SlideShare a Scribd company logo
1 of 95
Download to read offline
Characterizing Virtual
Population in Games


                         1
World of Warcraft




                    2
Use an add-on written in Lua




                               3
monitor
1100 random players
   over 71 days




                      4
median num of players per poll
  vs time of the day polled
                                 5
CDF of all session length, and
median session length of each player
                                       6
sufficiently long session length
     good for P2P games




                                  7
8
can use level to
predict session length?




                          9
CDF of median downtimes and
median session length of each player
                                       10
Availability of players (time online/total time)
                in February 2007
                                                   11
12
13
monitor
3 locations
Stormwind City
Stanglehorn Vale
Burning Steppes




                   14
15
can use zone to
predict session length?




                          16
density: num of players* in a
           zone
    *: out of 1100 players tracked




                                     17
Mean Location density of 117 locations

                                         18
Difference in Density between Dec and Feb

                                            19
20
dynamic load balancing not
         needed?




                             21
“My life is so great that I literally wanted a second one!”
- Dwight Schrute,The Office


                                                              22
256x256 m regions.

                     23
24
25
26
27
avatar mobility:
who is where, when


                     28
why do we care?



                  29
research in systems
 support for NVE


                      30
31
How to partition a world into regions
and assign regions to servers considering

- communication cost
- hand-over rate
- balancing server load
  :



                                            32
33
How to predict avatar movement (end
therefore what a user will see next)?




                                        34
35
AoI-based scheme




                   36
How many connections?

How stable are the connections?




                                  37
supernode-based scheme




                         38
How to pick supernodes?

How stable are the supernodes?




                                 39
how to simulate
 avatar mobility?


                    40
random walk
  random waypoint
clustered movement
          :

                     41
or,
  small-scale
implementation


                 42
no large-scale NVE
available until recently


                           43
482,594
residents logged in between 2-9 June 2008




                                            44
secondlife.com/whatis/economy-graphs.php
                                           45
• collect mobility traces of
 avatars in Second Life

• what it means w.r.t. systems
 design for NVEs?


                                 46
collecting traces



                    47
how do avatars move
 inside a distributed
virtual environment?


                        48
how are avatars
distributed within a
       region?


                       49
how long do they stay
   at a location?


                        50
do they move in
    groups?


                  51
etc.



       52
FPS   MMORPG   NVE




                     53
Linden, can we get access to
the server traces?


                   No.

                               54
• Wrote our own client
• Parses packets using libsecondlife
• Insert bots into regions
• Log positions of avatars every 10s

                                       55
difficulties



              56
running out of memory



                        57
anti-bots policy



                   58
over crowded region



                      59
inter-region tracking



                        60
• Wrote our own client
• Parses packets using libsecondlife
• Insert bots into regions
• Log positions of avatars every 10s

                                       61
who is
   where,
   when
(doing what)

               62
63
Freebies

           64
The Pharm

            65
Isis

       66
Ross

       67
Mobility Patterns



                    68
Freebies: number of visits to a cell

                                       69
Freebies: average pause time in a cell

                                         70
Freebies: average speed in a cell

                                    71
Isis: number of visits to a cell

                                   72
caching/prefetching
based on popularity of
      locations?


                         73
Isis: average pause time in a cell

                                     74
pick supernodes from
   sticky location?


                       75
Isis: average speed in a cell

                                76
mobility model:
random walk +
   pathway ?


                  77
churn rate



             78
79
80
Reasonably high churn (up to 6/min)

                                      81
1 min    10 min



                     1 hr   2 hr




Highly skewed. Some stay for hours.

                                      82
cannot pick
supernodes uniformly


                       83
clustering of
   avatars


                84
meeting: encounter
 between two avatars
(within each other AoI)


                          85
Meet many different avatars.

                               86
1 min
                       1 hr



              10 min          2 hr




   Most meetings are short.

                                     87
Meeting size is large.

                         88
high overhead in
maintaining AoI
   neighbors


                   89
meeting stability:

  avg meeting size
       over
 num of avatars met
                      90
Wide range of stability

                          91
other tidbits



                92
little temporal variations

can use historical information
      to predict future


                                 93
rotate 18% of the time

Second Life’s prefetching is
        wasteful


                               94
25-35% revisits the same
     region in a day

   region-based caching?


