@prefix vivo: . @prefix edm: . @prefix ns0: . @prefix dcterms: . @prefix dc: . @prefix skos: . vivo:departmentOrSchool "Applied Science, Faculty of"@en, "Electrical and Computer Engineering, Department of"@en ; edm:dataProvider "DSpace"@en ; ns0:degreeCampus "UBCV"@en ; dcterms:creator "Chen, Jian"@en ; dcterms:issued "2009-08-20T18:46:33Z"@en, "2002"@en ; vivo:relatedDegree "Master of Applied Science - MASc"@en ; ns0:degreeGrantor "University of British Columbia"@en ; dcterms:description """In this thesis, we analyze the behavior of real-time flows with hard time deadline using link layer retransmission to overcome wireless errors, and propose two new mechanisms to improve these flows' end-to-end Quality-of-Service (QoS) over a wireless network. Wireless channels have the characteristic that link quality varies according to propagation conditions. For real-time flows with hard time deadlines, link layer retransmissions over a wireless network, necessitated by fluctuations in link quality, may result in a high number of packets being dropped, as deadlines expire. The expired packets waste network resources and lead to long queuing delays for subsequent packets. After analyzing the characteristics of the Radio Link Control (RLC) layer of the General Packet Radio Service (GPRS)/Universal Mobile Telecommunication Services (UMTS) network, we have developed a new set of mechanisms to minimize expiration packet drops in favour of overflowlike packet drops. We propose to use active queue management to limit transmission queue length, hence queuing delay, thus eliminating expiration packet drops. This allows the buffer and wireless bandwidth, otherwise be wasted by expiring packets, to be released earlier for other packets. We apply this mechanism to the radio link control layer in the GPRS/ UMTS wireless networks. The effectiveness of the proposed mechanism is verified by simulations. Furthermore, we extend the similar idea from the radio link control layer to the whole GPRS/UMTS domain. We adapt the Early Regulation of Unresponsive Flows (ERUF) to the characteristics of wireless channels employing link layer retransmissions. We propose to regulate those congested flows at the DiffServ domain ingress edge nodes of the GPRS/UMTS core network, and drop undeliverable packets earlier to release some shared network resources for other flows. We also present simulation results to show that this new Wireless Early Regulation of Unresponsive Flows (WERUF) scheme can significantly improve the overall end-to-end quality-of-service."""@en ; edm:aggregatedCHO "https://circle.library.ubc.ca/rest/handle/2429/12416?expand=metadata"@en ; dcterms:extent "7253566 bytes"@en ; dc:format "application/pdf"@en ; skos:note "ACTIVE QUEUE MANAGEMENT TECHNIQUES TO IMPROVE QUALITY OF SERVICE FOR REAL-TIME FLOWS IN THIRD GENERATION WIRELESS NETWORKS by J I A N C H E N B . E . , Shanghai Jiao Tong University, China , 1995 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF T H E REQUIREMENTS FOR T H E D E G R E E M A S T E R OF APPLIED SCIENCE in T H E F A C U L T Y OF G R A D U A T E STUDIES Department of Electrical and Computer Engineering We accept this thesis as conforming to the required standard T H E U N I V E R S I T Y O F B R I T I S H C O L U M B I A October 2002 © Jian Chen, 2002 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department The University of British Columbia Vancouver, Canada Date OcX. / o t i DE-6 (2/88) Abstract In this thesis, we analyze the behavior of real-time flows with hard time deadline using link layer retransmission to overcome wireless errors, and propose two new mecha-nisms to improve these flows' end-to-end Quality-of-Service (QoS) over a wireless network. Wireless channels have the characteristic that link quality varies according to propa-gation conditions. For real-time flows with hard time deadlines, link layer retransmissions over a wireless network, necessitated by fluctuations in link quality, may result in a high number of packets being dropped, as deadlines expire. The expired packets waste network resources and lead to long queuing delays for subsequent packets. After analyzing the char-acteristics of the Radio Link Control (RLC) layer of the General Packet Radio Service (GPRS)/Universal Mobile Telecommunication Services (UMTS) network, we have devel-oped a new set of mechanisms to minimize expiration packet drops in favour of overflow-like packet drops. We propose to use active queue management to limit transmission queue length, hence queuing delay, thus eliminating expiration packet drops. This allows the buffer and wireless bandwidth, otherwise be wasted by expiring packets, to be released ear-lier for other packets. We apply this mechanism to the radio link control layer in the GPRS/ U M T S wireless networks. The effectiveness of the proposed mechanism is verified by simu-lations. Furthermore, we extend the similar idea from the radio link control layer to the whole GPRS/UMTS domain. We adapt the Early Regulation of Unresponsive Flows (ERUF) to the characteristics of wireless channels employing link layer retransmissions. We propose to regulate those congested flows at the DiffServ domain ingress edge nodes of the GPRS/ ii U M T S core network, and drop undeliverable packets earlier to release some shared network resources for other flows. We also present simulation results to show that this new Wireless Early Regulation of Unresponsive Flows (WERUF) scheme can significantly improve the overall end-to-end quality-of-service. iii Table of Contents Abstract ii Table of Contents iv List of Tables vi List of Figures. vii Acknowledgments x Chapter 1 Introduction 1 1.1 Background Knowledge 3 1.1.1 General Architecture of G P R S Domain 3 1.1.2 Some Mechanisms for QoS 5 1.2 Motivation 6 1.3 Contributions 8 1.4 Outline of the Thesis 9 Chapter 2 Overview of Previous Work 10 2.1 End-to-end QoS in G P R S / U M T S Architecture 10 2.2 G P R S / U M T S Core Network Mechanisms for QoS 12 2.2.1 IETF QoS Architectures 12 2.2.2 Mapping between G P R S / U M T S and DiffServ 16 2.2.3 Active Queue Management 17 2.3 Mechanisms at Wireless Access Nodes for QoS 21 2.3.1 Radio L i nk Control (RLC) Layer in G P R S / U M T S 21 2.3.2 Other Mechanisms at Wireless Access Nodes 24 Chapter 3 Applying A Q M to Link Layer Buffers for Real-time Traffic 27 iv 3.1 Analysis of Real-time Flows over R L C with A M 28 3.2 Simulation Results 34 3.2.1 Two-slope Piece-wise Linear A Q M Function II 35 3.2.2 Comparison of Different A Q M Functions with Varying Traffic Load 40 3.2.3 A Q M Functions with Different Error Rates 45 3.3 Summary 48 Chapter 4 Applying W E R U F to R L C Queue 50 4.1 Early Regulation in G P R S / U M T S Network 50 4.2 Simulation Results 54 4.2.1 Real-time Flow and T C P Flows 55 4.2.1.1 One Real-time Flow in a Noisy L ink and Multiple T C P Flows in Clean Links with Equal R T T 55 4.2.1.2 One Real-time Flow in a Noisy L ink and Multiple T C P Flows in Clean Links with Different R T T 60 4.2.2 Real-time Flows Over Different L ink Conditions 62 4.2.2.1 Two Real-time Flows in Different Links 62 4.2.2.2 Multiple Real-time Flows in Different Wireless Links 67 4.3 Summary 68 Chapter 5 Conclusions and Future Work 70 Bibliography 73 Appendix A.List of Abbreviations and Acronyms 78 Appendix B. Simulation Models in O P N E T 8.1 82 List of Tables Table 3.1 Parameters for Simulation of A Q M in R L C layer. 35 Table 4.1 Common Parameters for Simulation of W E R U F . 55 Table B . 