RBA-RIO：Rate Based Adaptive Red With In and Out
Algorithm for DiffServ AF PHB
Zhang Mingjie Zhu Peidong Su Jinshu Lu Xicheng
School of Computer, National University of Defense Technology, Changsha 410073, China [email protected]
Abstract-RIO is the active queue management algorithm for
supporting DiffServ AF PHB. When the subscription level or the load varies, the performance of RIO will change accordingly. The average queue delay and link utilization oscillate and do not converge to the ideal values. The paper proposes a new algorithm for DiffServ AF PHB, which is called RBA-RIO (Rate based Adaptive RED with In and Out). RBA-RIO consists of two sub-algorithms: adaptive RED and LPD (Loss Probability Divider). LPD calculates the drop probability of in and out packets dynamically based on their arrival rate. Compared with RIO, RBA-RIO only needs to configure one set of parameters. RBA-RIO can achieve smaller average queue delay and higher link utilization, and its performance advantage is verified using ns simulations by comparison with RIO.
Keywods: DiffServ, AF PHB, RIO, subscription level, utilization, average queue delay, adaptive
I. INTRODUCTION
The Internet, based on the TCP protocol, has succeeded in providing worldwide data communication service for the past few decades. However, Internet does not provide any Quality of Service (QoS) guarantee to applications. With increasing emergence of new service types, such as real-time audio/video applications, there is an increasing demand for providing QoS support in the Internet. The differentiated services (Diffserv) architecture [1] proposed by IETF has recently become the preferred method to address QoS issues in IP networks. Customer’s traffic is classified into different service classes and marked with different drop priorities (such as in/out packets) at edge routers. Core routers only need to implement simple packet scheduling and dropping algorithm. This packet marking based approach to IP QoS is attractive due to its simplicity and scalability.
In DiffServ networks, the externally observable forwarding behavior applied at a DiffServ-compliant node to a behavior aggregate is called Per-Hop-Behavior (PHB). IETF have standardized two basic PHBs: Expedited
Supported by the National Natural Science Foundation of China under Grant No. 90204005 and 90104001; the National Hi-Tech Research and Development Program of China under Grant No. 2003AA121510.
Forwarding (EF) [2] PHB and the Assured Forwarding (AF) [3] PHB. The EF PHB is used to build services that require low delay, low jitter and low loss like the Virtual Leased Line (VLL) services, while the AF PHB is used to build more “elastic” services that impose requirements only on throughput without any delay or jitter restrictions. This paper is focus on AF PHB.
To build an end-to-end service with AF, subscribed traffic profiles for customers are maintained at the traffic conditioning nodes at the edge of the network. The aggregated traffic is monitored and packets are marked at the traffic conditioner. When the measured traffic exceeds the committed target rate, the packets are marked with high drop precedence (out); otherwise, packets are marked with low drop precedence (in).
Core routers implement active queue management schemes, such as RED with In and Out (RIO) [4], and provide service differentiation to the traffic according to pre-assigned service classes and drop priorities carried in the packet header.
As illustrated in Fig.1, RIO uses the same mechanism as in RED [5] but is configured with two sets of parameters, one for in packets and the other for out packets. Upon each packet arrival at the router, the router checks whether the packet is tagged as in or out. If it is an in packet, the router calculates avgQ_in, the average queue for the in packets; if it is an out packet, the router calculates avgQ_out, the average total queue size for all (both in and out) arriving packets. The probability of dropping an in packet depends on avgQ_in, and the probability of dropping an out packet depends on avgQ_out. In Fig.1, min_in is bigger or equal to max_out.
Weighted RED (WRED) [6] is another AQM for supporting AF PHB. WRED calculates a single average
Fig. 1 RIO Algorithm
max_in min_in Pout max_out min_out avgQ_out Pmax_out 1 1 Pin avgQ_in Pmax_in
queue that includes arriving packets of all priorities. For an arrival or departure of in or out packets, WRED updates a single average queue based on total number of packets of in or out. However, multiple RED threshold parameters are maintained - one for each priority.
In [7] May et al perform some analytical modeling of DiffServ architecture schemes. Based on analytic evaluation of the loss probability, they conclude: “choice of different [RIO] parameter values can have a clear impact on performance”.
Reference [8], through experiments, evaluates the performance of WRED and RIO. Performance indicators used in the study included drop count of low drop precedence packets, transaction rate, throughput and number of retransmissions. They find that the performance of RIO is better than WRED.
This paper, through simulations, shows that when the subscription level or the load (connection number) varies, the performance of RIO will change accordingly. The average queue delay and link utilization oscillate and do not converge to the ideal values. To improve the performance of RIO, the paper proposes a new algorithm for AF PHB, which is called RBA-RIO (Rate based Adaptive RED with In and Out).
