Applying Active Queue Management to Link Layer Buffers for Real-time Traffic over Third Generation Wireless Networks

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1 Applying Active Queue Management to Link Layer Buffers for Real-time Traffic over Third Generation Wireless Networks Jian Chen and Victor C.M. Leung Department of Electrical and Computer Engineering The University of British Columbia Vancouver, BC, Canada, V6T 1Z4 {colinc, Abstract - Wireless channels have the characteristic that the link quality varies with propagation conditions. For real-time flows with hard time deadlines, link layer retransmissions over the wireless network due to fluctuations in link quality may result in many packets being dropped due to deadline expiry. The expired packets waste network resources and lead to long queuing delay for subsequent packets. In this paper, we propose to use active queue management to limit the transmission queue length and hence queuing delay, thus eliminating expiration packet drops. This allows the buffer and wireless bandwidth that would otherwise be wasted by expiring packets to be released earlier for other packets. We apply this mechanism to the radio link control layer in third generation wireless networks. The effectiveness of the proposed mechanism is verified by simulations. I. INTRODUCTION Wireless links are prone to transmission errors due to noise, interference and propagation impairments. Two main classes of techniques to address the effects of wireless errors on packet transmissions are Forward Error Correction (FEC), and Automatic Repeat Request (ARQ) protocols at the link layer that retransmit corrupted link layer packets [1][2]. They can be implemented separately, or combined together to get the best performance as in, e.g., hybrid ARQ. With ARQ schemes, the transmission buffer occupancy, or queue length, changes with the wireless link s bit-error rate (BER), because the number of packet retransmissions increases with the BER. For downlink packets sent to mobile terminals over third generation (3G) wireless networks, this transmission queue is in the Radio Link Control (RLC) layer implemented at the wireless-wireline interface node. Most real-time applications have hard time deadlines for packet delivery at the receiver. Received packets with expired deadlines are dropped by the receiver. If the transmission queue length grows to a large value, the end-to-end latency will be dominated by the queuing delay at the interface node. When this queuing delay approaches the maximum allowable delay tolerated by the real-time application, a large number of packets will be dropped by the mobile terminal due to deadline expiration. As these expired packets unnecessarily occupy transmit buffer and downlink bandwidth before they are dropped by the receiver, system performance is degraded due to inefficient use of resources. Furthermore, these packets add to the backlog of the transmission buffer resulting in more packet drops due to deadline expiration. This motivates us to develop a resource management scheme to control the transmission queue length so that long queuing delay is avoided, and expiring packets are prevented from using system resources and degrading system performance unnecessarily. In this paper we propose to use active queue management (AQM) [3] to achieve this goal. The rest of this paper is organized as follows. Section II discusses the behaviors of link layer queues in wirelesswireline interface nodes. Section III presents our AQM mechanism. Section IV discusses the simulation results, and Section V concludes the paper and suggests future work. II. ARQ MECHANISM AND QUEUE LENGTH As an example to illustrate our idea, we consider the RLC layer defined in UMTS as specified by the Third Generation Partnership Project (3GPP) [4]. A simplified illustration of RLC with Acknowledge Mode in UMTS is shown in Fig. 1. Fig. 1. Radio Link Control (RLC) layer in UMTS (window size = 8)