1 O P N E T modeling domains 82 vi List of Figures Figure 1.1 Overview of the G P R S packet domain architecture 3 Figure 2.1 Protocol stacks of the U M T S user plane 10 Figure 2.2 G P R S / U M T S QoS architecture 11 Figure 2.3 Mapping of the DiffServ domain and the G P R S / U M T S network 16 Figure 2.4 General algorithm for R E D gateways 19 Figure 2.5 R E D and Gentle-RED marking/dropping rate vs. average queue length 20 Figure 2.6 Acknowledged Mode in the Radio L ink Control layer (window size = 8) 22 Figure 3.1 Gaussian distribution with means MA and MB, variances a'A and a'B, where MA < M f i a n d o'A x 2 } = Q [—r-) (3.5) where Q(.) is the well-known Q function giving the tail area of a Gaussian distribution, as shown by the shaded area in Figure 3.1. In Figure 3.1, we draw the Gaussian probabi l i ty density Tj MA MB x 2 Figure 3.1 Gaussian distribution with means MA and MB, variances a ' A and a ' B , where MA < MB and ° ' A < ° ' B . functions for rc = A and n = B, where A < B. When the value of n is A, both the corresponding 31 Chapter 3 Applying AQM to Link Layer Buffers for Real-time Traffic mean and variance (M and o') are small, so the shaded area is also small. When n = B, its M and a' increase, thus the shaded area increases as well . This result matches the intuitive expectation earlier. Formula (3.5) is extremely important for the scheme we propose in this thesis. It is the nat-ural drop rate caused by expiration. Our active drop rate must not be higher than its value in a wide range of queue lengths, but must come as close to it as possible. F r o m (3.5) we can get the Gaussian curve I in Figure 3.2, which is the relationship O b s e r v e d q u e u e length Figure 3.2 Gaussian expiration drop rate and AQM approximations. between the expiration drop probability p and the observed queue length n. To obtain this result, we fix the long-term packet error rate at PB = 15%, and queue capacity to Lmax. To smooth the 32 Chapter 3 Applying AQM to Link Layer Buffers for Real-time Traffic changes in p, the observed queue length n here is actually made to be the calculated average queue size using a low-pass filter with an exponential weighted moving average wq [13]. Note that the above result is an approximation as the possible loss of some dedicated con-trol P D U s for status reports has been ignored. These losses introduce some extra queuing delay that causes the actual Gaussian curve I to be slight higher than shown in Figure 3.2. Because the dedicated control P D U s are much smaller than the data P D U s in length, for simplicity we ignore this additional delay. To actively drop packets with the exact probability obtained above is difficult to realize, due to computing complexity. Instead, we propose to use a simpler scheme similar to gentle-RED to approximate the expiration rate. In Figure 3.2 we present two piece-wise, linear A Q M func-tions, II and III, to approximate the Gaussian curve. We set these two approximation curves to be lower than the Gaussian one in most of the range of the observed queue length, so as to guarantee that the number of packets actively dropped is less than the natural expiration drops. Clearly, the better the approximation is, the better the performance should be. Therefore function III yields the best result as shown in the next section. However, in reality, it is hard to measure the Gaussian curve because of varying factors. We consider the two-slope, piece-wise, linear function II, defined in (3.6), to be the easiest to implement, as it can be uniquely fixed by Lmax and Lmin, while the other parameters are chosen somewhat arbitrarily. We can see that this simple scheme works wel l enough in controlling queue length and improving system perfor-mance. 33 Chapter 3 Applying AQM to Link Layer Buffers for Real-time Traffic max (n-L . ) P ,L . +L min max min. i f L • ^min + ^max (3.6) max 0 i f n < L min 3.2 Simulation Results The simplified system simulation model shown in Figure 3.3 has been built and exercised App R N C Server U D P / IP R L C UDP/ IP Internet & G P R S / U M T S core network Application UDP/ IP ~ - . . R L C Wireless Figure 3.3 Simulation model for AQM in the link layer buffer. in the O P N E T 8.1 environment. The R L C layer protocols shown above are carefully modeled. We simulate the behavior of l ink layer A R Q with poll ing functions and other conditions that can trigger the generation of status report, introduced in Section 2.3.1. For the status report, we modeled the L I S T Super-Fields (SUFI) [18] to carry the N A K and A C K information. The wireless channel model in O P N E T 8.1 takes into account multiple access interfer-ence, background noise and multipath fading, thus, it forms a more comprehensive error model 34 Chapter 3 Applying AQM to Link Layer Buffers for Real-time Traffic than the Gilbert-Elliott model [35] [36]. To focus the performance evaluations on the management of the downlink transmission queue, we make a simplifying assumption that the wireless uplink and the wireline Internet links are error-free. We assume that the arrivals of real-time packets fol-low the Poisson distribution, that packets have a constant length, and that the unit of queue length in the figures given below are the number of packets. Thus, the traffic load varies in direct propor-tion to the average inter-arrival time of real-time packets. 3.2.1 Two-slope Piece-wise Linear AQM Function II In this section, we take the two-slope piece-wise linear approximation A Q M function II as an illustrative example of the effects of the scheme. The parameters for the wireless link and real-time application are given in Table 3.1. Table 3.1 Parameters for Simulation of AQM in RLC layer. Parameters Value Effective wireless data rate R 1024 K bps Max. number of transmissions MaxDAT 5 40 packets (AMD PDUs) Lmax 210 packets (AMD PDUs) Maximum allowable end-to-end delay T 250ms Propagation delay in Internet & GPRS/UMTS core network 50ms Bandwidth of Internet link 2048 K bps Average long-term packet error rate PB 15% To get the values of the R E D parameters such as wq and maxp, we set them as suggested in [23], wq = 0.002 and maxp=0.1. Figure 3.4 ~ Figure 3.7 come from the two-slope, piece-wise, linear A Q M function II in (3.6) with 90% offered load. In this thesis, the term \"offered load\" refers to the ratio of the traffic 35 Chapter 3 Applying AQM to Link Layer Buffers for Real-time Traffic generating rate of a specific traffic source, and the effective service rate of the corresponding wireless link. From Figure 3.4 we can see the relationship between the wireless B E R , throughput, Wireless bit error rate 0. 0003 0. 0002 0. 0001 0. 0000 300 200 100 0 100 , 000 80 , 000 eo , 000 40 , 000 20 , 000 0 Average transmission queue length Receiving throughput (butes/sec) 100 200 Figure 3.4 BER, transmission queue length, and throughput without AQM. 300 time (sec) and the average transmission queue length (in packets) as functions of time, without A Q M . When the queue length becomes large with a high wireless bit error rate, the corresponding throughput drops sharply. We must note that wireless error does not directly cause packet loss because of the link layer A R Q recovery mechanism. However, the high wireless bit error rate causes frequent 36 **** Drops for exceeding MaxDAT (packets) Chapter 3 Applying AQM to Link Layer Buffers for Real-time Traffic retransmission at the link layer and large queuing delays, leading to expiration drops. Figure 3.5 Expiration drops (packets) 8 0 , 0 0 0 6 0 , 0 0 0 4 0 , 0 0 0 2 0 , 0 0 0 0 8 0 6 0 4 0 2 0 ! 