Name
Abstract | ||
|---|---|---|
Threshold-based performance monitoring in large 3G networks is very challenging for two main factors: large network scale and dynamics in both time and spatial domains. There exists a fundamental tradeoff between the size of threshold settings and the alarm quality. In this paper, we propose a scalable monitoring solution, called threshold-compression that characterizes the tradeoff via intelligent threshold aggregation. The main insight behind our solution is to identify groups of network elements with similar threshold behaviors across location and time dimensions, thus forming spatial-temporal clusters and generating the associated compressed thresholds within the optimization framework. Our evaluations on a commercial 3G network have demonstrated the effectiveness of our threshold-compression solution, e.g., threshold setting reduction up to 90% within 10% false/miss alarms. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/1993744.1993793 | SIGMETRICS |
Keywords | Field | DocType |
threshold-based performance monitoring,similar threshold behavior,threshold setting,fundamental tradeoff,scalable monitoring solution,threshold compression,large network scale,intelligent threshold aggregation,threshold-compression solution,main factor,network element,network monitoring,3g network | Cluster (physics),Compression (physics),Existential quantification,Performance monitoring,Computer science,ALARM,Real-time computing,Network element,Multiple time dimensions,Scalability,Distributed computing | Conference |
Volume | Issue | Citations |
39 | 1 | 1 |
PageRank | References | Authors |
0.41 | 1 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suk-Bok Lee | 1 | 426 | 28.58 |
| Dan Pei | 2 | 1540 | 128.64 |
| MohammadTaghi Hajiaghayi | 3 | 3082 | 201.38 |
| Ioannis Pefkianakis | 4 | 269 | 20.19 |
| Songwu Lu | 5 | 6137 | 504.90 |
| He Yan | 6 | 48 | 2.66 |
| Zihui Ge | 7 | 847 | 55.97 |
| Jennifer Yates | 8 | 790 | 64.51 |
| Mario Kosseifi | 9 | 15 | 1.45 |