CS4344 Lecture 10: Player Dynamics

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    CS4344 Lecture 10: Player Dynamics - Presentation Transcript

    1. Characterizing Virtual Population in Games 1
    2. World of Warcraft 2
    3. Use an add-on written in Lua 3
    4. monitor 1100 random players over 71 days 4
    5. median num of players per poll vs time of the day polled 5
    6. CDF of all session length, and median session length of each player 6
    7. sufficiently long session length good for P2P games 7
    8. 8
    9. can use level to predict session length? 9
    10. CDF of median downtimes and median session length of each player 10
    11. Availability of players (time online/total time) in February 2007 11
    12. 12
    13. 13
    14. monitor 3 locations Stormwind City Stanglehorn Vale Burning Steppes 14
    15. 15
    16. can use zone to predict session length? 16
    17. density: num of players* in a zone *: out of 1100 players tracked 17
    18. Mean Location density of 117 locations 18
    19. Difference in Density between Dec and Feb 19
    20. 20
    21. dynamic load balancing not needed? 21
    22. “My life is so great that I literally wanted a second one!” - Dwight Schrute,The Office 22
    23. 256x256 m regions. 23
    24. 24
    25. 25
    26. 26
    27. 27
    28. avatar mobility: who is where, when 28
    29. why do we care? 29
    30. research in systems support for NVE 30
    31. 31
    32. How to partition a world into regions and assign regions to servers considering - communication cost - hand-over rate - balancing server load : 32
    33. 33
    34. How to predict avatar movement (end therefore what a user will see next)? 34
    35. 35
    36. AoI-based scheme 36
    37. How many connections? How stable are the connections? 37
    38. supernode-based scheme 38
    39. How to pick supernodes? How stable are the supernodes? 39
    40. how to simulate avatar mobility? 40
    41. random walk random waypoint clustered movement : 41
    42. or, small-scale implementation 42
    43. no large-scale NVE available until recently 43
    44. 482,594 residents logged in between 2-9 June 2008 44
    45. secondlife.com/whatis/economy-graphs.php 45
    46. • collect mobility traces of avatars in Second Life • what it means w.r.t. systems design for NVEs? 46
    47. collecting traces 47
    48. how do avatars move inside a distributed virtual environment? 48
    49. how are avatars distributed within a region? 49
    50. how long do they stay at a location? 50
    51. do they move in groups? 51
    52. etc. 52
    53. FPS MMORPG NVE 53
    54. Linden, can we get access to the server traces? No. 54
    55. • Wrote our own client • Parses packets using libsecondlife • Insert bots into regions • Log positions of avatars every 10s 55
    56. difficulties 56
    57. running out of memory 57
    58. anti-bots policy 58
    59. over crowded region 59
    60. inter-region tracking 60
    61. • Wrote our own client • Parses packets using libsecondlife • Insert bots into regions • Log positions of avatars every 10s 61
    62. who is where, when (doing what) 62
    63. 63
    64. Freebies 64
    65. The Pharm 65
    66. Isis 66
    67. Ross 67
    68. Mobility Patterns 68
    69. Freebies: number of visits to a cell 69
    70. Freebies: average pause time in a cell 70
    71. Freebies: average speed in a cell 71
    72. Isis: number of visits to a cell 72
    73. caching/prefetching based on popularity of locations? 73
    74. Isis: average pause time in a cell 74
    75. pick supernodes from sticky location? 75
    76. Isis: average speed in a cell 76
    77. mobility model: random walk + pathway ? 77
    78. churn rate 78
    79. 79
    80. 80
    81. Reasonably high churn (up to 6/min) 81
    82. 1 min 10 min 1 hr 2 hr Highly skewed. Some stay for hours. 82
    83. cannot pick supernodes uniformly 83
    84. clustering of avatars 84
    85. meeting: encounter between two avatars (within each other AoI) 85
    86. Meet many different avatars. 86
    87. 1 min 1 hr 10 min 2 hr Most meetings are short. 87
    88. Meeting size is large. 88
    89. high overhead in maintaining AoI neighbors 89
    90. meeting stability: avg meeting size over num of avatars met 90
    91. Wide range of stability 91
    92. other tidbits 92
    93. little temporal variations can use historical information to predict future 93
    94. rotate 18% of the time Second Life’s prefetching is wasteful 94
    95. 25-35% revisits the same region in a day region-based caching? 95

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