Name
Abstract | ||
|---|---|---|
Cellular networks are constantly evolving due to frequent changes in radio access and end user equipment technologies, dynamic applications and associated trafflc mixes. Network upgrades should be performed with extreme caution since millions of users heavily depend on the cellular networks for a wide range of day to day tasks, including emergency and alert notifications. Before upgrading the entire network, it is important to conduct field evaluation of upgrades. Field evaluations are typically cumbersome and can be time consuming; however if done correctly they can help alleviate a lot of the deployment issues in terms of service quality degradation. The choice and number of field test locations have significant impacts on the time-to-market as well as confidence in how well various network upgrades will work out in the rest of the network. In this paper, we propose a novel approach — Reflection to automatically determine where to conduct the upgrade field tests in order to accurately identify important features that affect the upgrade. We demonstrate the effectiveness of Reflection using extensive evaluation based on real traces collected from a major US cellular network as well as synthetic traces. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICNP.2017.8117553 | 2017 IEEE 25th International Conference on Network Protocols (ICNP) |
Keywords | Field | DocType |
automated test location selection,radio access,end user equipment technologies,dynamic applications,field evaluations,field test locations,US cellular network,trafflc mixes,service quality,time-to-market | Radio access,Software deployment,End user,Computer science,Upgrade,Feature extraction,Software,Cellular network,Terms of service,Distributed computing | Conference |
ISSN | ISBN | Citations |
1092-1648 | 978-1-5090-6502-8 | 0 |
PageRank | References | Authors |
0.34 | 25 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mubashir Adnan Qureshi | 1 | 6 | 1.47 |
| Ajay Mahimkar | 2 | 206 | 17.45 |
| Lili Qiu | 3 | 3987 | 284.13 |
| Zihui Ge | 4 | 847 | 55.97 |
| Sarat C. Puthenpura | 5 | 3 | 1.50 |
| Nabeel Mir | 6 | 0 | 0.34 |
| Sanjeev Ahuja | 7 | 0 | 0.34 |