Task Failure Prediction and Migration in Cloud Environment

Cloud computing has become integral to modern IT infrastructure, enabling scalable and flexible services. However, the reliability of cloud-based applications is challenged by task failures, which can lead to service disruptions and degraded user experience. This paper presents a novel approach for task failure prediction and migration in cloud environments using Bidirectional Long Short-Term Memory (Bi-LSTM) networks. The proposed model leverages the sequential nature of task execution data to capture dependencies and patterns, enhancing the accuracy of failure predictions. Additionally, a dynamic task migration strategy is introduced to mitigate the impact of predicted failures. The migration algorithm intelligently reallocates tasks to alternative resources, minimizing service downtime and optimizing resource utilization. Experimental results on a real-world dataset demonstrate the effectiveness of the Bi- LSTM-based prediction model in identifying impending task failures. Moreover, the integration of the migration strategy significantly improves system resilience, reducing the overall impact of failures on cloud-based applications. This research contributes to the advancement of proactive fault management in cloud computing, enhancing the reliability and performance of cloud services.