Generative AI Agents for Automated Infrastructure Management in DevOps: Reducing Downtime and Enhancing Resource Efficiency in Cloud-Based Applications
Published 07-03-2024
Keywords
- Generative AI,
- infrastructure management,
- DevOps,
- cloud-based applications
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
In recent years, the proliferation of cloud-based applications has necessitated a paradigm shift in infrastructure management within DevOps environments. The increasing complexity of cloud infrastructures, combined with the demand for continuous integration and deployment, has underscored the need for innovative solutions that can ensure system reliability while optimizing resource utilization. This study investigates the role of generative artificial intelligence (AI) agents in automating infrastructure management processes in DevOps, specifically targeting the reduction of downtime, the enhancement of resource efficiency, and the overall improvement of system reliability.
Generative AI agents possess the capacity to learn from vast datasets, enabling them to model complex interactions within cloud-based environments effectively. By leveraging advanced machine learning techniques, these agents can analyze historical performance data, identify potential bottlenecks, and proactively mitigate issues before they escalate into critical failures. The deployment of generative AI in infrastructure management can lead to a significant reduction in operational downtime, thus improving the availability and reliability of cloud services. This paper elucidates the methodologies employed by generative AI agents to facilitate predictive maintenance, automated scaling, and intelligent resource allocation.
The research draws upon empirical evidence and case studies from leading organizations that have successfully integrated generative AI into their DevOps practices. By examining real-world implementations, we highlight the practical applications of these technologies in streamlining infrastructure management processes, minimizing human intervention, and promoting a culture of continuous improvement. Furthermore, the paper discusses the implications of generative AI on team dynamics and organizational culture, addressing potential challenges such as resistance to change and the necessity for upskilling personnel to work effectively alongside AI-driven tools.
Key challenges in the adoption of generative AI agents for infrastructure management are also explored, including data privacy concerns, the need for robust governance frameworks, and the importance of establishing trust in AI-driven decisions. The study emphasizes the critical need for organizations to develop comprehensive strategies that encompass both technological advancements and human factors to fully realize the benefits of generative AI.
Additionally, the paper presents an in-depth analysis of the architectural considerations for integrating generative AI agents into existing DevOps frameworks. This includes discussions on the deployment of AI models, the role of APIs in facilitating communication between various components, and the importance of monitoring and evaluation systems to ensure optimal performance. The findings indicate that organizations adopting generative AI can achieve not only enhanced operational efficiency but also a competitive edge in the rapidly evolving cloud landscape.
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References
- Praveen, S. Phani, et al. "Revolutionizing Healthcare: A Comprehensive Framework for Personalized IoT and Cloud Computing-Driven Healthcare Services with Smart Biometric Identity Management." Journal of Intelligent Systems & Internet of Things 13.1 (2024).
- Jahangir, Zeib, et al. "From Data to Decisions: The AI Revolution in Diabetes Care." International Journal 10.5 (2023): 1162-1179.
- Pushadapu, Navajeevan. "Artificial Intelligence and Cloud Services for Enhancing Patient Care: Techniques, Applications, and Real-World Case Studies." Advances in Deep Learning Techniques 1.1 (2021): 111-158.
- Rambabu, Venkatesha Prabhu, Munivel Devan, and Chandan Jnana Murthy. "Real-Time Data Integration in Retail: Improving Supply Chain and Customer Experience." Journal of Computational Intelligence and Robotics 3.1 (2023): 85-122.
- Priya Ranjan Parida, Chandan Jnana Murthy, and Deepak Venkatachalam, “Predictive Maintenance in Automotive Telematics Using Machine Learning Algorithms for Enhanced Reliability and Cost Reduction”, J. Computational Intel. & Robotics, vol. 3, no. 2, pp. 44–82, Oct. 2023
- Kasaraneni, Ramana Kumar. "AI-Enhanced Virtual Screening for Drug Repurposing: Accelerating the Identification of New Uses for Existing Drugs." Hong Kong Journal of AI and Medicine 1.2 (2021): 129-161.
- Pattyam, Sandeep Pushyamitra. "Data Engineering for Business Intelligence: Techniques for ETL, Data Integration, and Real-Time Reporting." Hong Kong Journal of AI and Medicine 1.2 (2021): 1-54.
- Qureshi, Hamza Ahmed, et al. "Revolutionizing AI-driven Hypertension Care: A Review of Current Trends and Future Directions." Journal of Science & Technology 5.4 (2024): 99-132.
- Ahmad, Tanzeem, et al. "Hybrid Project Management: Combining Agile and Traditional Approaches." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 122-145.
- Bonam, Venkata Sri Manoj, et al. "Secure Multi-Party Computation for Privacy-Preserving Data Analytics in Cybersecurity." Cybersecurity and Network Defense Research 1.1 (2021): 20-38.
- Sahu, Mohit Kumar. "AI-Based Supply Chain Optimization in Manufacturing: Enhancing Demand Forecasting and Inventory Management." Journal of Science & Technology 1.1 (2020): 424-464.
- Pushadapu, Navajeevan. "The Value of Key Performance Indicators (KPIs) in Enhancing Patient Care and Safety Measures: An Analytical Study of Healthcare Systems." Journal of Machine Learning for Healthcare Decision Support 1.1 (2021): 1-43.
- Sreerama, Jeevan, Venkatesha Prabhu Rambabu, and Chandan Jnana Murthy. "Machine Learning-Driven Data Integration: Revolutionizing Customer Insights in Retail and Insurance." Journal of Artificial Intelligence Research and Applications 3.2 (2023): 485-533.
- Rambabu, Venkatesha Prabhu, Amsa Selvaraj, and Chandan Jnana Murthy. "Integrating IoT Data in Retail: Challenges and Opportunities for Enhancing Customer Engagement." Journal of Artificial Intelligence Research 3.2 (2023): 59-102.
- Selvaraj, Amsa, Bhavani Krothapalli, and Venkatesha Prabhu Rambabu. "Data Governance in Retail and Insurance Integration Projects: Ensuring Quality and Compliance." Journal of Artificial Intelligence Research 3.1 (2023): 162-197.
- Althati, Chandrashekar, Venkatesha Prabhu Rambabu, and Munivel Devan. "Big Data Integration in the Insurance Industry: Enhancing Underwriting and Fraud Detection." Journal of Computational Intelligence and Robotics 3.1 (2023): 123-162.
- Thota, Shashi, et al. "Federated Learning: Privacy-Preserving Collaborative Machine Learning." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 168-190.
- Kodete, Chandra Shikhi, et al. "Hormonal Influences on Skeletal Muscle Function in Women across Life Stages: A Systematic Review." Muscles 3.3 (2024): 271-286.