Adaptive Virtual Buffer(AVB)-An Active Queue Management Scheme for Internet
Quality of Service
Xidong Deng, George Kesidis, Chita Das
Department of Computer Science and Engineering The Pennsylvania State University
State College, PA 16802
[email protected], [email protected], [email protected]
Abstract- In this paper we present a virtual queue based active queue management scheme called Adaptive Virtual Buffer(AVB). We study its properties in satisfying the QoS requirement such as low packet loss rate and high throughput; its ability in main-taining stable queue length under various network configurations such as the number of connections and round trip delay; and its robustness in the presence of extremely short flows. Using a math-ematical tool based on control theory, we present a simple rule in choosing the parameters for this algorithm. Through extensive simulations, we validate our design and compare the performance with some other AQM schemes such as RED, REM and AVQ.
I. INTRODUCTION
There has been a strong demand for QoS among flows in today’s Internet. As a result, the links are supposed to work with the sources to play an active role in congestion control and avoidance. There are two forms of congestion notification for TCP connections: one is packet dropping and the other is Explicit Congestion Notification (ECN) marking [10]. With ECN marking, the links on detecting incipient congestion set a bit in the packet header that notifies the user of the congestion and the user then reacts to the mark as if a packet has been lost. Thus, the link avoids dropping the packet and still manages to convey congestion information to the user.
To drop packets or provide ECN marks, the routers have to select packets intelligently in a manner that conveys informa-tion about the current network state to the users. Algorithms that the router employs to convey such information are called Active Queue Management (AQM) schemes. Designing AQM schemes has been a very active research area in Internet com-munity. For example, Random Early Drop (RED) [1] and many of its variations [4,5] are typical queue length based AQM schemes, in which a packet is dropped/marked according to a probability based on the average queue length when the packet arrives at the queue; Adaptive Virtual Queue (AVQ) [8], which is a rate based scheme, maintains a virtual queue whose link capacity adapts to the current packet arrival rate to achieve a certain level of link utilization; Random Exponential Marking (REM) [6], however, uses a combination of both queue length and rate information to probabilitically drop/mark the incom-ing packets and aims to regulate both to some target values.
In a queue length based AQM like RED, the queue length must steadily increase with the number of connections to gen-erate enough congestion signal, which means the queueing de-lay or network latency increases with the traffic load. In con-trast, under REM, the mean queue length is stablized around a target level regardless of the number of connections. But as we will see in the simulation, it exhibits sluggishness in con-verging to the stable state, especially when short flows are in-troduced. For AVQ, since it is a pure rate based algorithm and does not regulate queue length in any explicit way, it’s not clear how the queue will evolve under different network and traffic scenarios.
In this paper, we propose a virtual queue based scheme called AVB (Adaptive Virtual Buffer). As in REM, it aims to stablize both the packet arrival rate around the link capacity and the queue around a target value regardless of the number of connections sharing the link. But instead it uses a virtual queue which is maintained and updated like in AVQ and is more quicker in responding to the changing network status and more robust in the presence of short flows. When a packet ar-rives at the real queue, the virtual queue occupancy is also up-dated to reflect the new arrival. Unlike AVQ, where the service rate of the virtual queue is adaptable and the packet is dropped or marked whenever the virtual queue overflows the physical buffer limit, we instead fix the service rate as the link capac-ity of the real queue and adapt the limit of the buffer size of the virtual queue(virtual buffer) to the packet arrival rate. The incoming packets are then dropped or marked at a probabil-ity which is calculated based on both the current virtual buffer limit and the virtual queue occupancy. We then give a rule for finding the proper rate at which this probability adaptation can take place to make the system stable given certain system pa-rameters such as the maximum round trip delay and the num-ber of connections and use simulation results to validate our design.
The rest of this paper is organized as follows: The AVB al-gorithm is presented in section II. The results of a set of de-tailed simulations using ns-2 [11] are given in section III. Con-clusions are provided in section IV.
