Develop (1) design-time techniques for assessing vulnerabilities in networks being constructed thus improving 

reliability; and (2) run-time methods for predicting onset of congestion failure by detecting precursor signals 

prior to collapse of deployed networks. This will help industry to better build, monitor, and control networks. 

Developing Predictors of Failure Regimes in Real Communication Networks 

C. Dabrowski, K. Mills, Y. Wan, and J. Xie

http://www.nist.gov/itl/antd/emergent_behavior.cfm 

• Developed and evaluated five potential predictors of congestion failure in routers under: (1) increasing load, where packet injection, p, is raised to 300 packets / ms in the network; and (2) steady load, where p is held at 10 packets / ms. Results were 

simulated in a 218-router, single-ISP network with 258 158 nodes and realistic factors, such as TCP and variable router speeds. 

• Detrended by subtracting system in steady load and smooth curves. 

• For each router, computed if a precursor signal was real (i.e., not noise). 

• If precursor signal was declared real, alarm could be raised. 

As the packet injection rate p increased, packet queues at routers increased and 

transmission delays increased, degrading network performance. A critical point was 

said to be approached and a phase transition to a congested state occurred. When p 

was held steady, packet queues remained low and the phase transition never occurred. Of the five methods tested in the realistic, single-ISP network under both scenarios, three methods based on measuring the fullness of the 218 router buffers 

made successful predictions from 94.7% to 100%. However, two method (including 

Autocorrelation) predicted correctly at a lower rate, from 45.9% to 75.7%. For full results, see: 

K. Mills, C. Dabrowski, J. Filliben, F. Hunt, B. Rust. Early Warning of Network Catastrophes, FY2015 Innovations in Measurement Science (IMS) proposal.

K. Mills, J. Filliben, D. Cho, E. Schwartz and D. Genin. Study of Proposed Internet Congestion Control Mechanisms, NIST Special Publication 500-282, May 2010.

M. Scheffer, J. Bascompte, W. Brock, V. Brovkin, S. Carpenter, V. Dakos, H. Held, E. van Nes, M. Rietkerk, and G. Sugihara. Early warning signals for critical transitions. Nature. 461(53-59) 2009. 

C. Dabrowski. Catastrophic Event Phenomena in Communication Networks: A Survey, Computer Science Review, 18(10-45), November 2015. 

APPROACH: 

Single-ISP router topology with 

218 routers in which measures 

were developed and evaluated. 

Router L1c (see below) is starred. 

EXAMPLE: Consider two potential predictors of congestion in router L1c: (a) autocorrelation, used as a predictor in many domains, 

measured by Matlab xcorr, and (b) a buffer threshold method which signals an alarm when the router packet queue reaches 25% 

of the router buffer (minus the router queue at steady load). Both Autocorrelation (a) and Buffer Threshold (b) alarm before router 

L1c congests (below left), but only Buffer Threshold correctly is zero and does not alarm when there is no congestion (below 

right). However, Autocorrelation (a) also alarms incorrectly and yields a false positive, when there is no congestion (right). 

RESULTS: 

C. Dabrowski. And K. Mills. Evaluating Predictors of Congestion Collapse in Communications Networks. 

Submitted to NOMS 2017, October, 2017.