Related papers: Anomaly Resilient Temporal QoS Prediction using Hy…
Accurate prediction of financial and electricity markets, especially under extreme conditions, remains a significant challenge due to their intrinsic nonlinearity, rapid fluctuations, and chaotic patterns. To address these limitations, we…
Recently, with the rapid deployment of service APIs, personalized service recommendations have played a paramount role in the growth of the e-commerce industry. Quality-of-Service (QoS) parameters determining the service performance, often…
High throughput satellites (HTSs) outpace traditional satellites due to their multi-beam transmission. The rise of low Earth orbit mega constellations amplifies HTS data rate demands to terabits/second with acceptable latency. This surge in…
Quality of Experience (QoE) prediction plays a crucial role in optimizing resource management and enhancing user satisfaction across both telecommunication and OTT services. While recent advances predominantly rely on deep learning models,…
Next-generation services demand stringent Quality of Service (QoS) guarantees, such as per-flow bandwidth assurance, ultra-low latency, and traffic prioritization, posing significant challenges to 5G and beyond networks. As 5G network…
Modern industrial networks transport both best-effort and real-time traffic. Time-Sensitive Networking (TSN) was introduced by the IEEE TSN Task Group as an enhancement to Ethernet to provide high quality of service (QoS) for real-time…
More refined resource management and Quality of Service (QoS) provisioning is a critical goal of wireless communication technologies. In this paper, we propose a novel Business-Centric Network (BCN) aimed at enabling scalable QoS…
Graph-based reasoning over skeleton data has emerged as a promising approach for human action recognition. However, the application of prior graph-based methods, which predominantly employ whole temporal sequences as their input, to the…
Recommender systems that can learn from cross-session data to dynamically predict the next item a user will choose are crucial for online platforms. However, existing approaches often use out-of-the-box sequence models which are limited by…
Weather Forecasting is an attractive challengeable task due to its influence on human life and complexity in atmospheric motion. Supported by massive historical observed time series data, the task is suitable for data-driven approaches,…
With the rapid advancement of internet technologies, network services have become critical for delivering diverse and reliable applications to users. However, the exponential growth in the number of available services has resulted in many…
The rapid advancement of communication technologies has established cellular networks as the backbone for diverse applications, each with distinct quality of service requirements. Meeting these varying demands within a unified…
Devising and analyzing learning models for spatiotemporal network data is of importance for tasks including forecasting, anomaly detection, and multi-agent coordination, among others. Graph Convolutional Neural Networks (GCNNs) are an…
HTTP based adaptive video streaming has become a popular choice of streaming due to the reliable transmission and the flexibility offered to adapt to varying network conditions. However, due to rate adaptation in adaptive streaming, the…
Continuous QoE prediction is crucial in the purpose of maximizing viewer satisfaction, by which video service providers could improve the revenue. Continuously predicting QoE is challenging since it requires QoE models that are capable of…
The explosion of cloud services on the Internet brings new challenges in service discovery and selection. Particularly, the demand for efficient quality-of-service (QoS) evaluation is becoming urgently strong. To address this issue, this…
Accurate traffic forecasting is vital to intelligent transportation systems, which are widely adopted to solve urban traffic issues. Existing traffic forecasting studies focus on modeling spatial-temporal dynamics in traffic data, among…
Industrial Time-Sensitive Networking (TSN) provides deterministic mechanisms for real-time and reliable flow transmission. Increasing attention has been paid to efficient scheduling for time-sensitive flows with stringent requirements such…
Today, numerous web services with similar functionalities are available on the Internet. Users often evaluate the Quality of Service (QoS) to choose the best option among them. Predicting the QoS values of these web services is a…
Accurate traffic forecasting is a fundamental problem in intelligent transportation systems and learning long-range traffic representations with key information through spatiotemporal graph neural networks (STGNNs) is a basic assumption of…