Let C be the capacity of the link and B be the physical buffer limit. The AVB algorithm works as follows:
A. AVB-Marking
The router maintains a virtual queue whose service rate is equal to C. The packet in the real queue is marked accord-ing to the probability as follows:
(1) where
is the virtual queue occupancy and
is
the virtual buffer limit when the packet arrives at time .
The rationale behind (1) is that the router should be more aggressive in marking when
exceeds
. At each packet arrival, the virtual buffer limit
is up-dated according to (2): !"# (2)
where" is current aggregated packet arrival rate at the real
queue and is a constant that determines how fast should
the
adapts to " . The rationale behind (2) is that more
packets should be marked if" exceeds the link capacity.
At each packet arrival, the virtual queue occupancy
is updated as follows: $"% (3)
Notice that the difference between a virtual queue and the physical queue is that the virtual queue has no physical boundary. i.e. There is no overflow or underflow in the virtual queue so
is always differentiable.
Motivated by earlier work in [3] and [8], we model the above TCP/AVB system as a feedback control system. When is chosen properly, the system will reach its
equi-librium point so that " converges to and the queue
length converges to a target value
&
. Theorem 1 gives a rule on how to select to achieve the stable state: Theorem 1 Fix the link capacity C, the round trip
prop-agation delay d, the number of connections N, and the target queue length
&
, find the smallest )('
satisfying ' *,+.-0/ 2143657 98 :<; ' * :>= ? /A@- B DC (4)
for k=0,1,2... and EF
/HGI , :J= K L4MON , :<; PRQ L M N MOS K MT P = SVU W TYX K M L4M K[ZY\] , ' * _^ :<;
. Then for all a`b' ,
the system is asympotically stable.
The derivation of (4) is based on the fluid model of TCP congestion avoidance algorithm as proposed in [11] and
stability analysis of the linearized TCP/AQM model based on control theory as in [3] and [8]. A complete derivation of theorem 1 can be found in [12] and is ignored here due to the space limit.
B. AVB-Dropping
When there is no ECN support in the network, packets must be dropped from the queue to trigger the congestion notifica-tion so the link only services those packets that are admitted to the real queue. This results in a couple between the drop proba-bility and the arrival rate to decide the actual admitted rate and thus it’s more difficult to model the system and the stability analysis is much complicated. On the other side, we note that the stability of a TCP/AQM with dropping is less sensitive to the dynamics of network than a marking scheme if they were put under the same traffic scenario. The reason is that it takes longer for the source to detect and respond to a congestion sig-nal in the case of dropping. (on detection of three duplicate ACKs) Based on these observations, we move our focus from achieving a stablized system where both the total arrival rate matches the link capacity and the average queue size matches the target value to a much simpler design which only tries to maintain the queue length at the target value. The modified AVB algorithm for dropping is stated as follows:
, 2c (5) where
is the actual queue size at time t, and is a
pre-specified threshold value which is less or equal to the target average queue length
&
.
Note that equation(5) is a queue-based algorithm like RED using instantaneous queue length and without maximum threshold. However, We replace the pre-defined parameter
max-p in RED with to decide the aggressiveness of the
drop-ping. Moreover, is the only parameter need to be configured,
while we have to carefully choose 5 different parameters for RED. In the next section, we can see that superior performance can be achieved with such a simple design.
III. SIMULATIONRESULTS
A. Simulation Setups
We will use the network simulator ns-2 [9] to simulate the proposed AVB algorithm. As shown in Fig.1, we consider a single link with capacity of 10Mbps. There are N independent sources connected to the router, each by a link of 2Mbps. TCP-reno is used as the default transport protocol and FTP is used as the default application protocol. We assumed that the packets have a fixed size of 1000 bytes.
We will compare AVB with three other representative AQM schemes: RED[1], REM[6] and AVQ[8]. If not otherwise stated, they are configured by default as follows:
RED: Most of the parameters were chosen as
recom-mended in [2]. In particular, 0.002 for q-weight, 20 pack-ets for min-threshold and 80 for max-threshold. max-p is set to 0.5 to allow for heavy traffic load.