2 The packet error recovery mechanism of RLC employs a window-based, selective-repeat ARQ protocol. The sender keeps sending packets or Protocol Data Units (PDUs) in its transmission queue, and simultaneously storing copies of these in the retransmission buffer, until it receives a status report or reaches the sending window boundary. Once a status report arrives, RLC will take one of two actions according to the status being a positive acknowledgment (ACK) or negative acknowledgment (NAK). ACKed PDUs are deleted from the retransmission buffer, while NAKed PDUs are copied from the retransmission buffer to the transmission queue with a higher priority than PDUs yet to be transmitted for the first time. If the receiver has not received the expected packet successfully, it will request the sender to retransmit the packet by sending a NAK. There are several conditions at the receiver that indicate a missing packet and trigger the generation of a NAK. However, the specific condition triggering the status report does not affect our analysis and conclusions. When a packet has been transmitted N (a value pre-configured by the system) times, but still has not been successfully received by the receiver, it will be discarded by the sender. For real-time flows with hard delivery time deadlines, another kind of packet drop is caused by deadline expiration at the receiver. When the wireless channel is suffering from a high BER, packets will accumulate at the transmission queue. The resulting large queuing delay will increase the probability of packet expiration at the receiver. We assume a real-time flow with service data rate R. Each link layer packet with fixed size l will take l/r seconds for one transmission. In the extreme case that the wireless channel is clean enough to transmit all PDUs successfully the first time, the lower bound of transmission queue length that guarantees expiration packet drop is approximated as, L ( T t1 t2) R max = l (1) where T is the maximum allowable transfer delay of the realtime application, t1 is the latency caused by the Internet, and t2 is sum of the one-way wireless propagation delay and processing/scheduling delays at both ends. We assume that the traffic entering the UMTS core network is conditioned with shaping functions defined in 3GPP [9] such that the Internet latency t1 seen by the wireless terminal is effectively constant. When a new packet arrives at the transmission queue, if the observed queue length is longer than L max, then even if the wireless channel is clean, this packet will still be dropped by the receiver due to expiration. A queue capacity larger than L max is clearly not useful. If the wireless channel is so noisy that every PDU has to be transmitted N times before they are accepted or dropped, every packet will approximately spend t3 = N l R -- (2) in the transmission queue waiting for its transmissions to complete, during which all following packets will have to wait. On the other hand, under this situation every packet also has to spend approximately t4 = ( N 1) t2 2 (3) in the retransmission buffer waiting for an ACK or NAK coming from the receiver, during which it does not have any impact to the transmission of other packets. The factor 2 is to take into account of the round trip delays. We assume that the delay t2 is exactly the same on both forward and backward paths. Thus the upper bound of queue length that guarantees no packet expiration is, L T t1 t2 t3 t4 min = (4) t3 If the observed queue length is equal to or less than L min, a newly arrived PDU will never be dropped due to expiration. III. APPLYING AQM TO RLC QUEUE If the observed queue length falls into the range [L min, L max ], there is certain probability that a newly arrived PDU will be dropped due to expiration. Intuitively, a longer queue causes a larger queuing delay, and hence a higher probability of packet expiration. We propose to actively drop some packets at the RLC transmission queue according to the above observation, so that buffer length is limited to avoid packet expirations, and bandwidth can be released earlier to improve utilization. The relationship between average packet delay and average queue length has been analyzed in [5][6]. Here, we derive the relationship between the expiration probability of real-time packets and the queue length. Assuming the wireless channel has an average packet loss rate P B due to transmission errors, we can approximate the wireless link with an error-free data link that has a variable date rate ranging between [R/N, R] with average data rate ρ = (1 - P B )R. Note that this approximation does not change (1) - (4) above. The transmission time for each packet is thus independent of each other, with mean m = l/ρ and variance σ 2 upper bounded by { max( l ρ l R, l N R l ρ) } 2. If a newly arrived packet observes n packets in the queue, L min < n < L max, its queuing delay will be given by a random variable V, which is the sum of n independent random variables of the transmission times of packets already in the queue. From simulation results, we can see that when the traffic load is high, n will be large enough for the sum of n packet transmission times to have a distribution approximated by a Gaussian distribution, with mean M = m n and variance σ 2 = n σ 2, according to the central-limit theorem.

