Providing Multiple Service Classes for Bursty Data Tra c in ... - CiteSeerX

Providing Multiple Service Classes for Bursty Data Trac in Cellular Networks Zhimei Jiang

Li Fung Chang

N. K. Shankaranarayanan

AT&T Labs - Research Red Bank, NJ 07701 fjiang,lifung,[email protected]

Abstract { This paper evaluates the use of packet scheduling to provide multiple service classes for bursty data trac. In particular, we study the performance of the weighted fair queueing algorithm by simulating various scenarios in a packet cellular wireless network similar to an EDGE system. The results show that, due to the burstiness of data trac, traditional work-conserving packet scheduling algorithms such as weighted fair queueing can not achieve the desired performance unless the system is heavily loaded. Similar results hold even when using priority queueing, which is an extreme form of weighted fair queueing. We also nd that best eort trac should be managed carefully to avoid inecient usage of the network, especially in a low capacity cellular environment.

1 Introduction As the popularity of wireless networks grows at an unprecedented rate, in addition to the traditional voice services, wireless service providers and equipment vendors are working closely on providing higher speed data access over cellular wireless networks. For example, Enhanced Data rate for GSM Evolution (EDGE) oers a shared average data rate of about 300kbps. Although this rate is considerably higher than what is currently available over cellular networks (19.2kbps in CDPD, for example), it is still much lower than the capacity of the rest of the Internet and the wireless link will continue to be the bottleneck of the entire system. Because of the scarcity of wireless channel capacity, the potentially large user population, and the burstiness of data trac, aggressive admission control will likely be employed to fully utilize the wireless link. As a result, long delays might occur in the system. Instead of having all the users suer from the same level of delays, from the service providers' as well as the customers' point of view, it might be more desirable to oer preferential treatment to those who are willing to pay more for their services. This implies that the network will need to be able to provide multiple

data service classes. The need of having multiple service classes applies to both multimedia trac as well as bursty data trac. The focus of this paper is on the latter case. In fact, as we will show throughout the paper, it is the burstiness of the data trac that presents new challenges to the packet scheduling problem.

1.1 Related work Researchers have been studying the problem of providing per-connection guaranteed quality of service in packet switching networks for many years. In general, call admission control is used to determine whether a service request should be accepted. Once a request is accepted, the network tries to guarantee the speci ed QoS requirement in the request through packet scheduling, as long as the trac follows the speci cations in the request. The packet scheduling algorithm allocates each connection its fair share of the channel either in a work-conserving or non-work-conserving way [11, 15]. Almost all the existing packet scheduling algorithms are proved to be eective based on the assumption that the data streams last a long period of time and have stringent delay requirements, which is typical of video/audio trac. In this paper, we investigate how these algorithms perform when applied to bursty data trac. As far as we know, there has been no previous work speci cally addressing the problem of providing dierent service classes for bursty data trac. In fact, little is known on the exact meaning of having multiple bursty data service classes and what would be a reasonable goal to achieve with such services. We hope to shed some light on these critical questions through the discussions in this paper. In [2], the authors investigate how the adaptation capability of \elastic" multimedia applications diminishes the dierences between user perceived performance in the cases with/without reservation or prioritization. Our study focuses on the performance of networks that support data applications running on top of TCP/IP. The presence of TCP/IP has a profound impact on the out-

come of packet scheduling for bursty data trac. The rest of this paper is organized as follows. In the next section, we describe the system structure and the user workload model used in our study. In section 3, using the weighted fair queueing algorithm (WFQ) as an example, we study in detail whether packet scheduling can eectively provide multiple service classes for bursty data trac and analyze some of the parameters that might affect its performance. Section 4 brie y discusses several other issues related to packet scheduling in cellular networks. And we conclude in section 5.

