supporting first person shooter games in wireless local area networks

The 18th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’07)

SUPPORTING FIRST PERSON SHOOTER GAMES IN WIRELESS LOCAL AREA NETWORKS Brian Carrig School of Engineering Institute of Technology, Carlow Kilkenny Road, Carlow, Ireland Email: [email protected]

David Denieffe School of Engineering Institute of Technology, Carlow Kilkenny Road, Carlow, Ireland Email: [email protected]

A BSTRACT First Person Shooter (FPS) games are a popular online gaming genre played predominately over wired networks. FPS games are highly interactive and have stringent delay requirements. However, the explosive growth in wireless LAN (WLAN) deployment has seen an increase in the use of such networks for gaming purposes. The varied performance of the IEEE 802.11 MAC Distributed Coordination Function (DCF) has tended to make WLANs unsuitable for FPS games. The IEEE 802.11e standard introduces Quality of Service (QoS) mechanisms including Enhanced Distributed Channel Access (EDCA) allowing prioritization of competing flows. Using the NS-2 simulator, we evaluate the capability of 802.11g and 802.11e WLANs to support Quake IV games traffic in the presence of web traffic. We compare the results achieved using EDCA with those achieved by a non-elevated differentiated services scheduler known as Best Effort with Loss Trade-off (BELT). We find that the BELT scheduler compares favorably with EDCA in this context. I.

I NTRODUCTION

First Person Shooter (FPS) games are so named because of the viewpoint displayed during the game. Each player is responsible for a single character, sharing a three dimensional world with other game players with the objective of shooting and destroying objects or other players. The architecture is typically client-server, to preserve consistency and regulate the high degree of interaction among players in the shared virtual environment. In comparison with other multi-player gaming genres like Role Playing Games (RPGs) the number of players engaged in a game is low. Game times are also quite short, measurable in minutes. Increased availability of high-speed residential broadband networks and the addition of suitable connections to gaming consoles has greatly facilitated gamers wishing to compete with other players over the Internet. The most important characteristics of an Internet connection are throughput, latency, latency variation (jitter) and packet loss. Most Internet connections have sufficient bandwidth to accommodate multiple FPS game flows. Even a busy server may need less than 1Mb/s bandwidth on average [1]. The main issues for FPS games are latency, jitter and to a lesser extent packet loss. For games like Quake IV1 gaming performance is strongly correlated with network performance [2]. Although gaming clients predominately connect to game 1 http://www.quake4game.com/

c 1-4244-1144-0/07/$25.00°2007 IEEE

John Murphy School of Computer Science and Informatics University College Dublin, Belfield Dublin 4, Ireland Email: [email protected]

servers over wired networks, in recent years wireless data networks such as IEEE 802.11 (Wi-Fi) [3] have been widely deployed. Wi-Fi interfaces are automatically built into laptops and the recently released Nintendo Wii2 console does not provide any other network interface for multiplayer gaming. Studies indicate that users on WLANs behave in a similar manner to those on traditional wired LANs, with the same usage patterns and application range [4]. It is possible to extrapolate therefore that increasing volumes of gaming traffic on the Internet will have an affect on WLANs. As with most Internet application traffic, FPS games exhibit uplink/downlink asymmetry with a larger volume of downlink traffic. The legacy Wi-Fi MAC-layer protocol, Distributed Coordination Function (DCF), does not provide differentiation between the Access Point (AP) and client stations (STAs), or between traffic types on within the network, causing unfairness [5]. The 802.11e standard [6] addresses Quality of Service (QoS) concerns by introducing two MAC-layer enhancements: Enhanced Distributed Channel Access (EDCA) and an optional enhancement Hybrid Coordination Function (HCF) Controlled Channel Access (HCCA). This paper only considers EDCA. EDCA provides QoS through medium access control and traffic classification. For each mechanism a number of parameters must be configured. The standard calls for mandatory admission control policies although there are no guidelines on how this should be implemented by the AP. In this paper we examine WLANs which include a mix of gaming clients and web browsing clients. It is hoped to provide some guiding for setting admission control policies for APs that carry gaming traffic. By way of further evaluation we use a non-elevated Best Effort with Loss Trade-off (BELT) [7] scheduler in conjunction with the 802.11g AP and compare the results with those obtained using 802.11e EDCA. The BELT scheduler is an IP-layer rather than a MAC-layer service differentiation mechanism. As such, it is not strictly a replacement for MAC-layer differentiation. However, it is most useful in wireless environments where MAC-layer differentiation is not feasible. In the next section we provide an overview of 802.11, 802.11e and related research. This is followed by section 3, which provides a detailed description of the BELT scheduler. Section 4 defines the testing scenarios and simulation setup. While section 5 presents the experiment results. The paper concludes with a discussion of the main contributions of the paper as well as some ideas for future work. 2 http://wii.com/


