In this work, we propose to predict the network throughput using its past measurements. As the analysis shows, the network throughput forms a long range-dependent process; thus, for the throughput prediction, we
Torres et al.[199]proposed an ML-based approach for forecasting the average downlink throughput per user for a specific LTE cell. The average throughput is predicted using a variant of Autoregressive Integrated Moving Average (ARIMA) and supervised naive persistence model. A QoS forecast based on ...
A system is proposed based on aggregation approach for traffic flow prediction based on the moving average (MA), exponential smoothing (ES), ARIMA, and neural network (NN) models. The MA, ES, and ARIMA models were used to obtain three relevant time series, which were the basis of the NN...
Gated recurrent neural network The rise of deep learning has revolutionized predictive analytics, with gated architectures like LSTM and GRU becoming mainstream for time series forecasting. LSTM excels in modeling nonlinear, non-stationary sequences, while GRU (Cho et al.16) simplifies the architecture...
Encoder–decoder networks function better when attention mechanisms are used, and the Temporal Fusion Transformer is a neural network architecture that was specifically designed for time series forecasting tasks. It combines the strengths of LSTMs, attention mechanisms, and transformers to produce ...
Using a bandwidth checker module, SDNManager merges these proposed states (PS, the expected bandwidth utilization) into a target state (TS) that is guaranteed to maintain the safety and performance of the system. Updater module then updates the network to the target state. What is more, ...
Deep learning involves the complex application of machine-learning algorithms, such as Bayesian fusions and neural network, for data extraction and logical inference. Deep learning, combined with information fusion paradigms, can be utilized to provide more comprehensive and reliable predictions from big ...
To say a gene-pair involved in cross-talk between two particular signaling pathways has high potential of being involved in acquired drug-resistance, our research hypothesis was it should have high probability of appearing in the resistant network and low probability in the parental network. The ...
The future demand of resources of each group is predicted by using ARIMA based methods. The predicted demand is fed into a group controller that manages the cloud nodes of the group. A dispatcher also dispatchs requests among groups. Smith et al. in Smith et al. (1998) predict the run...
An LSTM network was proposed to forecast the CPU workload of the machine and results show significant improvement over the ARIMA model. Shan et al. [35] predicted the CPU and Bandwidth one time step ahead and compared it to a multi-time step ahead. The prediction was determined using RNN-...