0 2 0 , 0 0 0 1 5 , 0 0 0 1 0 , 0 0 0 5 , 0 0 0 0 •7* Overflow drops (packets) 3 0 0 time (sec) Figure 3.5 Different packet drops without AQM. shows this clearly. We can see that there are three types of packet drops when no A Q M is applied: expiration, transmission time exceeding maximum value (MaxDAT), and overflow. Among them, the expira-tion packet drops account for the largest share. When the queue length or wireless B E R increases, the number of all three types of packet drops increases as well , but expiration drops are the most 37 Chapter 3 Applying AQM to Link Layer Buffers for Real-time Traffic harmful to system performance, due to their large share of total packet drops. Figure 3.5 shows how serious the problem of expiration can become when the system load is high. App ly ing the A Q M scheme in (3.6) to constrain the queue length changes the situation dramatically. Figure 3.6 shows the relationship between the B E R , queue length (in packets), and 0.0003 0 .0002 0 . 0 0 0 1 0 .0000 200 100 .000 6 0 , 0 0 0 W i r e l e s s b i t e r r o r r a t e i . . . i . I. Average t r a n s m i s s i o n queue l e n g t h R e c e i v i n g t h r o u g h p u t ( b y t e s / s e c ) 300 t i m e ( s e c ) Figure 3.6 BER, transmission queue length, and throughput with AQM. throughput over time, wi th A Q M function II. We can see that the average queue length is 38 Chapter 3 Applying AQM to Link Layer Buffers for Real-time Traffic effectively controlled near the middle point of (Lmin + Lmax) I 2, and that the throughput curve does not fluctuate much on the time scale, which is a better situation than in Figure 3.4. The simulations also show that the maximum queue length increases with the B E R and traffic arrival rate, presented later. Figure 3.7 shows the reasons behind the improvements. We can see that one of the main Expiration drops (packets) 300 200 1 I _ _ 1 . B • . 1 ! 1 ! 1 3 0 , 0 0 0 100 50 0 l Active random, drops (packets) Drops for exceeding MaxDAT (packets) _mM . _ B B 4^ ^ da B *° B B B 100 200 300 time (sec) Figure 3.7 Different packet drops with AQM. 39 Chapter 3 Applying AQM to Link Layer Buffers for Real-time Traffic differences between Figure 3.5 and Figure 3.7 is that the types of packet drops have been changed. With A Q M , all overflow and most expiration drops are eliminated, replaced by a new kind of packet drop, active random drops. The figure shows that the amount of active random drops is much less than of expiration drops in Figure 3.5, but still dominates other types of packet drops. This demonstrates the effectiveness of A Q M in improving system throughput, without degrading the overall packet drop rate. Note that the latter condition is an important criteria in maintaining the quality of service for real-time applications, as mentioned above. 3.2.2 Comparison of Different AQM Functions with Varying Traffic Load In this section, we present the effect of the two A Q M functions II and III with different traffic loads. We fix al l other parameters as in Table 3.1, and vary the traffic load from 50% to 100%. The A Q M function III has been presented in Figure 3.2. We set the two turning points corresponding to the drop rate 0.1 and 0.9 in the A Q M function III as 100 and 180, with the parameters listed in Table 3.1. Figure 3.8 and Figure 3.9 show the throughput and end-to-end delay of these dropping functions. Under heavy traffic (traffic load exceeding 70%), the A Q M schemes yield great improve-ments to the overall throughput and end-to-end delay. A s expected, the A Q M function III gives better performance than function II, thanks to better approximation of the Gaussian curve. Figure 3.10 shows the moving averaged queue length of the above three dropping schemes with varying traffic loads. Here, the average queue length increases with the traffic load when the B E R is constant. 40 Chapter 3 Applying AQM to Link Layer Buffers for Real-time Traffic 0.5 0.S 0.7 0.8 0.9 1.0 T r a f f i c l o a d Figure 3.8 Throughput vs. traffic load with three schemes. 0 . 5 0 . 6 0 .7 0 .8 0 . 9 1.0 T r a f f i c l o a d Figure 3.9 End-to-end delay vs. traffic load with three schemes. Figure 3.11, Figure 3.12 and Figure 3.13 show different types of packet loss with varying 41 Chapter 3 Applying AQM to Link Layer Buffers for Real-time Traffic 200 0.5 0.6 0.7 0.8 0.9 1.0 Traffic load Figure 3.10 Average transmission queue length with three schemes. traffic loads, without A Q M , and with different types of A Q M functions (II and III). Owing to the rule that the active packet drops rate is less than the Gaussian drop rate, the total numbers of drops with A Q M functions II and III are much less than that without A Q M . It is apparent that when traffic is light, the queue length is not long enough to trigger the A Q M (see Figure 3.10), and the expiration packet drop rate is small enough not to damage system performance. Therefore the performances in both scenarios, with and without A Q M , are similar. With an increasing traffic load, both the expiration drop rate and queue length grow. When the traffic load exceeds 70%, the advantages of our proposed A Q M schemes become quite obvious. Under such conditions, without A Q M , the wireless link is saturated by an ever increasing number of retransmissions, resulting in the reduction of throughput as traffic load increases, as shown in Figure 3.8. A s the transmission queue length approaches Lmax, the expiration drop rate becomes 42 Chapter 3 Applying AQM to Link Layer Buffers for Real-time Traffic 140000 Traffic load Figure 3.11 Different packet drops vs. traffic load without AQM. the dominant (Figure 3.11) and most harmful factor to system performance, in both throughput and end-to-end delay. With A Q M , we can control queue length and limit it to a lower value, thus eliminating expiration packet drops. Since the A Q M releases resources that would otherwise be used ineffectively by expired packets, it is also able to mitigate throughput saturation, as shown in Figure 3.8, where the throughput curve for the A Q M continues to increase up to a traffic load of 100%. From these figures we can also see that the better the A Q M functions approximate the Gaussian curve, the better they can constrain queue length, and the better performance becomes. When we compare Figure 3.12 to Figure 3.13, we see that the A Q M function III does a better job of eliminating expiration packet drops, and it outperforms the A Q M function II. In Figure 3.12 43 Chapter 3 Applying AQM to Link Layer Buffers for Real-time Traffic Traffic load Figure 3.12 Different packet drops vs. traffic load with AQM function II. some expiration packet drops still remain, while in Figure 3.13, none do. This complete conver-sion from expiration drops to active random drops is critical to improving the end-to-end Quality-of-Service in this scenario. A s we can see from Figure 3.2, the A Q M function II can be uniquely fixed by Lmin and Lmax, while the A Q M function III lacks a middle point from which to determine the turning points, and requires more accurate information regarding the factors effecting the Gaussian curve for approximation. If we have all the necessary information to derive the Gaussian curve, we can easily determine an A Q M function with a good approximation. A n y A Q M function can be actually chosen to approximate the Gaussian curve, i f the computing capacity is powerful enough. 44 Chapter 3 Applying AQM to Link Layer Buffers for Real-time Traffic 45000 40000 35000 a o •J5 30000 a? o a n E 25000 20000 15000 10000 5000 Total packet drops Active random packet drops Expiration packet drops 0.4 0.5 0.6 0.7 0.8 0.9 1.1 Traffic load Figure 3.13 Different packet drops vs. traffic load with AQM function III. One of the parameters most difficult to determine, but deeply effecting the Gaussian drop rate, is the long-run wireless packet error rate PB. In the next section we show results using A Q M functions to approximate different Gaussian curves caused by different B E R s . 