2 Simulation Setup Fig. 1 illustrates a general network architecture, where mobile users communicate with servers elsewhere through a wireless access network. Modules that are implemented in our simulator are shown in Fig. 2. The simulation program is written in PARSEC, a C-based simulation language developed at UCLA [17]. In our simulator, les are delivered from servers to mobile users through the base-station using the TCP protocol. Section 2.1 will discuss how the trac is generated in detail. The TCP portion of the simulator is ported directly from the FreeBSD kernel. All the key elements of TCP, such as slow start, congestion control, retransmission, etc., are included. For simplicity, in our simulator, we characterize the backbone network connecting the servers and the wireless access network only by the propagation delay incurred in the path. The default value of the propagation delay is assumed to be 40msec. Within the access network, the delay is comprised of the queueing delay at the base-station and over the air delay (see next paragraph). The packet scheduling algorithms discussed in this paper schedule downstream packets to users within the coverage area of a single base-station (BS). We assume the majority of the trac is from servers outside the cellular access network to the mobile users, and the sole congestion point of the system is the downstream radio link. The amount of uplink trac, such as TCP acknowledgments, is assumed to be negligible and to have no queueing delay. The link layer assumptions in our simulation follow the speci cations for EDGE [9, 16]. Without going into greater detail, one can assume that there is one time frame every 20msec, which is divided into 8 time-slots. Each user is allocated its own queue at the base-station. The packet scheduling algorithm goes around and assigns time-slots to the non-empty queues based on the corresponding user's service class. A user is allowed to occupy one or multiple time-slots in one time frame. EDGE also de nes eight dierent combinations of modulation and coding schemes, which have dierent rates and robustness. A link adaption algorithm tries to select the most

Air Interface

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Figure 1: A general network architecture.

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Figure 2: Network structure implemented in the simulation eective combination based on current channel conditions. For this study, we x the modulation and coding scheme to MCS-7, the one with the second highest rate. It allows 112bytes per time slot (excluding header), which results in a peak data rate of 44.8Kbytes/sec (358kbps) [9]. In addition to using a xed modulation and coding scheme, we also assume there is no packet error. Channel error is one of the most important issues that any wireless packet scheduling algorithm must deal with very carefully. There have been some active research work in this area recently [5, 6, 7, 12, 14]. However, the main purpose of this study is to present the fundamental challenges in providing multiple bursty data service classes over a low capacity link. We avoid the complexities introduced by the instability of wireless channels by assuming that packets are error free. Future studies will address the impact of channel errors on the performance of scheduling

algorithms.

the total time. As the number of users in the system goes up, it takes longer to deliver les, thus the ON periods are extended, and the average data throughput of individual users decreases.

2.1 User workload model page request

...

OFF ( think )

ON

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ON

...

multiple files/page

Figure 3: User workload model. The workload model shown in Fig. 3 is used to generate trac for each individual user in our system. The model was derived from studies on WWW trac [3, 10], but may also be applied to most of the other popular Internet applications, such as email, ftp, etc. In a typical web access environment, upon a user request for a new page, one or more les are transmitted back to the user, which corresponds to an ON period of the model. Once the burst of les has nished, there is a period of think (OFF) time during which the user studies the information just downloaded thus generating no trac. A new ON period is started when the user requests a new page. The workload model generates trac by mimicking the web user access behavior described above, namely, requests a set of les, stops for a while, and then requests for another set of les, and so on. It is completely characterized by the distribution of the requested le sizes, the distribution of the number of les (embedded references) delivered in the ON periods, and the distribution of the OFF periods. Table 1 details the distributions and parameters used in the model for this study. The max parameter is the maximum value allowed for the corresponding variable in our simulations. The distributions and parameters listed in table 1 are based on studies at Boston University [4], with some modi cations on the maximum value of the parameters to represent wireless data trac. In particular, due to the low data rate in cellular networks, we expect mobile users to be more cautious about downloading very long les. So we set the maximum le size to be 100Kbytes, and the maximum number of les in one ON period to be 30. In addition, the device power consumption is typically a major concern to mobile users. It is therefore unlikely that a user will stay online for a long time without sending any data, so we set the maximum OFF period to be 15 minutes. For the workload model given in table 1, the average le size is 8.2Kbytes and the median is 3Kbytes. With a single user in the system, the average data throughput of the user is 20kbps, which is about 5% of the total link capacity, and the ON periods account for about 14% of

3 Packet Scheduling for Multiple Data Service Classes Data trac is known to be bursty. For instance, with the workload model described above, the user is in the ON period, i.e. in the middle of a page transmission, only about 14% of the time. Even though the per user average data throughput is merely 20kbps, during the ON periods, the rates can frequently reach 300kbps or well above that had the link capacity been higher. With multiple data service classes, a service class can either be characterized by (1) a nominal average rate over some period of time, which is the typical approach for multimedia trac, or by (2) its relative performance to other classes. Given the highly bursty and unpredictable nature of the data trac, the rst option may require conservative call admission control, which is undesirable for the precious wireless link. Moreover, it may prevent sources from delivering data faster than its assigned rate even when there is capacity available, and thus increase the likelihood of congestion in the future. Based on these considerations, we choose to explore the second option in this paper. Namely, we would like to provide better service for higher data service classes, but there is no absolute rate or any other quality guarantees for each class. Most of the work-conserving scheduling algorithms can be considered for this task. Let's start with the weighted fair queueing algorithm.

