On the Sensitivity of Online Game Playing Time to Network QoS

Kuan-Ta Chen, Polly Huang, Guo-Shiuan Wang, Chun-Ying Huang, and Chin-Laung Lei

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Online gaming is one of the most profitable businesses on the Internet. Among various threats to continuous player subscriptions, network lags are particularly notorious. It is widely known that frequent and long lags frustrate game players, but whether the players actually take action and leave a game is unclear. Motivated to answer this question, we apply survival analysis to a 1,356-million-packet trace from a sizeable MMORPG, called ShenZhou Online.
We find that both network delay and network loss significantly affect a player's willingness to continue a game. For ShenZhou Online, the degrees of player "intolerance" of minimum RTT, RTT jitter, client loss rate, and server loss rate are in the proportion of 1:2:11:6. This indicates that 1) while many network games provide "ping time," i.e., the RTT, to players to facilitate server selection, it would be more useful to provide information about delay jitters; and 2) players are much less tolerant of network loss than delay. This is due to the game designer's decision to transfer data in TCP, where packet loss not only results in additional packet delays due to in-order delivery and retransmission, but also a lower sending rate.
Human Factors, Internet Measurement, Network Games, Quality of Service, Survival Analysis

1  Introduction

The prevalence of MMORPGs (Massive Multiplayer Online Role Playing Games) has broadened the definition of network games. Until recently, most real-time interactive network games were distributed and peer-to-peer; and no more than several dozen players could participate in a game at any time. Nowadays, however, it is not uncommon to have thousands of participants playing in an MMORPG world simultaneously. The MMORPG business also plays an important part in the economic well being of the Internet. The MMORPGs have generated 5 billion US dollars of business worldwide in 2004 and the market is expected to double by 2009 [9]. Among various threats to continuous player subscriptions, network lags are particularly notorious. It is widely known that frequent or long lags frustrate game players but whether the players will take action and leave a game remains unclear. This work is dedicated to answer this question.
Online games in general have been considered QoS-sensitive Internt applications [13] and there have been studies, although no one consensus reached, on the QoS-sensitivity of FPS (First-Person Shooting) games, RTS (Real Time Strategy) games, sports games, and car racing games [11,[18,[3,[16,[2,[17,[13] (cf. Section II-A). MMORPGs are different in that there are no explicit victories or defeats, scores, or rankings, and the playing time is a more appropriate indicator of the player's gaming experience. Therefore, in this attempt to understand MMORPG players' QoS-sensitivity, we ask the question: "Once a player is in a game, how does network QoS affect his decision to continue or leave the game?" This work is, as far as we know, the first quantitative analysis on the relationship between network QoS and online game playing times.
In this paper, we analyze the lifetimes of game sessions derived from ShenZhou Online [20], a commercial MMORPG. Using a survival analysis approach, we investigate the relationship between network QoS and session times. Although, logically, the relation of cause and effect cannot be clarified from a cross-sectional study, we assume the correlation between game session times and network QoS implies that premature departures are caused by unfavorable network experience. The major findings are as follows. First, we show that both network delay and network loss significantly affect players' willingness to continue a game or leave it, whereas earlier studies indicate that players have remarkable tolerance of unfavorable network conditions [11,[3,[18].
Second, while many network games provide "ping time," i.e. the round trip time (RTT), to players to facilitate server selection, we show that the delay jitters are more important than absolute delays in terms of playing time. Therefore, in addition to the "ping time," its variations should also be considered in the server selection process.
Third, quantitatively, the degrees of player "intolerance" to minimum RTT, RTT jitter, client loss rate, and server loss rate are in the proportion of 1:2:11:6. To be specific, a player's decision to leave a game due to unfavorable network conditions is based on the following levels of intolerance: client packet loss (55%), server packet loss (30%), RTT fluctuations (10%), and minimum RTT (5%). While most QoS-sensitivity studies focus on the impact of delay, we argue that delay jitters and the packet loss (error) rate are more important, since, from our modeling, absolute delay times only contribute 1/20 of the influence on average to the QoS-intolerance of MMORPG players. Furthermore, we believe that only considering transit delay times and the different characteristics of transport protocols used, e.g., TCP or UDP, could be the major reasons for the inconsistent results of earlier works.
The remainder of this paper is organized as follows. Section II describes related works and the design of the game ShenZhou Online. We present the measurement methodology, preprocessing, and a summary of the game traffic trace in Section III. In Section IV, we explain why we adopt survival analysis and present a summary of this methodology. In Section V, we analyze the general lifetime patterns exhibited by game sessions, and confirm the correlation between network QoS and session duration. Next, in Section VI, we develop a lifetime model that describes the relationship between QoS covariates and game session times. We then discuss the model's results and implications. Finally, in Section VII, we present our conclusions.

2  Background

2.1  Related Work

In the QoS spectrum of network applications, realtime interactive network games are generally considered QoS-sensitive. Although a QoS infrastructure is not widely available on the Internet, network games are already prevalent. The reason is that either QoS is unnecessary, or players usually struggle with adverse network conditions. Motivated by the question, Henderson and Bhatti conducted controlled experiments to examine the QoS tolerance of network game players [13]. They found that degraded QoS affects whether a user joins a game in the first place; however, once a user is in a game, the decision to leave is not significantly related to increased network delay. Henderson and Bhatti also found that the effect of network delay is outweighed by game design or exogenous effects, and players seem to be remarkably tolerant of network conditions [11]. The impact of network conditions on different game genres has been investigated in many ways. Armitage, for example, suggests that players prefer the Quake 3 server with a ping time of 150 to 180 milliseconds [2]. Beigbeder et al. find that typical ranges of packet loss and latency do not significantly affect the outcome of the game in Unreall Tournament 2003 [3]. Sheldon et al. conclude that, overall, high latency has a negligible effect on the outcome of Warcraft III [18]. In [16], Nichols and Claypool show that user performance is degraded by almost 30% for latencies higher than 500 ms in NFL Football. While most previous works suggest remarkable QoS tolerance on the part of game players, our results show that both network latency and network loss have a significant influence on game playing times in MMORPG.
The modeling of player lifetime is one of the important aspects in characterizing network gaming traffic. Henderson and Bhatti model session durations in Quake and Half-Life as an exponential distribution, the only constant failure-rate lifetime distribution [12]. On the other hand, the duration of Half-Life game sessions is closely fitted to a Weibull distribution in [4]. The authors attribute the inconsistent results to the influence of various game add-ons. Our modeling differs from earlier works in that it incorporates network QoS factors as predictors, which transforms it into a (multiple) regression problem of lifetime hazard functions. As a result, we can assess the influence of individual network QoS factors on game session times.