3.2.3 A Q M Functions with Different Error Rates Different wireless packet error rates in the long term (during transmission periods) lead to different Gaussian drop rates, as illustrated in Figure 3.14 below. The Gaussian curves vary with different long-term packet error rates in a trend where the larger PB moves the Gaussian curves closer to the Lmin. Understandably, the larger PB leads to 45 Chapter 3 Applying AQM to Link Layer Buffers for Real-time Traffic Observed queue length Figure 3.14 Packet drop rate with different long-run packet error rates. more retransmission and longer queue length, thus causing a higher probability of real-time packet expiration. The wisest way to choose an A Q M function is to sample the error rate for a short period at the beginning of the transmission, and use the sampled values to determine an initial Gaussian curve, and then derive the appropriate A Q M function to approximate it. Because such short periods may not generate reliable enough sample values, the Gaussian curve and the A Q M function may need to be further adjusted during the following transmission period. The basic rule, however, that the A Q M drop rate be not higher than the Gaussian drop rate in most queue length ranges, must be adhered to. Every A Q M function may perform wel l within a certain queue length range, even i f it does not accurately approach to the Gaussian curve. Be low we show the effect of the A Q M functions II and III in Figure 3.2 with different error rates. 46 Chapter 3 Applying AQM to Link Layer Buffers for Real-time Traffic Again, we use the parameters listed in Table 3.1. The packet error rate PB ranges from 5% to 35%. The traffic load is constant at 80%. The system performance of the A Q M functions and the n o n - A Q M (Gaussian) cases with a varying long-term packet error rate of PB is presented in Figure 3.15 and Figure 3.16. 0.4 Long-term packet error rate Figure 3.15 Throughput vs. long-term packet error rates PB. When the long-term packet error rate PB varies within a certain range (around 15%), both A Q M functions achieve very good performance. If the error rate grows to higher values, as in Figure 3.14, the Gaussian curve moves closer to Lmin. Here, the effect of the A Q M function II gradually weakens because the gap between the Gaussian curves widens, while the A Q M function III still performs well because it is closer to the Gaussian curves. When PB is small, the average queue length rarely grows to a large enough value, thus the A Q M rarely takes effect, and the results with and without A Q M are similar. This shows the importance of accurately measuring the 47 Chapter 3 Applying AQM to Link Layer Buffers for Real-time Traffic Long-term packet error rate Figure 3.16 End-to-end delay vs. long-term packet error rates PB. Gaussian curve and carefully choosing a good A Q M function. Also , in order to get the best A Q M function, the long-term packet error rate must be measured before the A Q M function is chosen, and further adjustments may be needed during the following transmission period. In this simulation scenario, the R E S E T function of the R L C layer in G P R S / U M T S is not considered. If PB grows to a high value, the wireless link is very likely to be reset by this function. 3.3 Summary In this chapter, we analyze the behavior of the 3GPP G P R S / U M T S R L C layer's transmis-sion queue in Acknowledged Mode, and derive a novel expression for the relationship between the average queue length and drop rate due to expiry of real-time flows with hard time deadlines. We prove that this drop rate can be approximated as a Gaussian curve on the average queue 48 Chapter 3 Applying AQM to Link Layer Buffers for Real-time Traffic length. Wi th this approximation, we propose to actively drop those packets that are l ike ly to expire, according to probability, following piece-wise, linear functions before they are enqueued. This action guarantees no QoS degradation by making the active drop rate to be lower than the Gaussian drop rate in every case, and on this basis, improves overall performance when traffic load or the wireless error rate is high. Through simulations, we prove the effectiveness of the mechanism. We also prove that the better the active drop rate curve is at approximating the Gaussian curve, the more the improve-ment. 49 Chapter 4 Applying WERUF to RLC Queue Chapter 4 Applying WERUF to RLC Queue We not only apply the idea of dropping real-time packets earlier at the wireless-wireline interface nodes, but also extend it to the whole G P R S / U M T S domain in this chapter. The reasons for and advantages to l imit ing unresponsive best-effort traffic have been proven in [15] [16]. The idea behind E R U F is that when unresponsive flows encounter network congestion at certain nodes over the end-to-end path, causing some packets to miss their delivery deadlines or overflow, dropping them at an earlier node frees shared network resources for other flows. We apply a similar idea in G P R S / U M T S wireless networks. In these networks, although the shaping functions at edge (both wireless and fixed sites) nodes limits the volume of unrespon-sive flows, the problem of these flows unnecessarily using up valuable network resources is exacerbated by the costliness of wireless bandwidth. Furthermore, propagation-induced variabil-ity of transmission quality over wireless links causes congestion at the wire-wireless interface nodes, that is, R N C s in U M T S networks. We adapt the E R U F scheme to address these problems. The new scheme is called Wireless Early Regulation of Unresponsive Flows ( W E R U F ) . Ref. [14] also proposes an algorithm to identify flows that are not TCP-friendly for regulating. In our case, however, there is no need for identification because the link layer transmission queue length is the best indicator of the flows to be regulated. 4.1 Early Regulation in GPRS/UMTS Network Figure 4.1 demonstrates the general idea of W E R U F . Leaky Bucket 1 (LB1) and Leaky Bucket ( L B 2 ) can be realized by the DiffServ traffic conditioners and the wireless channel resource management functions, respectively. When packets accumulate at R N C , due to R L C retransmission caused by wireless error, some signals are sent back to the DiffServ edge node, 50 Chapter 4 Applying WERUF to RLC Queue R N C Core routers G G S N Figure 4.1 The general concept behind Wireless Early Regulation of Unresponsive Flows (WERUF). namely, G G S N in the G P R S domain, to notify it to shrink the token rate of the corresponding traffic shaper (the token bucket L B 2 ) . With this operation, the shared network resources in the forwarding nodes (or core routers) such as S G S N s are released for other flows. Because the packets accumulating at R N C may expire, as presented in discussed last chapter, we intend to generate the congestion signal using a lower probability and drop these packets at the G G S N . In this way, we actually convert the expiration packet drops to overflow-like packet drops at the DiffServ ingress edge node, and at the same time, guarantee performance degradation w i l l not occur at the constrained flows. It is worth noting that the packets in R N C are the A M D P D U s , while the packets in G G S N are the IP packets. They are handled by different layers. Again , we need to analyze the behavior of the R L C layer retransmission queue length as stated in previous chapter. Equations (3.1) - (3.5) s