Steady-state Start-time Fairness Experiment

This experiment evaluates how the start time influences the steady-state operating point of a congestion control algorithm (CCA). The sending rates of all flows are controlled by the same congestion control algorithm (CCA), i.e., it is an intra-protocol competition scenario.

Similar to the steady-state experiment with a single flow, the flows should jointly exhaust the bandwidth (bottleneck rate) of the dumbbell network and avoid self-inflicted queueing delay. The CCA should let the sending rates of the flows eventually converge to a steady-state equilibrium. At best, the flows share the shared bandwidth resources fairly. Network metrics quantify wheter or not efficiency (bandwidth utilization) and fairness is reached by a CCA.

Scenario

Multiple flows are set up to operate compete against each other in a static dumbbell network. Greedy source traffic ensures that flows are network-limited. The flows start at different times, but have the same two-way propagation delay. The number of flows can be varied with the experiment parameter k. The start times are set with the parameter start_times.

To summarize the setup:

  • Topology: Dumbbell topology (\(K>1\)) with static network parameters defined by the path parameter

  • Flows: Multiple flows (\(K>1\)) starting at different times using the same CCA (intra-protocol competition)

  • Traffic Generation Model: Greedy source traffic

Experiment Results

Experiment #20

Parameters

Command: ns3-dev-ccperf-static-dumbbell-default --experiment-name=steady_state_start_time_fairness --db-path=benchmark_TcpNewReno.db '--parameters={aut:TcpNewReno,k:2,path:static.default,start_times:[0s,1.87s]}' --aut=TcpNewReno --stop-time=15s --seed=42 --start-times=0s,1.87s --bw=32Mbps --loss=0.0 --qlen=40p --qdisc=FifoQueueDisc --rtts=15ms,15ms --sources=src_0,src_1 --destinations=dst_0,dst_1 --protocols=TCP,TCP --algs=TcpNewReno,TcpNewReno --recoveries=TcpPrrRecovery,TcpPrrRecovery --stop-times=15s,15s '--traffic-models=Greedy(bytes=0),Greedy(bytes=0)'

Flows

src dst transport_protocol cca cc_recovery_alg traffic_model start_time stop_time
src_0 dst_0 TCP TcpNewReno TcpPrrRecovery Greedy(bytes=0) 0.00 15.00
src_1 dst_1 TCP TcpNewReno TcpPrrRecovery Greedy(bytes=0) 1.87 15.00

Metrics

The following tables list the flow, link, and network metrics of experiment #20. Refer to the the metrics page for definitions of the listed metrics.

Flow Metrics

Flow metrics capture the performance of an individual flow. They are measured at the endpoints of a network path at either the source, the receiver, or both. Bold values indicate which flow achieved the best performance.

Metric flow_1 flow_2
cov_in_flight_l4 0.37 0.23
cov_throughput_l4 0.31 0.18
flow_completion_time_l4 15.00 13.12
mean_cwnd_l4 37.10 31.20
mean_delivery_rate_l4 17.72 14.82
mean_est_qdelay_l4 8.82 8.90
mean_idt_ewma_l4 0.60 0.68
mean_in_flight_l4 36.61 30.70
mean_network_power_l4 777.73 646.78
mean_one_way_delay_l7 1788.53 1926.28
mean_recovery_time_l4 30.70 30.81
mean_sending_rate_l4 17.84 14.89
mean_sending_rate_l7 19.86 17.26
mean_srtt_l4 23.82 23.90
mean_throughput_l4 17.74 14.83
mean_throughput_l7 17.74 14.83
mean_utility_mpdf_l4 -0.06 -0.07
mean_utility_pf_l4 2.84 2.68
mean_utilization_bdp_l4 0.95 0.80
mean_utilization_bw_l4 0.55 0.46
total_retransmissions_l4 107.00 37.00
total_rtos_l4 0.00 0.00

Network Metrics

Network metrics assess the entire network as a whole by aggregating other metrics, e.g., the aggregated throughput of all flows. Hence, the network metrics has only one column named net.

Metric net
mean_agg_in_flight_l4 67.32
mean_agg_throughput_l4 32.56
mean_agg_utility_mpdf_l4 -0.13
mean_agg_utility_pf_l4 5.52
mean_agg_utilization_bdp_l4 1.75
mean_agg_utilization_bw_l4 1.02
mean_entropy_fairness_throughput_l4 0.69
mean_jains_fairness_throughput_l4 0.99
mean_product_fairness_throughput_l4 262.98

Figures

The following figures show the results of the experiment #20.

Time Series Plot of the Operating Point

Time series plot of the number of segments in flight, the smoothed round-trip time (sRTT), and the throughput at the transport layer.

Mean Operating Point Plane

The mean throughput and mean smoothed round-trip time (sRTT) at the transport layer of each flow.

Distribution of the Operating Point

The empirical cumulative distribution function (eCDF) of the throughput and smoothed round-trip time (sRTT) at the transport layer of each flow.

Comparison of Congestion Control Algorithms (CCAs)

Figures