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Ch 13 Traffic Measurement and Player Analysis
Magda El Zarki Prof. of CS
Univ. of CA, Irvine Email: [email protected]
http://www.ics.uci.edu/~magda
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MMORPG
Different from FPS games No requirement for high precision – can tolerate some
delay Tracking accuracy lower No explicit game rounds No limit on number of players Use TCP instead of UDP – timing/delay not such an issue
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Trace Collection of Lineage II
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Overview of Data Collection
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No. of Users connected to server
Network or Server Outage
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Distribution of Client Packet Size
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CDF of Client Packet Size
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Client Packets
73% of client bytes are used for headers
38% of client packets are dedicated TCP ACKs TCP uses delayed ACKS – 200ms
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Distribution of Server Packet Size
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CDF of Client Packet Size
Most TCP ACKS are piggybacked
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Bandwidth Usage
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Bandwidth and No. of Players S->C
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Bandwidth and No. of Players C->S
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Packet Inter-arrival distr.
200msec update cycle
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PSD of Packet Arrivals
5Hz – 200msec cycle
6Hz is automated mouse click frequency
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Summary
Bytes
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Paper
Traffic Characteristics of a Massively Multi-player Online Role Playing Game, J. Kim, et al., NetGames’05, October 10–11, 2005, Hawthorne, New York, USA.
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Studying Player Populations
When they play
How long they play
How often they play
Player loyalty
Player retention over time
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Questions of Interest to Providers
How many players will there be next week? Provision servers to support them
What can I do to increase this number? Impact of game updates and promotions
What can I do to make sure players don’t quit? Detecting disinterested players
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Difficult questions to answer Requires player data from a successful game over a
long time period – very difficult to obtain
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Questions
How many players will there be next week? Provision servers to support them
What can I do to increase this number? Impact of game updates and promotions
What can I do to make sure players don’t quit? Detecting disinterested players
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Games Studied
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Game Popularity over time (FPS)
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Game Popularity over time contd.
MMORPG
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Game workloads Periodic
Strong daily peaks with weaker weekend peaks
Gamespy FPS EVE Online MMORPG
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Game Workloads contd.
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Comparing Across games
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FFT of Weekly Usage
Daily Usage is similar
Weekend Usage is similar
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Gamespy EVE Online
Game workloads Predictable over short-term
Workload fluctuations small from week-to-week
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Where are the players?
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Questions
How many players will there be next week? Provision servers to support them
What can I do to increase this number? Impact of game updates and promotions
What can I do to make sure players don’t quit? Detecting disinterested players
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EVE Online
Single world sci-fi MMORPG
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EVE Online statistics
Launched in UK and USA on May 6, 2003 Europe on May 23, 2003 China on June 12, 2006
As of August 3, 2007 190,000 active subscriptions 35,000+ peak concurrent on-line players
How does it stack up against other MMOs?
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Decent Player Populations
Source: http://mmogchart.com (6/2006)
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EVE Online trace
Anonymized authentication log of EVE Online throughout its existence All session-related events for each player
Duration May 6, 2003 – March 12, 2006
Total sessions 67,060,901 Total unique players 925,928
Total player time 17,204 years
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EVE Online growth Active player population throughout trace
EVE Online
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Mechanisms for increasing population
New game content and updates
Promotions and marketing
Price reduction
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Impact of game updates Gain in players after each game update in trace
Large gains after initial release Modest gains after subsequent game updates (2+3 below)
Castor spikes Competing sci-fi MMORPG shuttered Marketing blitz during game conference (free accounts)
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Impact of game updates on player time
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Questions
How many players will there be next week? Provision servers to support them
What can I do to increase this number? Impact of game updates and promotions
What can I do to make sure players don’t quit? Detecting disinterested players
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Player churn A fact of MMORPG life
MMORPGs notorious for low acquisition rates EVE Online player acquisition rate drops over time
Potential reasons New players at a disadvantage Hard-core player population “tapped” out 70% playing 1 month later
Only 25% still playing one month later
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Can we measure disinterest?
Examining play history to detect waning interest Minutes played per week Session length statistics Inter-session time statistics
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Metric #1: Minutes played per week Minutes played per week throughout play history
Players play less over time
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Metric #2: Session times Session time distribution
Session length of “final” session shorter than normal
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Metric #3: Intersession times Intersession time distribution
“Final” intersession time significantly longer than normal
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Catching a disinterested player Aggregate not individual statistics
Addicts thrown in with casual gamers Normalize per-player
What percentile does final session and final inter-session times fall into versus player’s prior times? “Final” intersession time a very good predictor!
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Paper
Characterizing On-line Games, Chris Chambers, Wu-chang Feng, Sambit Sahu, Debanjan Saha, David Brandt, IEEE/ACM Transactions on Networking, vol. 18, no. 3, June 2010