II.

R ELATED W ORK

than half this number for UDP streams [8].

A. IEEE 802.11

B.

Legacy 802.11 supports DCF and an optional Point Coordination Function (PCF) module. DCF provides contention based access to the wireless medium. PCF provides contention free access, with the AP choosing which wireless STA can transmit. In 802.11 DCF, STAs use a Carrier Sense Multiple Access Collision Avoidance (CSMA/CA) mechanism. When a STA wishes to transmit, it must first listen on the channel. If the channel is idle (i.e., no other STA is transmitting) for the duration of a DCF Inter-Frame Spacing (DIFS), then the STA can send its data. If the channel is busy, the STA must defer until the transmission has ended and the channel has been idle for a DIFS. The STA then sets a back-off timer, where the duration of the timer is a function dependent on the physical layer and the Contention Window (CW) parameter. When the back-off timer reaches zero, the STA can transmit. After a station has completed transmission of a frame, it must wait at least as long as its contention window before attempting to transmit again. The STA that picks the shortest random back-off time gains access to the medium. The 802.11e standard introduces two QoS-based medium access protocols, which offer traffic prioritization and improved channel access over DCF. EDCA is similar to a priority-based DCF scheme, while HCCA is a polled protocol offering QoS guarantees. There are four basic Access Categories (ACs) used to assign priorities in EDCA. These represent, from highest to lowest priority, Voice (VO), Video (VI), Best Effort (BE) and Background (BG) traffic. The priorities are implemented using a separate transmit queue and different EDCA parameter values for each of the four ACs. The adjusted EDCA parameters are the CW range (CWmin ≤ CW ≤ CWmax ), the Arbitration Inter-Frame Spacing (AIFS), and the Transmission Opportunity (TXOP) limit. EDCA is similar to DCF in that there is a back-off period if the channel is busy. The CW parameter determines the length of the back-off period. The AIFS is similar to the DIFS in DCF. A QoS-STA (QSTA) must observe the channel idle for the AIFS before transmitting. The TXOP limit defines how long a station can continue to transmit once it has gained access to the channel. The highest priority AC has the lowest values for the CW and AIFS parameters. The smaller the values, the shorter the amount of time the station has to wait to transmit data. High-priority flows should have larger values for the TXOP limit so they can send multiple frames without needing to re-contend for the medium. The AP maintains its own set of EDCA parameters to govern medium access. EDCA parameters are assigned to QSTAs when they associate with the AP. EDCA does not provide explicit QoS guarantees, it simply provides a very high statistical likelihood that the highest priority traffic gains the most frequent access to the medium. Multimedia support for Wi-Fi networks has been studied extensively. Although the raw data rate for legacy 802.11 is 11Mb/s and 54Mb/s for 802.11g, in practice protocol overhead limits the maximum achievable throughput to less

