0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessMany applications based on block transmission have strict latency requirements. Existing solutions to reduce latency primarily focus on enhancing packet loss resistance through redundancy and scheduling transmission orders. However, the performance of these algorithms depends on accurate block transmission time estimation. Current evaluation methods, which do not account for the impacts of complex network protocols and packet loss, result in significant estimation errors.In this paper, we propose Block Completion Time (BCT) as a transmission delay index for block-based applications. We develop a BCT distribution model that incorporates packet-level block transmission under complex protocols and apply it to predict BCT for typical TCP and QUIC protocols. As a use case, we demonstrate how our model can assist in optimizing redundancy configurations. The model is evaluated under varying network conditions, application types, and transport protocols, showing improved accuracy in predicting both the mean and distribution of BCT compared to baseline methods.
Gang Yi, Lei Zhang, Cui Ma, Yong Cui (2025). BCT: Modeling Block Completion Time for Transport Protocols. , DOI: https://doi.org/10.1109/iwqos65803.2025.11264000.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2025
Authors
4
Datasets
0
Total Files
0
DOI
https://doi.org/10.1109/iwqos65803.2025.11264000
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access