2.2  Design of ShenZhou Online

Figure 1: A screen shot of ShenZhou Online
ShenZhou Online is a mid-scale, commercial MMORPG in Taiwan [20]. There are thousands of players online at any time. To play, participants must purchase "game points" either from a convenience store or online. A screen shot of ShenZhou Online is shown in Fig. 1. The character played by the author is the man in the center of the screen and with a smiling face above his head. He is in a market place, where other players keep stalls. As is typical of an MMORPG, the players can engage in fights with random creatures, train themselves in particular skills, participate in marketplace commerce, or take on a quest.
ShenZhou Online is provided through a number of independent game sets, each of which is equivalent in content, but isolated. The reason for providing identical game sets is to distribute the workload over a number of servers with limited game content, e.g., terrain, missions, and creatures in the virtual world. A game set is logically a "game server" from the view point of players. Each game set comprises an entry server, several map servers, and a database server. The entry server guards the entrance to the game world, and game clients must connect to it first to "recall" the specified character from the database before their adventure. The game world is partitioned into a number of maps, provided by several map servers. When a character moves across map boundaries, if the new map is provided by a different map server, the game client will disconnect from the original map server and establish a connection with the new map server. In addition, since a credit account is allowed to own up to four characters in each game set, players may switch to another character during the game. The difference between a "character switch" and a "map switch" is that clients must contact the entry server to save and load character data in the former case, while switching maps does not require contact with the entry server.
Based on the above-mentioned mechanisms, we define three types of session: 1) map session: the period a character remains on a map; 2) role session: the period the same character is used; and 3) game session: the period a player remains in a game. Since we focus on the time players have been in the game, i.e., the duration of game sessions, only game sessions are considered in the rest of this paper. For brevity, we use "session" to denote game session(s), unless otherwise stated.
ShenZhou Online uses TCP as its network transport protocol. Although TCP is not designed for real-time communication, it has not been proved as yet whether it is suitable for MMORPG message transmission. However, from our analysis, TCP's loss recovery mechanism makes packet loss the major component of those disturb game play, whereas the situation could be relieved by using a more lightweight protocol which only recovers dropped packets whenever necessary.

3  Trace Description

In this section, we first describe the setup and network topology of the traffic measurement. We then address the method to infer game sessions from the packet-level trace. Finally the extracted game sessions are summarized.

3.1  Measurement Setup

Figure 2: Network setup and topology of the network monitor
To properly evaluate the relationship between game playing times and network conditions, a packet-level trace is necessary to infer QoS metrics, such as packet delay times and packet loss rates. With the assistance of ShenZhou Online staff, we set up a traffic monitor alongside the game servers. The traffic monitor is attached to a layer-4 switch upstream of the LAN containing the game servers (we call it the "game LAN"). The "port forwarding" capability of the tapped switch is enabled so that all inbound/outbound game traffic is forwarded to our monitor as a copy. To minimize the impact of monitoring, all remote management operations are conducted via an additional network path, i.e., the game traffic and management traffic do not interfere with each other. The network configuration of the game servers and the traffic monitor is depicted in Fig. 2. The traffic monitor is a FreeBSD PC equipped with Pentium 4, 1.5 GHz and 256 MB RAM. We use tcpdump with kernel built-in BPF to obtain traffic traces. Because of the restrictions of the network topology, the switch forwards all traffic sent to and from the game LAN to the monitor, including non-game-playing traffic such as HTTP, DNS and SMB packets. These unwanted traffic types are filtered out using the filtering support of tcpdump. Due to data privacy and storage considerations, only IP and TCP headers are recorded.
Owing to the large volume of game traffic, logging only packet headers takes less than three hours to fill up our 70 GB hard disk; however, a three-hour trace is too short for a lifetime analysis as the average session time of MMORPGs is between 70-120 minutes according to statistics from Japan [1]. To prolong the trace time, we randomly choose a subset of game sets for each trace-only packets corresponding to the selected game sets are logged. We then take two packet traces, N1 and N2, to record traffic for two- and three-game sets, respectively. Since each game set is identical in content and configuration, we assume players in different game sets do not exhibit significantly different behavior. We purposely captured two traces (one on a Sunday and one on a Monday) and took them as representatives of lifetime patterns on weekends and weekdays, respectively. A summary of the traffic traces is listed in Table I.
Table 1: Summary of Game Traffic Traces
Trace Sets Date Time Period Drops Conn. Pkt. (in / out / both) Bytes (in / out / both)
N1 3 8/29/04 (Sun.) 15:00 8 hr. 0.003% 57,945 342M / 353M / 695M 4.7TB / 27.3TB / 32.0TB
N2 2 8/30/04 (Mon.) 13:00 12 hr. ? 54,424 325M / 336M / 661M 4.7TB / 21.7TB / 26.5TB
The drop count reported by tcpdump is zero, but we actually found some packets are dropped at the monitor.

