## 3 Materials and Methods
### Trace Anonymization
\begin{table}
\begin{tabular}{l l l} \hline  & **Off-Peak** & **Peak** \\ \hline Time frame & 20:00–21:00 UTC, 6 August & 13:00–14:00 UTC, 5 August \\ \hline Observed sessions & 252,045 & 608,896 \\ \hline Server-to-client messages & 3,507,062 & 8,638,416 \\ \hline \end{tabular}
\end{table} 
Table 4: Statistics about the least busy time frame and the busiest time frame. Figure 5: Distribution of packet inter-arrival times for Game \(B\) during off-peak and peak periods. We conducted a similar analysis on the traffic trace of Game \(B\). Figure 7a,b present the variance-time plots for the client-to-server traffic and the overall traffic, respectively. Both graphs exhibit similar patterns, which can be attributed to Game _B_'s use of HTTP protocol that operates on a request-response basis. In this setup, the server-to-client responses closely follow the corresponding client-to-server requests, typically after a short processing delay of less than 100 milliseconds. Consequently, including server-to-client traffic essentially doubles the packet arrivals in 100-millisecond buckets, but does not significantly alter the overall pattern of the graph. The linear and slow decay nature of both graphs suggests strong self-similarity in the traffic pattern. Contrary to Game \(A\), the traffic in this type of game is strongly linked to player engagement, which partially explains the burstiness pattern observed. ## 5 Conclusions

Initially, we hypothesized that different game genres would exhibit unique network traffic patterns and that mobile games would significantly differ from PC online games due to traditionally less reliable mobile networking environments. However, our analysis of two mobile games representing opposite genre spectrums suggests that modern mobile games maintain high expectations for network reliability, similar to PC online games, regardless of genre. Contrary to our assumption that the wireless environment would influence packet lengths, our findings indicate that there are no substantial differences in packet lengths between recent mobile and PC online games. Both environments predominantly use short packets, closely linked to player action patterns, which aligns with findings from previous PC game research. Additionally, our analysis revealed that inter-packet arrivals within a

Figure 6: Variance–time plot for Game \(A\). Both axes are on a logarithmic scale (base 10). The x-axis indicates how many 100-millisecond buckets are aggregated. The y-axis represents the variance of aggregated packet counts normalized by the square of packet counts per 100 milliseconds. Figure 7: Variance–time plot for Game \(B\). Both axes are on a logarithmic scale (base 10). The y-axis represents the variance of aggregated packet counts normalized by the square of packet counts per 100 milliseconds. mobile game session are very short, resembling the patterns observed in PC online games. This suggests that modern mobile games do not implement packet aggregation, as they have high expectations for network robustness. On the other hand, the presence of traffic self-similarity, commonly observed in PC online games, varies across mobile game genres. While the HTTP-based, less-synchronized game exhibits self-similarity in its traffic, the TCP-based, tightly synchronized game does not appear to have strict self-similarity. This observation contradicts our assumption that similar game genres (e.g., MMORPG) might share similar traffic characteristics. The difference may arise from variances in genre and/or player behavioral patterns between wired and wireless networks. While further research is needed for a more comprehensive understanding, there is a potential opportunity to devise a traffic model for mobile games that better suits their unique characteristics compared to the self-similar process observed in PC online games. Despite the critical role of network traffic in the current Internet landscape, research on mobile game traffic patterns is surprisingly sparse.