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This paper explores the impacts of path selection on end-to-end network performance, focusing on latency, loss rates, and bandwidth. We reveal that default routing paths often underperform compared to alternate paths, particularly during peak usage hours. Key findings indicate that 30%-55% of default paths exhibit longer round-trip times, and 70%-80% have lower bandwidth. Through synthetic topology generation and comprehensive measurements, we highlight potential sources of routing inefficiency and suggest improvements such as dynamic multipath routing and adaptive routing strategies.
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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
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.
Metrics & Measurements • Measure paths between pairs of hosts • Generate synthetic topology – full N2 mesh • Find best alternate path through this graph
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
Loss Rate • 75%- 85% of default paths have higher loss rates
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
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
Time of day Variation • Alternate paths are better during peak hours • Reason: Routing instability and congestion
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.
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
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
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
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
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)
How good is my Routing? CALL: 1-800-SAVAGE