3.2  Session Composition

From the packet trace, we can easily identify a map session since it is semantically equivalent to a TCP connection. Unfortunately, a game session cannot be easily recognized because it involves a set of connections. However, as we know the game's design, we can obtain game sessions by the following composition rules: The thresholds, 30 sec and 120 sec, are conservative estimates so that most map switches and character switches are finished in the interval. We note that different threshold values do not noticeably affect our modeling, since only a few sessions involve long-duration map or character switches.
Table 2: Summary of Game Sessions
Trace # Sess. # Cens. Min. Median Max.
N1 7,597 3,331 (44%) 27 sec 122 min 487 min
N2 7,543 1,774 (24%) 22 sec 86 min 729 min
Total 15,140 5,105 (34%) 22 sec 100 min 729 min
Estimated with the Kaplan-Meier curve (Equation 1).
Table 3: Summary of Network Performance Experienced by Game Sessions
Trace RTTmin RTTmean RTTmax RTTsd LossclientLossserver Losstotal
N1 48.8 ms 176.8 ms 839.0 ms 63.4 ms 0.13% / 57.9% / 46.2% 0.10% / 27.0% / 61.3% 0.08% / 35.5% / 64.1%
N2 49.3 ms 176.4 ms 792.3 ms 61.9 ms 0.12% / 62.5% / 48.3% 0.10% / 18.5% / 61.5% 0.08% / 50.1% / 64.8%
Total 49.1 ms 176.6 ms 815.7 ms 62.6 ms 0.12% / 62.5% / 47.2% 0.10% / 27.0% / 61.4% 0.08% / 50.1% / 64.5%
The format of these columns: "geometric mean / maximum / percentile of sessions with at least one packet loss."

3.3  Trace Summary

We summarize the derived game sessions in Table II. A total of 15,140 sessions were observed with 5,105 sessions censored. The median session time of 100 minutes agrees with the statistics in [1], which reports the average session time is around 70-120 minutes for a number of Korean MMORPGs played in Japan.
The round-trip times and packet loss rates for game sessions are listed in Table III. The average RTT of around 180 ms looks reasonable for playing MMORPGs [13]. However, 10% of the sessions experienced an average loss rate ≥ 1%, and 3% of the sessions had a loss rate ≥ 5%. Does such a degree of loss influence players to continue a game or leave it? To answer this question, in the later sections, we progressively demonstrate how players' game times are related to their network experiences.

4  Methodology

In this paper, we analyze the relationship between game playing times and network QoS by a survival analysis approach [14]. We adopt this statistical methodology for two reasons: 1) a significant number (34%) of observed sessions are censored, i.e., only a portion of a session's duration is observed by our measurement, while methods in survival analysis are capable of handling such uncertainty; and 2) the relationship between game playing times and network QoS can be properly assessed by a transformation to a (multiple) regression problem, which corresponds to the notable Cox Proportional Hazards model [7] in survival analysis.
Figure 3: Our measurement setup leads to explicit censoring of game sessions. The four possible censoring scenarios are depicted with notation (t, s), where t is the observed duration and s is the censoring status.
Even though our traces take 8 and 12 hours respectively, 34% of the sessions are censored. Since game servers shutdown and carry out maintenance on a daily basis, we argue that the censoring of sessions is inevitable, either explicitly or implicitly. Our approach leads to explicit censoring, i.e., sessions are censored by the choice of trace periods (see Fig. 3). On the other hand, if we take traces over an entire day, players are implicitly censored by the prearranged daily shutdown. In this scenario, some players may leave the server ahead of the scheduled shutdown, while others may stay until they are forcibly disconnected. For example, if the server shuts down at 11:00 AM, a player who leaves at 10:30 may be due to the daily maintenance or other reasons. Because of the uncertainty of implicit censoring, we use explicit censoring as it reflects the "true" censoring status more accurately.
By the conventions of survival analysis, we denote a player's departure as an event or a failure. An indicator variable, si, the censoring status, is used to indicate whether a session, i, is censored: thus si=1 means an event has occurred (not censored) and vice versa, as illustrated in Fig. 3.
A survival function is commonly used to describe the lifetime pattern of an object or a set of observations. In our context, the survival function is defined as:
(a session that survives longer than time t)
1− Pr
(a session that fails before,
or is equal to time t)
where F(t) is the cumulative distribution function (CDF) of session times. A standard estimator of the survival function, proposed by Kaplan and Meier [15], is called the Product-Limit estimator or the Kaplan-Meier estimator. Suppose there are n distinct session times t1, t2, …, tn in ascending order such that t1 < t2 < … < tn, and that at time ti there are di events and Yi active sessions. The estimator is then defined as follows for all values of t ≤ tn:
ti ≤ t Pr
[T > ti |T ≥ ti ]

if t < t1,
ti ≤ t [1 − di

if t1 ≤ t.
For observations with ties, if the times are continuous in essence and later discretized by measurement, which is the case with game session times, a practical solution is to add a small amount of "noise" so that all times are unique. Following the estimate of the survival function, the pth quantile of the lifetime, tp, can then be obtained by
tp= inf
(t) ≤ 1−p}.
We use this equation repeatedly to estimate the median session time as t0.5 for a group of sessions.
In addition to the survival function, a frequently used quantity in survival analysis is the hazard function, or the hazard rate. It is also known as the conditional failure rate in reliability engineering, or the intensity function in stochastic processes. The hazard rate is defined by
h(t) =
∆t → 0
[t ≤ T < t + ∆t|T ≥ t]

A related quantity is the cumulative hazard function H(t) which is defined by
H(t) =
h(u)du = − ln[S(t)].
The hazard function gives the instantaneous rate at which failures occur for observations that have survived at time t. The quantity h(t)∆t may therefore be seen as the approximate probability that a player who has been in a game for time t will leave the game in the next ∆t period, given that ∆t is small. The hazard function plays an important role in the Cox regression model in that the hazard rate of session times h(t) is taken as the response variable of network QoS factors, as we shall discuss in Section VI.

