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Rahul Mangharam

The End- to- End Effects of Internet Path Selection Stefan Savage, Andy Collins, Eric Hoffman, John Snell, Tom Anderson Department of Computer Science and Engineering University of Washington. Rahul Mangharam. Motivation.

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Rahul Mangharam

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  1. The End- to- End Effects ofInternet Path SelectionStefan Savage, Andy Collins, Eric Hoffman, John Snell, Tom AndersonDepartment of Computer Science and EngineeringUniversity of Washington Rahul Mangharam

  2. Motivation • An optimal routing system would always choose the best available path between any two points • Best path? • Minimum average Latency • Minimum average Loss rate • Maximum average Bandwidth • Goal: Quantify and understand the impact of path selection on end- to- end performance • Focus on how “good” current routing is and not on alternative routing policies.

  3. Metrics & Measurements • Measure paths between pairs of hosts • Generate synthetic topology – full N2 mesh • Find best alternate path through this graph

  4. RTT Latency Performance Trans-Atlantic link Metric: CDF of difference between the mean RTT and the mean value derived for the best alternate path 30%- 55% of default paths have longer round- trip times

  5. Loss Rate • 75%- 85% of default paths have higher loss rates

  6. Bandwidth BW calculated from TCP RTT and loss rate • Optimistic : Max. loss rate of any component of synthetic path. • Pessimistic: Loss rates are independent • 70%- 80% of default paths have lower bandwidth

  7. Room for Improvement ! • The default path is usually not the best • True for latency, loss rate, and bandwidth • RTT:Alternate path performance over default • Loss Rate:Alternate path performance 95% confidence interval

  8. Time of day Variation • Alternate paths are better during peak hours • Reason: Routing instability and congestion

  9. Congestion versus Propagation Delay • Estimate propagation delay (10th percentile) • Queuing delay = RTT – propagation delay • Congestion contribution: Difference between prop. Delay for each path and the best prop. Delay of alt path. CDF of mean RTT. • Congestion and prop. Delay are equally dominant.

  10. Influence of popular hosts and AS’s • RTT: Marginal difference in performance upon removing 10 hosts that caused the largest difference in CDF. • Contribution: CDF of number of paths in which a host appears • AS’s appear almost with the same frequency in both default and alternate paths

  11. Potential sources of Routing Inefficiency • Poor Routing Metrics • Minimize number of AS traversed • Exchange only connectivity information • Restrictive Routing Policies • Co-operative or contractual? • Early-exit policy (could be in opp. geographic dir) • DiffServs and “accelerators” (starvation) • Manual Load Balancing • Single Path Routing

  12. Transport Inefficiencies • Effective data-rate is a small fraction of Available data-rate • Prevention of “congestion collapse” – Adaptive Routing • Slow start (the learning curve) • Congestion avoidance • Timeouts (conservative) and fast retransmit

  13. Transport Inefficiencies • 10KB web page over a 10Mbps, 70ms RTT link • Ideal : BW < WIN/RTT (7.5 Mbps) • Long Flows : BW < (MSS/RTT) p-0.5 [Mathais97] • P is the probability that a packet is dropped (1.2 Mbps) • Short Flows : BW <TransferSize RTT.[log1.5(TransferSize/2.MSS) + 1] • Connection setup (ack), timeout, connection loss • Effective BW is reduced to 75 Kbps

  14. Detour • Host needs more network status information • Detour routers measure and exchange latency, drop rate and bandwidth status • Adaptive routing over long time scale • Dynamic multi-path routing + load balancing • TCP adaptation: Cannot treat network as black box • RTT, timeouts, WIN, back pressure (early drop)

  15. How good is my Routing? CALL: 1-800-SAVAGE

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