Continual deep learning for time series modeling
The multi-layer structures of Deep Learning facilitate the processing of higher-level
abstractions from data, thus leading to improved generalization and widespread …
abstractions from data, thus leading to improved generalization and widespread …
Intelligent traffic management in next-generation networks
The recent development of smart devices has lead to an explosion in data generation and
heterogeneity. Hence, current networks should evolve to become more intelligent, efficient …
heterogeneity. Hence, current networks should evolve to become more intelligent, efficient …
Deep learning with long short-term memory for time series prediction
Time series prediction can be generalized as a process that extracts useful information from
historical records and then determines future values. Learning long-range dependencies …
historical records and then determines future values. Learning long-range dependencies …
Optimal VNF placement via deep reinforcement learning in SDN/NFV-enabled networks
The emerging paradigm-Software-Defined Networking (SDN) and Network Function
Virtualization (NFV)-makes it feasible and scalable to run Virtual Network Functions (VNFs) …
Virtualization (NFV)-makes it feasible and scalable to run Virtual Network Functions (VNFs) …
Multiple strategies differential privacy on sparse tensor factorization for network traffic analysis in 5G
Due to high capacity and fast transmission speed, 5G plays a key role in modern electronic
infrastructure. Meanwhile, sparse tensor factorization (STF) is a useful tool for dimension …
infrastructure. Meanwhile, sparse tensor factorization (STF) is a useful tool for dimension …
Intelligent routing based on reinforcement learning for software-defined networking
DM Casas-Velasco, OMC Rendon… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Traditional routing protocols employ limited information to make routing decisions, which
can lead to a slow adaptation to traffic variability, as well as restricted support to the Quality …
can lead to a slow adaptation to traffic variability, as well as restricted support to the Quality …
A long short-term memory recurrent neural network framework for network traffic matrix prediction
Network Traffic Matrix (TM) prediction is defined as the problem of estimating future network
traffic from the previous and achieved network traffic data. It is widely used in network …
traffic from the previous and achieved network traffic data. It is widely used in network …
Scalable tensor factorizations for incomplete data
The problem of incomplete data–ie, data with missing or unknown values–in multi-way
arrays is ubiquitous in biomedical signal processing, network traffic analysis, bibliometrics …
arrays is ubiquitous in biomedical signal processing, network traffic analysis, bibliometrics …
Applying deep learning approaches for network traffic prediction
Network traffic prediction aims at predicting the subsequent network traffic by using the
previous network traffic data. This can serve as a proactive approach for network …
previous network traffic data. This can serve as a proactive approach for network …
Network traffic prediction using recurrent neural networks
N Ramakrishnan, T Soni - 2018 17th IEEE International …, 2018 - ieeexplore.ieee.org
The network traffic prediction problem involves predicting characteristics of future network
traffic from observations of past traffic. Network traffic prediction has a variety of applications …
traffic from observations of past traffic. Network traffic prediction has a variety of applications …