t i l l apply, except the meaning of some variables are changed. In this chapter we are concerned with finding an algorithm that caters to the QoS requirement of the U M T S bearer service, and making improvements. Focusing on the G P R S / U M T S domain, we assume the latency caused by the Internet, 51 Chapter 4 Applying WERUF to RLC Queue external to the G P R S / U M T S domain, is a fixed value. The variable T i n (3.1) and (3.4) becomes the amount of transfer delay over the U M T S bearer service. According to [7], T < 250 ms. In the simulation we use 250 ms for simplicity. A s a result, Lmin and Lmax in these equations becomes the minimal and maximal transmission queue length that can cater to the transfer delay require-ment of the U M T S bearer service instead of the end-to-end delay requirement of real-time applications. We also assume that no P D U is dropped in the R L C layer due to expiry, so that the independence of each P D U ' s transmission time is st i l l maintained. Another change is that the meaning of tl in these equations becomes the latency caused by the G P R S / U M T S core network, excluding the Internet. The equations still hold true, despite the changes listed. Further, we use O P N E T 8.1 to model the DiffServ functional elements in the G P R S / U M T S routers, as shown in Figure 2.3. In G G S N , we build the Mult i - f ie ld classifier, the traffic shaper, the multiplexer and the dropper. In S G S N we build only a dropper. These droppers are R I O droppers handling I N and O U T packets. They strictly follow the I E T F document [21] to complete the Assured Forwarding (AF) architecture accurately. One of the base of this scheme is that every flow must be independent, except at the core routers. Therefore we assume the Complete Partitioning (CP) [37] policy is used at the G G S N for each flow shaped by the Leaky Bucket. A t R N C , i f no explicit traffic conditioner is implemented and the shaping relies on wireless resource management, some assumptions must be clarified. If these flows are multiplexed at the same transmission buffer, our scheme can either apply to cases where each flow goes through different R N C s , served by the same core network router, or the C P policy is also used at the R N C for every flow. In order to separate the flows, a Mult i-f ield Classi-fier resides at the R N C (Figure 2.3). Whether there are one or more R N C s in the simulation, the 52 Chapter 4 Applying WERUF to RLC Queue network topology figures below show only one R N C for simplicity. Source quenches employ Internet Control Message Protocol ( ICMP) [38] packets, sent to the G G S N to adjust the shaper's token rate for the flow (here we consider the per-flow shaping only). These I C M P packets are generated according to the A Q M functions described in last chapter. In the G G S N shapers, the token rate adjustment is made by multiplicative-decreases/ additive-increases. When an I C M P packet is received, and the token rate has not been cut within the current estimated end-to-end round trip time (RTT), the new token rate is calculated by new rate=min{guaranteed token rate, current token rate/2) (4-1) where the guaranteed data rate is pre-configured by the system [7]. This cut-by-RTT scheme protects the regulator from being rapidly driven down by a sequence of source quenches before the source has had a chance to respond to the congestion. The end-to-end R T T is difficult to measure for real-time traffic, due to the lack of return link acknowledgments. [14] proposes to use twice the propagation delay on the next link towards the congested router to approximate the RTT, but this proves unfeasible due to the extra delay caused by link layer retransmissions. With \"lossy\" wireless links, the queuing delay dominates the propagation delay. When this type of estimate is made at the DiffServ ingress edge node, G G S N , there is no easy way to predict the queuing delay at the egress edge node, l ike R N C . To resolve this problem, we consider that the 3GPP-defined maximum transfer delay in the U M T S bearer service, T, to be a reasonable estimate for real-time applications for several reasons. First, in most cases, the latency introduced by the external Internet is much less than the latency in the G P R S / U M T S domain with noisy wireless links. We can assume the delay in the G P R S / U M T S domain 53 Chapter 4 Applying WERUF to RLC Queue represents the total trip time. Second, i f the one trip delay in the G P R S / U M T S network for al l real-time applications is uniformly distributed, the mean value of this delay is l ikely to be located in the middle of [0, 250 ms]. Therefore using 250 ms to approximate the round trip delay is quite reasonable. The token rate recovery mechanism proposed in [16] consists of the bandwidth of the rate-reduced shaper associated with a flow being increased by one average packet size for every R T T estimate that passes, without the arrival of a source quench for the flow unti l the token rate recovers to the original value. This scheme recovers the token rate too slowly, however, and does not work well in wireless networks. For congestion caused by wireless errors, which normally has a much smaller time scale than congestion periods in wired networks, the edge-node token rate should preferably not stay at a low level for an extended period of time. Otherwise, i f the wireless link has recovered but the token rate has not, the throughput degrades unnecessarily. Our recovery rule is that after a token rate reduction, the token rate increases by one average packet size every time a new downlink packet arrives at the token bucket, until the token rate has recovered to the initial value. Our simulation results prove the effectiveness of this mechanism. A s mentioned before, we use a complete partitioning (CP) policy to manage the buffer of the G G S N for each token bucket. The buffer size is set to a small value so new packets start dropping earlier, after the token rate is reduced. 4.2 Simulation Results We use the same O P N E T 8.1 R L C models as in last chapter with the new models of GSNs . Some parameters commonly used in al l below simulations are listed in Table 4.1. A l l of the fol lowing simulation scenarios are with some background traffic in the core network. I C M P 54 Chapter 4 Applying WERUF to RLC Queue packets are generated by the R N C with probabilities determined by the A Q M function II. The Table 4.1 Common Parameters for Simulation of WERUF. Parameters Value Max. number of transmissions MaxDAT 3 Packet error rate in noisy link 15% Packet error rate in clean link 0.2% Lmin 30 packets (AMD PDUs) Lmax 180 packets (AMD PDUs) Propagation delay in the GPRS/UMTS core network 1.0 (ms) Wireless effective service data rate for each real-time flow 1 M bps Wireless effective service data rate for each TCP flow 384 K bps token rate adjustment and R T T estimate methods at the G G S N , upon receiving an I C M P packet, are exactly the same as described in section 4.1. The term \"offered load\" in scenarios given below refers to the offered load for every real-time flow, for example, a 90% offered load means every real-time flow has a 90% offered traffic load, no matter what its wireless link condition. We also let the wireless links be independent of each other. The wireless errors are caused by a separated interference source. 4.2.1 Real-time Flow and TCP Flows Real-time flows and T C P flows normally have different t iming requirements. In the following two scenarios, we apply the W E R U F to real-time flows only because T C P flows do not have any expired packet drops. 