S1
Sn
Router Dest.
Fig. 1: Network Configuration
REM: We will use the ns-2 patch code got from [7] and
use the parameter setting recommended there. In particu-lar,de BAf AAg , e f 9 B ,hi fjB
and the update time
T is ten packet transmission time 8ms. The target queue
length is set to 50.
AVQ: The target link utilization is set to 1.0. And we
will use the designing rules given in [8] to decide h ,
the constant which decides how fast should the algorithm adapt to the network status.
B. AQM with dropping
In the first simulation we will run a single experiment for each AQM scheme using dropping as congestion notification mechanism. We have a bunch of homogeneous, persistent FTP connections fromkl
/
9
sources competing for the bottle-neck link withmn
B
9
packets buffer. By homogeneous we mean that the round-trip propagation delay+
for each connec-tion are identical.(100ms) To achieve a target queue length at 50 packets, we set[ o
g
for AVB.
In addition to the four AQM schemes, we also simulate a simple drop-tail buffer and use it as a baseline for performance comparasion. Table 1 shows the results for different perfor-mance measurements.
TABLE1. COMPARISON OF AQM WITH DROPPING AQM Loss Rate Goodput(Packets/s) Avg. q (packets) p
Z (Packets) Droptail 11.8% 1249.2 92.7 8.7 RED 13.1% 1245.8 42.1 26.0 REM 13.6% 1237.0 40.1 28.3 AVQ 14.0% 1218.0 10.5 11.8 AVB 13.2% 1249.2 51.4 5.9
From Table 1. We can see that AQM does not help in reduc-ing the packet loss rate when compared with drop tail. More-over, the system goodput may be degarded due to large amount of packet loss. On the other side, AQM does help in bringing the average queue length down thus reducing the network la-tency. In the four AQM schemes under investigation, AVQ has the lowest average queue length, but it’s at the price of a much lower goodput. The average queue length for RED and REM are near the target value, but with large variance. AVB out-performs the other schemes by achieving the highest goodput with a moderate packet loss rate. Moreover, it maintains the queue at a level that is most closed to the target level and has the least variance.
Since ECN marking is believed to be able to improve per-formance for TCP/AQM systems, we will only study the AQM schemes with marking in the rest of simulations.
C. Persistent Traffic a) Number of Connections
In this set of simulation, we use the same network configu-ration and traffic patterns as in the previous one. With+
fixed to 100ms andm to 100 packets, the maximum round trip delay
is 180ms. We run 10 simulations for each AQM scheme, each with a differentk ranging from 20 to 200, and last for 400
seconds. With q Br/ g9 ,ks / ,+ fjBt and & g9 , we solve equation (4) and set to 0.2 for AVB. The results are
plotted in Fig. 2 through 4 and summarized below:
Packet Loss Rate: AQM with ECN marking is very
ef-fective in reducing the packet loss rate. For example.
when k
/
A
, from table 1, the loss rate for all schemes is above 10%, while from Fig.2, even the largest value(generated by RED with ECN) is less than 4%. In RED, REM and AVQ, the packet drop increases with the number of connections, while in AVB, it remains at a pretty low level (less than 0.1%) even whenk is large.
Goodput: From Fig.3 we can see that RED suffer a quite
low goodput when N is small. We can get some improve-ment by reducing the value of max-p to make the algo-rithm less aggressive in marking. But on the other side, a small max-p may hurt the performance when N is large since more overflow may occur if not enough early con-gestion signals are sent back to slow down the sources. Regardless of the number of connections, An AVB queue delivers the packets at a rate which is almost the full link capacity(1250 packets/s).
