Modern API Design: AI-First Architecture, Event-Driven Patterns, and Zero-Trust Security |
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| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-11 |
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| Year of Publication : 2024 | ||
| Authors : Ganapathy Subramanian Ramachandran | ||
| DOI : 10.14445/22312803/IJCTT-V72I11P123 | ||
How to Cite?
Ganapathy Subramanian Ramachandran, "Modern API Design: AI-First Architecture, Event-Driven Patterns, and Zero-Trust Security," International Journal of Computer Trends and Technology, vol. 72, no. 11, pp. 220-227, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I11P123
Abstract
This paper presents a comprehensive framework for modern API design that addresses the challenges of integrating machine learning operations into traditional and contemporary API architectures. The proposed approach combines three key elements: an AI-first architecture design for efficient vector operations and model serving capabilities alongside traditional data operations, event-driven patterns that enhance ML workflows and standard request-response interactions, and zero-trust security principles adaptable to both ML workloads and conventional API usage. The research demonstrates how these architectural patterns can be effectively implemented to create APIs that support traditional web services and modern computational workload operations while maintaining system scalability, security, and performance.
Keywords
Event-driven architecture, Zero-trust security, AI integration, Feature store, Model serving, Vector operations, Distributed systems, Scalable systems, Edge computing, Real-time processing, Batch processing, Stream processing, Continuous authentication, Context-aware security, ML model security, AI-first design, Edge AI, Federated learning, Scalability, High throughput, Low latency.
Reference
[1] Andrei Paleyes, Raoul-Gabriel Urma, and Neil D. Lawrence, “Challenges in Deploying Machine Learning: A Survey of Case Studies,” ACM Computing Surveys, vol. 55, no. 6, pp. 1-29, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] J. Johnson, M. Douze and H. Jégou, "Billion-Scale Similarity Search with GPUs," in IEEE Transactions on Big Data, vol. 7, no. 3, pp. 535-547, 1 July 2021, doi: 10.1109/TBDATA.2019.2921572.
[3] Kim Hazelwood et al., “Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective,” 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA), Vienna, Austria, pp. 620-629, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Daniel Crankshaw et al., “Clipper: A Low-Latency Online Prediction Serving System,” Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2017.
[Google Scholar] [Publisher Link]
[5] Chaoyun Zhang, Paul Patras, and Hamed Haddadi, “Deep Learning in Mobile and Wireless Networking: A Survey,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2224-2287, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Samyam Rajbhandari et al., “ZeRO: Memory Optimizations Toward Training Trillion Parameter Models,” SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, Atlanta, GA, USA, pp. 1-16, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Paris Carbone et al., “State Management in Apache Flink®: Consistent Stateful Distributed Stream Processing,” Proceedings of the VLDB Endowment, vol. 10, no. 12, pp. 1718–1729, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Shivaram Venkataraman et al., “Drizzle: Fast and Adaptable Stream Processing at Scale,” SOSP '17: Proceedings of the 26th Symposium on Operating Systems Principles, Shanghai China, pp. 374–389, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Chen Li et al., “The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-scale, Unbounded, Out-of-order Data Processing,” Proceedings of the VLDB Endowment, vol. 8, no. 12, pp. 1792–1803, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Roy Thomas Fielding, “Architectural Styles and the Design of Network-based Software Architectures,” Ph.D. dissertation, Department of Information Computer Science, University of California, Irvine, CA, USA, 2000.
[Google Scholar] [Publisher Link]
[11] Yu A. Malkov, and D.A. Yashunin, “Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 4, pp. 824-836, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] MongoDB, MongoDB Atlas Vector Search. [Online]. Available: https://www.mongodb.com/products/platform/atlas-vector-search
[13] Kafka, Documentation. [Online]. Available: https://kafka.apache.org/documentation/
[14] Amazon Web Services, Amazon EventBridge Developer Guide. [Online]. Available: https://aws.amazon.com/eventbridge/
[15] OpenAI, OpenAI API Documentation, 2020. [Online]. Available: https://openai.com/blog/openai-api/
[16] Google LLC, "TensorFlow Serving Documentation," 2021. [Online]. Available: https://www.tensorflow.org/tfx/guide/serving
[17] Google Cloud, BeyondCorp: A New Approach to Enterprise Security. [Online]. Available: https://cloud.google.com/beyondcorp
[18] Microsoft Corporation, Azure API Management Documentation, Microsoft Technical Documentation. [Online]. Available: https://azure.microsoft.com/en-us/services/api-management/
[19] Kabir Nagrecha, and Arun Kumar, “Hydra: A System for Large Multi-Model Deep Learning,” arXiv, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Alexey Ilyushkin et al., “An Experimental Performance Evaluation of Autoscaling Policies for Complex Workflows,” ICPE '17: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, L'Aquila Italy, pp. 75-86, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Cloudflare Workers Documentation. [Online]. Available: https://workers.cloudflare.com/
[22] J. Hermann, and M. Del Balso, Meet Michelangelo: Uber's Machine Learning Platform, Uber Engineering Blog, 2017. [Online]. Available: https://eng.uber.com/michelangelo
[23] Herve Jégou, Matthijs Douze, and Cordelia Schmid, “Product Quantization for Nearest Neighbor Search,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 1, pp. 117-128, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Matei Zaharia et al., “Resilient Distributed Datasets: A Fault-tolerant Abstraction for In-memory Cluster Computing,” Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation (NSDI'12), 2012.
[Google Scholar] [Publisher Link]
[25] Fabrizio Montesi, and Janine Weber, “Circuit Breakers, Discovery, and API Gateways in Microservices,” arXiv, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Scott Rose et al., “Zero Trust Architecture,” NIST Special Publication 800-207, National Institute of Standards and Technology, 2020.
[CrossRef] [Publisher Link]
[27] Dick ardt, “The OAuth 2.0 Authorization Framework,” Internet Engineering Task Force (IETF), 2012.
[Google Scholar]
[28] E. Rescorla, “The Transport Layer Security (TLS) Protocol Version 1.3,” Internet Engineering Task Force (IETF), 2018.
[Google Scholar] [Publisher Link]
[29] Robin Sommer, and Vern Paxson, “Outside the Closed World: On Using Machine Learning for Network Intrusion Detection,” 2010 IEEE Symposium on Security and Privacy, Oakland, CA, USA, pp. 305-316, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Maria-Irina Nicolae et al., “Adversarial Robustness Toolbox v1.0.0,” arXiv, pp. 1-34, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Haokun Fang, and Quan Qian, “Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning,” Future Internet, vol. 13, no. 4, pp. 1-20, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Qiang Yang et al., “Federated Machine Learning: Concept and Applications,” ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 2, pp. 1-19, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Solon Barocas, Moritz Hardt, and Arvind Narayanan, Fairness and Machine Learning, 2017.
[Publisher Link]