                           95

More Related Content

More from Wei Tsang Ooi

SIGMM Education Effort Presentation at Business Meeting
SIGMM Education Effort Presentation at Business MeetingSIGMM Education Effort Presentation at Business Meeting
SIGMM Education Effort Presentation at Business MeetingWei Tsang Ooi
 
The Computer Science behind YouTube
The Computer Science behind YouTubeThe Computer Science behind YouTube
The Computer Science behind YouTubeWei Tsang Ooi
 
CS4344 09/10 Lecture 10: Transport Protocol for Networked Games
CS4344 09/10 Lecture 10: Transport Protocol for Networked GamesCS4344 09/10 Lecture 10: Transport Protocol for Networked Games
CS4344 09/10 Lecture 10: Transport Protocol for Networked GamesWei Tsang Ooi
 
CS4344 09/10 Lecture 9: Characteristics of Networked Game Traffic
CS4344 09/10 Lecture 9: Characteristics of Networked Game TrafficCS4344 09/10 Lecture 9: Characteristics of Networked Game Traffic
CS4344 09/10 Lecture 9: Characteristics of Networked Game TrafficWei Tsang Ooi
 
CS4344 09/10 Lecture 8: Hybrid Architecture
CS4344 09/10 Lecture 8: Hybrid ArchitectureCS4344 09/10 Lecture 8: Hybrid Architecture
CS4344 09/10 Lecture 8: Hybrid ArchitectureWei Tsang Ooi
 
CS4344 09/10 Lecture 7: P2P Interest Management
CS4344 09/10 Lecture 7: P2P Interest ManagementCS4344 09/10 Lecture 7: P2P Interest Management
CS4344 09/10 Lecture 7: P2P Interest ManagementWei Tsang Ooi
 
CS4344 09/10 Lecture 6: P2P Synchronization
CS4344 09/10 Lecture 6: P2P SynchronizationCS4344 09/10 Lecture 6: P2P Synchronization
CS4344 09/10 Lecture 6: P2P SynchronizationWei Tsang Ooi
 
Cs4344 09/10 Lecture 5: Interest Management
Cs4344 09/10 Lecture 5: Interest ManagementCs4344 09/10 Lecture 5: Interest Management
Cs4344 09/10 Lecture 5: Interest ManagementWei Tsang Ooi
 
CS4344 09/10 Lecture 3: Dead Reckoning and Local Perception Filter
CS4344 09/10 Lecture 3: Dead Reckoning and Local Perception FilterCS4344 09/10 Lecture 3: Dead Reckoning and Local Perception Filter
CS4344 09/10 Lecture 3: Dead Reckoning and Local Perception FilterWei Tsang Ooi
 
CS4344 09/10 Lecture 2: Consistency
CS4344 09/10 Lecture 2: ConsistencyCS4344 09/10 Lecture 2: Consistency
CS4344 09/10 Lecture 2: ConsistencyWei Tsang Ooi
 
CS4344 09/10 Lecture 1: Introduction
CS4344 09/10 Lecture 1: IntroductionCS4344 09/10 Lecture 1: Introduction
CS4344 09/10 Lecture 1: IntroductionWei Tsang Ooi
 
CS5229 09/10 Lecture 11: DNS
CS5229 09/10 Lecture 11: DNSCS5229 09/10 Lecture 11: DNS
CS5229 09/10 Lecture 11: DNSWei Tsang Ooi
 
CS5229 09/10 Lecture 10: Internet Routing
CS5229 09/10 Lecture 10: Internet RoutingCS5229 09/10 Lecture 10: Internet Routing
CS5229 09/10 Lecture 10: Internet RoutingWei Tsang Ooi
 
CS5229 09/10 Lecture 9: Internet Packet Dynamics
CS5229 09/10 Lecture 9: Internet Packet DynamicsCS5229 09/10 Lecture 9: Internet Packet Dynamics
CS5229 09/10 Lecture 9: Internet Packet DynamicsWei Tsang Ooi
 
CS5229 09/10 Lecture 6: Simulation
CS5229 09/10 Lecture 6: SimulationCS5229 09/10 Lecture 6: Simulation
CS5229 09/10 Lecture 6: SimulationWei Tsang Ooi
 
Lecture 4: TCP and TFRC
Lecture 4: TCP and TFRCLecture 4: TCP and TFRC
Lecture 4: TCP and TFRCWei Tsang Ooi
 
Lecture 2: Congestion Control and Avoidance
Lecture 2: Congestion Control and AvoidanceLecture 2: Congestion Control and Avoidance
Lecture 2: Congestion Control and AvoidanceWei Tsang Ooi
 
Lecture 0: Introduction to CS5229
Lecture 0: Introduction to CS5229Lecture 0: Introduction to CS5229
Lecture 0: Introduction to CS5229Wei Tsang Ooi
 

More from Wei Tsang Ooi (20)

SIGMM Education Effort Presentation at Business Meeting
SIGMM Education Effort Presentation at Business MeetingSIGMM Education Effort Presentation at Business Meeting
SIGMM Education Effort Presentation at Business Meeting
 
CS2106 Tutorial 2
CS2106 Tutorial 2CS2106 Tutorial 2
CS2106 Tutorial 2
 
The Computer Science behind YouTube
The Computer Science behind YouTubeThe Computer Science behind YouTube
The Computer Science behind YouTube
 