4.2.1.1 One Real-time Flow in a Noisy Link and Multiple TCP Flows in Clean Links with Equal RTT In this scenario, M l , M 2 , M 3 and M 4 are downloading traffic from H I , H2 , H3 and H4, 55 Chapter 4 Applying WERUF to RLC Queue respectively (Figure 4.2). The M l - H l pair is transferring real-time traffic, experiencing high i G P R S / U M T S domain j Internet Figure 4.2 Network topology of one real-time plus three TCP flows. B E R in the wireless link, and has a maximal token rate of up to 1 M bps, as well as a 256 K bps guaranteed rate. The other pairs are running long-lived F T P applications, and have maximal token rates of up to 384 K bps with clean wireless links, but do not have guaranteed rates. The total core network bandwidth is limited to 1 M bps. The external Internet introduces a 30 ms delay to al l hosts. We set the real-time packets with a higher priority over T C P packets in the core network to cater to their different delay requirements. In this network topology, the S G S N acts as the core router in the DiffServ domain. Figures 4.3 and 4.4 show that after applying W E R U F , the throughput of the real-time flow in the noisy wireless l ink improves significantly, when its offered load increases of more than 60%. Further, the end-to-end transfer delay reduces to less than 65%. Figure 4.5 shows the improvement to T C P flows. When the real-time flow's offered load is higher than 60%, the improvement for T C P flows also increases significantly, due to the network resources in the core network being released by the real-time flow. 56 Chapter 4 Applying WERUF to RLC Queue 5 5 0 0 0 J O CO 5 0 0 0 0 h CD Z 3 &r 4 5 0 0 0 h Z 3 O o cu CO cu on - © - W i t h o u t W E R U F - + - • W i t h W E R U F ,4- -. 4 0 0 0 0 1 -3 5 0 0 0 3 0 0 0 0 0 . 5 0 . 6 0 . 7 Offered load Figure 4.3 Real-time flow throughput vs. offered load. 1 1 0 W i t h W E R U F - © - W i t h o u t W E R U F -e~ 0 . 8 0 . 9 4 0 0 . 5 0 . 6 0 . 7 Offered load 0 . 8 0 . 9 Figure 4.4 End-to-end delay of the real-time flow vs. offered load. We can see that improvements only occur when the real-time traffic flow is heavy. In this case, the noisy wireless l ink is close to saturation and many real-time P D U s accumulate in the 57 Chapter 4 Applying WERUF to RLC Queue 3 0 0 0 0 0 \" 1 • 1 X 0 . 5 0 . 6 0 . 7 0 . 8 0 . 9 Offered load of the real-time flow Figure 4.5 Total TCP throughput vs. real-time traffic offered load R N C , the average transmission queue length is long enough to trigger the W E R U F . On the other hand, there is much competition for the shared bandwidth/buffer in the core network, thus the saved resources due to W E R U F can make a positive impact on overall performance. Next, we investigate what causes to the improvements. In our simulation, the real-time packet drops due to transmission time exceeding M a x D A T are almost equal in both cases, so they are not presented in the figures. Figure 4.6 shows that without W E R U F , most packets are dropped due to expiration, but with W E R U F applied, packets drop due to overflow in the G G S N only, while none drop due to expiry (Figure 4.7). More importantly, the total packet drops in Figure 4.7 is less than the total packet drops in Figure 4.6. This results from our strategy of making the active packet drop rate (actually the I C M P packets', generating rates fol lowing the A Q M function II here) less than the natural packet drop rate. The fewer expired drops results in more efficient resource utilization, discussed in Chapter 3. 58 Chapter 4 Applying WERUF to RLC Queue 35000 30000 CO § \" 2 5 0 0 0 ~CD - g 20000 CO Q_ M — ° 1 5 0 0 0 CD 10000 50001-| Total packets d rops D Expired packet d rops J Over f low packet d rops at RNC 0.5 0.6 0.7 Of fe red load 0.8 0.9 Figure 4.6 Real-time flow packet drops vs. offered load without WERUF. 25000 20000 CO Q . O \" O \"CD 15000 O CO Q_ o » - 10000 CD 5000 h • • T o t a l p a c k e t d r o p s [_ I O v e r f l o w p a c k e t d r o p s a t G G S N E x p i r e d p a c k e t d r o p s 0.5 11 0.6 0.7 Of fe red load 0.8 0.9 Figure 4.7 Real-time flow packet drops vs. offered load with WERUF. This effect is similar to the packet drops described in the last chapter, except we also 59 Chapter 4 Applying WERUF to RLC Queue improve the performance of low priority T C P flows by early regulation. The undeliverable packets are dropped at the G G S N because of the overflow caused by a smaller token rate and l imited buffer space. This mechanism has a drop-tail effect, thus, it is l ike ly to drop packets continuously, contrary to the A Q M schemes discussed in Chapter 3. Different real-time applica-tions may favour different dropping disciplines. This topic would be interesting for future research. 4.2.1.2 One Real-time Flow in a Noisy Link and Multiple T C P Flows in Clean Links with Different R T T In this scenario, the Internet latency of the real-time flow ( M l - H l pair) is 30 ms, while the latencies from H2, H3, . . . , H6 to G G S N are 5 ms, 20 ms, 80 ms, 160 ms and 250 ms, respec-tively (Figure 4.8). We set the real-time traffic load to be constant at 80%. Again , the real-time Figure 4.8 Network topology of one real-time and five TCP flows with different Internet latencies. flow has a higher priority in the core network, but suffers from a noisy wireless link, while T C P flows with lower priority in the core network are sent over clean wireless links. Their token rates 60 Chapter 4 Applying WERUF to RLC Queue in G G S N and wireless data rates in R N C are the same as in section 4.2.1.1. The total core network bandwidth is 2 M bps. The simulations yield throughputs of 43357 bytes/s and 51719 bytes/s for the real-time flow without W E R U F and with W E R U F , respectively - an improvement of better than 19% for W E R U F . The total packet drop rate with W E R U F is also less than the packet expiry rate without W E R U F . T C P throughput with different Internet latencies are shown in Figure 4.9. Here the Q | 1 1 , 1 1 1 0 50 100 150 200 250 Internet latency (ms) Figure 4.9 TCP throughput per flow vs. Internet latency. improvement favors T C P flows with smaller Internet latency. Because T C P uses A C K s to \"clock out\" new segments [8], flows with shorter RTTs occupy more core network bandwidth given up by the real-time flow under W E R U F . 61 Chapter 4 Applying WERUF to RLC Queue 4.2.2 Real-time Flows Over Different Link Conditions These scenarios show cases when multiple real-time flows compete for l imited core network bandwidth. W E R U F is applied to their transmission queues at R N C s . 4.2.2.1 Two Real-time Flows in Different Links In this scenario, only two real-time flows are investigated (Figure 4.10). These two real-Internet Figure 4.10 Network topology of two real-time flows. time flows both get a 1 M bps token rate in the G G S N , and the same Internet latency of 30 ms. The flow in the noisy link gets a higher priority than the one in the clean link, that is, their packets in the core network are handled by different drop precedences. The purpose of this setting is to more easily show the effects of W E R U F . When these two flows have the same drop precedence when competing for core network resources, most packets are dropped by the RIO dropper in the core network, while the surviving packets arriving the R N C are not dense enough to accumulate a long enough queue to trigger the W E R U F . The Internet latency is 30ms for both flows. The other parameters are the same as in Table 62 Chapter 4 Applying WERUF to RLC Queue 4.1. From Figures 4.11 to 4.14, we can see that W E R U F not only significantly improves the low drop precedence real-time flow performance in the noisy link, but also the performance of the high drop precedence flow in the clean link, under heavy load condition. F r o m Figures 4.11 and 4.13, we can see that without W E R U F , when the traffic load 5 2 0 0 0 \"575 C L =3 o 50000Y 4 8 0 0 0 1 -4 6 0 0 0 4 4 0 0 0 h 4 2 0 0 0 4 0 0 0 0 3 8 0 0 0 3 6 0 0 0 3 4 0 0 - € > - W i t h o u t W E R U F - + - W i t h W E R U F 3 2 0 0 0 0 . 