Name
Abstract | ||
|---|---|---|
Computer network use is becoming increasingly widespread, both in terms of number of users and variety of applications. To provide consistently high-quality service, network engineers and managers must monitor several aspects of the network, including traffic volumes on its links. As networks' sizes expand, such monitoring becomes demanding in terms of resources required. Motivated by the prospect of monitoring only a small subset of links, this article explores the problem of using observed traffic measurements on selected links to predict the traffic on other, unobserved links. The characteristics of such unobserved links are learned through auxiliary data. Although more expensive to obtain, this extra dataset provides the necessary information to represent important structure in the network, and can significantly improve the results of prediction as compared with more naive approaches. In addition, we introduce an adjusted control chart methodology that shows possible applications of our prediction results in situations where all links may be observed. Supplementary materials are available online. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1080/00401706.2012.723959 | TECHNOMETRICS |
Keywords | Field | DocType |
Control charts,Kriging,Long-range dependent data,Spatial statistics | Kriging,Spatial analysis,Data mining,Control chart,Statistical model,Statistics,Mathematics | Journal |
Volume | Issue | ISSN |
55.0 | 1.0 | 0040-1706 |
Citations | PageRank | References |
1 | 0.37 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joel Vaughan | 1 | 13 | 1.44 |
| Stilian Stoev | 2 | 78 | 8.03 |
| George Michailidis | 3 | 303 | 35.19 |