3.1 Weighted Fair Queueing The weighted fair queueing scheduling algorithm (WFQ) can easily be adopted to provide multiple data service classes by assigning each trac source a weight determined by its class. The weight controls the amount of trac a source may deliver relative to other active sources during some period of time. From the scheduling algorithm's point of view, a source is considered to be active if it has data queued at the base-station. For two users with weights w0 and w1 respectively, if both of them are active continuously for a relatively long period of time, then the ratio of their average data transmission rates should be w0/w1. This rate ratio indicates the performance of the WFQ in terms of how eectively it can provide dierent services to dierent classes of users. In the rest of this section, we investigate the performance of WFQ in detail. For simplicity, only two classes of users, class 0 and class 1, are simulated. We would like class 0 users' expe-

Table 1: Distributions and parameters of the trac model.

Model

Probability Density Function

Body

Lognormal

p(x) = xp1 2 e(? ln x?)2 =22

Tail

Pareto

p(x) = k x?(+1)

Embedded References

Pareto

p(x) = k x?(+1)

OFF ( Think ) Times

Pareto

p(x) = k x?(+1)

Component Request Sizes

rience to be three times better than that of class 1 users, so naturally we assign them weights 3 and 1 respectively. We start by having just one user in each class, and then gradually increase the number of users in both classes at the same time to obtain dierent channel loadings. For each setting, we determine the end-to-end transmission rate of every le delivered, where the end-to-end transmission rate of a le is obtained by dividing its size with the time it takes to deliver the entire le. To evaluate the performance of WFQ, we compute the average end-to-end transmission rate for class 0 and class 1 users respectively and then compute the ratio of the two rates. Given the weights of 3 and 1 for the two classes, the ideal value of the ratio would be close to 3. However, the actual result is very dierent as we will show next. 14 Ratio of End-to-End Rates Class 0 User End-to-End Rate Class 1 User End-to-End Rate

12

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Figure 4: WFQ performance for multiple data service classes. Fig. 4 plots the average end-to-end transmission rates experienced by the two classes of users as well as the ratio

Parameters  = 7:881;  = 1:339 k = 34102;  = 1:177 ( max = 100Kbytes ) k = 2;  = 1:245 ( max = 30 ) k = 1;  = 1:4 ( max = 900sec )

of the two for dierent network utilization. The left Y-axis is for the ratio of the rates, while the right Y-axis is for the actual rates. The result is quite surprising in that the ratio stays consistently at a very low level for utilization lower than 80%, and barely reaches 1.4 when the system is almost 80% loaded. Only when even more users join the system after it is already over 80% loaded, does the ratio eventually approaches 3, which is our target value. Note that, as discussed in section 2, when the number of users in the system goes up, the increased delay will cause `back-pressure' to the sources, which will eectively reduce the amount of trac generated by each source. As a result, the system load never exceeds one, only the delay keeps increasing as the number of users goes up. The low rate ratios in Fig. 4 indicate that, unless the system is heavily congested, the service that class 0 users receive is only slightly better than what class 1 users receive, instead of 3 times better as we hoped when setting the weights to 3 and 1. The key reason for this poor performance is the burstiness of the data trac. When trac is bursty, sources are active only from time to time for a short period of time. As a result, when class 1 sources have data to send, there may not even be any active class 0 sources to compete with them, so they can transmit at relatively high rates. Clearly, this is more likely to happen with low network utilization. We can certainly increase the weights assigned to class 0 users to improve their relative performance as will be demonstrated in section 3.2. However, before doing that, let's rst examine a few other factors that might have some impact on the performance of WFQ. 1) TCP What Fig. 4 compares is the average end-to-end transmission rates perceived by users at the receiving end. As mentioned previously, TCP is used as the end-to-end

2) Propagation delay Propagation delay is another factor that can aect the end-to-end transmission rate signi cantly, especially when combined with TCP slow start procedure. By considering eective serving rate at the base-station, most of its impact should also be removed. Fig. 6 and 7 show the ratio of the end-to-end rates and the eective serving rates for propagation delays of 1 msec and 100 msec respectively. The default setup with propagation delay of 40 msec is

40 Effective Serving Rate (Ratio) End-to-End Data Rate (Ratio) Class 0 Effective Serving Rate Class 1 Effective Serving Rate

