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Hideyuki Yamamoto, Hiroyuki Ohsaki Graduate School of Information Sci. & Tech.

Modeling and Performance Evaluation of DRED (Dynamic Random Early Detection) using Fluid-Flow Approximation. Hideyuki Yamamoto, Hiroyuki Ohsaki Graduate School of Information Sci. & Tech. Osaka University, Japan hideymmt@ist.osaka-u.ac.jp. 1. State Goals and Define the System. Goals

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Hideyuki Yamamoto, Hiroyuki Ohsaki Graduate School of Information Sci. & Tech.

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  1. Modeling and Performance Evaluation of DRED (Dynamic Random Early Detection) using Fluid-Flow Approximation Hideyuki Yamamoto, Hiroyuki Ohsaki Graduate School of Information Sci. & Tech. Osaka University, Japan hideymmt@ist.osaka-u.ac.jp

  2. 1. State Goals and Define the System • Goals • Confirm validity of our approximate analysis • Investigate DRED’s steady-state/transient-state performance • System Definition • IP network including... • DRED routers and links • source and destination hosts

  3. 2. List Services and Outcomes • Services Provided • Congestion control for TCP flows • Outcomes • High link bandwidth utilization? • Low packet loss probability? • Low packet transfer delay/jitter?

  4. 3. Select Metrics • Speed (case of successful service case) • Individual • TCP throughput, round-trip time, packet loss probability • Global • Queue occupancy, link utilization, packet loss probability • Reliability (case of error) • None • Availability (case of unavailability) • None

  5. 4. List Parameters • System parameters • Network related • Topology • Link bandwidth, latency, loss ratio • DRED router related • Control parameters (Dt, α, β, θ, T, L) • Queue size • Workload parameters • # of TCP flows, TCP traffic pattern • Background traffic pattern

  6. 5. Select Factors to Study • System parameters • Network related • Topology • Link bandwidth, latency, loss ratio • DRED router related • Control parameters (Dt, α, β, θ, T, L) • Queue size • Workload parameters • # of TCP flows, TCP traffic pattern • Background traffic pattern

  7. 6. Select Evaluation Technique • Use analytical modeling? • Yes • Use simulation? • Yes • Use measurement of real system? • No

  8. 7. Select Workload • TCP flows • Persistent traffic • # of TCP flows: 1 -- 1000 • Background traffic • 0 -- 70% of the bottleneck link bandwidth

  9. 8. Design Experiments

  10. 9. Analyze and Interpret Data

  11. 10. Present Results

  12. Network Topology Source 1 Sink 1 TCP Flows dred R1 R2 : : : : c [packet/ms] DRED Queue 100Mbps 10ms 100Mbps 10ms source Sink Source N Sink N Identical round trip time [ms] Router

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