Average Queue Length: From Fig.4, among all four
schemes, AVQ has the lowest average queue length. But again the trade-off here is a lower goodput. In RED, the average queue length steadily increases with the number of connections due to its queue-based nature. For both REM and AVB, the average queue length stays around the target value 50.
b) Round Trip Time
The value of Round Trip Time(RTT) has great impact on the performance and the stability of TCP/AQM systems because it determines how fast the source can detect and respond to the congestion signals. A system with large RTTs may be too sluggish to be effective, while a small RTT may lead to large oscillatory behavior or in the worst case even instability. The typical RTT for traffic traversing continental United States is about 200ms. And we will study the influence of RTT on dif-ferent AQM schemes by setting the RTT at a range between 40ms to 400ms with N fixed to 200 and B to 100. From Fig.5 we can see that RED degrades most quickly with the increase of RTT. The loss rate increases sharply when RTT is between 80ms and 240ms. Beyond 240ms, it begins to decrease be-cause the traffic injected to the network has fallen to a pretty low level and the link is highly under-utilized. In REM, how-ever, the loss rate decreases with the increase of RTT. This is
due to a relative sluggish nature of REM-its speed of updat-ing of the markupdat-ing probability is slower than other schemes thus a larger RTT is needed to allow for the adaptation. AVB out-performs other schemes in a wide range of practical RTT settings. There is almost no packet loss when RTT is small and the loss rate increases slightly but still no significant degrada-tion in goodput with the increase of RTT.
D. Responsivenss
In this set of simulation we will compare the responsive-ness of the AQM schemes when flows are introduced and then dropped later on. Most of the system parameters are identi-cal to the previous simulation but we will let the propagation delay for each connection uniformly distributed between 40ms and 200ms to make the scenario more realistic. The number of FTP flows is 50 at time=0.0, from time=50.0, 50 new connec-tions are established every 50 seconds until time=300.0, from then on 50 flows are dropped every 50 seconds until the system goes back to 50 flows at time=500.0.
Fig. 6 plots the queue length evolution for different AQM schemes. In RED, the queue follows the change of traffic load thus often reaches the top of the buffer when N is large; Both AVQ and REM often over-react to the sudden change of the traffic thus drive the queue to either the top or the bottom of the buffer at the time when flows are added or dropped. REM acts slowly in bringing the queue length to the target value so that the queue keeps at a high position when traffic load keeps increasing or at a low position when traffic load keeps decreas-ing. AVB, however, produced a much smoother queue length evolution than other schemes, the average queue length stays around the target value and there is no sharp increasing or de-creasing point at the time when bunches of flows are added or dropped.
E. Short Flows
Till now we have been comparing AVB and other AQM schemes in the absence of short flows. However, a large part of the connections in the Internet comprise of short flows. As a result, it is important to study the performance of an AQM scheme in the presence of short flows. In this set of simulation, we start with 50 FTP flows, running as some background traffic throughout the whole 400 seconds’ simulation time. And we will introduce short flows at a rate ofu flows per second withu
ranging from 20 to 100. Each short flow sends 20 packets then stops and the round trip propagation delay for each flow is also uniformly distributed between 40ms and 200ms.
Fig.7a shows that in RED and REM, the loss rate increases with the number of short flows introduced per second. How-ever, it will not hurt the performance of the other two virtual queue based scheme AVQ and AVB. For AVQ, there is no packet loss in the whole simulation duration and the average queue length is the least of the four AQM schemes. The trade-off here is again a lower goodput as shown in Fig.7b. In con-trast, AVB has slightly more packet loss larger goodput. The average queue length still stays around the target value, despite
of the frequent add or drop of short flows.
IV. CONCLUSIONS
This paper has described an virtual queue based active queue management scheme called Adaptive Virtual Buffer(AVB). We considered both dropping and marking for the bottle-neck router to detect and send congestion signals. Through extensive simulations, we have compared this scheme with RED,REM and AVQ and have shown that our algorithm can achieve lower packet loss rate, higher throughput and less queue flunctuation under various traffic conditions than other schemes and concluded that it is a better scheme in satisfying the QoS requirement for aggregated TCP traffics going through the bottleneck router.
REFERNCES
[1] S. Floyd and V. Jacobson. “Random early detection gateways for con-gestion avoidance.” IEEE/ACM Transactions on Networking.
1(4):397-413, August 1993.