5  Session Characteristics

In this section, we first examine the day of the week effect. We then clarify the correlation between game playing times and network QoS by correlational plots and statistical tests.

5.1  The Day of the Week Effect

Figure 4: Survival curves for sessions on weekday and weekend respectively
Having two traces, captured on a weekend day and a weekday respectively, an intuitive question we want to answer is: Do game playing times on these two days differ significantly? We use the estimated survival functions for sessions on both days to answer the question. As depicted in Fig. 4, the median lifetimes are 123 minutes and 84 minutes for the weekend and weekday, respectively. We can highlight this difference in another way: while 30% of users play for more than 5 hours on a weekend, only 18% of users stay for the same time on a weekday.
We use the the Mantel-Haenszel test (also known as the log-rank test) [10] to judge whether a set of survival functions is statistically equivalent. The log-rank test, with the null hypothesis that both survival functions are equivalent, reports p=1−Prχ2,1(245) ≈ 0, which strongly suggests the existence of a day of the week effect.

5.2  Correlation with Network QoS

MMORPGs are different in that there are no explicit victories or defeats, scores, or rankings, and the playing time is a more appropriate indicator of the player's gaming experience. Therefore, we expect players' staying times in MMORPGs will be affected, to some extent, by the network QoS. Instead of asking how network QoS affects game playing times, we begin with a more fundamental question: "Do lifetime patterns differ significantly under different network conditions?" To answer this, we plotted the survival curves for sessions grouped by the minimum RTT experienced by each session, and then checked the significance of the differences between the groups. In Fig. 5, the survival curves of three session groups, divided by 25% (38 ms) and 75% (56 ms) percentiles, are plotted. Visually these three curves diverge significantly from each other, and the log-rank test reports p=1−Prχ2,2(342) ≈ 0, which indicates the sessions in these groups were far from equivalent. The median session times of groups 1 and 3 were 145 minutes and 66 minutes, respectively, which gives a high ratio of 2.2. Therefore, we confirm a pronounced correlation between game session times and the minimum RTT the sessions experienced.
Figure 5: Survival curves for sessions with different levels of minimum RTT
In addition to network latency, network loss is also considered an important QoS factor related to gaming experience. Thus, we also assess the relevance of network loss to session times by contrasting the survival curves of sessions that experience different levels of network loss, as shown in Fig. 6. The sessions are classified into three groups by zero loss rate and 0.5% ( ≈ 90 percentile). Intuitively we expect that higher loss rates lead to shorter game sessions; however, group 1, which incurs no packet loss, has a much shorter average duration (52 minutes) than groups 2 and 3 (191 and 97 minutes respectively). This can be explained by the fact that short sessions are more likely to be lucky enough not to incur any packet loss. If we focus on those sessions with at least one packet loss, the median session time in group 3 is almost half that in group 2. Also, the log-rank test reports p=1−Prχ2,2(1277) ≈ 0, which suggests a significant connection between packet loss rates and session times.
Figure 6: Survival curves for sessions with different levels of loss rate
As the relevance of network QoS has been established, we can now check the correlation between gaming times and various QoS factors, namely, minimum RTT, average RTT, standard deviation of RTT (RTT std dev for short), mean queueing delay, client packet loss rates, and server packet loss rates. For brevity, hereafter, we use "client loss rate" for the estimated loss rate of client packets; "server loss rate" is similarly used. In Fig. 7, the median times for session groups with different levels of network quality as well as smoothed lowess curves [6] are plotted. For network delay factors, we first detect a "threshold" effect, that is, a negative correlation between playing times and network delay is only apparent within certain range. For example, the negative correlation of session times with minimum RTT exists only when the minimum RTT is smaller than 120 ms (cf. Fig. 7(a)). Despite the threshold effect, all network delay factors show a negative correlation with game times within certain ranges. On the other hand, the network loss shows a more consistent negative correlation with gaming times without the threshold effect, while the slope of the downward trend gradually becomes flatter for higher loss rates.
However, we note that simple correlational analysis does not reveal the true impact of individual QoS factors, because they are highly collinear. For example, the correlation coefficient between average RTT and minimum RTT and that between average RTT and RTT std dev are both higher than 0.6. Given that all the three RTT-related factors have significant correlations with session times, which one is the "true source" of user dissatisfaction is unclear. Players could be particularly unhappy because of one of the factors, or be sensitive to all of them. Thus, to determine the impact of individual factors, we adopt regression analysis, which models game playing times as responses to various QoS factors, in the next section.
Figure 7: Correlation of session times with network QoS factors

6  Proportional Hazards Regression

In this section, using the Cox proportional hazards model, a semi-parametric regression method, we assess how each individual QoS factor influences players' willingness to continue with a game or leave it. In the following, we briefly introduce the Cox regression model. Before the model can be fitted, we check the validity of the assumptions and carry out necessary adjustments. Then after developing the model, we assess its adequacy by checking the outliers and performing goodness-of-fit tests. Finally we validate the model by prediction, and conclude this section with a discussion on the modeling results.
In the correlational analysis (Section V-B), we showed that sessions with and without packet loss exhibit different lifetime patterns. To make the regression model parsimonious, and since sessions that incur no packet loss are of no interest to us as they contain no information about the relationship between session times and network loss, we exclude those sessions from our modeling. As a result, 6,680 out of 15,140 sessions remain.

