Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 2125-2129· 0 citations· 19 references
Abstract
Accurate workload prediction in cloud data centers is essential for efficient resource management, yet high-dimensional and noisy operational data often hinder forecasting performance. This work extends the original CVCBM model by integrating a lightweight Bidirectional GRU (BiGRU) with Bidirectional LSTM (BiLSTM) to enhance prediction efficiency while maintaining temporal feature extraction. Initially, workload signals are denoised and decomposed using a two-stage process—Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) followed by Variational Mode Decomposition (VMD). Sample Entropy (SE) selects meaningful components, and K-Means clustering prioritizes high workload data for training. The hybrid Conv1D-BiLSTM-BiGRU architecture captures multi-scale temporal patterns and both short-term and long-term dependencies. The trained model is deployed using the Flask framework for real-time workload prediction, allowing interactive input of datasets and immediate forecasting. Experimental evaluation demonstrates that the extended model reduces computational overhead while improving prediction accuracy, providing robust, scalable, and real-time forecasting for cloud data center resource management.
Accurate prediction of computing-resource workloads is important for capacity planning, overload warning, and intelligent system management. Large-scale computing systems exhibit complex temporal fluctuations, sudden variations, and multi-resource coupling characteristics, making accurate workload prediction challenging. To address these challenges, this paper proposes a Hybrid Temporal-Feature Attention enhanced Multi-task Stacking model (HTAM-Stack) for multivariate cluster workload forecasting. First, a Temporal-Feature Hybrid Attention (TFHA) module is designed to jointly capture temporal dependencies and cross-resource feature interactions, enabling adaptive extraction of critical temporal patterns and important resource characteristics. Second, a Multi-Task Learning (MTL) framework is introduced to simultaneously predict CPU and Memory workloads by exploiting the correlations among heterogeneous resource variables. Furthermore, a Dynamic Stacking (DS) mechanism is developed to adaptively adjust the contributions of heterogeneous base learners through a weight generation network, and a Residual Corrector (RC) is incorporated to further enhance prediction robustness. Extensive experiments conducted on two widely used public cluster workload datasets, including Google Cluster Trace and Alibaba Cluster Trace, demonstrate that HTAM-Stack achieves competitive prediction performance under complex and dynamic workload conditions. The proposed model achieves MAE values of 0.0012 and 0.0010, RMSE values of 0.0031 and 0.0027, MAPE values of 0.20% and 0.16%, and R2 values of 0.9715 and 0.9782 on the two datasets, respectively. Moreover, HTAM-Stack requires only 3.20 ms inference time with 8.60 M parameters, achieving a favorable balance between prediction accuracy and computational efficiency. The results validate the general effectiveness of the proposed framework on public cluster workload benchmarks rather than its direct applicability to railway IT systems. Because no representative railway workload dataset was available, railway IT is discussed only as a potential application context that requires domain-specific validation.
A dynamic recurrent neural network is proposed to accurately predict workloads and integrates an auto-encoder to effectively extract representations from the original workload data with high dimensionality to enable adaptive and accurate predictions for highly variable workloads.
