With the evaluation of cellular network internet data traffic, forecasting and understanding traffic patterns become the critical objectives for managing the network-designed Quality of Service (QoS) benchmark. For th...
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With the evaluation of cellular network internet data traffic, forecasting and understanding traffic patterns become the critical objectives for managing the network-designed Quality of Service (QoS) benchmark. For this purpose, cellular network planners often use different methodologies for predicting data traffic. However, traditional traffic forecasting approaches are erroneous. As well as most of the time, traditional traffic forecasts are high-level or a generously large regional cluster level. Also, eNodeB-level utilization concerning with traffic forecasting is not readily available. As a result, user experience degradation or unnecessary network expansion is triggered based on the traditional method. This research focuses on extensive 6.2 million real network time series Long-Term Evolution (LTE) data traffic and other associate parameters, including eNodeB-wise Physical Resource Block (PRB) utilization, which mainly focuses on building a traffic forecasting model with the help of multivariate feature inputs and deep learning algorithms. A state-of-the-art Deep learning algorithm-based fusion model is proposed. The combination of different deep learning algorithms, namely Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU), enables traffic forecasting at a granular eNodeB-level and also provides eNodeB-wise forecasted PRB utilization. In this research R-2 score value for the proposed fusion model is 0.8034, which outperforms traditional models. Apart from the PRB utilization, QoS threshold was devised as 70% from a real network experience to trigger soft parameter tuning decisions. Based on the forecasted PRB utilization, this research proposed a unique algorithm that estimates eNodeB-level soft capacity parameter optimization for a short-term step-up solution or long-term network expansion to ensure a guaranteed QoS benchmark.
Fog Computing, Software Define networking, RESTful API and machinelearning are new technologies in the area of ICT as well as in networking. Fog computing brought high performance computing at the edge of network and...
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ISBN:
(纸本)9783031807121;9783031807138
Fog Computing, Software Define networking, RESTful API and machinelearning are new technologies in the area of ICT as well as in networking. Fog computing brought high performance computing at the edge of network and Software Defined networking. As we intended to head towards QoS and Load balancing in SDN. In this work we cover an initial segment which is machine leaning model implementation on a network traffic information to predict future outcomes for traffic flow. Meanwhile, in comprehensive research we intended to reduce network congestion, jitter, QoS and load balancing by learning past traffic flow information. As well as, we aim to improve maximum utilization of link-bandwidth. Through RESTful API of SDN, we can embed machinelearning based prediction model to the network server which can modify the network policies by learning from past experiences. In this paper, we proposed a QoS and load balancing framework. A Server and SDN based application for network monitoring, management and controlling the policies over network gateway for better performance in regard of traffic flow. Experiment result shows that implementation of machinelearning over network traffic flow information immensely important for new emerging technologies. The evaluation results of machinelearning model we implemented in this work depicts that model performs well. Meanwhile the model improves by increasing with the number of epochs.
The landscape of network management has undergone significant transformation with the advent of diverse Internet applications, smart devices, and the shift towards software-defined networks (SDN). This evolution has a...
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ISBN:
(纸本)9798350361261;9798350361278
The landscape of network management has undergone significant transformation with the advent of diverse Internet applications, smart devices, and the shift towards software-defined networks (SDN). This evolution has amplified the complexities of managing and measuring network traffic, necessitating more sophisticated and dynamic traffic classification methods to maintain optimal network performance and ensure user Quality-of-Experience (QoE). This paper presents a novel approach to network traffic classification, leveraging the capabilities of Gaussian Mixture Models (GMM) to classify network traffic based on user behavior patterns and temporal data. Our methodology distinctly categorizes network traffic into business or pleasure-oriented activities by analyzing various features such as the number of connected users, traffic volume, the day of the week, and the time of day. This classification is crucial not only for traffic management but also for understanding evolving network usage patterns, which are vital for ensuring robust network operations and efficient resource allocation.
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