Deep Buffers Experiment

Buffer-filling congestion control algorithms (CCAs) are designed to continuously probe for free bandwidth. By doing that, they may keep filling up a standing queue at the bottleneck, which leads to self-inflicted queueing delay. When the bottleneck queue has a large queue size, i.e., when the buffer is deep, large queueing delays may be a consequence. The presence of deep buffers in many networks today and the consequences thereof is known as the problem of bufferbloat. A CCA that is resilient against bufferbloat should refrain from inflicting queueing delays that are proportional to the queue size.

Scenario

In the deep buffers experiment, a single flow operates in a static dumbbell network with a queue size that is larger than the bandwidth-delay product (BDP). The flow generates greedy source traffic and uses a CCA. The experiment has one parameter named qlen that sets the size of the bottleneck queue. It can be repeated for different values of qlen to evaluate the influence of the queue size on the operating point.

To summarize the experiment setup:

Topology: Dumbbell topology (\(K=1\)) with static network parameters
Flows: A single flow (\(K=1\)) that uses a CCA
Traffic Generation Model: Greedy source traffic

Experiment #18

Command: ns3-dev-ccperf-static-dumbbell-default --experiment-name=deep_buffers --db-path=benchmark_TcpNewReno.db '--parameters={aut:TcpNewReno,k:1,qlen:20p}' --aut=TcpNewReno --stop-time=15s --seed=42 --qlen=20p --bw=16Mbps --loss=0.0 --qdisc=FifoQueueDisc --rtts=15ms --sources=src_0 --destinations=dst_0 --protocols=TCP --algs=TcpNewReno --recoveries=TcpPrrRecovery --start-times=0s --stop-times=15s '--traffic-models=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

Metrics

The following tables list the flow, link, and network metrics of experiment #18. 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
cov_in_flight_l4	0.21
cov_throughput_l4	0.04
flow_completion_time_l4	15.00
mean_cwnd_l4	33.60
mean_delivery_rate_l4	15.36
mean_est_qdelay_l4	9.76
mean_idt_ewma_l4	0.76
mean_in_flight_l4	33.08
mean_network_power_l4	647.17
mean_one_way_delay_l7	1925.55
mean_recovery_time_l4	33.91
mean_sending_rate_l4	15.44
mean_sending_rate_l7	17.50
mean_srtt_l4	24.76
mean_throughput_l4	15.37
mean_throughput_l7	15.37
mean_utility_mpdf_l4	-0.07
mean_utility_pf_l4	2.73
mean_utilization_bdp_l4	1.72
mean_utilization_bw_l4	0.96
total_retransmissions_l4	68.00
total_rtos_l4	0.00

Link Metrics

Link metrics are recorded at the network links of interest, typically at bottlenecks. They include metrics that measure queue states. Bold values indicate which link achieved the best performance.

Metric	link_5
mean_qdisc_delay_l2	7.90
mean_qdisc_length_l2	11.03
mean_sending_rate_l1	15.95
total_qdisc_drops_l2	68.00

Figures

The following figures show the results of the experiment #18.

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.

Comparison of Congestion Control Algorithms (CCAs)

Figures

Mean Bandwidth Utilization vs Buffer Size

The mean bandwidth utilization vs the buffer size for large buffers (larger or equal to the BDP). High utilization indicates good performance. CCAs that suffer from the bufferbloat problem inflict high queueing delays as the buffer size increases.