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transport protocol in our simulation. Due to the slow start procedure in TCP, the link is typically not fully utilized at the beginning of a le transfer, assuming separate connection is established for each le as the way it is done in our simulation. Since the median le size is only 3Kbytes, most of the le transfers are nished before reaching the full available capacity, and slow start is likely to have a big impact on the average transmission rate. This performance compromise caused by TCP is not within the control of the cellular access network. To express the service provided by the cellular network more accurately, it is more appropriate to compare the actual rates at which data are served at the basestation for the two classes of users. Consider there is a timer for each queue. Whenever a packet arrives at an empty queue, the queue becomes active and its timer is started. While the packet is waiting in the queue to be served, there might be additional packets arriving at the same queue; and even more packets may arrive while these packets are waiting, and so on. So the timer keeps running until the queue becomes inactive, i.e. when it is empty and stays empty for at least 20msec. Recall that 20msec is what takes for the last piece of data from the queue to be delivered over the air. If we divide the total amount of data delivered when the queue is active by the length of the active period, we obtain a rate which indicates how fast the base-station serves this queue when it has data to send. We call this rate the eective serving rate the corresponding user receives from the wireless access network. When measuring eective serving rate, the time a user spends waiting for the acknowledgments or for the packets to get to the wireless access network is not counted. Fig. 5 plots the average eective serving rates (right Y-axis) for class 0 and class 1 users as well as the ratio of the two rates (left Y-axis), under dierent network loads. For comparison, the ratio of the end-to-end transmission rates is also plotted as a gray dotted line. Fig. 5 shows that indeed TCP has a large impact on the end-to-end performance of WFQ. When comparing the eective serving rates, which capture more accurately the service provided by the wireless access network, the rate ratio reaches 1.5 at 60% load and reaches 2 at 80% load.

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Figure 5: WFQ performance in terms of ratio of the effective serving rates at the base-station. also shown in the two gures in gray dotted lines. The actual end-to-end transmission rates with the three propagation delays are plotted in Fig. 8. Fig. 6-8 indicate that, although the absolute values of the end-to-end rates are quite dierent for dierent propagation delays, the rate ratios are relatively stable. When the propagation delay is 40 msec, the ratio of the end-to-end data rates is slightly higher than when the propagation delay is 100 msec, since higher propagation delay reduces the end-to-end rate of class 0 users by a larger percentage as their rates are higher. When the propagation delay is 1 msec, at TCP slow start periods, it takes 21 msec (including 20 msec over the air) for an acknowledgment to get back to the source, and then 1 msec for the new packets to arrive at the base-station, where they have to wait at least another 18 milliseconds for the next time frame to be delivered. In this case, the round trip time is not much lower than when the propagation delay is 40 msec, so the two yield very similar results. The relation between eective serving rate and propagation delay is more subtle. When the propagation delay is lower, the packet inter-arrival times within the same ON period are smaller. So if there are both class 0 and class 1 active users, class 0 users can take advantage of WFQ, and be transmitted faster. In other words, class 0 users will spend less time in ON periods. On the other hand, since the ON periods of the class 0 users are shorter, class 1 users have less chance to share the channel with class 0 users, so they also can be served faster. The net result is both classes are faster, while the relative performance, i.e. the ratio of the eective serving rates, remains almost the same. With a higher propagation delay, the average packet inter-arrival time becomes larger, but the relative

performance of the two classes remains the same as shown in Fig. 7. This is because, although packet arrivals are less bursty in this case, overall, the amount of data that need to be delivered for a given utilization is about the same.

3

Propagation delay = 1 msec Propagation delay = 1 msec Propagation delay = 40 msec Propagation delay = 40 msec

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Figure 6: WFQ performance with propagation delay of 1 msec. 3

Propagation delay = 100 msec Propagation delay = 100 msec Propagation delay = 40 msec Propagation delay = 40 msec

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3) Persistent TCP connections Recall that in our trac model, each ON period might consist of several le transfers. In our previous simulations, a separate TCP connection was created for every le. If we assume les requested within a single ON period are all from the same server, we can then keep the same TCP connection open during the entire burst to reduce the overhead from TCP hand-shaking and slow start. Such a mechanism, which is referred to as using persistent TCP connections, has been adopted by HTTP 1.1 standard [8]. Fig. 9 compares the rate ratios with and without using persistent TCP connections. Similar to the situation with propagation delays, although persistent TCP connections improve network eciency, it has little impact on the ratios of the end-to-end rates and the eective serving rates, because it bene ts class 0 and class 1 users equally well.

