switchboard: a matchmaking system for multiplayer mobile games
DESCRIPTION
Battling demons and vampires on your lunch break…. Switchboard: A Matchmaking System for Multiplayer Mobile Games. Justin Manweiler , Sharad Agarwal , Ming Zhang, Romit Roy Choudhury, Paramvir Bahl ACM MobiSys 2011. Breakthrough of Mobile Gaming. iPhone App Store 350K applications - PowerPoint PPT PresentationTRANSCRIPT
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Switchboard: A Matchmaking System for Multiplayer Mobile Games
Justin Manweiler, Sharad Agarwal, Ming Zhang, Romit Roy Choudhury, Paramvir Bahl
ACM MobiSys 2011
Battling demons and vampires on your lunch break…
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Breakthrough of Mobile Gaming
Windows Phone 7Top 10+ apps are games
John Carmack (Wolfenstein 3D, Doom, Quake)…“multiplayer in some form is where
the breakthrough, platform-defining things are going to happen in the
mobile space”
iPhone App Store350K applications
20% apps, 80% downloads47% Time on Mobile AppsSpent Gaming
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Mobile Games: Now and Tomorrow
Increasing Interactivity
Single-playerMobile
(mobile today)
Multiplayer Turn-based
(mobile today)
Multiplayer Fast-action
(mobile soon)
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Key Challenge
Game Type Latency Threshold
First-person, Racing ≈ 100 ms
Sports, Role-playing ≈ 500 ms
Real-time Strategy ≈ 1000 msChallenge: find groups of peers
than can play well together
Bandwidth is fine: 250 kbps to host 16-player Halo 3 game
Delay bounds are much tighter
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The Matchmaking Problem
Match to satisfy total delay bounds
End-to-end Latency Threshold
Clients
Conn
ecti
on L
aten
cy
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Instability in a Static Environment
9:36 9:50 10:04 10:19 10:33 10:48 11:02 11:16 11:31 11:45 12:00150
170
190
210
230
250
270
290
310
Time of Day (AM)
Med
ian
Late
ncy
(ms)
Due to instability,must consider latency distribution
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End-to-end Latency over 3G
0 100 200 300 400 500 6000
0.2
0.4
0.6
0.8
1
AT&T to AT&T DirectAT&T to AT&T via BingAT&T to AT&T via DukeAT&T to AT&T via UW
RTT (in ms)
Empi
rical
CDF
First-person Shoot. Racing Real-time StrategySports
Peer-to-peer reduces latency and is cost-effective
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The Matchmaking Problem
Link Performance P2P ScalabilityGrouping
Targeting 3G:play anywhere
Latency not Bandwidthinteractivity is key
Measurement / Predictionat game timescales
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Requirements for 3G Matchmaking● Latency estimation has to be accurate
Or games will be unplayable / fail
● Grouping has to be fast Or impatient users will give up before a game is initiated
● Matchmaking has to be scalable For game servers For the cellular network For user mobile devices
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State of the Art● Latency estimation
Pyxida, stable network coordinates; Ledlie et al. [NSDI 07] Vivaldi, distributed latency est.; Dabek et al. [SIGCOMM 04]
● Game matchmaking for wired networks Htrae, game matchmaking in wired networks;
Agarwal et al. [SIGCOMM 09]
● General 3G network performance 3GTest w/ 30K users; Huang et al. [MobiSys 2010] Interactions with applications; Liu et al. [MobiCom 08] Empirical 3G performance; Tan et al. [InfoCom 07] TCP/IP over 3G; Chan & Ramjee [MobiCom 02]
Latency estimation and matchmaking are established for wired networks
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A “Black Box” for Game Developers
InternetIP network
RNC
SGSN
RNC
SGSN
GGSNGGSN
Link Performance (over time)
End-to-end Performance
“Black Box”
CrowdsourcedMeasurement
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Latency Similarity by Time
Crowdsourcing 3G over Time
Time
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Crowdsourcing 3G over Space
Latency Similarity by Distance
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Can we crowdsource HSDPA 3G?● How does 3G performance vary over time?
