The primary objective of this research is to employ artificial intelligence, machine learning, and neural networks in order to construct a network traffic prediction model. The analysis of network traffic data obtaine...
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This paper presents a GPU-based massively parallel implementation of the Best- Worst-Play (BWP) metaphor-less optimization algorithm, which results from the combination of two other simple and quite efficient populati...
This paper presents a GPU-based massively parallel implementation of the Best- Worst-Play (BWP) metaphor-less optimization algorithm, which results from the combination of two other simple and quite efficient population-based algorithms, Jaya and Rao-l, that have been used to solve a variety of prob-lems. The proposed parallel GPU version of the algorithm is here used for solving large nonlinear equation systems, which have enormous importance in different areas of science, eng.neering, and economics and are usually considered the most difficult class of problems to solve by traditional numerical methods. The proposed parallelization of the BWP algorithm was implemented using the Julia programming language on a GeForce RTX 3090 GPU with 10496 CUDA cores and 24 GB of VRAM and tested on a set of challeng.ng scalable systems of nonlinear equations with dimensions between 500 and 2000. Depending on the tested problem and dimension, the GPU-based implementation of BWP exhibited a speedup up to $283.17\times$ , with an average of $161.21\times$ , which shows the efficiency of the proposed G PU - based parallel version of the BWP algorithm.
The primary objective of this research is to employ artificial intelligence, machine learning, and neural networks in order to construct a network traffic prediction model. The analysis of network traffic data obtaine...
详细信息
ISBN:
(数字)9798350379433
ISBN:
(纸本)9798350379440
The primary objective of this research is to employ artificial intelligence, machine learning, and neural networks in order to construct a network traffic prediction model. The analysis of network traffic data obtained from a digital media and entertainment provider operating in Turkey is conducted through the application of multivariate time-series analysis techniques in order to get insights into the temporal patterns and trends. In model development, Vector Autoregression (VAR), Vector Error Correction Model (VECM), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) algorithms have been utilized. LSTM and GRU models have performed better with low Mean Absolute Percentage Error (MAPE) and high R-squared Score (R
2
). LSTM model has reached 0.98 R2 and 8.95% MAPE. These results indicate that the models can be utilized in network management optimization as resource allocation, congestion detection, anomaly detection, and quality of service.
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