Okore Kalu, C. Okafor, P. Asuquo et al.· E3S Web of Conferences· 0 citations
With the advancement of data science technologies and the growing demand for real-time applications, streaming data has assumed an increasingly prominent role in modern data processing systems, continuously challenging traditional batch-processing paradigms. To address key challenges in streaming learning—such as extreme class imbalance, concept drift, and the requirement for millisecond-level latency response—this paper proposes STRAP (Streaming Temporal deep learning with Robust Attentive Prediction), an end-to-end deep streaming prediction framework. STRAP constructs temporal inputs using a sliding-window mechanism combined with causal slicing, ensuring predictions rely solely on current and historical information. For representation learning, it integrates bidirectional gated recurrent units (BiGRUs) with a multi-head attention mechanism to capture complex contextual dependencies. Furthermore, Focal Loss is incorporated to dynamically adjust class weights, thereby enhancing the model’s ability to learn from minority classes. Recognizing the lack of standardized evaluation protocols in streaming learning, this paper introduce WASE (Window- Adaptive Streaming Evaluation), a comprehensive assessment framework that jointly evaluates deployment performance along both accuracy and efficiency dimensions. WASE combines online AUC, latency percentiles (p95, p99), and system throughput, featuring a window-level adaptive evaluation mechanism that aligns with evolving data distributions and real-time operational requirements. In experiments on a streaming-reconstructed version of the IEEE-CIS Fraud Detection dataset, STRAP achieves a 1.5% absolute improvement in test-set AUC over an MLP baseline while maintaining a p99 latency below 7 ms, and attains a throughput of 1,258.2 samples/s. Ablation studies confirm that the multi-head attention mechanism and the dynamic reweighting strategy jointly improve minority-class recognition. These results demonstrate that STRAP delivers both high predictive accuracy and ultra-low-latency responsiveness in highly dynamic, high-concurrency streaming environments, exhibiting strong practical deployability for latency-sensitive real-time forecasting scenarios such as smart grid anomaly detection, financial risk control, and edge-based monitoring systems.
Fangrui Yu, Xiangyu Lin· International Conference on...· 0 citations
In recommendation and advertising systems, model training pipelines rely on high-throughput ingestion of fresh, collected behavioral data. These data are typically organized into time-partitioned, multi-level table paths. The access patterns driven by highly concurrent model training tasks exhibit complex cross-path correlations, multi-scale temporal dependencies, and frequent hot-cold transitions, making effective data placement under limited SSD capacity challenging. In this paper, we propose FLEDA, a forecasting-based data partition placement framework that selectively places daily-level training data partitions onto SSDs under constrained storage capacity. We first model historical data access patterns as multivariate and multiscale time series to capture both temporal dynamics and cross-path dependencies. By leveraging dependencies across two dimensions, we propose a model that extracts multi-scale cross-path temporal features and fuses mixture-of-experts to forecast the future throughput of all table paths. Besides, we design a ranking-based placement policy that balances predicted throughput, recent access history, and SSD residency continuity to maximize coverage of future data accesses while avoiding excessive data migration. Experiments on large-scale, real-world training workloads show that FLEDA consistently outperforms state-of-the-art multivariate time-series forecasting models and significantly improves SSDs' throughput coverage with low data migration overhead.
Yuchen Yang, Teng Yin, Yang Guo et al.· Proceedings of the 32nd ACM...· 0 citations
A deep learning-based failure prediction model that integrates Convolutional Neural Networks and Bidirectional Long Short-Term Memory networks to identify job failures before they occur is presented, improving the performance of cloud computing applications by reducing job failures and optimising resource utilisation.
Wunukhen Shehu Awudu, P. Asuquo, B. Agbor et al.· E3S Web of Conferences· 0 citations
This paper proposes a time-series data processing model based on the Transformer Encoder, designed to address the limitations of traditional Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTM) in handling long sequences, such as long-term dependency issues, vanishing or exploding gradients, and low computational efficiency. By employing a self-attention mechanism along with learnable positional encoding and multi-head self-attention, the model effectively captures global dependencies in sequential data and significantly enhances its expressive power. To improve training stability, layer normalization is applied after the Transformer encoder, and the data processing flow is optimized through reorganization across batch and node dimensions. The model demonstrates clear advantages in parallel computation and long-term dependency modeling, enabling more efficient processing of complex time-series data. It exhibits superior computational efficiency and stronger generalization capabilities, particularly in real-time traffic flow prediction tasks based on large-scale datasets and long sequences. Experimental results show that the proposed model outperforms traditional RNN and LSTM models across various tasks, indicating promising potential for practical applications.
Ze Zhao, Ming-Yan Jiang, Feng Wang· International Conference on...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