Initial work on QoS in IP networks relied on admission control increasing complexity of deployment. High priority traffic was associated with low delay, improving the throughput achieved. A fundamentally different approach to service differentiation was analyzed in [9] where a ’1-bit’ scheme provides delay differentiation without the need for admission control. The packets receive serving priority but are constrained to a smaller buffer. Ref. [10] proposes a proportional differentiated services model where the service offered by different priority levels is proportional and independent of the load within individual classes. These proposals still couple low delay with improved throughput, offering some form of priority or elevated service level. A non-elevated service known as Alternative Best Effort (ABE) is outlined in [11]. This scheduler offers a trade-off between packet delay probability and packet loss probability. A packet marked low delay will experience limited queuing delay but an increased likelihood of packet loss. Since this operates as a form of trade-off, classes are not expected to be priced differently or require any form of admission control. The Equivalent Differentiated Services (EDS) [12] proposal is similar to ABE but provides an infinite range of classes. C.

Non-elevated Scheduling

FPS Games

FPS games are the third largest selling computer game genre in the US. 44% of frequent gamers play online; up from 19% in 2000. Combined video and computer games sales exceeded $7 billion in 2005 and 42% of Americans purchased a game in 2006 [13]. Within this growth industry, the percentage of those playing games online is increasing. The move toward ubiquitous availability of broadband is positive for this trend. Frequent comments referring to ”lag” and ”ping” on gaming forums and a survey in [14] suggest that gamers are sensitive to network latency. In an analysis of popular on-line multiplayer games [1], it is observed that FPS games traffic is highly predictable. The same characteristics are exhibited across numerous titles, such as periodic bursts of small UDP packets and low latency requirements. The sensitivity of Quake 3 gamers to latency is estimated in [15] although the conclusion that most Quake 3 gamers prefer roundtrip times of below 150ms has been vigorously argued against by gamers on popular online forums like Slashdot. In [16], a more empirical approach to measuring the sensitivity of Quake 3 players to delay is taken and though the sample size is small, the results largely agree with [15] and [2]. III.

BELT S CHEDULER

The BELT scheduler is similar in design to the ABE scheduler. Both make use of work-conserving Earliest Deadline First (EDF) scheduling of packets and both offer only two classes of traffic. The primary benefit of the BELT scheduler is its lack of complexity.


duplicate queue. This queue then emulates the behavior of a First In, First Out (FIFO) queue. A router that was to be upgraded to provide an ABE service would need to double its existing memory to maintain the same overall queue size. The BELT scheduler on the other hand uses a counter to assign deadlines to the non-DS packets when they arrive. This counter is incremented by the size of the packet in bytes every time a packet is received. Unlike a wired network experiment in which the BELT scheduler was applied [7], the nature of using a shared medium means that the available bandwidth will fluctuate. Therefore, the dequeue rate for the virtual queue cannot be assumed to be fixed. Instead, we constantly update the dequeue rate value using an Exponentially Weighted Moving Average (EWMA) of continuous throughput as shown in equation (1). The use of an EWMA helps smooth temporary fluctuations in delay, while still giving precedence to the most recent delay value. Ti is the dequeue rate average in bytes/sec after the ith sample; Mi is the ith measurement and α is 0.94. This decay value is often used in stock market volatility or Value-at-Risk (VaR) calculations [17]. It is best suited to a series of data points where the process generating the data moves randomly around an underlying mean. A flowchart for the BELT scheduler is shown in fig. 1. Ti = αTi−1 + (1 − α)Mi IV. Figure 1: Flowchart for BELT Scheduler BELT is governed by the following simple rules: • Packets are either marked Delay Sensitive (DS) or not • DS packets receive a bounded delay at each hop • Non-DS packets are not adversely affected by the number of DS packets on the network BELT is implemented without the need to separate the buffer memory into two distinct virtual queues. This is an important issue because in the ABE system the network administrator must allocate a certain portion of the total router buffer space to DS (green) traffic. The amount allocated will depend on the volume of DS traffic anticipated on the network - something the administrator cannot be assumed to know with any degree of accuracy. The green buffer cannot be set larger than the perhop delay bound, as packets held for longer than this period are dropped. However, configuring the queue to this size may render the green acceptance test component of the scheduler somewhat superfluous. This algorithm drops green packets when they arrive if they will spend longer than the delay bound in the queue. Regardless of the proportion of the buffer assigned to the blue or green virtual queue, the combined amount of buffer space allocated to the two virtual queues cannot exceed half of the total buffer space available in the router. The Duplicate Scheduling with Deadlines (DSD) technique used by ABE requires that a copy of each incoming packet be placed in a