CS4344 09/10 Lecture 10: Transport Protocol for Networked Games
CS4344 09/10 Lecture 10: Transport Protocol for Networked GamesCS4344 09/10 Lecture 10: Transport Protocol for Networked Games
CS4344 09/10 Lecture 10: Transport Protocol for Networked Games
 
CS4344 09/10 Lecture 9: Characteristics of Networked Game Traffic
CS4344 09/10 Lecture 9: Characteristics of Networked Game TrafficCS4344 09/10 Lecture 9: Characteristics of Networked Game Traffic
CS4344 09/10 Lecture 9: Characteristics of Networked Game Traffic
 
CS4344 09/10 Lecture 8: Hybrid Architecture
CS4344 09/10 Lecture 8: Hybrid ArchitectureCS4344 09/10 Lecture 8: Hybrid Architecture
CS4344 09/10 Lecture 8: Hybrid Architecture
 
CS4344 09/10 Lecture 7: P2P Interest Management
CS4344 09/10 Lecture 7: P2P Interest ManagementCS4344 09/10 Lecture 7: P2P Interest Management
CS4344 09/10 Lecture 7: P2P Interest Management
 
CS4344 09/10 Lecture 6: P2P Synchronization
CS4344 09/10 Lecture 6: P2P SynchronizationCS4344 09/10 Lecture 6: P2P Synchronization
CS4344 09/10 Lecture 6: P2P Synchronization
 
Cs4344 09/10 Lecture 5: Interest Management
Cs4344 09/10 Lecture 5: Interest ManagementCs4344 09/10 Lecture 5: Interest Management
Cs4344 09/10 Lecture 5: Interest Management
 
CS4344 09/10 Lecture 3: Dead Reckoning and Local Perception Filter
CS4344 09/10 Lecture 3: Dead Reckoning and Local Perception FilterCS4344 09/10 Lecture 3: Dead Reckoning and Local Perception Filter
CS4344 09/10 Lecture 3: Dead Reckoning and Local Perception Filter
 
CS4344 09/10 Lecture 2: Consistency
CS4344 09/10 Lecture 2: ConsistencyCS4344 09/10 Lecture 2: Consistency
CS4344 09/10 Lecture 2: Consistency
 
CS4344 09/10 Lecture 1: Introduction
CS4344 09/10 Lecture 1: IntroductionCS4344 09/10 Lecture 1: Introduction
CS4344 09/10 Lecture 1: Introduction
 
CS5229 09/10 Lecture 11: DNS
CS5229 09/10 Lecture 11: DNSCS5229 09/10 Lecture 11: DNS
CS5229 09/10 Lecture 11: DNS
 
CS5229 09/10 Lecture 10: Internet Routing
CS5229 09/10 Lecture 10: Internet RoutingCS5229 09/10 Lecture 10: Internet Routing
CS5229 09/10 Lecture 10: Internet Routing
 
CS5229 09/10 Lecture 9: Internet Packet Dynamics
CS5229 09/10 Lecture 9: Internet Packet DynamicsCS5229 09/10 Lecture 9: Internet Packet Dynamics
CS5229 09/10 Lecture 9: Internet Packet Dynamics
 
CS5229 09/10 Lecture 6: Simulation
CS5229 09/10 Lecture 6: SimulationCS5229 09/10 Lecture 6: Simulation
CS5229 09/10 Lecture 6: Simulation
 
Lecture 5: RED
Lecture 5: REDLecture 5: RED
Lecture 5: RED
 
Lecture 4: TCP and TFRC
Lecture 4: TCP and TFRCLecture 4: TCP and TFRC
Lecture 4: TCP and TFRC
 
Lecture 2: Congestion Control and Avoidance
Lecture 2: Congestion Control and AvoidanceLecture 2: Congestion Control and Avoidance
Lecture 2: Congestion Control and Avoidance
 
Lecture 0: Introduction to CS5229
Lecture 0: Introduction to CS5229Lecture 0: Introduction to CS5229
Lecture 0: Introduction to CS5229
 

Recently uploaded

DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsMemoori
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...shyamraj55
 
My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024The Digital Insurer
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxhariprasad279825
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsMark Billinghurst
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationRidwan Fadjar
 
Vertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsVertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsMiki Katsuragi
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...Fwdays
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfAddepto
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyAlfredo García Lavilla
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University
 

Recently uploaded (20)

DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial Buildings
 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
 
My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptx
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR Systems
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 Presentation
 
Vertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsVertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering Tips
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdf
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easy
 
Hot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort Service
Hot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort ServiceHot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort Service
Hot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort Service
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
 
DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special EditionDMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
 

Characterizing Virtual World Populations Using Game Add-Ons