ns2

DOI : https://doi.org/10.5281/zenodo.14831444

Abstract : As cloud computing continues to evolve, serverless architectures have gained significant traction due to their scalability, cost-efficiency, and ease of deployment. This research focuses on optimizing serverless architectures for high- throughput systems, specifically leveraging AWS Lambda and DynamoDB. Serverless computing removes the need for managing server infrastructure, allowing developers to focus on writing code while the cloud provider handles scaling, load balancing, and fault tolerance. AWS Lambda, combined with DynamoDB, provides an ideal environment for building applications with varying workloads, offering both flexible execution and efficient data storage solutions. The paper explores key performance optimization techniques for AWS Lambda and DynamoDB, considering their integration to handle high-throughput use cases. It addresses concerns related to function execution time, cold start latency, and resource allocation in Lambda, as well as optimizing data access patterns and throughput capacity in DynamoDB to minimize cost and improve performance. By analyzing the behavior of AWS Lambda in terms of invocation frequency and duration, the research proposes strategies for improving efficiency, such as optimal memory allocation, function-level caching, and avoiding unnecessary re-invocations. Furthermore, the research delves into the design of DynamoDB tables, including partition key selection, global secondary indexes, and item size optimization. Strategies for managing write and read capacity units, as well as reducing table read and write contention, are examined to ensure the system can scale to handle large volumes of requests without significant performance degradation. Emphasis is placed on achieving the right balance between provisioning capacity and using on-demand scaling to meet throughput demands dynamically. Through extensive testing and case studies, the paper demonstrates the effectiveness of these optimization strategies in real-world scenarios, highlighting the performance gains in terms of reduced latency, improved scalability, and optimized cost structures. It provides a roadmap for architects and developers aiming to design and deploy high-throughput serverless systems using AWS Lambda and DynamoDB, ensuring that applications can efficiently handle large-scale workloads while maintaining flexibility, cost control, and high availability.

Keywords : AWS Lambda, DynamoDB, Serverless Architecture, High-Throughput Systems, Performance Optimization, Cold Start Latency, Scalability, Cost-Efficiency.

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71. Sai Ram Paidipati, Sathvik Pothuneedi, Vijaya Nagendra Gandham and Lovish Jain, S. Kumar,  “A Review: Disease Detection in Wheat Plant using Conventional and Machine Learning Algorithms,” In 5th International Conference on Contemporary Computing and Informatics (IC3I) on December 14-16, 2022.
72. Vijaya Nagendra Gandham, Lovish Jain, Sai Ram Paidipati, Sathvik Pothuneedi, S. Kumar, and Arpit Jain “Systematic Review on Maize Plant Disease Identification Based on Machine Learning” International Conference on Disruptive Technologies (ICDT-2023).
73. Sowjanya, S. Kumar, Sonali Swaroop and  “Neural Network-based Soil Detection and Classification” In 10th IEEE International Conference on System Modeling &Advancement in Research Trends (SMART) on December 10-11, 2021.
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75. Communication Protocols for Real-Time Data Transfer in Embedded Devices. International Journal of Applied Mathematics & Statistical Sciences (IJAMSS) 9(4):31-56.
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77. Kyadasu, Rajkumar, Ashvini Byri, Archit Joshi, Om Goel, Lalit Kumar, and Arpit Jain. 2020. DevOps Practices for Automating Cloud Migration: A Case Study on AWS and Azure Integration. International Journal of Applied Mathematics & Statistical Sciences (IJAMSS) 9(4):155-188.
78. Kyadasu, Rajkumar, Vanitha Sivasankaran Balasubramaniam, Ravi Kiran Pagidi, S.P. Singh, S. Kumar, and Shalu Jain. 2020. Implementing Business Rule Engines in Case Management Systems for Public Sector Applications. International Journal of Research and Analytical Reviews (IJRAR) 7(2):815. Retrieved (www.ijrar.org).
79. Krishnamurthy, Satish, Srinivasulu Harshavardhan Kendyala, Ashish Kumar, Om Goel, Raghav Agarwal, and Shalu Jain. (2020). “Application of Docker and Kubernetes in Large-Scale Cloud Environments.” International Research Journal of Modernization in Engineering, Technology and Science, 2(12):1022-1030. https://doi.org/10.56726/IRJMETS5395.
80. Gaikwad, Akshay, Aravind Sundeep Musunuri, Viharika Bhimanapati, S. P. Singh, Om Goel, and Shalu Jain. (2020). “Advanced Failure Analysis Techniques for Field-Failed Units in Industrial Systems.” International Journal of General Engineering and Technology (IJGET), 9(2):55–78. doi: ISSN (P) 2278–9928; ISSN (E) 2278–9936.
81. Dharuman, N. P., Fnu Antara, Krishna Gangu, Raghav Agarwal, Shalu Jain, and Sangeet Vashishtha. “DevOps and Continuous Delivery in Cloud Based CDN Architectures.” International Research Journal of Modernization in Engineering, Technology and Science 2(10):1083. doi: https://www.irjmets.com.
82. Viswanatha Prasad, Rohan, Imran Khan, Satish Vadlamani, Dr. Lalit Kumar, Prof. (Dr) Punit Goel, and Dr. S P Singh. “Blockchain Applications in Enterprise Security and Scalability.” International Journal of General Engineering and Technology 9(1):213-234.
83. Vardhan Akisetty, Antony Satya, Arth Dave, Rahul Arulkumaran, Om Goel, Dr. Lalit Kumar, and Prof. (Dr.) Arpit Jain. 2020. “Implementing MLOps for Scalable AI Deployments: Best Practices and Challenges.” International Journal of General Engineering and Technology 9(1):9–30. ISSN (P): 2278–9928; ISSN (E): 2278–9936.
84. Akisetty, Antony Satya Vivek Vardhan, Imran Khan, Satish Vadlamani, Lalit Kumar, Punit Goel, and S. P. Singh. 2020. “Enhancing Predictive Maintenance through IoT-Based Data Pipelines.” International Journal of Applied Mathematics & Statistical Sciences (IJAMSS) 9(4):79–102.
85. Akisetty, Antony Satya Vivek Vardhan, Shyamakrishna Siddharth Chamarthy, Vanitha Sivasankaran Balasubramaniam, Prof. (Dr) MSR Prasad, Prof. (Dr) S. Kumar, and Prof. (Dr) Sangeet. 2020. “Exploring RAG and GenAI Models for Knowledge Base Management.” International Journal of Research and Analytical Reviews 7(1):465. Retrieved (https://www.ijrar.org).