(1)

S IMULATION SETUP

The network topology is shown in fig. 2. A single AP is connected directly to wired game server and web server nodes by means of a 100Mb/s link with 1ms delay. Stationary wireless nodes are equi-distant from the AP and sufficiently close to mitigate channel errors. The network is 802.11g with a raw data capacity of 54Mb/s. The number of STAs and QSTAs is varied over numerous simulations. Two types of traffic are seen on the network. A web-like traffic mix of long and short-lived TCP connections (based on data supplied in [18]) and Quake IV gaming clients. The Quake IV traffic is actual Quake IV client traffic taken from [19]. Since the largest trace is for only nine players, server packet sizes for simulations with a larger number of clients have been calculated using the methodology described in [20]. The are four standard ACs in EDCA, these are relatively prioritized using an input vector consisting of the following parameters: AIFSN, CWmin , CWmax and TXOP Limit. The AIFS parameter is calculated using equation (2) and the Short Inter-Frame Spacing (SIFS) time. All QSTAs use the EDCA provided in Table 1 with data supplied in [6]. In both the first and last experiments the 802.11e enhancement is not used and the parameters for STAs are simply the default PHY and MAClayer parameters as per standard. CWmin and CWmax dictate the back-off period upon contention and are thus smaller for high priority queues AC VO and AC VI. Note that AC VI has the longest transmit time once the channel is grabbed. Where the TXOP Limit is 0, the queue can only transmit one frame at a time. The waiting period following channel idle is lowest for AC VO which along with the smaller CWmin and CWmax values ensures AC VO traffic will experience the lowest delay.


802.11g WLAN (Quake IV Clients Only) 5 Queue = 10 packets Queue = 50 packets Queue = 100 packets Queue = 200 packets Queue = 300 packets Queue = 500 packets

Mean Opinion Score

4

Figure 2: Network topology

AC VO VI BE BG

AC BE and AC BG have the same back-off values but differ in the wait period after channel is idle, ensuring AC BE will obtain more frequent access to the medium than AC BG. (2)

M OS = −0.00000587X 3 +0.00139X 2 −0.114X +4.37 (3) X = 0.104 ∗ average ping + average jitter

(4)

Performance for gaming clients is evaluated by mapping metrics such as packet delay, delay jitter and packet loss to a Mean Opinion Score (MOS). This is calculated using equations 3 and 4 for Quake IV traffic MOS prediction provided in [2]. This gives an accurate mapping of subjective gameplay measurements within our simulation. Measuring the performance of web browsing QSTAs is somewhat simpler, as we simply track the number of pages downloaded during the course of the simulation. All simulations are run for 300 seconds after 100 seconds warm-up time. The network simulator used is NS-2 version 2.30 [21] with the TKN 802.11e extension [22]. The queue threshold for delay sensitive packets with the BELT scheduler is set to 20 packets or roughly 7ms at optimum transmission rates. Where there is a mix of gaming and web traffic, the number of QSTAs using a particular application is evenly divided. V.

