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Ensuring Access to Emergency Services in the Presence of Long Internet Dial-Up Calls

Author

Listed:
  • V. Ramaswami

    (AT&T Labs, 180 Park Avenue, Florham Park, New Jersey 07932)

  • David Poole

    (AT&T Labs, 180 Park Avenue, Florham Park, New Jersey 07932)

  • Soohan Ahn

    (Department of Statistics, University of Seoul, Seoul 130-743, South Korea)

  • Simon Byers

    (AT&T Labs, 180 Park Avenue, Florham Park, New Jersey 07932)

  • Alan Kaplan

    (Unique Technical Solutions, 52 Brookside Drive, Upper Saddle River, New Jersey 07458)

Abstract

Telephone availability is critical, particularly in emergency situations when people need immediate help. We used statistical data analysis and queueing models to identify the root cause of dial-tone unavailability in parts of the AT&T network and to develop remedies. Our solutions restored quality service, protecting the AT&T brand name and ensuring the safety of our customers. This work also gave AT&T opportunities to reduce transit charges paid to other carriers by $15 million per year. In addition, we have filed five patent requests, of which two have been granted and the rest are pending (Chaudhury et al. 2004, Kaplan and Ramaswami 2004). Furthermore, our findings have important implications for several current areas of research related to Internet and broadband technologies, call-center engineering, and network security.

Suggested Citation

  • V. Ramaswami & David Poole & Soohan Ahn & Simon Byers & Alan Kaplan, 2005. "Ensuring Access to Emergency Services in the Presence of Long Internet Dial-Up Calls," Interfaces, INFORMS, vol. 35(5), pages 411-422, October.
    • Handle: RePEc:inm:orinte:v:35:y:2005:i:5:p:411-422
    DOI: 10.1287/inte.1050.0155
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    References listed on IDEAS

    as
    1. Shlomo Halfin & Ward Whitt, 1981. "Heavy-Traffic Limits for Queues with Many Exponential Servers," Operations Research, INFORMS, vol. 29(3), pages 567-588, June.
    2. Winfried K. Grassmann & Michael I. Taksar & Daniel P. Heyman, 1985. "Regenerative Analysis and Steady State Distributions for Markov Chains," Operations Research, INFORMS, vol. 33(5), pages 1107-1116, October.
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