Bounded Low Latency via Inverse Reinforcement Learning
Accurate traffic prediction is essential for effective resource utilization and improving user
experience quality in next generation wireless networks. Machine Learning (ML) techniques …
experience quality in next generation wireless networks. Machine Learning (ML) techniques …
Taming the Elephants: Affordable Flow Length Prediction in the Data Plane
Machine Learning (ML) shows promising potential for enhancing networking tasks by
providing early traffic predictions. However, implementing an ML-enabled system is a …
providing early traffic predictions. However, implementing an ML-enabled system is a …
A meta-learning scheme for adaptive short-term network traffic prediction
Network traffic prediction is a fundamental prerequisite for dynamic resource provisioning in
wireline and wireless networks, but is known to be challenging due to non-stationarity and …
wireline and wireless networks, but is known to be challenging due to non-stationarity and …
Lens: A Foundation Model for Network Traffic
Network traffic refers to the amount of information being sent and received over the internet
or any system that connects computers. Analyzing and understanding network traffic is vital …
or any system that connects computers. Analyzing and understanding network traffic is vital …
Poster: ISOML: Inter-Service Online Meta-Learning for Newly Emerging Network Traffic Prediction
The increasing utilization of newly emerging networks (eg, private-5G) across industries
underscores the need for accurate traffic prediction to manage network resources effectively …
underscores the need for accurate traffic prediction to manage network resources effectively …
Deep Learning for Network Traffic Prediction: An Overview
M Fu, P Wang, Z Wang, Z Li - … , Intl Conf on Cloud and Big Data …, 2023 - ieeexplore.ieee.org
Accurately predicting metrics such as bandwidth utilization in future networks can assist
service providers in predicting network congestion, allowing for proactive network …
service providers in predicting network congestion, allowing for proactive network …
Meta-NWDAF: A Meta-Learning based Network Data Analytic Function for Internet Traffic Prediction
KH Chen, HS Huang - 2022 23rd Asia-Pacific Network …, 2022 - ieeexplore.ieee.org
With the coming of the B5G and 6G era, lots of research reports held on predictions that the
number of connected devices will keep exploding. According to specifications determined by …
number of connected devices will keep exploding. According to specifications determined by …
MAMRL: Exploiting Multi-agent Meta Reinforcement Learning in WAN Traffic Engineering
Traffic optimization challenges, such as load balancing, flow scheduling, and improving
packet delivery time, are difficult online decision-making problems in wide area networks …
packet delivery time, are difficult online decision-making problems in wide area networks …
MetaSTNet: Multimodal Meta-learning for Cellular Traffic Conformal Prediction
Network traffic prediction techniques have attracted much attention since they are valuable
for network congestion control and user experience improvement. While existing prediction …
for network congestion control and user experience improvement. While existing prediction …
DeepFlow: Towards network-wide ingress traffic prediction using machine learning at large scale
S Fischer, K Katsarou… - … Symposium on Networks …, 2020 - ieeexplore.ieee.org
Describing incoming web traffic-as seen from large eyeball networks, ie ingress traffic-and
estimating it into the future, are necessary operations for network service providers who …
estimating it into the future, are necessary operations for network service providers who …