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On Class-based Isolation of UDP, Short-lived and Long-lived TCP Flows

by

Selma Yilmaz Ibrahim Matta

Computer Science DepartmentBoston University

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Motivation

• Internet traffic is mixture of flows with different

characteristics: – Smooth, unresponsive real-time traffic (UDP)

– Bursty, congestion-sensitive traffic (TCP)

• Problems with “single class of best effort” service:

– Real-time traffic:• do not perform adequately because of delay variations

• do not back off in presence of congestion

– TCP:• congestion control

• unfairness

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Motivation (cont.) • Short-lived TCP flows

– generally carry interactive/delay sensitive data

– mostly operate in slow-start phase• have smaller window size

– produce smaller bursts– takes longer to recover from a single packet loss

– arrival of flows are more bursty• prevents long TCP flows from operating in a predictable mode

• Long-lived TCP flows

– mostly operate in congestion avoidance phase• have larger window size

– produce big bursts

• can more easily recover from multiple packet losses– may shut off short-lived TCP flows

– more stable

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Architecture: Class-Based Isolation

Idea: Isolate flows with different characteristics into different classes

CBQ Logically Separate Communication Paths

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Advantages of Architecture

• Protect each class from negative effects of the other classes

• Per-flow state is kept only at the edge routers – scalable

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Analytic Model

• Extends that of

T. Bonald, M. May, J. Bolot, “Analytic evaluation of RED Performance”, IEEE/INFOCOM 2000.

• Overview: - Bursts of B packets arrive according to Poisson process - Processing times of packets at the router are exponentially distributed - Burst size models average window size of a TCP flow - Number of packets buffered in the queue defines a Markov chain - Drop probability for a packet in a Tail-Drop and RED router is computed - Model does not capture congestion-sensitivity of TCP flows

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• RED– For simplicity, instantaneous queue size is used– minth=K/2, maxth=K, maxp=1

• Remove Bonald et al. approximation:– “All packets in the same burst see the same drop probability d(k), where k

is instantaneous queue size at the time the first packet in burst arrives at the router”

>> accurate only if B is not too big than K>> for the same queue size, gives less accurate results for

longer-lived flows

Analytic Model (cont.)

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Experiments• Effects of

– Fraction of service rate allocated to different classes– Burst size– Isolation into 2 classes: TCP, UDP– Isolation into 3 classes: UDP, short-lived TCP, long-lived TCP

• Each experiment is repeated for– FIFO with RED and Tail-drop– High load (total load=2) and low load (total load=1)– sharing (mixed): Flows with different characteristics compete for shared

resources (queue size and service rate)– isolated: Resources are split in proportion to the load introduced by each class

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Performance Measures

• Drop probability: measures effective goodput

• Fairness: Chui and Jain’s fairness index

xi is the drop probability of flow-typei.

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Observations

• 2 types of flows (UDP,TCP), 2 classes (UDP,TCP)– At high load, isolation improves fairness over shared Tail-drop by

increasing drop probability of UDP

– RED performs as good as isolated Tail-drop queues

• Isolation provides better control over QoS of each traffic type

– At low load, static isolation suffers from loss in statistical multiplexing

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Observations (cont.)• 3 types of flows (UDP, short- and long-lived TCP), 2

classes (UDP, TCP)– At high load, isolation significantly reduces drop probability of

short-lived flows independent of buffer management scheme

• Improves QoS of interactive/delay sensitive data

• Less timeouts, higher goodput

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Observations (cont.)

• 3 types of flows (UDP, short- and long-lived TCP), 3 classes– At high load, isolation provides

• Better and predictable drop probability for all classes

• Perfect fairness across different flow types

• Better control over QoS of each traffic type

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Conclusion

• At high load, class-based isolation provides

– better fairness among different types of flows

– TCP • reduced drop probability

• improved fairness

– short-lived TCP• lower transmission delay

– UDP• generally sees increased drop probability

• improved service predictability

• Shared Tail-drop: isolation is necessary– no protection against misbehaving flows

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Conclusion (cont.)

• Shared RED : isolation provides little gain

– reduces bias against bursty traffic– fair

• Class-based isolation

– better control over quality of service for each class

– improved fairness

– improved predictability

– simple and scalable

• Use RED within each class to provide intra-class fairness

• At low load with static isolation, statistical multiplexing gains are lost

– must implement dynamic resource allocation as in CBQ

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Future Work

• More detailed models

• Non-homogeneous flows in each class and fairness among them

• Identify minimum number of classes for mix of TCP flows with various lifetime and RTT

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