3 Now with the Gaussian distributed random variable V, we can analyze the relationship between the expiration drop rate p and the observed queue length n. When a packet arrives at time τ 1, its deadline is computed as τ 2 = τ 1 + ( T t1). Since V is a Gaussian variable, the probability of expiration p is τ 2 M p = Pr{τ 1 + v > τ 2 } = Q (5) σ' where Q(.) is the well-known Q function giving the tail area of a Gaussian distribution, as illustrated by the shaded areas in Fig. 2. The figure shows the Gaussian probability density functions for n = A and n = B, with A < B. When the value of n is A, the corresponding mean and variance (M and σ ) are both small, so the shaded area is also small. When n = B, its M and σ increase, thus the shaded area increases as well. This result matches the intuition mentioned above. σ Α M A σ B τ 1 τ 2 Fig. 2. Gaussian distributions with means M A and M B, variances σ A and σ B, where M A < M B and σ A < σ B From (5) we can get curve I (labelled as Gaussian) in Fig. 3, which is the relationship between expiration probability p and observed queue length n assuming a Gaussian distributed queuing delay. To obtain this result, we fix the packet error rate P B = 15%, and queue capacity to L max. To smooth the changes in p when applying this result in performance evaluations, the observed queue length n is obtained from the average queue size calculated using a low-pass filter with an exponentially weighted moving average w q [3]. Note that the above result is an approximation as the M B possible loss of control PDUs for status reports has been ignored. These losses will introduce some extra queuing delay that will increase the mean value of the Gaussian curve I in Fig. 3. For simplicity we ignore this additional delay. To actively drop packets with the exact probability obtained above is difficult to realize due to the computation complexity. Instead we propose to use a simpler scheme similar to the gentle variant of Random Early Detection (RED) [7] to approximate the expiration rate. For the values of RED parameters like w q and max p, we set them as suggested in [8]. In Fig. 3 we present two piece-wise linear AQM functions II and III to approximate the Gaussian curve. It is understandable that the better the approximation is, the better the performance should be. So function III yields the best result shown in the next section. However in reality it is hard to estimate the Gaussian curve due to uncertainty in system parameters. We consider the two-slope piece-wise linear function II defined in (6) as the easiest to implement as it can be uniquely fixed by L max and L min. p = 0 if n L min The results in the next section show that this simple scheme works well enough in controlling the queue length and improving the system performance. IV. SIMULATION RESULTS L min ( n L max min ) + L p if L L min + L min n max < < max L 2 2 min L n min + L (6) max 2 L ( 1 max p ) if n min + L max L L min + L max max The system simulation model shown in Fig. 4 has been built and exercised in the OPNET 8.1 simulation environment. It includes the wireless channel model in the OPNET 8.1 library, which takes into account the multiple access interference, background noise and multipath fading, and gives quite a comprehensive channel error model. Receiver Basestation Internet Fig. 4 Simulation model Server Fig. 3. Gaussian expiration drop rate and AQM approximations To focus the performance evaluations on the management of the downlink transmission queue, we make the simplifying assumption that the uplink is error-free. The parameters for the wireless link and real-time application are given in Table 1.

4 TABLE 1. SIMULATION PARAMETERS Parameters Value Effective wireless data rate R 1024 Kbps Maximum number of transmissions N 5 L min 40 L max 210 Maximum allowable end-to-end delay T 250 ms Propagation delay in Internet 50 ms Bandwidth of Internet link 2048 Kbps Average packet error rate P B 15% We assume that the arrivals of real-time packets follow the Poisson distribution and packets have a constant length. This accentuates the queue occupancy and hence the effectiveness of AQM. The unit of queue length in the figures below is number of packets. Thus the traffic load varies in direct proportion to the average inter-arrival time of real-time packets. As explained above, we assume a fixed Internet latency as the result of traffic shaping [9] at the input to the UMTS core network. Other system parameters, such as values of the end-to-end delay T and wireless data rate R, also come from [9]. Figs. 5-8 were obtained using the two-slope piece-wise linear AQM function II in Fig. 3, with 90% offered load. From Fig. 5 we can see the relationship between the wireless BER, throughput and the time average transmission queue length as functions of time, without AQM. When the queue length becomes large, the corresponding throughput drops sharply. Fig. 6 shows the reasons behind the results shown in Fig. 5. We can see that without AQM there are three types of packet drops: expiration, number of transmissions exceeding maximum value (N), and buffer overflow. Among them expiration packet drops account for the largest share. When the queue length or wireless BER increase, the numbers of all Fig. 5 BER, transmission queue length and throughput without AQM Fig. 7 BER, transmission queue length and throughput with AQM Fig. 6 Packet drops without AQM Fig. 8 Packet drops with AQM