The rest of the paper is organized as follows. In Section II we demonstrate the weakness of RIO through simulations. We then propose RBA-RIO algorithm in Section III. In Section IV, the validity of RBA-RIO is verified through ns simulations. Finally, we conclude our research in Section V.
II.WEAKNESS of RIO
This section demonstrates the weakness of RIO through ns-2 [13] simulations. The simulation topology is illustrated as Fig.2. In Fig.2, FTP/TCPReno connections are established between Si and Ri. E1、E2are edge routers and mark packets according to the traffic profile. Time Sliding Window (TSW) [4] marker is adopted by E1. The bottleneck link is between core routers C1 and C2. RIO is run on C1 and its parameters are listed in Table I.
Different scenarios have been simulated on this network to evaluate the performance of RIO. The subscription level is changed from under-subscription (20%) to over-subscription (140%), and connection number is changed from 20 to 100.
Each Si→Ri connection pair has a target rate of 10SL N
Mb/s, where SL is subscription level, and N is the connection number.
TABLE I RIO PAREMETER SETTINGS
IN-profile OUT-profile
Minth 5 15
Maxth 15 30
Maxp 0.02 0.2
Wq 0.002 0.002 Total simulation time lasted 400s and link utilization from C1 to C2 is calculated by dividing total sent packet count during interval [100s-300s] by the maximal packet count that can be sent on the link. Fig. 3 shows the link utilization.
Fig. 3. Link Utilization of RIO
From Fig.3, we can observe that when resource is under subscribed, link utilization increases along with the connection number increase. When subscription level approaches saturation, link utilization changes little as the connection number increases. We also find if connection number is fixed, link utilization increases along with the subscription level increase. This phenomenon is more significant when connection number is small.
Average queue length on router C1, which is calculated by dividing the summation of sampled average total queue length by the sampling count, is depicted in Fig.4.
Fig. 4. Average Queue Length of RIO
From Fig.4, we can observe that when subscription level is fixed, average queue length increases along with the
E1 C1 C2 E2 S1 S2 Sn R1 R2 Rn 200M 5ms 200M 200M 200M 10M 5ms 5ms 5ms 50ms
connection number increase, and when connection number is fixed, average queue length increases along with the subscription level increase.
Average queue length (delay) of RIO will reach different values at different network scenarios and therefore cannot be predicted in advance.
Network operators wish the link utilization is as high as possible and independent of subscription level or connection number. At the same time, real-time audio and video customers wish average queue delay is stable. But from the above simulations, we can see that RIO cannot achieve high link utilization and stable average queue delay simultaneously.
III. THE RBA-RIO ALGORITHM
In this section, we discuss the design of the RBA-RIO algorithm in detail.
A. RBA-RIO Design
To improve the performance of RIO, the paper design RBA-RIO for AF PHB. RBA-RIO is depicted in Fig. 5.
In RBA-RIO, packet arriving rate of in and out is
estimated using TSW [4] method. Packet drop probability of in and out packet is calculated according to the following equations. p p rate rate rate p rate rate rate out out in out in out in in (1) out in p p d d 0 (2) ¯ ® in out in p p p 0
1 1 t out in in out in in rate rate rate p rate rate rate p (3) When calculating, first let pin 0, and then calculate
out
p according to (1). If pout!1, then let pout 1, calculate in
p according to (1).
Above method can be extended simply to three-drop priorities (GREEN/YELLOW/RED). In RBA-RIO, the maximal packet drop probability is constantly tuned to adjust to current traffic conditions. The detailed adjusting procedure is described in [9].
B. Characteristics of RBA-RIO
Compared with RIO, RBA-RIO can achieve high link utilization and stable average queue delay simultaneously. Furthermore, RBA-RIO only needs to configure one set of parameters rather than two or three sets of parameters.
IV. SIMULATIONS
In this section we evaluate the performance of RBA-RIO in various traffic conditions using simulations and compare it with RIO. In simulations, parameters of RBA-RIO are listed in Table II. Parameter maxp is the maximal packet drop
probability and is adjusted to current traffic conditions. Parameter WinLen is used for estimating packet arrival rate.
TABLE II
RBA-RIO PAREMETER SETTINGS
minTH 5
maxTH 15
maxp 0.02
Wq 0.0008
WinLen 5s
A. Static Traffic Scenario
Here we repeat the simulations in section II, but core router C1 runs RBA-RIO this time. Link utilization and average queue length are depicted as Fig. 6 and Fig. 7, respectively.
