Active Queue Management

Active Queue Management

TELCOM2321 – CS2520

Wide Area Networks

Dr. Walter Cerroni

University of Bologna – Italy

Visiting Assistant Professor at SIS, Telecom Program

Slides partly based on Dr. Znati’s material

2

Reading

• Textbook, Chap. 17, Section 17.3

• S. Floyd, V. Jacobson

Random Early Detection Gateways for

Congestion Avoidance

IEEE/ACM Transactions on Networking

Vol. 1, No. 4, August 1993, pp. 397-413.

http://ieeexplore.ieee.org

• B. Braden et al.

Recommendations on Queue Management

and Congestion Avoidance in the Internet

IETF RFC 2309, April 1998

Queue Management in Network Nodes

• Networks should adopt a congestion control mechanism

– open-loop proactively tries to avoid congestion – closed-loop reacts to congestion events

• Network nodes should implement weighted fair

scheduling algorithms

– to meet QoS requirements of different traffic flows

• Network nodes should decide when to drop a packet

– can they do better than just dropping a packet when it arrives and finds a full queue? (drop-tail, passive approach)

• Active queue management techniques come into play

– to reduce or avoid the chance of severe congestion – to achieve unbiased packet dropping in queues shared by

4

Drop-tail in case of congestion

• Drop-tail is simple but has several drawbacks

– a few connections may monopolize the queue space and prevent other flows to enter the queue (lock-out)

– queues become full without any signal of raising congestion – bursty connections are penalized

– e2e closed-loop congestion control schemes react to packet drops only when sources are notified; meanwhile the queue stays full and congestion continues

– global synchronization problem

• all sources adopting implicit notification based on packet loss back off simultaneously

• total sending rate is suddenly reduced and congestion is relieved • network is underutilized

• sources start increasing sending rate • congestion occurs again

Passive vs. Active Queue Management

• Passive alternatives

– drop-head: when a packet arrives to a full queue, drop the first packet in line

– random drop: when a packet arrives to a full queue, drop random packet (more complex)

– they avoid the lock-out problem, but keep queues full and do not solve global synchronization

• Active approach: early dropping when congestion arises

– give sources enough time to react to congestion before queues fill up

– do not keep queues full

6

Random Early Detection (RED)

• RED is an Active Queue Management scheme for

Internet routers

– tailored for TCP connections across IP routers

• RED design goals

– congestion avoidance

– global synchronization avoidance – avoidance of bias against bursty traffic – bound on average queue length to limit delay

• RED basic steps

1. define two thresholds for queue size THMIN, THMAX

2. at packet arrival, compute the average queue size AVG 3. if AVG < THMINqueue packet (no congestion)

4. else if AVG > THMAXdrop packet (severe congestion)

RED: statistical approach to early drop

AVG

P_a 1

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RED: Average Queue Size

• The average queue size at arrival k is calculated using an

Exponential Weighted Moving Average (EWMA) of

current and previous queue lengths Q

, Q

, Q

, ...

AVG

= (1 – w) AVG

+ w Q

• Why an average queue size is used, when the actual

current queue size is known at the router when the

packet arrives?

– using average allows filtering out transient congestion at the router (low-pass filter)

– weighted average allows to tune the contribution of the current queue size (typically w = 0.002)

RED: Average Queue Size

• During idle periods the average is not updated

• If the queue is empty at the arrival of a packet (Q

= 0),

the number M of packets that could have been

transmitted by the router during the idle period is

estimated

t

, k-th packet arrival time

t

, start of queue idle time

M = F(t

– t

), linear function of idle interval

AVG

= (1 – w)

_AVG

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RED: Drop Probability

• Critical region, TH

_≤

AVG

_≤

TH

• count, number of consecutive packets queued since last

discard while in the critical region

• First, the linear probability P

is computed

P

= P

typically P

= 0.02

• Then, the drop probability is defined as

P

= 0

_≤

count < 1/P

AVG – TH

TH

– TH

P

1 – count P

RED: Drop Probability

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RED: Drop Probability

• Why such a complex probability model?

• Probabilistic phenomena typically produce clusters

– flipping a fair coin many times does not necessarily produce a uniform alternating series of heads and tails

• Using the simple linear probability P

results in geometric

drop distribution after a packet drop

consider a constant AVG

X = no. of arrivals between two consecutive drops

Prob[X = n] = (1 – P

)

_P

b

• Packets close together are likelier to be dropped

– global synchronization – bursty sources are penalized

RED: Drop Probability

• RED attempts to space the drops evenly so that a bursty

source is not penalized

• Using P

results in uniform drop distribution after a packet

drop

Prob[X = 1] = P

= P

Prob[X = 2] = (1 – P

) P

= P

Prob[X = 3] = (1 – P

) (1 – P

) P

= P

...

Prob[X = n] = P

0 < n

_≤

1/P

Prob[X = n] = 0

n > 1/P

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RED: Variants

• Weighted RED (WRED)

– drops different flows with different probabilities – THMIN, THMAX, PMAXare chosen based on flow priority

– low priority flows are dropped more aggressively

• RED with In/Out (RIO)

– edge nodes mark non-conforming packets (out-of-profile) according to traffic description

– different THMIN, THMAX, PMAXare chosen for in-profile and

out-of-profile packets

– out-of-profile packets are dropped more aggressively – EWMA for in-profile packets is computed considering those

packet only

– EWMA for out-of-profile packet is computed considering all packets