5 three types of packet drops will increase as well, but expiration drops are most harmful to the system performance due to its large share in the total packet drops. This figure shows how serious the problem of expiration could be when the system load is high. Applying the AQM scheme in (6) to constrain the queue length changes the situation dramatically. Fig. 7 shows the relationship between the BER, queue length and throughput over time. We can see that the average queue length is effectively controlled near the mid-point of (L min +L max )/2, and the throughput curve does not have as much fluctuations over time, which is much better than the results in Fig. 5. The simulations also show that the maximum queue length increases with the BER and traffic arrival rate. Due to space limitations these latter results are not presented here. Fig. 8 shows the reasons behind the improvements. We can see that one of the main differences between Figs. 6 and 8 is that the types of packet drops have been changed. With AQM, all overflow and most expiration drops are eliminated, replaced by a new kind of packet drop, i.e., active random drops. The figure shows that the amount of the active random drops is much less then the expiration drops in Fig. 6, but still dominates over other types of packet drops. This illustrates the effectiveness of AQM in improving system throughput without degrading the overall packet drop rate experienced by the realtime applications. Note that the latter condition is an important criteria in maintain the quality of service for these applications. Figs. 9 and 10 show the effects of AQM on throughout and end-to-end delay, respectively, under different traffic load and comparing the piece-wise linear AQM functions II and III. We fix all other parameters as in Table 1, and vary the traffic load from 50% to 100%. Under heavy traffic (traffic load exceeding 70%), our AQM scheme yields great improvements to the overall throughput and end-to-end delay. As we expect, AQM function III gives a better performance than function II. It is apparent that when traffic is light, the queue length is not long enough to trigger the AQM, and the expiration packet drop rate is small enough not to impact the system performance. So the performances of both scenarios, with and without AQM, are similar. With increasing traffic load, both the expiration drop rate and queue length grow. When the traffic load exceeds 70%, the advantages of our proposed AQM scheme become quite obvious. Under such condition, without AQM the wireless link is saturated by ever increasing number of retransmissions, resulting in the reduction of throughput as traffic load increases as shown in Fig. 9. As the transmission queue length approaches L max, the expiration drop rate becomes the dominating and most harmful factor to the system performance in both the throughput and end-to-end delay. With AQM we can control the queue length to limit it to a lower value, thus eliminating expiration packet drops. Since AQM releases resources that would otherwise be used ineffectively by expired packets, it is also able to mitigate throughput saturation, as shown in Fig. 9, where the throughput curve for AQM continues to increase up to a traffic load of 100%. V. CONCLUSIONS AND FURTHER WORK In this paper, we have analyzed the relationship between the real-time expiration packet drop rate and the transmission queue length, when link layer retransmissions are used to recover packet losses over the wireless channel. We propose a novel application of active queue management at the transmission queue to actively drop the potentially expiring packets before they are transmitted, in order to free the network resources and control the transmission queue length effectively. We have presented simulation results to show that the proposed AQM method is effective in reducing the queuing delay and eliminating the expiration packet drops, thus improving overall system performance by increasing throughput and reducing end-to-end delay. While we have not distinguished in this paper between different types of packets that may exist in real-time flows, there may be a need for such differentiation because in many real-time applications, some types of packets may carry more Fig. 9 Throughput vs. traffic load Fig. 10. End-to-end delay vs. traffic load

6 important information than others. In this case Random Early Detection with IN-and-OUT (RIO) [10] could be used as a basis of our AQM method to implement the discrimination. Furthermore, how to apply AQM effectively to a mix of different real-time applications remains to be investigated. ACKNOWLEDGMENTS This work was supported by Motorola Canada Ltd., and the Canadian Natural Sciences and Engineering Research Council under grant CRDPJ REFERENCES [1] J. B. Cain and D. N. McGregor, A Recommended Error Control Architecture for ATM Networks with Wireless Links, IEEE Journal on Selected Areas in Communications, vol. 15, pp , Jan [2] N. Guo and S. D. Morgera, Frequency-Hopped ARQ for Wireless Data Services, IEEE Journal on Selected Areas in Communications, vol. 12, pp , Oct [3] S. Floyd and V. Jacobson, Random early detection gateways for congestion avoidance, IEEE/ACM Transactions on Networking, vol. 1, pp , Aug [4] 3G TS v5.0.0 ( ), Radio Link Control (RLC) protocol specification, [5] E. Chan and X. Hong, Analytical model for an assured forwarding differentiated service over wireless links, IEE Proc. on Communications, vol. 148, pp Feb [6] R. Fantacci, Queuing analysis of the selective repeat automatic repeat request protocol wireless packet networks, IEEE Transactions on Vehicular Technology, vol. 45, pp May [7] V. Rosolen, O. Bonaventure and G. Leduc, A RED discard strategy for ATM networks and its performance evaluation with TCP/IP traffic, ACM Computer Communication Review, vol. 29, no. 3, Jul [8] S. Floyd, Discussion of Setting Parameters, REDparameters.txt. [9] 3G TS v5.3.0 ( ), QoS Concept and Architecture, [10] D. D. Clark, and W. Fang, Explicit Allocation of Best-Effort Packet Delivery Service, IEEE/ACM Transactions on Networking, vol. 6, pp , Aug

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