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The Ninth IEEE Workshop on Future Trends of Distributed Computing Systems (FTDCS'03)
Network Tomography-Based Unresponsive Flow Detection and Control
San Juan, Puerto Rico
May 28-May 30
ISBN: 0-7695-1910-5
| ASCII Text | x | ||
| Ahsan Habib, Bharat Bhargava, "Network Tomography-Based Unresponsive Flow Detection and Control," Future Trends of Distributed Computing Systems, IEEE International Workshop, pp. 258, The Ninth IEEE Workshop on Future Trends of Distributed Computing Systems (FTDCS'03), 2003. | |||
| BibTex | x | ||
| @article{ 10.1109/FTDCS.2003.1204345, author = {Ahsan Habib and Bharat Bhargava}, title = {Network Tomography-Based Unresponsive Flow Detection and Control}, journal ={Future Trends of Distributed Computing Systems, IEEE International Workshop}, volume = {0}, year = {2003}, issn = {1071-0485}, pages = {258}, doi = {http://doi.ieeecomputersociety.org/10.1109/FTDCS.2003.1204345}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Future Trends of Distributed Computing Systems, IEEE International Workshop TI - Network Tomography-Based Unresponsive Flow Detection and Control SN - 1071-0485 SP EP A1 - Ahsan Habib, A1 - Bharat Bhargava, PY - 2003 KW - null VL - 0 JA - Future Trends of Distributed Computing Systems, IEEE International Workshop ER - | |||
To avoid a congestion collapse, network flows should adjust their sending rates. Adaptive flows adjust the rate, while unresponsive flows do not respond to congestion and keep sending packets. Unresponsive flows waste resources by taking their share of the upstream links of a domain and dropping packets later when the downstream links are congested. We use network tomography — an edge-to-edge mechanism to infer per-link internal characteristics of a domain — to identify unresponsive flows that cause packet drops in other flows. We have designed an algorithm to dynamically regulate unresponsive flows. The congestion control algorithm is evaluated using both adaptive and unresponsive flows, with sending rates as high as four times of the bottleneck bandwidth, and in presence of short and long-lived background traffic.
Citation:
Ahsan Habib, Bharat Bhargava, "Network Tomography-Based Unresponsive Flow Detection and Control," ftdcs, pp.258, The Ninth IEEE Workshop on Future Trends of Distributed Computing Systems (FTDCS'03), 2003
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