On Delay Tomography: Fast Algorithms and Spatially Dependent Models
Access StatusFull text of the requested work is not available in DASH at this time ("dark deposit"). For more information on dark deposits, see our FAQ.
MetadataShow full item record
CitationKe Deng, Yang Li, Weiping Zhu, Zhi Geng, and J. S. Liu. 2012. “On Delay Tomography: Fast Algorithms and Spatially Dependent Models.” IEEE Transactions on Signal Processing 60 (11) (November): 5685–5697. doi:10.1109/tsp.2012.2210712.
AbstractAs an active branch of network tomography, delay tomography has received considerable attentions in recent years. However, most methods in the literature assume that the delays of different links are independent of each other, and pursuit sub-optimal estimate instead of the maximum likelihood estimate (MLE) due to computational challenges. In this paper, we propose a novel method to implement the EM algorithm widely used in delay tomography analysis for multicast networks. The proposed method makes use of a “delay pattern database” to avoid all redundant computations in the E-step, and is much faster than the traditional implementation. With the help of this new implementation, finding MLE for large networks, which was considered impractical previously, becomes an easy task. Taking advantage of this computational breakthrough, we further consider models for potential spatial dependence of links, and propose a novel adaptive spatially dependent model (ASDM) for delay tomography. In ASDM, Markov dependence among nearby links is allowed, and spatially dependent links (SDLs) can be automatically recognized via model selection. The superiority of the new methods is confirmed by simulation studies.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:33719929
- FAS Scholarly Articles