on efficient on-line grouping of flows with shared bottlenecks at loaded servers by o. younis and s....
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On Efficient On-line Grouping of Flows with Shared Bottlenecks at Loaded Servers
by O. Younis and S. Fahmy
Department of Computer Sciences, Purdue University
Presented by Felix Lam
Introduction
Bottleneck Sharing is common among different connections
Ordinary TCP is simple but NOT efficient.
Internet Internet
FTP/Web/Streaming Server
Clients
Introduction
Coordinated Congestion Management Flows sharing the same bottleneck(s) are
grouped together for congestion control Each flow in a group is given different qu
ality of service by the server (e.g. different sending rates, different error protection… etc.)
Collective performance improved
Introduction
How to group flows that share the same bottleneck(s) ?
FlowMate Make use of packet delay information to
group flows sharing the same bottleneck(s)
Basic Architecture
The FlowMate module is embedded into the TCP implementation of the sender to collect delay samples for correlation test.
Delay Computation
Packet delay information can be collected in two ways Timestamping ACKs
Option fields supported in TCP implementations in most operating systems. TS Value – time at which the data packet / ACK is sent TS Echo Reply – the previously received TS Value, only present in ACK. TS Recv. Time – extension proposed by the paper Clock-skewness does not affect the correlation values as long as it is constant.
Delay = TS Value (Sender) – TS Recv. Time (Receiver)
Delay Computation
Round Trip Time (RTT) samples For each ACK received, TCP sender get a
RTT sample. However, using RTT samples instead of
forward delay may degrade grouping accuracy because: Bottlenecks of reverse path and forward path may
be different Delayed acknowledgement affect RTT
The reduction in grouping accuracy < 5%
Correlation Test
Correlation test is performed after each flow has got N (e.g. N=6) delay samples
Apply Pearson’s correlation function on the delay samples
Packet Sent Time1s 2s 3s 4s 5s 6s 8s 11s 12s 15s
x1x2 x3 x5 x6y1 y2 y3 y4 y5x4 y6
7s 14s
Correlation Test
Cross-measure Mx Select delay samples from the two flow
s that a packet of flow x must precede a packet of flow y immediately
The correlation of the 3 consecutive pairs of delay samples is computed
Packet Sent Time1s 2s 8s 12s
x1x5y1 y4x4 y6
7s 14s
Correlation Test
Auto-measure Ma
Select pairs of delay samples from one of the two flows that the spacing between each pair is larger than the average spacing between the pairs used to compute Mx
(e.g. 1.33)
Packet
Sent Time
1s 5s 6s 12s
x1x2 x3 x5
Correlation Test
If Mx > MaThen the two flows are grouped together (i.e. they share the same bottleneck(s))
Partitioning
If more than one groups succeed in the test, join the one with highest Mx
Each flow will be repartitioned after it gets enough new delay samples
G1
G2Representative Flow
A new flow
Correlation tests
G3
form a new group if fail
Performance Evaluation
kj is the number of splits of the correct group j. correct: {1,2,3},{4,5,6}
result: {1,2},{3,4,5},{6}Accuracy Index =0.67
flowsTotal
k
flowsTotal
flowssharedfalseIndexAccuracy
Groups
jj
#
)1(
#
#1
#
1
Conclusions
FlowMate is an on-line partitioning algorithm, requires no active-probing
Robust under heavy background traffic, and different router buffer sizes
High burstiness degrades the performance
Useful for many applications that require inter-flow coordination
Comments and Discussions
The high accuracy, robustness under high background traffic load makes FlowMate quite useful in practical network environments.
Although burstiness degrades performance, it does not affect its usefulness in the scenario of TCP video streaming.
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