att_abstract={{Traditional DSL troubleshooting solutions are reactive, relying
mainly on customers to report problems, and tend to
be labor-intensive, time consuming, prone to incorrect resolutions
and overall can contribute to increased customer
dissatisfaction. In this paper, we propose a proactive approach
to facilitate troubleshooting customer edge problems
and reducing customer tickets. Our system consists of: i) a
ticket predictor which predicts future customer tickets; and
ii) a trouble locator which helps technicians accelerate the
troubleshooting process during field dispatches. Both components
infer future tickets and trouble locations based on
existing sparse line measurements, and the inference models
are constructed automatically using supervised machine
learning techniques. We propose several novel techniques to
address the operational constraints in DSL networks and to
enhance the accuracy of NEVERMIND. Extensive evaluations
using an entire years worth of customer ticket and measurement
data from a large network show that our method
can predict thousands of future customer tickets per week
with high accuracy and reduce significantly reduce the time
and effort for diagnosing these tickets. This is beneficial as it
has the effect of both reducing the number of customer care
calls and improving customer satisfaction.}},
	att_authors={nd1321, ag1971, ph2326, ss2864},
	att_categories={C_CCF.5, C_NSS.9},
	att_copyright_notice={{(c) ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM CoNext 2010 {{, 2010-11-30}}.}},
	author={Yu Jin and Nicholas Duffield and Alexandre Gerber and Patrick Haffner and Subhabrata Sen and Zhi-Li Zhang},
	institution={{in Proc. of ACM CoNext}},
	title={{NEVERMIND, the Problem Is Already Fixed: Proactively Detecting and Troubleshooting Customer DSL Problems}},