2

1

Table 1: EDCA Parameter Settings CWmin CWmax AIFSN TXOP Limit (ms) 1 7 1 1.5424 7 15 2 3.264 16 1023 3 0 16 1023 7 0

AIF S[AC] = AIF SN [AC] ∗ SlotT ime + SIF S

3

S IMULATION RESULTS

A. 802.11g with Gaming Clients Only Results have been aggregated for the hundreds of experiments conducted with different queue sizes and STA numbers. The results displayed in fig. 3 are the client MOS averaged over an entire simulation. Although a larger queue size causes a more rapid degradation of QoS experienced by gaming clients, we note that across all queue sizes up to 20 simultaneous players can engage with acceptable quality. Such a large number of concurrent players would not be uncommon but is definitely at

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Figure 3: MOS for 802.11g WLAN with Quake IV STAs only the higher end of the scale. However, as the number of players is increased beyond this, the QoS experienced degrades quicker for larger queue sizes. The server throughput average peaks at 9.4Mb/s for 20 clients with a queue size of 200 but falls as low as 3Mb/s for games involving over 50 players. However, this entails a significantly higher packet loss rate of over 90%. The average individual client upstream throughput peaks at just over 50kb/s for the case of 20 clients with the queue limited to 20 packets (downstream throughput is 436kb/s). Irrespective of the number of STAs, aggregate upstream throughput appears capped at around 1.4Mb/s. A similar experiment conducted with only web browsing clients downloaded over 21,000 pages for 52 STAs and a buffer size of 200 packets. B.

802.11g with Gaming Clients and Web Clients

The results for an 802.11g WLAN with a mix of contending Quake IV and web downloading traffic can be seen in fig. 4 and 5 respectively. The former displays the average MOS obtained by the gaming STAs and the latter the number of web pages downloaded over the course of each experiment. Individual client throughput is remarkably consistent across all experiments, regardless of the number of competing STAs or queue size, averaging around 24kb/s. Lost uplink packets only exceed 6% when the total number of STAs is greater than 42 (21 concurrent gaming clients). Aggregate downstream packet losses for the same simulation are just over 13% but both are still within an acceptable range for most FPS applications. Similarly, the MOS average remains acceptable until the total number STAs exceeds 30. Considerably less impressive in this experiment is the performance of the web traffic STAs. The largest number of pages downloaded is 4,442 for a total of 16 STAs (8 web clients) and a buffer size of 500. This compares with just 255 pages for the same experiment conducted when the buffer is only 10 packets in size. Thus it is clear that in this experiment, even without traffic differentiation, the gaming clients dominate the AP queue. This is because FPS games traffic is rarely TCP-friendly and will not reduce its


transmission rate in the face of losses. C. 802.11e with Gaming Clients and Web Clients

802.11g WLAN (Quake IV & Web Browsing STAs) 5

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802.11g WLAN with BELT scheduler (Quake IV & Web Browsing QSTAs) 5 Queue = 10 packets Queue = 50 packets Queue = 100 packets Queue = 200 packets Queue = 300 packets Queue = 500 packets

4

Mean Opinion Score

In this experiment the BELT scheduling mechanism described in section 3 is employed in the AP buffer queue. Quake IV packets are marked as DS and web packets are marked as non-DS. Since the AP is the network bottleneck, this results in improved throughput for non-DS traffic while maintaining low delays for DS traffic. However, this comes at the expense of increased packet losses for DS traffic. Throughput is correspondingly lower as well, peaking at just over 7Mb/s for aggregate downlink games server traffic. Client losses are very well contained measuring only fractions of a percent with less than 30 STAs. The MOS average remains acceptable for buffer sizes greater than 50 where there is 48 STAs present. Losses are higher where a smaller buffer is used, as there is less room for DS packets to be ”inserted” into the queue without penalizing non-DS packets already waiting in the queue. Most impressive is the number of webpages downloaded during the experiments. This peaks at 6,840 for 24 STAs and a buffer size of 200. Thus we can see that the use of a non-elevated scheduler is beneficial for both traffic types when compared to the previous experiments conducted. Similar performance can be obtained using 802.11e EDCA, provided the AC VO queue is limited in size relative to the AC BE queue. This provides a balance of low delay for gaming clients without allowing them to dominate the medium.

