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
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…
2
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
3
Mobile Games: Now and Tomorrow
Increasing Interactivity
Single-playerMobile
(mobile today)
Multiplayer Turn-based
(mobile today)
Multiplayer Fast-action
(mobile soon)
4
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
5
The Matchmaking Problem
Match to satisfy total delay bounds
End-to-end Latency Threshold
Clients
Conn
ecti
on L
aten
cy
6
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
7
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
8
The Matchmaking Problem
Link Performance P2P ScalabilityGrouping
Targeting 3G:play anywhere
Latency not Bandwidthinteractivity is key
Measurement / Predictionat game timescales
9
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
10
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
11
A “Black Box” for Game Developers
InternetIP network
RNC
SGSN
RNC
SGSN
GGSNGGSN
Link Performance (over time)
End-to-end Performance
“Black Box”
CrowdsourcedMeasurement
12
Latency Similarity by Time
Crowdsourcing 3G over Time
Time
13
Crowdsourcing 3G over Space
Latency Similarity by Distance
14
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)
15
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
16
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
17
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
18
Switchboard Cloud Serviceon MSFT Azure
Switchboard: crowdsourced matchmaking
Game
Phone Client
Network Testing Service
Latency Data
Latency Estimator
Measurement Controller
Grouping Agent
19
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
20
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
21
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
23
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
24
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
25
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
26
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
27
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
28
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
29
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