Name
Abstract | ||
|---|---|---|
Finding the root causes of network performance anomalies is critical to satisfy the quality of service requirements. In this paper, we introduce machine learning (ML) models to process TCP socket statistics to pinpoint underlying reasons of performance issues such as packet loss and jitter. More importantly, we introduce a novel feature engineering method to transform network-dependent metrics (e.... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LCN52139.2021.9525015 | 2021 IEEE 46th Conference on Local Computer Networks (LCN) |
Keywords | DocType | ISSN |
Training,Computational modeling,Sockets,Training data,Packet loss,Transforms,Production | Conference | 0742-1303 |
ISBN | Citations | PageRank |
978-1-6654-1886-7 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Md Arifuzzaman | 1 | 0 | 0.68 |
| Shafkat Islam | 2 | 0 | 0.34 |
| Engin Arslan | 3 | 116 | 12.12 |