Cloud service providers face significant challenges in preventing hardware and software failures due to the large-scale and heterogeneous nature of cloud computing. Although many studies have focused on characterising failed jobs, fewer have explored proactive failure prediction. This paper presents a deep learning-based failure prediction model that integrates Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks to identify job failures before they occur. The proposed model improves the performance of cloud computing applications by reducing job failures and optimising resource utilisation. Using the Google Cluster Traces dataset, we analyse failure patterns and evaluate the effectiveness of the model across multiple performance metrics. The results demonstrate the robustness of the proposed scheme, achieving an accuracy of 99.96%, along with a high F1-score of 99.92% when compared to existing models. These findings highlight the potential of deep learning in proactive failure mitigation, providing a foundation for future advances in cloud workload reliability.
Wunukhen Shehu Awudu, P. Asuquo, B. Agbor et al.· E3S Web of Conferences· 0 citations
This work proposes an intelligent, lightweight Tiny LSTM–GRU hybrid IDS on the edge to monitor device-generated behavioral patterns in real time, with minimal computational and energy overhead, and proposes an adaptive FedProx-based weighted federated learning framework.
Emmanuel Udok, B. Stephen, U. Luke et al.· E3S Web of Conferences· 0 citations
Comparative evaluation against existing machine learning and deep learning approaches indicates that the proposed framework achieves competitive accuracy while maintaining deployment-oriented processing speeds, suggesting that the CNN-GRU model is well-suited for SDN security monitoring under controlled experimental conditions.
Victor Anaga, B. Stephen, E. Adediji et al.· E3S Web of Conferences· 0 citations
The results show a success in implementing a real time, scalable, privacy-preserving, and adaptive IDS in large-scale IoT deployments through intelligent workload distribution between edge and cloud layers.
Chidera Winifred John, Eduediuyai Ekerete Dan, P. Asuquo et al.· E3S Web of Conferences· 0 citations
