International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 08 | Aug 2024
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p-ISSN: 2395-0072
AI for Resilient Infrastructure in Cloud: Proactive Identification and Resolution of System Downtimes Karthik Chowdary Tsaliki Bytedance, USA. -----------------------------------------------------------------------***-------------------------------------------------------------------------Abstract: Artificial Intelligence (AI) has emerged as a transformative solution for proactively identifying and addressing system downtimes in IT operations. By leveraging machine learning algorithms and predictive analytics, AI systems continuously monitor system health, analysing data from system logs, performance metrics, and historical patterns to detect anomalies and predict potential issues. The real-time analysis and anomaly detection capabilities of AI enable the generation of proactive alerts and notifications to IT teams, facilitating preventive measures to reduce the likelihood of system downtime. The integration of AI in IT operations management enhances the ability to detect and address potential issues, minimizes disruptions, and ensures continuous business operations. However, challenges such as integration with existing infrastructure, data quality, and ethical considerations must be addressed. Future directions include advancements in AI algorithms, integration with emerging technologies, collaborative approaches, and continuous improvement based on feedback and evolving needs. Embracing AI for identifying system downtimes signifies a commitment to maintaining robust and resilient IT infrastructures in the cloud era. Keywords: Artificial Intelligence (AI), System Downtimes, Predictive Analytics, Anomaly Detection, IT Operations Management
I. Introduction In the rapidly evolving landscape of information technology (IT) operations, ensuring the availability and reliability of systems has become a critical priority [1]. As organizations increasingly rely on cloud-based infrastructure to support their business processes, the impact of system downtimes can be severe, leading to financial losses, customer dissatisfaction, and reputational damage [2]. Traditional approaches to system monitoring and downtime identification often involve manual processes and reactive measures, which can be time-consuming, error-prone, and ineffective in preventing disruptions [3]. Artificial Intelligence (AI) has emerged as a transformative solution to address these challenges, offering a proactive and efficient approach to identifying system downtimes [4]. By leveraging the power of machine learning algorithms and predictive analytics, AI systems can continuously monitor various aspects of system health, analyse vast amounts of data in real-time, and detect subtle anomalies that may indicate potential issues [5]. This proactive approach enables IT teams to take preventive measures and minimize the impact of downtimes on business operations [6]. The application of AI in identifying system downtimes has gained significant attention in recent years, with numerous studies exploring its potential benefits and challenges [7]. Researchers have investigated the use of various AI techniques, such as anomaly detection [8], predictive modeling [9], and log analysis [10], to enhance the efficiency and accuracy of downtime identification. The integration of AI with cloud-based infrastructure has also been a focus of research, as it enables scalable and distributed monitoring capabilities [11]. This article aims to provide a comprehensive overview of the transformative use of AI in identifying system downtimes in cloud-based infrastructure. It explores the key techniques and approaches employed, the advantages offered by AI-driven monitoring, and the impact on IT operations management. Additionally, the article discusses the challenges and considerations associated with implementing AI solutions, such as integration with existing infrastructure, data quality, and ethical concerns. Finally, it presents future directions and opportunities for further research and development in this field.
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