5 0 . 6 0 . 7 Offered load 0 . 8 0 . 9 Figure 4.11 Throughput of the low drop precedence real-time flow in a noisy link vs. offered load. becomes heavy, the low drop precedence flow in a noisy l ink approaches to saturation, that is, from 70% to 90%, the throughput hardly increases, while the end-to-end delay grows. But with W E R U F , its throughput continues to increase with the offered load, and the incremental end-to-end delay is less than that without W E R U F . Without W E R U F , the performance of the high drop precedence f low in a clean l ink 63 Chapter 4 Applying WERUF to RLC Queue 5 4 0 0 0 • H 5 2 O 0 O - X \"~ ~—— / \\ — — - . 4 . ~ — 5 0 0 0 0 - -:es/s) :es/s) 4 8 0 0 0 - S ->-» - O -*—' —s 4 6 0 0 0 roughpi 4 4 0 0 0 • -4 2 0 0 0 - O - W i t h o u t W E R U F 4 0 0 0 0 — 1 — W i t h W E R U F \\ -3 8 0 0 0 0 . 5 O . S 0 . 7 0 . 8 0 . 9 Offered load Figure 4.12 Throughput of the high drop precedence real-time flow in a clean link vs. offered load. 2 01 , , , 1 0.5 0.6 0.7 0.8 0.9 Offered load Figure 4.13 End-to-end delay of the low drop precedence real-time flow in a noisy link vs. offered load. degrades when the total traffic load increases (Figure 4.12 and Figure 4.14), because packets of 64 Chapter 4 Applying WERUF to RLC Queue 2 0 ' • • • 1 0.5 0.6 0.7 0.8 0.9 Offered load Figure 4.14 End-to-end delay of the high drop precedence real-time flow in a clean link vs. offered load. the low drop precedence flow uses up shared resources of the core network. M a n y of these packets are actually undeliverable due to expiration, thus network resources are wasted. Wi th W E R U F , these undeliverable packets are dropped earlier at G G S N , network resources are utilized more efficiently, and the performance of this high drop precedence flow significantly improves. Figure 4.15 and Figure 4.16 show the packet drops of the low drop precedence flow in the noisy l ink. The total number of packet drops in these two figures does not include those drops made due to transmission times exceeding M a x D A T , which are roughly equal in both cases for the same PB and offered load. Obviously, W E R U F converts the expired packet drops and overflow at the R N C to the overflow packet drops at the G G S N . Earlier packet drops prevent undeliverable packets from 65 Chapter 4 Applying WERUF to RLC Queue 8 0 0 0 0 7 0 0 0 0 «/) O \" 6 0 0 0 0 T3 a> 5 0 0 0 0 o ns ° ~ 4 0 0 0 0 _g 3 0 0 0 0 2 0 0 0 0 1 0 0 0 0 • • T o t a l p a c k e t d r o p s I I E x p i r e d p a c k e t d r o p s I I O v e r f l o w p a c k e t d r o p s a t R N C 0 . 5 0 . 6 0 . 7 Offered load 0 . 8 0 . 9 Figure 4.15 Packet drops of the low drop precedence real-time flow in a noisy link vs. offered load without WERUF. Q . g \"3 o ro CL. J O 4 5 0 0 0 4 0 0 0 0 3 5 0 0 0 3 0 0 0 0 2 5 0 0 0 2 0 0 0 0 -1 5 0 0 0 -1 0 0 0 0 -5 0 0 0 -o T o t a l p a c k e t d r o p s E x p i r e d p a c k e t d r o p s O v e r f l o w p a c k e t d r o p s a t G G S N 0 . 5 0 . 6 0 . 7 Offered load 0 . 8 0 . 9 Figure 4.16 Packet drops of the low drop precedence real-time flow in a noisy link vs. offered load with WERUF. occupying the limited core network resources, and improve overall performance. Another 66 Chapter 4 Applying WERUF to RLC Queue phenomenon worth mentioning is that for this flow (with low drop precedence, and over the noisy l ink) the total packet drops in W E R U F are less than without W E R U F , which guarantees no observable QoS loss due to the introduction of W E R U F from end users' viewpoint. 4.2.2.2 Multiple Real-time Flows in Different Wireless Links The network topology of this scenario is illustrated in Figure 4.17 below. Five real-time Figure 4.17 Network topology of multiple real-time flows in noisy and clean links. flows come from five Internet sources, with the same Internet latency of 30 ms. The maximum token rate of each of them is 1 M bps, and the guaranteed token rate is 256K bps. The other parameters are listed in Table 4.1. We set the offered load of these flows to 90%, and vary the number of noisy links from 1 to 4. A s in the last scenario, in the core network we assign a low drop precedence to those flows experiencing noisy wireless links, and assign a high drop precedence to flows experiencing clean l i nks . The total throughput of f lows in noisy and clean l inks is measured. The result is demonstrated in Figure 4.18. 67 Chapter 4 Applying WERUF to RLC Queue o' ' ' • 1 2 3 4 Number of noisy l inks Figure 4.18 Total throughput in noisy and clean links vs. number of noisy links In this f igure , the number o f n o i s y l i n k s increases f r o m left to r i gh t (1 to 4). C l e a r l y , W E R U F improves the performance not on ly i n noisy l inks but also i n c lean ones. T h e t r end o f the i m p r o v e m e n t is i n t e r e s t i ng . T h e i m p r o v e m e n t o f c l e a n l i n k f l o w s gradual ly shrinks w i t h the growth o f the number o f noisy l i nks . A s the number o f f lows over c lean l i n k s decreases, the traffic l oad on these f lows becomes too l igh t to f u l l y u t i l i z e core ne twork resources made avai lable by constrained f lows over noisy l i nks . O n the other hand, the improve -ment o f n o i s y l i n k f l o w s g r o w s w i t h the number o f n o i s y l i n k s , because ea r ly r e g u l a t i o n is t r iggered i n more and more f l o w s ' t ransmiss ion queues, s h o w i n g a greater and greater pos i t ive effect. 4.3 Summary In this chapter, we extend the idea o f active packet drops discussed i n the last chapter f rom 68 Chapter 4 Applying WERUF to RLC Queue the wireless-wireline interface nodes to the whole G P R S / U M T S domain by Wireless Ear ly Regulation of Unresponsive Flows ( W E R U F ) . We utilize DiffServ functional elements at the edge nodes to restrict real-time flows with hard time deadlines, so as to release shared core network resources to other flows. These specific real-time flows are identified by the transmission queue length at the wireless-wireline interface nodes with the probability generated by the piece-wise linear A Q M functions. We also propose some important rules for the rate adjustment at the edge nodes to make the scheme work efficiently. In this way the congestion caused by wireless error at the interface nodes can be propagated to the edge nodes. Simulations are excised in a wide spectrum of scenarios. The results prove the W E R U F can not only significantly improve the QoS of congested real-time flows experiencing high traffic load and wireless error rates, but can also improve the QoS of other flows when shared core network resources are scarce. 