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
The convergence of the Internet of Things (IoT) with Web3 ecosystems introduces new opportunities for automation, trust, and decentralized coordination. However, the same decentralized nature also amplifies security vulnerabilities, as conventional centralized intrusion detection and response systems are unable to provide real-time, tamper-proof protection at scale. This paper presents the Smart Contract-Based Automated Response System (SC-ARS), a novel framework that integrates blockchain smart contracts, machine learning (ML)-based anomaly detection, and automated mitigation policies. SC-ARS leverages lightweight consensus mechanisms and decentralized storage to ensure resilience against single points of failure, while smart contracts provide transparent and auditable enforcement of security actions. The ML pipeline, implemented using Random Forest, XGBoost, and LSTM models, is trained on benchmark datasets (NSL-KDD and CICIDS2017) to enable accurate anomaly detection. Experimental evaluation demonstrates up to 95% detection accuracy, a 50% reduction in response latency, and scalability to over 100,000 IoT devices without performance degradation. These results highlight the suitability of SC-ARS for deployment in smart cities, industrial IoT, and decentralized critical infrastructures where trust, transparency, and real-time responsiveness are essential.
S. Bassey, B. Stephen, Emediong Bassey Obot 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