2 3

With Persistent TCP Connections With Persistent TCP Connections No Persistent TCP Connections No Persistent TCP Connections

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Figure 7: WFQ performance with propagation delay of 100 msec.

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Propagation Delay = 100 ms

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Figure 9: WFQ performance with persistent TCP connections.

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We also tested TCP pipelining, which allows multiple requests or les to be placed in a single TCP packet to further reduce the overhead. Again, it improves the rates for both classes, while leaving their relative performance unchanged.

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Figure 8: End-to-end transmission rates of class 0 users for dierent propagation delays.

4) Distribution of class 0 and class 1 users So far, we have been assuming that there are equal number of users from class 0 and class 1. We now investigate whether the exact number of users from the two classes

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5 Class 0 Users ( Serving Rate ) 5 Class 0 Users ( End-to-End Rate ) Equal Class 0/1 Users ( Serving Rate )

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Figure 10: WFQ performance with a xed number (5) of class 0 users.

3.2 The limit of Weighted Fair Queueing

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5 Class 1 Users ( Serving Rate ) 5 Class 1 Users ( End-to-End Rate ) Equal Class 0/1 Users ( Serving Rate ) Equal Class 0/1 Users ( End-to-End Rate )

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Figure 11: WFQ performance with a xed number (5) of class 1 users. 40

Eqaul Number of Users 5 Class 0 Users

Effective Serving Rate ( Kbytes/sec)

In the last section, we showed that the ratio of the eective serving rates at the base-station provides a more justi ed view of the performance of WFQ in the cellular network. Moreover, factors such as propagation delay, persistent TCP connections, and distribution of the users, could all change the actual rates, yet they all have little impact on the ratio of the rates, which is used as the performance measurement for WFQ. To further evaluate WFQ, the next step is to nd out how the weights might change its performance. To compare the results with dierent weights, we repeat the simulations with the weight of class 0 users set to 7 and 5 respectively, while keeping the weight of class 1 users at 1. The results are plotted in Fig. 13, together with the previous results with a weight of 3 for class 0 users. The graph shows that by assigning a higher weight to class 0 users, improvements on the performance can be achieved even when the system is lightly loaded. We can continue to assign higher weights to class 0 users to further improve their relative performance. Clearly, the upper-bound on the performance of WFQ can be obtained through priority queueing, in which class 1 users are not served whenever there are class 0 packets in the queue. Fig. 14 shows the rate ratios obtained using the priority queueing scheduling algorithm. The part of the curve in the region [1:3] is re-plotted in Fig. 15 in order to compare with previous results. Fig. 15 shows that, due to the burstiness of the trac, the best WFQ can achieve is to transmit class 0 data on average twice as fast as class 1 data when the system is 45% loaded, and 3 times faster when it is 60% loaded. Note that with priority queueing, the curves rise

Equal Class 0/1 Users ( End-to-End Rate )

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Rate Ratio

might aect the results. We test two dierent cases by xing the number of class 0 and class 1 users at 5 respectively, and the results are shown in gures 10 and 11. In both cases, the rate ratios are almost exactly the same as when there are equal number of users from both classes. However, the actual eective serving rates received by the users are dierent in all three cases as illustrated in Fig. 12 (only class 0 users' eective serving rates are shown). This is because, when there are more class 0 users in the system, there is more competition for the bandwidth for both classes of users. As a result, each user receives less bandwidth on average compared to his/her peers in a system with a smaller number of class 0 users under the same system load. The results above indicate that, for a given weight, the performance of WFQ is not aected by the exact number of users from each class, although the actual amount of bandwidth each user receives does depend on the distribution of users.

5 Class 1 Users

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Figure 12: Eective serving rates for class 0 users with dierent distributions of class 0 and class 1 users.

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Figure 13: WFQ performance with dierent weight assignments.

