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- Chiesa, Marco, 1987-, et al.
(author)
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Lying your way to better traffic engineering
- 2016
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In: CoNEXT 2016 - Proceedings of the 12th International Conference on Emerging Networking EXperiments and Technologies. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450342926 ; , s. 391-398
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Conference paper (peer-reviewed)abstract
- To optimize the flow of traffic in IP networks, operators do traffic engineering (TE), i.e., tune routing-protocol parameters in response to traffic demands. TE in IP networks typically involves configuring static link weights and splitting traffic between the resulting shortest-paths via the Equal- Cost-MultiPath (ECMP) mechanism. Unfortunately, ECMP is a notoriously cumbersome and indirect means for optimizing traffic flow, often leading to poor network performance. Also, obtaining accurate knowledge of traffic demands as the input to TE is elusive, and traffic conditions can be highly variable, further complicating TE.We leverage recently proposed schemes for increasing ECMP's expressiveness via carefully disseminated bogus information ("lies") to design COYOTE, a readily deployable TE scheme for robust and efficient network utilization. COYOTE leverages new algorithmic ideas to configure (static) traffic splitting ratios that are optimized with respect to all (even adversarially chosen) traffic scenarios within the operator's "uncertainty bounds". Our experimental analyses show that COYOTE significantly outperforms today's prevalent TE schemes in a manner that is robust to traffic uncertainty and variation. We discuss experiments with a prototype implementation of COYOTE.
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| 2. |
- Høiland-Jørgensen, Toke, et al.
(author)
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Measuring Latency Variation in the Internet
- 2016
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In: Proceedings of the 12th International on Conference on emerging Networking EXperiments and Technologies. - New York, NY, USA : ACM. - 9781450342926 ; , s. 473-480
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Conference paper (peer-reviewed)abstract
- We analyse two complementary datasets to quantify the latency variation experienced by internet end-users: (i) a large-scale active measurement dataset (from the Measurement Lab Network Diagnostic Tool) which shed light on long-term trends and regional differences; and (ii) passive measurement data from an access aggregation link which is used to analyse the edge links closest to the user.The analysis shows that variation in latency is both common and of significant magnitude, with two thirds of samples exceeding 100\,ms of variation. The variation is seen within single connections as well as between connections to the same client. The distribution of experienced latency variation is heavy-tailed, with the most affected clients seeing an order of magnitude larger variation than the least affected. In addition, there are large differences between regions, both within and between continents. Despite consistent improvements in throughput, most regions show no reduction in latency variation over time, and in one region it even increases.We examine load-induced queueing latency as a possible cause for the variation in latency and find that both datasets readily exhibit symptoms of queueing latency correlated with network load. Additionally, when this queueing latency does occur, it is of significant magnitude, more than 200\,ms in the median. This indicates that load-induced queueing contributes significantly to the overall latency variation.
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