Queue = 10 packets Queue = 50 packets Queue = 100 packets Queue = 200 packets Queue = 300 packets Queue = 500 packets

4

Mean Opinion Score

In this series of experiments, the 802.11e relative prioritization enhancement is employed. Gaming traffic flows are assigned to the AC VO queue and web traffic is assigned to the AC BE queue. The MOS averages for the are shown in fig. 4. They are broadly similar to those obtained in the previous subsection. However, packet loss rates are considerably higher particularly for downlink server traffic. As the number of QSTAs increases above 30, the aggregate percentage loss for server traffic exceeds 33%. Although Quake IV traffic displays remarkable resilience to packet loss, such high loss rates would not be acceptable for all FPS applications. The losses for uplink gaming QSTA traffic are far more contained and in line with those presented in the previous subsection. Probably as a direct result of the higher loss rates sustained by downlink game traffic, the number of webpages downloaded is significantly increased. This peaks at 5,403 pages for 24 QSTAs with a buffer of 10 packets. As can be seen in fig. 5 (b) the number of webpages downloaded increases as the buffer size decreases. This phenomenon is caused by the relative prioritization mechanism of 802.11e. If the network is heavily loaded a larger AC VI queue will produce fewer transmission opportunities for packets in the lower priority AC BE queue. If we increase the size of the AC BE queue but do not increase the size of the AC VI queue then a much greater number of webpages may be downloaded at the expense of higher losses for gaming traffic flows.

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Figure 4: MOS for mix of web and Q4 QSTAs


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802.11g WLAN (Quake IV & Web Browsing STAs) Queue = 10 packets Queue = 50 packets Queue = 100 packets Queue = 200 packets Queue = 300 packets Queue = 500 packets

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C ONCLUSIONS

In this work we investigated the capability of IEEE 802.11g and 802.11e WLANs to support a mix of web and gaming traffic clients using a simulation-based approach. We have shown that the introduction of a traffic prioritization scheme such as EDCA can be beneficial. However, caution is required because the prioritized traffic can easily dominate the network to the detriment of other flows. This may occur even if traffic is not differentiated where this is a significant amount of non-TCP friendly flows. The introduction of admission control as per the 802.11e standard would be extremely useful. A constraint of no more than 10 flows would allow feasible FPS game playing on a single access point without unduly compromising other traffic flows. The complexity of an admission control protocol could be avoided for EDCA in this context by constraining AC VO and AC VI flows to smaller buffers. This is similar to the approach advocated in [9]. A prioritized service is offered to certain traffic flows but starvation of other flows on the network is prevented by constraining the prioritized flows to a smaller buffer. In essence, this is providing a non-elevated service with a trade-off between delay and packet-loss. Alternatively, the BELT scheduling mechanism can be employed at the AP. For example in situations where traffic differentiation at the MAClayer is not possible. This would be applicable to 802.11 DCF with comparable performance to EDCA for our specific range of experiments. Future work will examine the role admission control policy can play in such a setup and how it might impact upon user fairness and perception. We would also like to expand the traffic mix and introduce new traffic flows such as voice, video and peer-to-peer file-sharing. Similar arrangements where mobility is included to encompass the expanding range of mobile game-playing devices will also be investigated.

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R EFERENCES [1] Wu chang Feng, Francis Chang, Wu chi Feng, and Jonathan Walpole, “A traffic characterization of popular on-line games,” IEEE/ACM Trans. Netw., vol. 13, no. 3, pp. 488–500, 2005.

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[2] A. F. Wattimena, “Performance modeling of interactive gaming,” M.S. thesis, Vrije Universiteit, Amsterdam, Netherlands, May 2006.

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Figure 5: Completed web page downloads for mix of web and Q4 QSTAs

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