measurement and estimation of network qos among peer xbox game players
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Measurement and Estimation of Network QoS among Peer Xbox Game Players
Youngki Lee, KAISTSharad Agarwal, Microsoft Research
Chris Butcher, Bungie StudioJitu Padhye, Microsoft Research
• A series of online multiplayer game via Xbox Live▫ First Person Shooter (FPS) game▫ 15 million copies sold worldwide
• We focus on Halo 3 for data collection and analysis.▫ Halo 3 has a large set of widely distributed player population.▫ released on September 25, 2007.
2
P2P architecture of Halo
3 Xbox console
P2P architecture of Halo
• P2P, a peer as a server
Xbox console
Xbox Live matchmaking service
• Network QoS between the server peer and other client peers is important for game quality.▫ excellent experience: latency (< 50ms), BW (50~70Kbps).▫ minimum requirement: latency (< 150ms), BW (>30Kbps).
QoS probing among peers
Xbox Live matchmaking service
Query: Give me a list of hosts that satisfy my criteria
Probing using the packet-pair technique
5
Candidate hosts
Motivation• Understand network path quality (NPQ) among peer game
players and characteristics of the players▫ NPQ in terms of network delay and capacity
• Address the problem of NPQ measurement overhead▫ improve user pre-game experience
probe fewer, better candidate hosts
• Limited publications on large-scale E2E network characteriza-tion▫ Planetlab-based end-to-end NPQ studies: O(100) nodes▫ king-based end-to-end NPQ studies O(1000) nodes▫ several studies of provisioned server based games
6
Methodology1. Collect probe data among peer game players
a) consoles report the probe results back to Xbox live service.
2. Understand characteristics of peer game playing
3. Understand NPQ between peer game players
4. Examine stability and predictability of NPQa) propose three simple predictors
IP history, prefix history, geographyb) examine robustness of the predictors
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Outline• Background• Motivation• Analysis on probe data
▫ general characteristics▫ NPQ results
• NPQ prediction▫ IP history predictor▫ prefix history predictor▫ geography predictor
• Conclusion
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Data• Session data (per game attempted)
▫ time, session-id, src IP
• NPQ measurement data (per probing to a host)▫ session-id, dest IP▫ # of packet-pairs sent, # of packet-pairs rcvd▫ minimum and median latency▫ average downstream and upstream capacity
• Player locations calculated from their IP addresses ▫ MaxMind database provides mapping between locations and
IP addresses
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Basic statistics
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•126 million probes among 5.6 million IP addresses !!!
11.14.2007 1.3.2008 (50 days)
sessions
distinct IPs
total probes
39,803,350
5,658,951
126,085,887
Geographic distribution
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85% in USA
13% in Europe 2% in
Asia, Australia
Player characterization• Strong diurnal pattern (peaks between 2 ~ 8PM, UTC time)• Most players played a few games, only some a lot• Probe distribution per game trial (session)
▫ 90% of sessions probed fewer than 10 hosts, but some a lot.
1 10 100 10000.1
1
# of probes
cum
ulat
ive
freq
. (s
essi
ons)
12
0.9
Delay distribution• 25% of the delay measurement are above 150ms.
▫ 150 ms: upper bound for responsive experience in FPS games.
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1 10 100 1000 100000.01
0.1
1
delay (ms)
cum
ulat
ive
freq
.(p
robe
s)
150
0.75 25%
0 2000 4000 6000 8000 100000
1000000
2000000
3000000
4000000
5000000
6000000192Kbps
1.6Mbps5.8Mbps
10Mbps
capacity (Kbps)
freq
uenc
y (x
1,0
00,0
00)
(pro
bes)
Capacity distribution• Peaks around typical broadband capacities in USA.
▫ marginal error due to the packet pair technique.
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Outline• Background• Motivation• Analysis on probe data
▫ general characteristics▫ NPQ results
• NPQ prediction▫ IP history predictor▫ prefix history predictor▫ geography predictor
• Conclusion
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Predictors• Predict NPQ without probing
▫ to disqualify a host, select a host, do quick re-probe▫ potentially reduce the user-wait time and probe traffic
• IP/Prefix history predictor▫ reuse the previous probe results between the same IP pair▫ reuse results between two peers within the same prefix pair
determine prefixes by BGP table (12/27/2007 RouteViews)
• Geography predictor▫ predict delay or capacity based on the geographic distance
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IP history predictor (delay)• Delays are very consistent over time, even for 50 days
▫ excellent predictor for delay
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0.1
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0.9 1
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e0
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Within 5 minWithin 30 minWithin 6 hrWithin 1 dayNo Constraints
coefficient of variation (CV)
cum
ulati
ve fr
eq.
(src
-dst
IP p
airs
)
• CV= Stdev/Mean, small CV = small variation
(50 days)
IP history predictor (capacity)• Capacities are also quite consistent over time.
▫ decent predictor for downstream capacity
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0.1
0.2
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0.9 1
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1.9 2
Mor
e0
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0.8
1
Within 5 minWithin 30 minWithin 6 hrWithin 1 dayNo Constraints
coefficient of variation (CV)
cum
ulati
ve fr
eq.
(src
-dst
IP p
airs
)
(50 days)
Prefix history predictor• Quite consistent, but more variation compared to IP pairs
▫ outliers mostly caused the variation. ▫ good predictor for delay after removing outliers.
00.10.20.30.40.50.60.70.80.9
1
Within 5 minWithin 30 minWithin 6 hrWithin 1 dayNo Constraints
coefficient of variation (CV)
cum
ulati
ve f
req.
(s
rc-d
st p
refix
pai
rs)
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(50 days)
Geography predictor• Distance has strong correlation with minimum delay
▫ good predictor for removing hosts with high latency
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Distance (miles)
1200
1000
800
600
400
200
2000 4000 6000 8000 10000 120000
0
Del
ay (m
s)
Conclusions• Large-scale end host latency and capacity characterization• Large-scale P2P game network characterization
▫ 126 million probes among 5.6 million unique IPs
• NPQ prediction for delay▫ IP history : great ! ▫ prefix history: good after removing outliers▫ geography : great for removing distant hosts
• NPQ prediction for capacity▫ IP history: decent!▫ prefix history: not feasible▫ geography: not feasible
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