1 / 29

Switchboard: A Matchmaking System for Multiplayer Mobile Games

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

adrina
Télécharger la présentation

Switchboard: A Matchmaking System for Multiplayer Mobile Games

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Battling demons and vampires on your lunch break… Switchboard: A Matchmaking System for Multiplayer Mobile Games Justin Manweiler, SharadAgarwal, Ming Zhang, Romit Roy Choudhury, ParamvirBahl ACM MobiSys 2011

  2. Breakthrough of Mobile Gaming iPhone App Store 350K applications 20% apps, 80% downloads 47% Time on Mobile Apps Spent Gaming Windows Phone 7 Top 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”

  3. Mobile Games: Now and Tomorrow Increasing Interactivity Single-player Mobile (mobile today) Multiplayer Turn-based (mobile today) Multiplayer Fast-action (mobile soon)

  4. Key Challenge Bandwidth is fine:250 kbps to host 16-player Halo 3 game Delay bounds are much tighter Challenge: find groups of peers than can play well together

  5. The MatchmakingProblem End-to-end Latency Threshold Match to satisfy total delay bounds Connection Latency Clients

  6. Instability in a Static Environment Due to instability, must consider latency distribution

  7. End-to-end Latency over 3G First-person Shoot. Racing Sports Real-time Strategy Peer-to-peerreduces latency and is cost-effective

  8. The MatchmakingProblem Targeting 3G: play anywhere Latencynot Bandwidth interactivity is key Measurement / Prediction at game timescales Link Performance Grouping P2P Scalability

  9. Requirementsfor 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 IP network Internet GGSN GGSN “Black Box” SGSN SGSN RNC RNC End-to-end Performance Crowdsourced Measurement Link Performance (over time)

  12. Crowdsourcing 3G over Time Latency Similarity by 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) Live characterization is necessaryand is feasible Performance drifts over longer time periods Similar latencies under the same tower Black line represents phone 1 (all other lines phone 2)

  17. Stability over Space(at the same time) Similarity at the same cell tower Substantial variation Divergence between nearby towers

  18. Switchboard: crowdsourced matchmaking Switchboard Cloud Service on MSFT Azure Phone Client Game Grouping Agent Network Testing Service Latency Data Measurement Controller Latency Estimator

  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 Switchboard clients benefit from deployment at scale 1 client arrival/sec

  21. Conclusion • Latency: key challenge for fast-action multiplayer • 3G latency variability makes prediction hard • Crowdsourcing enables scalable 3G latency estimation • Switchboard: crowdsourcedmatchmaking for 3G

  22. Thank you! Questions? cs.duke.edu/~jgm jgm@cs.duke.edu

  23. Stability over Time(in a Static Environment) At timescales longer or shorter than 15 minutes: successive interval pairs have less similarity

  24. How Many Measurements Req.? Reasonably small number of measurements are required per 15-minute interval

  25. End-to-End Performance End-to-end performance predictable as the sum of edge and Internet latencies

  26. ICMP Probes by Client Arrival Rate More clients = less probing overhead for each

  27. Scalability by Client Arrival Rate After the initial warming period, later clients reuse effort by earlier clients

  28. Group Size by Client Arrival Rate Availability of high-quality matches increases with utilization

  29. Timescale Statistical Analysis

More Related