Data-driven resource allocation with traffic load prediction

Yao, Chuting; Yang, Chenyang; Chih‐Lin, I

doi:10.1007/s41650-017-0005-y

Cited by 13 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the accuracy of future cellular traffic flow prediction benefits the effectiveness of demand-aware resource allocation [11], and traffic forecasting ensures that the predicted mobile and user capabilities will be performed even without capacity deadlock or usability evaluation destruction for MNOs to take into account. Machine learning (ML) has recently risen to prominence as a popular innovation aimed at balancing challenge computation costs with accuracy concerns, causing considerable consternation throughout the mathematical optimization field [12].…”

Section: Introductionmentioning

confidence: 99%

Network Traffic Forecasting in Network Cybersecurity: Granular Computing Model

Alzahrani

Aldhyani

Alsubari

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

Industry 4.0, also known as the Internet of Things, is a concept that encompasses the joint applicability of operation, the Internet, and information technologies to expand the efficiency expectation of automation to include green and flexible processes and innovative products and services. Industrial network infrastructures must be modified to accommodate extra traffic from a variety of technologies in order to achieve this integration. In order to successfully implement cutting-edge wireless technologies, high-quality service (QoS) must be provided to end users. It is thus important to keep an eye on the functioning of the whole network without impacting base station throughput. Improved network performance is constantly needed, even for already-deployed cellular networks, such as the 4th generation (4G) and 3rd generation (3G). For the purpose of forecasting network traffic, an integrated model based on the long short-term memory (LSTM) model was used to combine clustering rough k-means (RKM) and fuzzy c-means (FCM). Clustering granules derived from FCM and RKM were also utilized to examine the network data for each calendar year. The novelty of our proposed model is the integration of the prediction and forecasting results obtained using existing prediction models with centroids of clusters. The WIDE backbone network’s live network traffic statistics were used to evaluate the proposed solution. The integrated model’s outcomes were assessed using a variety of statistical markers, including mean square error (MSE), root mean square error (RMSE), and standard error. The suggested technique was able to provide findings that were very accurate. The prediction error of LSTM with FCM was less on the basis of the MSE of 0.00783 and RMSE of 0.0885 at the training phase, where the prediction values of LSTM with the RKM had an MSE of 0.00564 and RMSE of 0.7511. Finally, the suggested model may substantially increase the prediction accuracy attained using FCM and RKM clustering.

show abstract

Section: Introductionmentioning

confidence: 99%

Network Traffic Forecasting in Network Cybersecurity: Granular Computing Model

Alzahrani

Aldhyani

Alsubari

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…To realize intelligent management of cellular networks, it is very important to perform real-time or non-real-time regular analysis and accurate prediction of cellular traffic. For example, accurate prediction of future traffic can greatly increase the efficiency of demand aware resource allocation [8].…”

Section: Introductionmentioning

confidence: 99%

Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network

et al. 2020

View full text Add to dashboard Cite

With the arrival of 5G networks, cellular networks are moving in the direction of diversified, broadband, integrated, and intelligent networks. At the same time, the popularity of various smart terminals has led to an explosive growth in cellular traffic. Accurate network traffic prediction has become an important part of cellular network intelligence. In this context, this paper proposes a deep learning method for space-time modeling and prediction of cellular network communication traffic. First, we analyze the temporal and spatial characteristics of cellular network traffic from Telecom Italia. On this basis, we propose a hybrid spatiotemporal network (HSTNet), which is a deep learning method that uses convolutional neural networks to capture the spatiotemporal characteristics of communication traffic. This work adds deformable convolution to the convolution model to improve predictive performance. The time attribute is introduced as auxiliary information. An attention mechanism based on historical data for weight adjustment is proposed to improve the robustness of the module. We use the dataset of Telecom Italia to evaluate the performance of the proposed model. Experimental results show that compared with the existing statistics methods and machine learning algorithms, HSTNet significantly improved the prediction accuracy based on MAE and RMSE.

show abstract

“…For example, the forecast of long-term mobile traffic load is essential to determine the deployment of base stations (BSs), that is, network planning and design [2]. Moreover, the short-term mobile traffic load prediction can be utilized for the optimized resource management, congestion control and load balancing among nearby BSs [3,4]. It is worth noting that the prediction of mobile traffic load is also beneficial to reduce the overall power consumption at the mobile network by making BS go into the sleep mode when the overall mobile traffic load is low, as shown in [5] and [6].…”

mentioning

confidence: 99%

A mobile traffic load prediction based on recurrent neural network: A case of telecommunication in Afghanistan

Ahmadzai

Lee

2022

Electronics Letters

View full text Add to dashboard Cite

This paper investigates the prediction of mobile traffic load based on four variants of recurrent neural networks, which are the simple long short-term memory (LSTM), stacked LSTM, gated recurrent unit (GRU) and bidirectional LSTM. In the considered schemes, the mobile traffic load of 15 min ahead of time is estimated based on the previous mobile traffic load data. The performance of the proposed scheme is verified using realistic traffic load data collected from the base station located in Kabul city, Afghanistan, which belongs to the SALAAM telecommunication operator during December 2020 and January 2021. Through performance evaluation, the authors confirm that the traffic load can be predicted with high accuracy using considered schemes and the GRU-based scheme outperforms other schemes in terms of accuracy.

show abstract

Data-driven resource allocation with traffic load prediction

Cited by 13 publications

References 18 publications

Network Traffic Forecasting in Network Cybersecurity: Granular Computing Model

Network Traffic Forecasting in Network Cybersecurity: Granular Computing Model

Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network

A mobile traffic load prediction based on recurrent neural network: A case of telecommunication in Afghanistan

Contact Info

Product

Resources

About