International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 11 | Nov 2024
p-ISSN: 2395-0072
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Predicting and Preventing Cloud Downtime: How AI Enhances Resiliency in Multi-Cloud Architectures Nandakumar Ramachandran Pezhery Xoriant Inc., USA --------------------------------------------------------------------------***----------------------------------------------------------------------ABSTRACT: This paper aims at analysing the part played by AI in the flexibility of multi-cloud structures in the fight
against downtime. With a growing adoption of multi-cloud models across enterprises, cutting-edge solutions, such as predictive analytics and machine learning, deliver preventive approaches toward service disruption. By using real-time, real-time monitoring of applications, ability to detect abnormalities, and abilities to dynamically adjust the allocation of resources to optimize business operations, AI guarantees continuity of operations and stability. This paper will explore how AI presently secures multi-cloud environments and potential issues with implementing AI across different kinds of clouds. The studies establish the reformative and irreplaceable role of AI in minimizing operating time and maintaining efficient service delivery.
KEYWORDS: AI, multiple cloud architecture, service interruption, prognostics. I.INTRODUCTION Although as the main benefits of multi-cloud solutions, flexibility, scalability, and cost-efficiency, are widely implemented, the risks of a system-wide failure and the interruption of services are still present in companies. Multi-cloud is often defined as the use of services from various cloud providers within an organization thus creating levels of interdependency which make it possible to experience low performance, system crashes, or even outages. Whatever the reason for the disruption, even minutes can be critical when considering the cut off revenue, loss in brand reputation and customer trust [1]. Calendar activities in conventional cloud infrastructures can frequently involve traditional approaches to downtime which are generally elite, implying that issues are only fixed once they have arisen. However, with the trends in technology on Artificial intelligence (AI) businesses are moving to more strategic one focusing on predicting and avoiding a downtime even it occurs. Machine learning and predictive analytics are among the AI technologies impacting the multi-cloud systems across organizations. It enables organisations not only to monitor the problems in real time, but also predict possible failures based on the tendency identified in historical data, resource consumption and environmental factors. By identifying where and when issues are likely to occur, AI allows businesses to proactively make the necessary changes in areas of resources, configuration or automate failover [2]. This change from a reactive to a proactive approach guarantees that protected downtime is as low as could reasonably be anticipated and that business operations are not interfered with. AI’s adoption in multi-cloud environments also improves the imperatives for real-time resource optimization within the clouds. Multi-cloud environments are not always complex – although they can be, the services and the performance presented by different providers is not unalike from each other [3]. It can take into considerations such differences and make sound decisions on distribution of workload within cloud application and Cloud resources and thus prevent some of the following from happening; Bottlenecks, Underutilization of cloud resources could make some systems to fail. With the aid of AI in monitoring systems, business can assess the state of cloud systems on a real-time basis and find out if there are abnormalities within the cloud systems to make necessary changes to the setting of the systems to improve the functioning of the systems. Furthermore, utilizing AI helps having an automation level that is almost four times higher than the time and human resources it takes for cloud management. The fact that they can prescribe action for performance problems, resource shifting, or failover scenarios where the human is not required results in much faster recovery times and greatly increases the reliability of multi-cloud systems [4]. Hence, AI-enhanced resilience enables organizations guarantee their consumer a sound and reliable experience despite probable infrastructure issues. Drawing upon the different capabilities of AI, this paper aims to discuss how the various forms of AI can be employed to strengthen the resistance of multi-cloud systems against outages and ensure that business operations continuity is achieved in the cloud, and the facets of cloud structures are fortified [5]. Mult iCloud is slowly becoming a standard approach in modern organizations because AI solutions help corporations maximize the effectiveness of investments in
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