Host load prediction in cloud computing with Discrete Wavelet Transformation (DWT) and Bidirectional Gated Recurrent Unit (BiGRU) network

Computer Communications, Год журнала: 2022, Номер 198, С. 157 - 174

Опубликована: Ноя. 30, 2022

Язык: Английский

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A hybrid model based on discrete wavelet transform (DWT) and bidirectional recurrent neural networks for wind speed prediction

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 127, С. 107340 - 107340

Опубликована: Окт. 30, 2023

Язык: Английский

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Auto-scaling techniques in container-based cloud and edge/fog computing: Taxonomy and survey

Computer Communications, Год журнала: 2023, Номер 209, С. 120 - 150

Опубликована: Июнь 19, 2023

Язык: Английский

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Multi-Task Learning for Electricity Price Forecasting and Resource Management in Cloud Based Industrial IoT Systems

IEEE Access, Год журнала: 2023, Номер 11, С. 54280 - 54295

Опубликована: Янв. 1, 2023

Cloud computing has gained immense popularity in the logistics industry. This innovative technology optimizes operations by eliminating requirement for physical equipment calculations. Instead, specialized companies provide cloud-based services, relying heavily on computers and servers that consume substantial amounts of energy. Hence, ensuring availability affordable dependable electricity is paramount efficient design management these services. centers, which are power-intensive, face challenge reducing their energy consumption due to escalating power costs. To address this issue, data placement node strategies commonly employed operations. An AlexNet model been designed optimize storage relocation predict prices. The outcome initiative resulted a considerable reduction at centres. uses Dwarf Mongoose Optimization Algorithm (DMOA) produce an optimal solution increase its performance with real-world dataset from IESO Ontario, Canada. 75% available was used training assure model’s precision, remaining 25% allocated testing purposes. forecasts prices MAE 2.22% MSE 6.33%, resulting average 22.21% expenses. Our proposed method accuracy 97% compared 11 benchmark algorithms, including CNN, DenseNet, SVM having 89%, 88%, 82%, respectively.

Язык: Английский

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Proactive auto-scaling technique for web applications in container-based edge computing using federated learning model

Journal of Parallel and Distributed Computing, Год журнала: 2024, Номер 187, С. 104837 - 104837

Опубликована: Янв. 9, 2024

Язык: Английский

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MSACN-LSTM: A multivariate time series prediction hybrid network model for extracting spatial features at multiple time scales

International Journal of Machine Learning and Cybernetics, Год журнала: 2025, Номер unknown

Опубликована: Фев. 14, 2025

Язык: Английский

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Symmetry-Aware Multi-Dimensional Attention Spiking Neural Network with Optimization Techniques for Accurate Workload and Resource Time Series Prediction in Cloud Computing Systems

Symmetry, Год журнала: 2025, Номер 17(3), С. 383 - 383

Опубликована: Март 3, 2025

Cloud computing offers scalable and adaptable resources on demand, has emerged as an essential technology for contemporary enterprises. Nevertheless, it is still challenging work to efficiently handle cloud because of dynamic changes in load requirement. Existing forecasting approaches are unable the intricate temporal symmetries nonlinear patterns workload data, leading degradation prediction accuracy. In this manuscript, a Symmetry-Aware Multi-Dimensional Attention Spiking Neural Network with Optimization Techniques Accurate Workload Resource Time Series Prediction Computing Systems (MASNN-WL-RTSP-CS) proposed. Here, input data from Google cluster trace dataset were preprocessed using Multi Window Savitzky–Golay Filter (MWSGF) remove noise while preserving important maintaining structural symmetry time series trends. Then, (MASNN) effectively models symmetric fluctuations predict resource series. To enhance accuracy, Secretary Bird Algorithm (SBOA) was utilized optimize MASNN parameters, ensuring accurate predictions. Experimental results show that MASNN-WL-RTSP-CS method achieves 35.66%, 32.73%, 31.43% lower Root Mean Squared Logarithmic Error (RMSLE), 25.49%, 32.77%, 28.93% Square (MSE), 24.54%, 23.65%, 23.62% Absolute (MAE) compared other approaches, like ICNN-WL-RP-CS, PA-ENN-WLP-CS, DCRNN-RUP-RP-CCE, respectively. These advances emphasize utility achieving more forecasts, thereby facilitating effective management.

Язык: Английский

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An efficient prediction-based dynamic resource allocation framework in quantum cloud using knowledge-based offline reinforcement learning

Quantum Machine Intelligence, Год журнала: 2025, Номер 7(1)

Опубликована: Март 5, 2025

Язык: Английский

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Workload Prediction in Cloud Data Centers Using Complex‐Valued Spatio‐Temporal Graph Convolutional Neural Network Optimized With Gazelle Optimization Algorithm

Transactions on Emerging Telecommunications Technologies, Год журнала: 2025, Номер 36(3)

Опубликована: Март 1, 2025

ABSTRACT Workload prediction is the necessary factor in cloud data center for maintaining elasticity and scalability of resources. However, accuracy workload very low, because redundancy, noise, low center. In this manuscript, Prediction Cloud Data Centers using Complex‐Valued Spatio‐Temporal Graph Convolutional Neural Network Optimized with Gazelle Optimization Algorithm (CVSTGCN‐WLP‐CDC) proposed. Initially, input collected from two standard datasets such as NASA Saskatchewan HTTP traces dataset. Then, preprocessing Multi‐Window Savitzky–Golay Filter (MWSGF) used to remove noise redundant data. The preprocessed fed CVSTGCN a dynamic environment. work, proposed Approach (GOA) enhance weight bias parameters. CVSTGCN‐WLP‐CDC technique executed efficacy based on structure evaluated several performances metrics accuracy, recall, precision, energy consumption correlation coefficient, sum index (SEI), root mean square error (RMSE), squared (MPE), percentage (PER). provides 23.32%, 28.53% 24.65% higher accuracy; 22.34%, 25.62%, 22.84% lower when comparing existing methods Artificial Intelligence augmented evolutionary approach espoused centres architecture (TCNN‐CDC‐WLP), Performance analysis machine learning centered techniques (PA‐BPNN‐CWPC), Machine effectual utilization centers (ARNN‐EU‐CDC) respectively.

Язык: Английский

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Research on anti-error operation warning of power grid dispatching based on deep bidirectional gated recurrent neural network

EAI Endorsed Transactions on Energy Web, Год журнала: 2025, Номер 12

Опубликована: Апрель 11, 2025

To improve the security and overall efficiency of grid scheduling work accurately optimize decisions, a error-proof operation warning method based on deep bidirectional gated recurrent neural network is proposed. This paper combines principle hierarchical data construction, summarizes structured metadata tickets maintenance plans CIM model OMS frame model, constructs warehouse dispatching error prevention; natural language processing (NLP) technology, key information knowledge entities related to prevention are automatically identified extracted from warehouse. Based network, sequence used as input construct state reconstruction carried out according output prediction results. The experimental results show that: docking speed in different phases fast with fastest 71.254MB/s, convergence analysis calculation within 0.01MB/s, indicating that high, application performance good, it can determine whether there any misoperation process carry highly efficient, accurate, early warning.

Язык: Английский

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