Figure 14: Performance of priority queueing scheme. 3

Priority queueing Priority queueing Weighted - 3:1 Weighted - 3:1

sharply when the system is heavily loaded. There are two reasons for this, one is simply because there is too much class 0 trac, so class 1 sources have little chance to be served; the second reason is unnecessary TCP retransmissions of class 1 packets. Let's look at the second reason more closely. When there is no active class 0 user in the system, all the active class 1 users share the channel equally, and can generally deliver data at a decent speed. As soon as a class 0 packet arrives, class 1 users immediately lose the channel completely until all class 0 packets at the base-station are nished. If there happens to be several class 0 users in the system, from time to time, class 1 users will switch between the state of being able to send data smoothly, and the state of having no access to the system for a relatively long period of time, depending on whether there are class 0 packets at the base-station. This huge variation in the eective serving rate makes TCP unable to estimate the round trip time correctly, and trigger it to retransmit packets which are actually still queued at the base-station. Moreover, blocks of consecutive packets may be retransmitted, since once class 1 users nally regain access to the channel, TCP will mistake the new acknowledgments as the ones for the retransmitted packets, and then continue to transmit even more packets unnecessarily. The class 1 data in priority queueing are in fact the so called best eort trac. Therefore, what the above results indicate is that even the best eort trac should be managed carefully to avoid inecient usage of bandwidth, especially for cellular networks where capacity is very limited and congestion may occur frequently.

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Figure 15: Performance of priority queueing scheme. Magni cation of [1:3] region of Y-axix in Fig. 14.

3.3 Beyond Weighted Fair Queueing In addition to WFQ, there are many other workconserving packet scheduling algorithms such as virtual clock, worst-case fair weighted fair queueing (WF2 Q), packetized generalized processor sharing (PGPS), and earliest time line rst (ETLF), etc [15]. We expect them all behave similarly to WFQ, due to the nature of these algorithms and the burstiness of the data trac. In fact, priority queueing gives a performance upper-bound for all of these algorithms. To provide consistently better performance to higher service classes, we are currently investigating several hybrid weighted fair queueing algorithms. For instance, we may assign higher weights to class 0 users at low utilization, while decrease the weights as system load goes up to

keep the relative performance of dierent classes of users at the same level.

4 Issues Related to Packet Scheduling in Wireless Cellular Networks 4.1 Admission control The packet scheduling algorithms discussed in this paper try to divide bandwidth proportionally among users of the same or dierent classes. In order to provide some degree of desired delay performance to users, call admission control is required to limit the number of users accepted to the system. This applies to even the best eort trac as pointed out in section 3. In addition, for cellular networks, call admission control should consider not only the available capacity, but also the interference levels in the system [1].

4.2 Packet errors As we mentioned previously, channel error is one of the major challenges in wireless network protocol design. From a fair-queueing point of view, it remains unclear how packet errors should be taken into account by the scheduling algorithm. For example, should a user be compensated for lost bandwidth due to errors? How should the service guarantee be handled if the link layer predicts severe error? A similar problem exists when using dierent modulation schemes, where one time slot may deliver a dierent amount of data depending on the selected modulation scheme. In terms of fair queueing in wireless networks, one may argue that it is the amount of time that a user occupies the channel that determines whether it has received its share of the channel resource, rather than the actual amount of data that is successfully delivered. We didn't consider packet loss in this paper because we would like to rst identify the interactions between scheduling algorithms and other system parameters without bringing in the complexities caused by packet loss.

4.3 Link layer scheduling Because of the simpli cations we made to the wireless channel { error-free transmission and a xed modulation scheme, we are able to combine packet scheduling for multiple data classes with link layer scheduling in this study. In reality, link layer scheduling algorithms are often optimized based on many other factors such as channel conditions, air link eciency, etc [13]. Therefore, a complete solution for this problem will need coordination between the link layer and the network layer scheduling algorithms to support multiple data service classes without adding too much overhead to the system.

5 Conclusion In this paper, we studied the performance of the weighted fair queueing algorithm in providing multiple service classes for typical Internet users in a cellular packet data network. Due to the burstiness of the data trac, WFQ can provide dierentiated services only to a limited extent, even when considering the eective serving rates at the base-station. We show exactly how the performance of the algorithms improves slowly with system load and reaches the desired results only when the system is severely loaded. By increasing the weights assigned to higher class users, we are able to improve the performance at all utilization levels. Other factors such as propagation delay, persistent TCP connections, and distribution of users, have little impact on the performance of WFQ, which makes it relatively easy to manage the network. We also examine the performance of the priority queueing algorithm, which gives us an upper-bound on what can be achieved on providing multiple service classes for bursty data trac. The study on priority queueing scheme also reveals that best eort trac should not be treated as absolutely best eort. Instead, they should be managed carefully to avoid inecient usage of the channel. Results presented in this paper are not restricted to cellular networks. We chose to put it into this context simply because cellular networks are among the places where the provision of dierent classes of data services will be most attractive due to the modest channel capacity.

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