Building Autonomous AI Agents based AI Infrastructure |
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© 2024 by IJCTT Journal | ||
Volume-72 Issue-11 |
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Year of Publication : 2024 | ||
Authors : Apurva Kumar | ||
DOI : 10.14445/22312803/IJCTT-V72I11P112 |
How to Cite?
Apurva Kumar, "Building Autonomous AI Agents based AI Infrastructure," International Journal of Computer Trends and Technology, vol. 72, no. 11, pp. 116-125, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I11P112
Abstract
The emergence of autonomous AI agents-self-directed software entities capable of perceiving, reasoning, and acting independently within predefined parameters-has profound implications for artificial intelligence (AI) infrastructure. This paper explores the transformative role of autonomous AI agents in optimizing, securing, and scaling AI infrastructure. By autonomously managing AI Infrastructure, enhancing system resilience, facilitating real-time decision-making, and fortifying data security, autonomous AI agents can significantly increase the efficiency and robustness of AI infrastructures.
Keywords
Artificial Intelligence, Infrastructure, Autonomous AI agents, Security, Sustainability.
Reference
[1] Bala Sai Krishna Paladugu, “Artificial Intelligence Models for Digitized Operations and Maintenance of Large Infrastructure Systems, Arizona State University, pp. 1-108, 2023.
[Google Scholar] [Publisher Link]
[2] Vijay Ramamoorthi, “AI-Driven Cloud Resource Optimization Framework for Real-Time Allocation,” Journal of Advanced Computing Systems, vol. 1, no. 1, pp. 8-15, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Okechukwu Clement Agomuo, Osei Wusu Brempong Jnr, and Junaid Hussain Muzamal, “Energy-Aware AI-Based Optimal Cloud Infra Allocation for Provisioning of Resources,” 2024 IEEE/ACIS 27th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Beijing, China, pp. 269-274, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Xinyi Hou et al., “Large Language Models for Software Engineering: A Systematic Literature Review,” ACM Transactions on Software Engineering and Methodology, pp. 1-76, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Jose Pergentino de Araujo Neto, Donald M. Pianto, and Celia G. Ralha, “A Resilient Agent-Based Architecture for Efficient Usage of Transient Servers in Cloud Computing," 2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Nicosia, Cyprus, pp. 218-225, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Alfred R. Mele, Autonomous Agents: From Self-Control to Autonomy, 1st ed., OUP USA, pp. 1-288, 1995.
[Google Scholar] [Publisher Link]
[7] Adrian Lino, “Flexchip Signal Processor (MC68175/D),” Motorola, 1996.
[Google Scholar] [Publisher Link]
[8] Rebecca Hersher, Amazon and the $150 Million Typo, The Two-Way, NPR, 2017. [Online]. Available: https://www.npr.org/sections/thetwo-way/2017/03/03/518322734/amazon-and-the-150-million-typo
[9] Shilin He et al., “STEAM: Observability-Preserving Trace Sampling,” Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, San Francisco CA USA, pp. 1750-1761, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Wei Du, and Shifei Ding, “A Survey on Multi-Agent Deep Reinforcement Learning: From the Perspective of Challenges and Application,” Artificial Intelligence Review, vol. 54, pp. 3215-3238, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Iqbal H. Sarker, Md Hasan Furhad, and Raza Nowrozy, “AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions,” SN Computer Science, vol. 2, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Dan He et al., “Autonomous Anomaly Detection on Traffic Flow Time Series with Reinforcement Learning,” Transportation Research Part C: Emerging Technologies, vol. 150, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Tamzidul Mina et al., “Adaptive Workload Allocation for Multi-Human Multi-Robot Teams for Independent and Homogeneous Tasks,” IEEE Access, vol. 8, pp. 152697-152712, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Venkata Mohit Tamanampudi, “AI Agents in DevOps: Implementing Autonomous AI Agents for Self-Healing Systems and Automated Deployment in Cloud Environments,” Australian Journal of Machine Learning Research & Applications, vol. 3, no. 1, pp. 507-556, 2023.
[Google Scholar] [Publisher Link]
[15] Mohammad S. Islam et al., “Anomaly Detection in a Large-Scale Cloud Platform,” 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), Madrid, ES, pp. 150-159, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Amira Mahamat Abdallah et al., “Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques-Recent Research Advancements,” IEEE Access, vol. 12, pp. 56749-56773, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Allyson I. Hauptman et al., “Adapt and Overcome: Perceptions of Adaptive Autonomous AI Agents for Human-AI Teaming,” Computers in Human Behavior, vol. 138, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Petr Skobelev, “Towards Autonomous AI Systems for Resource Management: Applications in Industry and Lessons Learned,” Advances in Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection: 16th International Conference, Toledo, Spain, pp. 12-25, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Alan Chan et al., “Visibility into AI Agents,” Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, pp. 958-973, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Debanjan Ghosh et al., “Self-Healing Systems-Survey and Synthesis,” Decision Support Systems, vol. 42, no. 4, pp. 2164-2185, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Hasan Cam, “Cyber Resilience Using Autonomous AI Agents and Reinforcement Learning,” Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, vol. 11413, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[22] TunsAdrian, Google/Cluster-Data, Github, 2024. [Online]. Available: https://github.com/google/cluster-data