How quickly do old measurements “expire”? How many measurements needed to characterize the
latency distribution? …
● How does 3G performance vary over space? Signal strength? Mobility speed? Phones under same cell tower? Same part of the cellular network? …
Details of parameter space left for the paper (our goal is not to identify the exact causes)
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Methodology● Platform
Windows Mobile and Android phones HSDPA 3G on AT&T and T-Mobile
● Carefully deployed phones Continuous measurements Simultaneous, synchronized traces at multiple sites
● Several locations Princeville, Hawaii Redmond and Seattle, Washington Durham and Raleigh, North Carolina Los Angeles, California
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Stability over Time (in a Static Environment)
120 140 160 180 200 220 2400
0.2
0.4
0.6
0.8
1Redmond, AT&T, 15m Intervals
RTT (Msec)
Empi
rical
CDF
Black line represents phone 1 (all other lines phone 2)
Similar latencies under the same tower
Performance drifts over longer time periods
Live characterization is necessary and is feasible
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Stability over Space (at the same time)
0 20 40 60 80 100 120 140 160 180 2000
0.2
0.4
0.6
0.8
1
S-homeLatonaU VillageHerkimerNorthgate1st Ave
RTT difference at 90th percentile (ms)
Empi
rical
CDF
Similarity at the same cell tower
Divergence between nearby towers
Substantialvariation
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Switchboard Cloud Serviceon MSFT Azure
Switchboard: crowdsourced matchmaking
Game
Phone Client
Network Testing Service
Latency Data
Latency Estimator
Measurement Controller
Grouping Agent
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Scalability through Reuse…
● Across Time Stable distribution over 15-minute time intervals
● Across Space Phones can share probing tasks equitably for each tower
● Across Games Shared cloud service for any interactive game
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Client Matchmaking Delay
0 100 200 300 400 500 600 7000
0.2
0.4
0.6
0.8
1
Total 1 client/secTotal 10 clients/s
Time until placed in group (s)
Empi
rical
CDF
10 client arrival/sec1 client arrival/sec
Switchboard clients benefit from deployment at scale
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Conclusion
● Latency: key challenge for fast-action multiplayer
● 3G latency variability makes prediction hard
● Crowdsourcing enables scalable 3G latency estimation
● Switchboard: crowdsourced matchmaking for 3G
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Stability over Time (in a Static Environment)
0 50 100 150 200 2500
5
10
15
20
25
30
35
95th90th50th
Interval Size (in minutes)
Mea
n Se
quen
tial D
iffer
ence
(ms)
At timescales longer or shorter than 15 minutes: successive interval pairs have less similarity
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How Many Measurements Req.?
0 5 10 15 20 25 30 35 40 45 500
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
5s15s45s60s90s
RTT difference at 90th percentile (in ms)
Empi
rical
CDF
Reasonably small number of measurements are required per 15-minute interval
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End-to-End Performance
0 50 100 150 200 250 300 350 4000
0.1
0.2
0.3
0.4
0.5
0.6
0.70.8
0.9
1
Durham FRH to San Antonio FRH
Durham phone to FRH
San Antonio phone to FRH
phone to phone
RTT (ms)
Empi
rical
CDF
End-to-end performance predictable as the sum of edge and Internet latencies
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ICMP Probes by Client Arrival Rate
0 10 20 30 40 50 600
0.2
0.4
0.6
0.8
1
1 client/s2 clients/s5 clients/s10 clients/s
ICMP Probes per Client
Empi
rical
CDF
More clients = less probing overhead for each
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Scalability by Client Arrival Rate
0 15 30 45 600
10
20
30
40
50
60
70
80
1 client/s 2 clients/s5 clients/s 10 clients/s
Time (minutes)
Clie
nt-to
-ser
ver T
raffi
c (K
bps)
After the initial warming period,later clients reuse effort by earlier clients
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Group Size by Client Arrival Rate
2 3 4 5 6 7 8 9 10 110
0.2
0.4
0.6
0.8
1
1 client/s
2 clients/s
5 clients/s
10 clients/s
Size of Client Group
Empi
rical
CDF
Availability of high-quality matches increases with utilization
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Timescale Statistical Analysis
0.00001 0.0001 0.001 0.01 0.1 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1sig=90 α=0.1
240 minutes120 minutes60 minutes2 minutes15 minutes
KS Test P-Value (inter-interval instability where p < α), (log scale)
Empi
rical
CDF