69 Chapter 5 Conclusions and Future Work Chapter 5 Conclusions and Future Work In this thesis, we have analyzed the relationship between the real-time expired packet drop rate and the transmission queue length, when link layer retransmissions are used to recover packet losses over the wireless link. We propose a novel active queue management method for the trans-mission queue that actively drops potentially expiring packets before they are enqueued, in order to free network resources and control transmission queue lengths effectively. Using the RLC layer, defined in UMTS as specified by the 3GPP as an example, we have presented simulation results to show that the proposed AQM method is effective in reducing queuing delays and elimi-nating expiration packet drops. This improves overall system performance by increasing through-put and reducing end-to-end delay. Analysis and simulations have been made to prove the effectiveness of this scheme. In a similar vein, we extend source quenches to DiffServ ingress edge nodes, namely the Gateway GPRS Support Node (GGSN) in the GPRS/UMTS domain, to prevent undeliverable packets from consuming shared network resources in core networks, thus utilizing network resources more efficiently and improving overall system performance. We have proposed a new mechanism, Wireless Early Regulation of Unresponsive Flows (WERUF) in this thesis, which converts the undeliverable expired packet drops to overflow packet drops that can be applied to packet ingress nodes, freeing the network resources for other flows. This mechanism is a variation 70 Chapter 5 Conclusions and Future Work of active queue management. Simulation results have been presented to prove that this scheme can efficiently improve the end-to-end QoS of G P R S / U M T S networks. It achieves its highest efficiency when the real-time traffic load is heavy and the core network resources are limited. We, believe the mechanism can be applied to any wireless communication systems with l ink layer retransmissions and shaped traffic. The drawbacks of these mechanisms include needed extra computing power and uplink bandwidth for the source quenches. We minimize computing complexity by choosing simple active queue management functions at the wireless-wireline interface node, and using a simple token rate operation at the edge nodes. However, the impact of such actions on computing power is still unknown and needs further investigation. We believe the extra uplink bandwidth consumed by the source quenches has a minor effect, because in G P R S / U M T S , the uplink traffic is normally slight, thus the core network bandwidth of the uplink is redundant in most cases. The source quenches can also be treated as flow control signals, therefore, it is possible to implement them in control plane protocols, which can be guaranteed by the system [12]. While this paper does not distinguished between different types of packets that may exist in real-time flows, there may be a need for differentiation to be made, because in many real-time applications, some types of packets carry more important information than others. In these cases, Random Ear ly Detection with IN-and-OUT (RIO) [25] can be used as a basis for our A Q M 71 Chapter 5 Conclusions and Future Work method, at wireless-wireline interface nodes, to implement differentiation. Furthermore, the best way to apply A Q M effectively to a mix of different real-time applications remains to be investi-gated. A s we mentioned in the previous chapter, the W E R U F drops packets in a drop-tail mode. The side-effects of this dropping discipline to some real-time applications may need further analysis. A g a i n , for those packets carrying more important information than others, some effective mechanisms wi l l be developed to avoid inappropriate drops. Another question that may prove interesting to research is what impact the resource management schemes have on our proposed mechanism. 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List of Abbreviations and Acronyms Appendix A. List of Abbreviations and Acronyms 3GPP: 3rd Generation Partnership Project ACK: ACKnowledge AF: Assured Forwarding AM: Acknowledge Mode AMD: Acknowledged Mode Data AQM: Active Queue Management ARQ: Automatic Repeat Request BA: Behavior Aggregates BER. Bit Error Rate BSS: Base Station System CAC: Call Admission Control CN: Core Network CP: Complete Partitioning CS: Complete Sharing DiffServ: Differentiated Services 78 Appendix A. List of Abbreviations and Acronyms DSCP: DiffServ CodePoint EDD: Earlest-Due-Date E L F : Effort-Limited Fair EPC: Estimated PDU Counter E R U F : Early Regulation of Unresponsive Flows F E C : Forward Error Correction GGSN: Gateway GPRS Support Node GPRS: General Packet Radio Service G S M : Global System for Mobile ICMP: Internet Control Message Protocol ISP: Internet Service Provider IP : Internet Protocol IETF: Internet Engineering Task Force IWFQ: Idealized Wireless Fair-Queuing IntServ: Integrated Services LB: Leaky Bucket 79 Appendix A. List of Abbreviations and Acronyms M A C : Media Access Control M F : Multi-Field MS: Mobile Station M T : Mobile Terminal N A K : Negative Acknowledge PDN: Packet Data Network. PDP: Packet Data Protocol PDU: Protocol Data Unit PHB: Per-Hop Behavior P L M N : Public Land Mobile Network QoS: Quality of Service RED: Random Early Detection RIO: Random Early Detection with IN-and-OUT R L C : Radio Link Control RNC: Radio Network Controller SGSN: Serving GPRS Support Node 80 Appendix A. List of Abbreviations and Acronyms SLA: Service Level Agreement SUFI: Super-Fields TCP: Transmission Control Protocol TE: Terminal Equipment UMTS: Universal Mobile Telecommunications System UTRAN: UMTS Terrestrial Radio Access Network. WERUF: Wireless Early Regulation of Unresponsive Flows WFQ: Weighted Fair Queuing 81 Appendix B. Simulation models in OPNET 8.1 Appendix B. Simulation models in OPNET 8.1 O P N E T provides a comprehensive development environment supporting the modeling of communication networks and distributed systems. It is a discrete-event driven simulator. There are three modeling domains (Table B . l ) in O P N E T : Network, Node and Process modeling environments. These three modeling domains span all the hierarchical levels of a model. Table B.l OPNET modeling domains. Domain Editor Modeling Focus Network Project Network topo logy descr ibed in te rms of subne tworks , nodes, l inks, and geographica l context . Node Node Node internal archi tecture descr ibed in te rms of funct ional e l -ements and data f low be tween t h e m . Process Process Behavior of processes (protocols, a lgor i thms, appl icat ions) , speci f ied using finite state mach ines and ex tended high- level language. The topology of a system consists of both an inventory of its devices and the communica-tions links between them. The network domain refers to these devices as nodes, and has several types of links that can be defined to connect them together for the purpose of sending information between them. Groups of nodes and links can be used to form subnetworks, and subnetworks can in turn contain lower-level subnetworks to form unlimited hierarchies. Some system topologies can be entirely represented in the node domain by interconnecting the node level building blocks, called modules. In most cases the network domain is used to represent high-level system topology because of its direct support for unlimited depth hierarchies, sophisticated communication links, and measured physical layout. 82 "@en ; edm:hasType "Thesis/Dissertation"@en ; vivo:dateIssued "2002-11"@en ; edm:isShownAt "10.14288/1.0065262"@en ; dcterms:language "eng"@en ; ns0:degreeDiscipline "Electrical and Computer Engineering"@en ; edm:provider "Vancouver : University of British Columbia Library"@en ; dcterms:publisher "University of British Columbia"@en ; dcterms:rights "For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use."@en ; ns0:scholarLevel "Graduate"@en ; dcterms:title "Active queue management techniques to improve Quality of Service for real-time flows in third generation wireless networks"@en ; dcterms:type "Text"@en ; ns0:identifierURI "http://hdl.handle.net/2429/12416"@en .