Upon a packet arrival: if(Packet is in)
update ratein;
else
update rateout;
update avgQLen; if(avgQLen!maxTH)
drop the packet;
else if(minTHdavgQLendmaxTH)
calculate packet drop probability p;
calculate pin, pout according to p, ratein,rateout
if(packet is in)
with probability pin drop the arriving packet;
else
with probability pout drop the arriving packet;
else if(avgQLendminTH)
queue the arriving packet. Variable:
ratein/rateout in(out) packet arrival rate;
avgQLen average queue length;
minTH/maxTH low/high threshold of avgQLen;
p packet (in/out) drop probability;
pin/pout in/out packet drop probability;
Fig. 6. Link Utilization of RBA-RIO
Fig. 7. Average Queue Length of RBA-RIO From Fig.6 we can see that link utilization is high (>99%) for various subscription level and connection number. Fig.7 shows that average queue length, which is around target queue length (10~11), is much more stable than RIO,.
B. Dynamical Traffic Scenario
To evaluate the performance of RBA-RIO in dynamical conditions, the simulation topology depicted as Fig.8 is somewhat different from Fig.1. We add senders s1 ~sm, and receivers r1 ~rm. The latency from senders to edge routers is uniformly distributed between 10ms to 50ms.
B.1 Under subscription
In this experiment, n (20) FTP sources start to send bulk data during [0s-5s] and aggregate subscription is 4Mbps, and m (30) FTP sources begin to send data during [100s-105s] and aggregate subscription is also 4Mbps. The total subscription level is 80%.
Fig. 9-1 and Fig. 9-2 show the time evolution of link utilization and average queue length, respectively. From Fig.9-1 and Fig. 9-2, we can observe that RBA-RIO has higher link utilization and smaller average queue length oscillations than RIO.
Packet drop rate of in and out, which is calculated by dividing discarded packet count in 1.5s by received packet count, are depicted in Fig.9-3 and Fig.9-4, respectively. In Packet drop rate of RBA-RIO and RIO are both very small when the system is stable. But at time around 0s and 100s, in packet drop rate of RIO appears a peak. From Fig.9-4, we can observe that out packet drop rate is quite different during [100s-200s]. We measure the number of received packet and dropped packet between 140s and 150s. Duration [140s-150s], RIO received 2151 out packets and dropped 1057 out packets; the drop rate of out is 49%. RBA-RIO received 3324 out packets and dropped 723 out packets; the drop rate is 22%. Low out packets drop rate is consistent with high link utilization.
Fig. 8. Simulation Topology
E1 C1 C2 E3 S1 S2 Sn R1 R2 r1 200M 5ms 200M 200M 200M 10M 5ms 5ms 5ms E2 E4 s1 s2 sm Rn r2 rm
Fig. 9-1. Utilization Comparison
B.2 Over Subscription
Here, n (20) FTP sources start to send bulk data during [0s-5s] and aggregate subscription is 5Mbps, and then during [100s-105s], m (30) FTP sources begin to send data and aggregate subscription is 6Mbps. The subscription level is 110%.
Fig.10-1 and Fig.10-2 show the link utilization and average queue length evolution of RBA-RIO and RIO, respectively. From Fig.10-1, we can observe that link utilization of RIO and RBA-RIO has little difference between 100s and 200s. But from Fig.10-2, the average queue length and oscillation of RIO is much bigger than RBA-RIO.
Packet drop rate of in and out are depicted in Fig.10-3 and Fig.10-4, respectively. It is seen (Fig.10-3 and Fig.10-4) that when resource is over subscription, packet drop rate of in and out has little difference.
We also conducted extensive simulations using web traffic, and the simulation results show that the performance of RBA-RIO under web traffic is similar to that of FTP traffic.
Fig. 9-3. Loss Rate (IN) Comparison
Fig. 9-4. Loss Rate (OUT) Comparison
Fig.10-3. Loss Rate (IN) Comparison
Fig.10-4. Loss Rate (OUT) Comparison
Fig. 10-1. Utilization Comparison
V. CONCLUSIONS
The paper proposes a new algorithm for AF PHB to eliminate the weakness of RIO. The performance advantage of RBA-RIO over RIO is verified using ns-2 simulations. It is demonstrated that RBA-RIO can achieve higher utilization and more stable average queue delay than RIO. The stablibility of average queue delay is crucial for real-time audio and video applications.
The idea of RBA-RIO is not only applicable to RED but to some other active queue management algorithms. RBA-RIO can be abstracted as Fig. 11.
CBA (Color-blind AQM) block is the basic AQM for calculating total packet drop probability p, and LPD (Loss Probability Divider) block is responsible for dividing p into pGREEN, pYELLOWand pRED. Besides RED and its variants, PI
[10], PIP [11] and SFC [12] are also AQM algorithms used in best-effort networks. If CBA is substituted by these AQMs, we can design different buffer management algorithms for DiffServ AF PHB. Performance evaluation of these algorithms will be part of our future work..
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CBA LPD
pGreen
pYellow
pRed
