1. Issues with Measurement-based characterization of on- line games Prasad

2. Behavior of gamers Conclusions made may not be applicable to all categories of games – hence, the term gamers is misleading Conclusions made may not be applicable to Counter-Strike gamers! Data does not encompass gamer activity across all servers In subsequent slides, please read “gamers” as “Counter-Strike gamers”

3. Are gamers really impatient? How significant is the impact of a feature like QuickStart? Without analyzing data from many other prominent servers of Counter- Strike, how could one make such a conclusion?

4. Do gamers really have short attention spans? How significant is the effect of a player exploring server features? “As with player patience, it may be possible to fit a negative exponential for longer session times” So, is it Weinbull or negative exponential distribution?

5. Gamers aren’t loyal!? Makes a good headline, but, data doesn’t really say this Maybe, 42% of gamers didn’t like the game at all and hence never returned! Maybe they found another server closer to them “We hypothesize that, due to a large population of servers to choose from (over 30,000), clients rarely select the same server twice” If the clients are happy with their servers, and the game, would they really keep switching to new servers just because there are 30,000 of them!? Doesn’t make good sense!

6. Loss of Interest If no. of mins decreased, could it be because the gamer came close to the end and finished it! “The variance on this data is extremely high, due in part to the fact that players only spend a portion of their time on this single server, and therefore this data is unsuitable for predicting the interest of a given player.” In other words, the conclusion on gamer’s interest may be wrong!

7. Games and interactive application workloads are synchronized It’s hard to understand this Using one game’s data, collected for just one week, how can one make this emphatic conclusion?

8. Finally… Many conclusions are based on one week’s data - how was that particular week selected to analyze the data? Many graphs show daily data variations – what is the time-zone used to define “day” and “night”?