6.1  The Cox Regression Model

The Cox proportional hazards model [7] has long been the most used procedure for modeling the relationship between covariates and censored outcomes. In the Cox model, we treat potential network QoS factors, e.g., the average RTT, as risk factors or covariates; in other words, as variables that could cause failures. In this model, the hazard function of each session is decided completely by a baseline hazard function and the risk factors related to that session. We define the risk factors of a session as a risk vector Z. Cox's basic model is defined as:
h(t|Z) = h0 (t)exp(βtZ)=h0(t)exp( p

k = 1 
βk Zk ),
where h(t|Z) is the hazard rate at time t for a session with risk vector Z; h0(t) is the baseline hazard function, which is computed during the regression process; and β=(β1,…,βp)t is the coefficient vector that corresponds to the influence of each risk factor. Dividing both sides of Equation 2 by h0(t) and taking the logarithm, we obtain

log h(t|Z)

h0 (t)
= β1 Z1 + …+ βk Zk = p

k = 1 
βk Zk = β t Z,
where Zp is the pth factor of the session. The right side of Equation 3 is a linear function of covariates and their respective coefficients, i.e., it is transformed to a linear regression problem. The Cox model possesses the property that, if we look at two sessions with risk vectors Z and Z′, the hazard ratio (ratio of their hazard rates) is

h0 (t)exp[∑k = 1p βk Zk ]

h0 (t)exp[∑k = 1p βk Z′k ]
exp[ p

k = 1 
βk (Zk − Z′k )],
which is a time-independent constant, i.e., the hazard ratio of the two sessions is independent of time. For this reason the Cox model is often called the proportional hazards model. On the other hand, this imposes the most strict restriction when applying the Cox model, because the validity of the model relies on the assumption that the hazard rates for any two sessions must be in proportion all the time.
Figure 8: Graphical check for proportionality of the weekend factor

6.2  Proportional Hazards Check for Categorical Variables

We begin the model development by checking whether the proportional hazards assumption is met for our data set. We first check the assumption for the categorical variables in this subsection and for the continuous factors in the next subsection.
For modeling purposes, we set a dichotomous variable, weekend, indicating if a session was observed on the weekend. A graphical check for the proportional hazards assumption is first performed by grouping sessions by the categorical variable, and plotting the cumulative hazard function Hi(t) versus t for each group i in a log-log scale. If the proportional hazards assumption is met, the log survival curves should steadily drift apart. Specifically, for a dichotomous variable, the assumption requires that the hazard ratio between "true" and "false" sessions is a constant. As Fig. 8 shows, the two curves intersect at t = 2 minutes and gradually deviate from each other thereafter, which indicates that weekend violates the proportionality assumption.
Now that a non-proportional categorical variable is present, to accommodate the variable, we use the stratified Cox model. The model augments the basic Cox model by incorporating the support of strata, where each stratum has its own baseline hazard function. For a Cox model with m strata (m = 2 in our modeling), Equation 3 is generalized to
hi(t|Z) = h0i (t)exp(βtZ), i=1,…,m.
Note that, although the baseline hazard function for each stratum can be different, the coefficient vector β is shared by all strata.
Table 4: Time-dependent coefficients before adjustment
Variable tho chisq p Variable tho chisq p
rtt.min -0.04 5.40 0.02 cl -0.17 114.17 0.00
rtt.sd 0.03 3.03 0.08 sl -0.34 5.61 0.02
Table 5: Time-dependent coefficients after adjustment
Variable tho chisq p Variable tho chisq p
rtt.min 0.01 0.38 0.54 cl -0.03 1.85 0.17
rtt.sd 0.01 0.21 0.65 cl.med -0.01 0.19 0.67
sl -0.03 1.60 0.21 cl.hi 0.01 0.11 0.74
sl.hi 0.01 0.34 0.56

6.3  Functional Form Identification and Adjustment

Figure 9: The original (before adjustment) functional form of the four factors
For a continuous variable, Cox's proportional hazards assumption implies that a linear relationship between the covariates and the hazard function, i.e., it implies that the ratio of risks between a 20 ms- and a 30 ms-average RTT session is the same as that between a 90 ms- and 100 ms-average RTT session. Thus, to proceed with the Cox model, we must ensure our predictors have a linear influence on the hazard functions.
We explore the correct functional form for the covariates by
E[si ] = exp(β t f(Z))

I(ti \geqslant s)h0 (s)ds
where f(z) is the "true" functional form for the covariate z. This is just a Poisson regression model if h0(s) is known, while the value of h0(s) can be approximated by fitting a Cox model with unadjusted covariates. We can then fit the Poisson regression model with smoothing spline terms for each covariate [19]. The fitted terms for our QoS factors, as well as their two-standard-error confidence bands, are plotted in Fig. 9. Note that the average RTT and mean queueing delay are not included in the model, since these two terms become insignificant once the minimum RTT is incorporated into the model. Also, the minimum RTT, which can be seen as an approximation of round-trip propagation delay time, describes the observations much better from a log-likelihood point of view. From the graph, we observe that both the minimum RTT and RTT std dev have roughly proportional influence in the dense region, i.e., the region where observations are concentrated (note the "rugs" at the bottom of each plot). The vertical dashed lines denote a possible cutoff line that reflects the "threshold" effect we observed in Section V-B. However, the influence of loss rates is not proportional to their magnitude in any case; thus, modeling their influence as linear would not be realistic or accurate. A solution for non-proportional variables is the scale transformation. We find that after taking logarithms, the transformed variables, cl and sl, for client loss rates and server loss rates respectively, have a smoother and approximately proportional influence on the failure rate. That is, the failure rate is proportional to the scale of the loss rate, rather than their magnitude (Figs. 12 and 13).
Despite the threshold effect and the non-strict-linearity of our covariates, we first test whether the proportional hazard assumption holds. One test is to fit the same data to a more generalized Cox model which allows time-dependent coefficients [19]. In this model, Equation 3 is extended to
log h(t|Z)

