International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 02 | Feb 2025
p-ISSN: 2395-0072
www.irjet.net
AI-Driven Fault Detection and Recovery in Multi-Cloud Architectures Abhishek Sinha, St. Cloud State University, MN, USA Sreeprasad Govindankutty, Rochester Institute of Technology, USA ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Multi-cloud systems are complex and hard to
should be automated healing mechanisms to intelligently detect and fix the root cause.
manage when things go wrong. Organizations use multiple cloud providers to avoid dependence on a single vendor and to keep their systems running even if one provider fails. However, each cloud provider has different tools and ways of working, making it difficult to detect and fix problems quickly.
This study proposes a framework that uses "autoencoders" to spot unusual behavior, "graph-based algorithms" to trace the problem back to its source, and "reinforcement learning" to enable the system to learn and heal itself. This framework combines these techniques into a single, unified system that can handle real-time issues across different cloud environments.
We built an AI system that finds and fixes problems across different cloud providers automatically. The system collects data from AWS, Azure, and Google Cloud to spot patterns that indicate potential failures. When it detects a problem, it can often fix it without human help. Our tests show this system is significantly faster than traditional methods. It detects problems 40% faster and resolves them 50% more quickly than existing solutions. This matters most for systems that must stay running, like hospital equipment, banking services, and online stores. The system works well for large-scale operations and can adapt as organizations add more cloud services.
Figure 1: Autoencoders, graph-based algorithms and reinforced learning comes together as a single system for fault detection and recover in multi-cloud architectures.
We also discuss how to improve the system's ability to predict failures and how to make it work with newer types of cloud computing, like edge computing.
The primary contributions of this paper are as follows:
Key words: Multi-Cloud, AI-driven Fault Detection, crosscloud network performance, synthetic fault injection
Novel Framework: We introduce an end-to-end AI-driven fault management framework specifically designed for multi-cloud environments.
1.INTRODUCTION
Advanced ML Integration: The framework incorporates state-of-the-art ML algorithms tailored for anomaly detection, root cause analysis, and self-healing.
The traditional single cloud service focused on building distributed systems on top of services provided by a single vendor. To avoid vendor lock-in and ensure reliability, building distributed systems on multiple cloud providers ensures business continuity. Using multi cloud ensured that engineering workloads were executed on different cloud providers depending on which cloud solutions best fit the problem.
Comprehensive Evaluation: We present a rigorous experimental evaluation demonstrating the effectiveness of the framework in reducing downtime and improving fault resolution times. Scalability and Practicality: The framework is designed to scale with increasing numbers of cloud providers and services, ensuring its applicability to real-world deployments.
This approach of multi cloud deployments present significant challenges in terms of monitoring, error detection and automated recovery. As systems are deployed on multiple computing platforms, there is no unified system to view errors on all three platforms. Anomalies in metrics or behavior that occurs on one cloud provider will cause ripple effects slowly to other cloud providers and these correlated anomalies need to be detected early to minimize impact on business. There
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Impact Factor value: 8.315
This paper aims to bridge the gap between theoretical advancements in AI and their practical application in multi-cloud fault management, providing both researchers and practitioners with a robust solution to a critical problem.
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