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Cloud infrastructure provides computing services where computing resources can be adjusted on-demand. However, the adoption of cloud infrastructures brings concerns like reliance on the service provider network, reliability, compliance for service level agreements (SLAs), etc. Software-defined networking (SDN) is a networking concept that suggests the segregation of a network’s data plane from the control plane. This concept improves networking behavior. In this paper, we present an SDN-enabled resource-aware topology framework. The proposed framework employs SLA compliance, Path Computation Element (PCE) and shares fair loading to achieve better topology features. We also present an evaluation, showcasing the potential of our framework.
Aaqif Afzaal Abbasi; Shahab Shamshirband; Mohammed A. A. Al-Qaness; Almas Abbasi; Nashat T. Al-Jallad; Amir Mosavi. Resource-Aware Network Topology Management Framework. 2020, 1 .
AMA StyleAaqif Afzaal Abbasi, Shahab Shamshirband, Mohammed A. A. Al-Qaness, Almas Abbasi, Nashat T. Al-Jallad, Amir Mosavi. Resource-Aware Network Topology Management Framework. . 2020; ():1.
Chicago/Turabian StyleAaqif Afzaal Abbasi; Shahab Shamshirband; Mohammed A. A. Al-Qaness; Almas Abbasi; Nashat T. Al-Jallad; Amir Mosavi. 2020. "Resource-Aware Network Topology Management Framework." , no. : 1.
In the real-life, time-series data comprise a complicated pattern, hence it may be challenging to increase prediction accuracy rates by using machine learning and conventional statistical methods as single learners. This research outlines and investigates the Stacking Multi-Learning Ensemble (SMLE) model for time series prediction problem over various horizons with a focus on the forecasts accuracy, directions hit-rate, and the average growth rate of total oil demand. This investigation presents a flexible ensemble framework in light of blend heterogeneous models for demonstrating and forecasting nonlinear time series. The proposed SMLE model combines support vector regression (SVR), backpropagation neural network (BPNN), and linear regression (LR) learners, the ensemble architecture consists of four phases: generation, pruning, integration, and ensemble prediction task. We have conducted an empirical study to evaluate and compare the performance of SMLE using Global Oil Consumption (GOC). Thus, the assessment of the proposed model was conducted at single and multistep horizon prediction using unique benchmark techniques. The final results reveal that the proposed SMLE model outperforms all the other benchmark methods listed in this study at various levels such as error rate, similarity, and directional accuracy by 0.74%, 0.020%, and 91.24%, respectively. Therefore, this study demonstrates that the ensemble model is an extremely encouraging methodology for complex time series forecasting.
Mergani A. Khairalla; Xu Ning; Nashat T. Al-Jallad; Musaab O. El-Faroug. Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model. Energies 2018, 11, 1605 .
AMA StyleMergani A. Khairalla, Xu Ning, Nashat T. Al-Jallad, Musaab O. El-Faroug. Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model. Energies. 2018; 11 (6):1605.
Chicago/Turabian StyleMergani A. Khairalla; Xu Ning; Nashat T. Al-Jallad; Musaab O. El-Faroug. 2018. "Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model." Energies 11, no. 6: 1605.