h0 (t)
= p

k = 1 
β(t) k Zk = p

k = 1 
kkln(t)) Zk,
where the coefficient vector β(t) is not constant, but time-dependent. The null hypothesis, which indicates the conformance of the proportional hazards assumption, corresponds to θk ≡ 0,k=1,…,p. In this case, β(t) in the extended model reduces to β in the standard model. The test is similar to a standard linear trend test in that it tests whether a significant non-zero slope exists by a ordinary least square regression. The test results of our current model are listed in Table IV. In the table, the column rho is the Pearson product-moment correlation between the scaled Schoenfeld residuals and ln(t); chisq gives the test statistics, which has an asymptotic χ21 distribution. The significance values show that, except for rtt.sd, other covariates reject the proportional hazards assumption at significance level 0.05, and all are rejected at level 0.1. Thus, we need some adjustments for these covariates so that the proportionality assumption holds.
Figure 10: The functional form of the rtt.min factor
Figure 11: The functional form of the rtt.sd factor
Figure 12: The functional form of the cl factor with a linear-spline approximation
Figure 13: The functional form of the sl factor with a linear-spline approximation
First we inspect the functional form of rtt.min shown in Fig. 10. We consider the pronounced threshold effect is plausible in that a minimum RTT smaller than a certain threshold will not make a difference to the gaming experience, i.e., a 10 ms- and 20 ms-minimum-RTT should be indistinguishable by players. On the other hand, for large minimum-RTTs, which are nearly always experienced by sessions initiated in other countries, players must be accustomed to struggling against large network latency which is unavoidable. Therefore, to put rtt.min into the Cox model, we need to cut out the non-proportional-influence sections. For this purpose, we search for the best thresholds (cut-off points) by minimizing the chisq statistic in the above proportional hazard test. The resulting thresholds of 45 and 70 ms, shown in Fig. 10, bracket the linearly influential section of rtt.min. By similar arguments, rtt.sd also exhibits a threshold effect, but it only applies to large RTT fluctuations, i.e., RTT std dev causes players approximately proportional discomfort as long as its magnitude is not too high. The computed threshold is 470 ms. It can be shown that a linear approximation to RTT std dev's true influence with rtt.sd ≤ 470 ms is appropriate, as the line is consistently within the confidence band (Fig. 11).
The covariates of the packet loss rates, cl and sl, shown in Figs. 12 and 13 respectively, do not exhibit the threshold effect, but their influence is clearly non-linear. We choose to approximate their influence by linear splines, that is, piecewise linear segments connected by "knots," while the locations of the knots are obtained using a minimum partial log-likelihood approach. By incorporating new covariates, cl.med and cl.hi, for cl, and sl.hi for sl, we model the influence of cl and sl by three- and two-segment-linear splines, respectively, so that the whole linear spline function is within the corresponding confidence bands (see Fig. 12 and Fig. 13). The new covariates are defined by

if cl ≥ −3.72,

if cl ≥ −2.37,

if sl ≥ −3.71,
where the knots of cl are −3.72 ( ≈ 0.02%) and −2.37 ( ≈ 0.43%) respectively, and the knot of sl is −3.71 ( ≈ 0.02%). The integrated influence of the client loss rate and the server loss rate can then be computed by cl×βcl+cl.med×βcl.med+cl.hi×βcl.hi and sl×βsl+sl.hi×βsl.hi respectively. Incorporating these new covariate reduces the log-likelihood by 25.7, which is significant for a chi-square distribution with three degrees of freedom. After the covariates are adjusted for proportionality assumption, we perform the proportional assumption test again, and list the results in Table V. According to the table, all covariates do not reject the linearity hypothesis at significance level 0.1 after the adjustments, i.e., all covariates are approximately linear in terms of their influence on game playing times.
Now that the proportional hazards assumption is affirmed, we adopt a stepwise approach for the selection of significant interaction terms. As no interaction terms are significant at 0.05, we keep the model intact with the original seven covariates. We defer the presentation and discussion of the fitted model to later sections (Section VI-F and VI-G), following a check for outliers and a goodness-of-test for the model's adequacy.

6.4  Outlier Detection

To assess the impact of individual sessions in a regression model, the most direct measure of influence is the jackknife value Ji=[^(β)]−[^(β)](i), where [^(β)](i) is the result of a fit that includes all observations, except session i. Because the jackknife involves a significant amount of computation, we use the dfbeta residuals to approximate the jackknife value [19]. Note that dfbeta residuals have the opposite implication of the jackknife, i.e., they indicate the change of β with the inclusion of a particular individual. The potential outliers we identified are mostly sessions that experience unfavorable network conditions, but have rather long durations. We determine whether a session is "reasonable" by two metrics: cli.prate, the average client data packet rate; and srv.prate, the average server data packet rate. The former can be seen as an indicator of player activities, such as movement and attack, while the latter indicates the degree of interaction, since server packets primarily contain status updates of the nearby environment. We treat potential outliers whose cli.prate or srv.prate is smaller than their 20% percentile as actual outliers and remove them from the trace. The rationale behind this is that low cli.prate and srv.prate indicate that the participants did not actively play the game, or even left the game idle for some period; therefore their corresponding session times are less reliable. As a result, 38 out of 3,027 sessions were removed according to the above rules.

6.5  Assessment of Model Adequacy

We use the Cox and Snell residuals to assess the overall goodness-of-fit of our model [8]. If the model is correctly fitted, the random variable ri=[^H](ti,Zi) has an exponential distribution with a hazard rate of 1, where [^H](ti,Zi) is the estimated cumulative hazard rate for session i with risk vector Zi. Accordingly, the plot of ri and its Kaplan-Meier estimate of survival function [^S](r) will be a straight line through the origin with a slope of 1. The graphical check is plotted in Fig. 14, in which most sessions are along a 45° straight line, especially in the dense area. A few sessions ( ≈ 4%) deviate from the straight line. We believe these sessions are due to QoS-tolerant game fans who experience higher delay variations and loss rates, but still play the game about four times longer than regular players. Except for the divergence due to such game fans, most sessions fit the model very well; therefore, the adequacy of the fitted model is confirmed.
Figure 14: Cox-Snell residuals plot for overall goodness of test
Table 6: Coefficients in the Final Model
Variable Coef Exp(Coef) Std. Err. z P > |z|
rtt.min 19.20 2.2e+08 3.90 4.93 8.29e-07
rtt.sd 4.54 94 0.52 8.70 0.00e+00
cl 0.70 2 0.15 4.85 1.23e-06
cl.med -0.52 0.59 0.18 -2.87 4.07e-03
cl.hi 0.64 1.9 0.11 5.76 8.29e-09
sl 0.45 1.6 0.16 2.88 4.00e-03
sl.hi -0.35 0.7 0.17 -2.03 4.25e-02

