• Jeyasri Sekar

The purpose of this research is to explore and develop advanced techniques for autonomous cloud management using artificial intelligence (AI), focusing specifically on self-healing and self-optimization capabilities. Autonomous cloud management aims to reduce human intervention, improve reliability, and enhance the efficiency of cloud services. This study is significant because it addresses the growing complexity of cloud environments and the need for dynamic, real-time responses to ensure optimal performance and resilience. This research employs a multi-faceted approach to achieve self-healing and self-optimization in cloud environments. For self-healing, we utilize AI-driven anomaly detection algorithms, predictive maintenance models, and automated recovery protocols. These techniques are designed to identify and rectify faults without human intervention. For self-optimization, we apply machine learning algorithms to analyze workload patterns, predict resource demands, and dynamically allocate resources to maximize efficiency and minimize costs. The experimental setup involves a simulated cloud environment where these AI techniques are tested and validated using a range of performance metrics, including response time, throughput, and resource utilization. The implementation of AI-driven self-healing techniques resulted in a significant reduction in downtime and improved system reliability. The anomaly detection algorithms were able to identify potential issues with a high degree of accuracy, triggering automated recovery processes that restored normal operation swiftly. The predictive maintenance models successfully forecasted potential failures, allowing for preemptive measures. For self-optimization, the machine learning models effectively balanced workloads and resource allocation, leading to enhanced performance metrics. Compared to traditional methods, the AI-based approaches demonstrated superior efficiency in resource utilization and cost savings. The findings of this research highlight the potential of AI to revolutionize cloud management by enabling autonomous, self-healing, and self-optimization capabilities. These advancements not only improve the reliability and efficiency of cloud services but also reduce the need for human intervention, thus lowering operational costs. The successful implementation of these AI techniques in a simulated environment indicates their feasibility for real-world application. Future research could explore the integration of these techniques with other emerging technologies, such as edge computing and IoT, to further enhance the capabilities of autonomous cloud management.

Autonomous Cloud Management, Artificial Intelligence, Self-Healing, Self-Optimization, Cloud Computing

"AUTONOMOUS CLOUD MANAGEMENT USING AI: TECHNIQUES FOR SELF-HEALING AND SELF-OPTIMIZATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.p571-p580, May-2023, Available :http://www.jetir.org/papers/JETIR2305G78.pdf

"AUTONOMOUS CLOUD MANAGEMENT USING AI: TECHNIQUES FOR SELF-HEALING AND SELF-OPTIMIZATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppp571-p580, May-2023, Available at : http://www.jetir.org/papers/JETIR2305G78.pdf