[2] http://www.aciri.org/floyd/REDparameters.txt
[3] C. Hollot, V. Misra, D. Towlsey, and W. Gong. “A control theoretic anal-ysis of RED.” In Proceedings of IEEE Infocom 2001 Anchorage,Alaska,
April 2001,
[4] T. J. Ott, T.V. Lakshman, and L.H.Wong. “SRED: Stabilized RED.” In
Proceedings of IEEE Infocom 1999, New York, NY, March 1999.
[5] W.Feng, D. Kandlur, D.Saha, and K. Shin. “A Self-Configuring RED gateway.” In Proceedings of Infocom 1999, New York, NY, March 1999. [6] S. Athuraliya, D.E. Lapsley, and S.H. Low. “Random early marking for internet congestion control.” In Proceedings of IEEE Globecom 1999. [7] http://netlab.caltech.edu/pub.html
[8] S. Kunniyur, R. Srikant. “Analysis and Design of an Adaptive Virtual Queue(AVQ) Algorithm for Active Queue Management.” In
proceed-ings of IEEE Sigcomm 2001, San Diego, California, August 2001.
[9] http://www.isi.edu/nsnam
[10] S. Floyd. “TCP and explicit congestion notification.” ACM Computer
Communication Review, 24:10-23, October, 1994.
[11] S.Kunniyur and R.Srikant. “End-to-end congestion control: utility func-tions, random losses and ECN marks.” In Proceedings of Inforcom 2000,
Tel Aviv, Israel, March 2000
[12] http://www.cse.psu.edu/ xdeng/research.html 20 40 60 80 100 120 140 160 180 200 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 Number of Connections
Packet Loss Rate
RED−ECN REM−ECN AVQ−ECN AVB−ECN
20 40 60 80 100 120 140 160 180 200 1160 1170 1180 1190 1200 1210 1220 1230 1240 1250 Number of Connections Goodput(Packets/s) RED−ECN REM−ECN AVQ−ECN AVB−ECN
Fig. 3: Goodput v.s Number of Connections, with ECN
20 40 60 80 100 120 140 160 180 200 25 30 35 40 45 50 55 60 65 Number of Connections
Average Queue Length(Packets)
RED−ECN REM−ECN AVQ−ECN AVB−ECN
Fig. 4: Average Queue Length v.s. Number of Connections, with ECN
50 100 150 200 250 300 350 400 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
Packet Loss Rate
RTT(ms) RED REM AVQ AVB RED−ECN REM−ECN AVQ−ECN AVB−ECN
Fig. 5a: Loss Rate v.s. Round Trip Delay, with ECN
50 100 150 200 250 300 350 400 1080 1100 1120 1140 1160 1180 1200 1220 1240 1260 Goodput(Packets/s) RTT(ms) RED−ECN REM−ECN AVQ−ECN AVB−ECN
Fig. 5b: Goodput v.s Round Trip Delay, with ECN
0 100 200 300 400 500 600 0 50 100 0 100 200 300 400 500 600 0 50 100 0 100 200 300 400 500 600 0 50 100 0 100 200 300 400 500 600 0 50 100 RED−ECN REM−ECN AVQ−ECN AVB−ECN
Fig. 6: Queue Length Evolution with time, with ECN
20 30 40 50 60 70 80 90 100 0 1 2 3 4 5 6 7 8 9x 10 −3
Number of Short Flows per second
Packet Loss Rate
RED−ECN REM−ECN AVQ−ECN AVB−ECN
Fig. 7a: Packet loss rate v.s number of short flows per second
20 30 40 50 60 70 80 90 100 1244.5 1245 1245.5 1246 1246.5 1247 1247.5 1248 1248.5 1249
Number of Short Flows per second
Goodput(Packets/s)
RED−ECN REM−ECN AVQ−ECN AVB−ECN
Fig. 7b: Goodput v.s number of short flows per second
20 30 40 50 60 70 80 90 100 30 35 40 45 50 55 60 65
Number of Short Flows per second
Average Queue Size(Packets)
RED−ECN REM−ECN AVQ−ECN AVB−ECN