6.6  Model Validation and Interpretation

In Table VI, we present the estimated coefficients along with their standard errors and significance values for the final model. All covariates in the model are significant at level 0.05. We can validate the model by prediction; that is, given a network QoS vector Z, we can predict the most probable session time as the median time of the estimated survival curve, i.e., inf{t:S(t|Z) ≤ 0.5}, while S(t|Z) = exp(−H(t|Z)) is the computed survival function for the session with risk vector Z. By the relation, we sort and group all sessions by their risk scores, βtZ, and predict session times based on the median risk score in each group. The actual median times, predicted times, and their 50% confidence bands are depicted in Fig. 15. Note the confidence bands are asymmetric, since the standard errors are in the form of hazard rates. We find that the predicted times are rather close to the actual median times, especially on weekdays, and for most groups the actual median times are within the 50% predicted confidence bands.
Figure 15: Predicted times vs. actual median times for session groups sorted by their risk scores
The coefficients in the model, as listed in Table VI, can be interpreted by hazard ratios (Equation 4). For example, assuming two players enter a game at the same time and experience similar network conditions, except for minimum RTT, where the minimum RTTs they have are 70 ms and 50 ms respectively. The hazard ratio between session times of these two players can then be computed by exp((0.07 − 0.05)×19.2) ≈ 1.47, where 19.2 is the coefficient of covariate rtt.min. That is, as long as both players are still online, in every instant, the probability that player 1 will leave the game is 1.47 times the probability that player 2 will leave. By this rule, given QoS factors experienced by any two players, we can compute the hazard ratio between their game sessions.

6.7  Discussion

Since we have shown that network conditions significantly impact on game playing times, one may ask: How do these QoS factors influence the behaviors of game players "in practice"? To answer this, we try to determine a factor's actual influence by predicting session times with measured values, i.e., by applying the magnitude of QoS factors from their distributions in our game trace, as shown in Fig. 16. When observing a QoS factor, the other factors are set equal to their respective medians. Note that we purposely place four curves separately, since the predictions for different factors cannot be compared directly. We observe that the RTT std dev and client loss rate show rapidly declining trends at their tails (top quantiles). Specifically, sessions with the top 10% RTT std dev ( ≥ 80 ms) are affected by RTT fluctuations (also known as delay jitters) much more than other sessions. Similarly, sessions with the top 20% client loss rate ( ≥ 0.5%) are affected by client packet loss much more than other sessions. We remark that RTT fluctuations and client packet loss are two major potential opponents to a smooth game playing experience because of their strong impact at high quantiles.
Figure 16: The influence of each covariate in practice, i.e., predicting duration by quantiles of QoS factors in our game trace
We can also determine the factors' actual influence by the ratio of predicted duration between different quantiles. As shown in Table VII, the predicted duration of 1% percentile client loss rate ( ≈ 0.002%) is 9.3 times more than that of 99% percentile client loss rate ( ≈ 25%)! In contrast, the same quantity for the minimum RTT is only 1.5. We can also combine the influence of network latency and loss, the 1% versus 99% scenario shows that network loss has a much higher impact (ratio of 6.8) than latency (ratio of 2.6).
Table 7: Ratio of predicted duration between different quantiles
Ratio rtt.min rtt.sd closs sloss
25% vs. 75% 114:96=1.2 109:105=1.0 118:98=1.2 111:102=1.1
5% vs. 95% 121:85=1.4 112:79=1.4 192:59=3.3 144:93=1.6
1% vs. 99% 122:82=1.5 117:43=2.7 252:27=9.3 171:90=1.9
Ratio rtt.min + rtt.sd closs + sloss
25% vs. 75% 128:123=1.0 173:147=1.2
5% vs. 95% 132:95=1.4 271:69=3.9
1% vs. 99% 142:54=2.6 373:55=6.8
Figure 17: Relative influence of different QoS factors in each session
Another of our concerns is to quantify the relative influence of QoS factors. We assess their relative weights by computing the risk score for each QoS factor with the other factors set to their respective minimum values. The relative influence of each QoS factor, which is normalized by a total risk score of 100, is shown in Fig. 17. On average, the degrees of players' "intolerance" to minimum RTT, RTT std dev, client loss rate, and server loss rate are in the proportion of 1:2:11:6. That is, a player's decision to leave a game due to unfavorable network conditions is based on the following levels of intolerance: client packet loss (55%), server packet loss (30%), RTT fluctuations (10%), and minimum RTT (5%).
The above results highlights the fact that delay jitters are less tolerable than absolute delay. While most earlier QoS-sensitivity studies completely neglected the impact of delay jitters, we argue they are more relevant in players' network experience. Therefore, while current network games primarily rely on a "ping time" to select a server for a smooth game play, delay jitters should also be considered in the server selection process. We also find that server packet loss is relatively more tolerable than client packet loss. We consider this to be reasonable, since client packet loss delays players' commands to the server, while server packet loss delays the response and state updates. Nowadays MMORPGs are server-centric so that no command becomes valid until it has been processed by the server. Therefore, delaying players' commands, such as attack or casting spells in combat, is much more annoying than just delaying the response and screen updates.
Comparing the influence of network loss and network latency, we find a ratio of 17:3, or nearly six to one. However, in an earlier study on Unreal Tournament 2003 [3], the authors reported that though network latency in a typical range (0 ms - 200 ms) has a statistically weak impact on user performance, network loss of a typical range ( < 6%) has no impact on user performance. We consider that the difference between our result and those of [3] is due to the choice of underlying transport protocol. That is, while most FPS games transmit messages via UDP, many MMORPGs, including ShenZhou Online, use TCP. Since TCP provides in-order delivery and congestion control, a lost packet will cause the subsequent packets to be buffered until it is successfully delivered, and, furthermore, cut down TCP's congestion window. On the other hand, packet loss incurs no overhead in UDP. In short, for TCP-based online games, packet loss incurs additional packet delay and delay jitters, and therefore causes further annoyance to players. From this point of view, and because of TCP's high communication overhead [5], we consider that more lightweight protocols would be more appropriate for realtime interactive network games.

7  Conclusion

In this paper, we analyze the lifetimes of game sessions derived from ShenZhou Online, a commercial MMORPG. Using a survival analysis approach, we investigate the relationship between network QoS and session times, and find that both network delay and network loss significantly affect a player's willingness to continue a game or leave it. For ShenZhou Online, the degrees of player "intolerance" of minimum RTT, RTT jitter, client loss rate, and server loss rate are in the proportion of 1:2:11:6. This indicates that: 1) while many network games provide "ping time" to players to facilitate server selection, it would be more useful to provide information about delay jitters; and 2) players are much less tolerant of network loss than delay. This is due to the game designer's decision to transfer data in TCP, where packet loss incurs additional delay and delay jitters, and therefore causes further annoyance to players.


This work would not have been possible without the extensive traffic trace. The authors are much indebted to the following people who helped us gather the trace: Tsing-San Cheng, Lawrence Ho, Chen-Hsi Li, and especially to Yen-Shuo Su, who made the datasets available. The authors also wish to thank the anonymous referees for their constructive criticisms.


[1] "Gametrics weekly Korea MMORPG population survey." [Online]. Available: http://www.4gamer.net/news.php?url=/specials/gametrics/gametrics.shtml
[2] G. Armitage, "An experimental estimation of latency sensitivity in multiplayer Quake 3," in 11th IEEE International Conference on Networks (ICON 2003), 2003.
[3] T. Beigbeder, R. Coughlan, C. Lusher, J. Plunkett, E. Agu, and M. Claypool, "The effects of loss and latency on user performance in Unreal Tournament 2003," in NetGames '04: Proceedings of the 3nd Workshop on Network and System Support for Games.    ACM Press, 2004, pp. 144-151.
[4] F. Chang and W. chang Feng, "Modeling player session times of on-line games," in NetGames '03: Proceedings of the 2nd Workshop on Network and System Support for Games.    ACM Press, 2003, pp. 23-26.
[5] K.-T. Chen, P. Huang, and C.-L. Lei, "Game Traffic Analysis: An MMORPG Perspective," Computer Networks, Article In Press.
[6] W. S. Cleveland, "LOWESS: a program for smoothing scatterplots by robust locally weighted regression," The American Statistician, vol. 35, no. 54, 1981.
[7] D. R. Cox and D. Oakes, Analysis of Survival Data.    Chapman & Hall/CRC, June 1984.
[8] D. R. Cox and E. J. Snell, "A general definition of residuals (with discussion)," Journal of the Royal Statistical Society, vol. B 30, pp. 248-275, 1968.
[9] DFC Intelligence, "The online game market 2004."
[10] D. P. Harrington and T. R. Fleming, "A class of rank test procedures for censored survival data," Biometrika, vol. 69, pp. 553-566, 1982.
[11] T. Henderson, "Latency and user behaviour on a multiplayer game server," in Proceedings of the Third International COST Workshop (NGC 2001).    Springer-Verlag, 2001, pp. 1-13.
[12] T. Henderson and S. Bhatti, "Modelling user behaviour in networked games," in MULTIMEDIA '01: Proceedings of the Ninth ACM International Conference on Multimedia.    ACM Press, 2001, pp. 212-220.
[13] --, "Networked games: a QoS-sensitive application for QoS-insensitive users?" in RIPQoS '03: Proceedings of the ACM SIGCOMM Workshop on Revisiting IP QoS.    ACM Press, 2003, pp. 141-147.
[14] J. D. Kalbfleisch and R. L. Prentice, The Statistical Analysis of Failure Time Data, 2nd ed.    Wiley-Interscience, August 2002.
[15] E. L. Kaplan and P. Meier, "Nonparametric estimation from incomplete observations," Journal of the American Statistical Association, vol. 53, pp. 437-481, 1958.
[16] J. Nichols and M. Claypool, "The effects of latency on online madden NFL football," in NOSSDAV '04: Proceedings of the 14th International Workshop on Network and Operating Systems Support for Digital Audio and Video.    ACM Press, 2004, pp. 146-151.
[17] L. Pantel and L. C. Wolf, "On the impact of delay on real-time multiplayer games," in NOSSDAV '02: Proceedings of the 12th International Workshop on Network and Operating Systems Support for Digital Audio and Video.    ACM Press, 2002, pp. 23-29.
[18] N. Sheldon, E. Girard, S. Borg, M. Claypool, and E. Agu, "The effect of latency on user performance in Warcraft III," in NetGames '03: Proceedings of the 2nd Workshop on Network and System Support for Games.    ACM Press, 2003, pp. 3-14.
[19] T. M. Therneau and P. M. Grambsch, Modeling Survival Data: Extending the Cox Model, 1st ed.    Springer, August 2001.
[20] "ShenZhou Online," UserJoy Technology Co., Ltd. [Online]. Available: http://www.ewsoft.com.tw/


1. This research is supported in part by the National Science Council of the Republic of China under grant NSC 94-2213-E-002-043.

Sheng-Wei Chen (also known as Kuan-Ta Chen)
Last Update September 28, 2019