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Infrastructures and Architectures

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Title: Infrastructures and Architectures


1
Infrastructures and Architectures
  • Distributed Computing
  • Spring 2007

2
Focus
  • Christophe Diot (SPRINT, ATL) and Laurent
    Gautier (INRIA) A Distributed Architecture for
    Multiplayer Interactive Applications on the
    Internet in Network, IEEE, 1999 (MiMaze)
  • Rajesh Krishna Balan (CMU), Maria Ebling, Paul
    Castro, and Archan Misra (IBM)Matrix Adaptive
    Middleware for Distributed Multiplayer Games in
    Middleware 2005 (Matrix)

3
Organization
  • Compare aims and contents of the papers
  • Motivations
  • Identify Take Aways from the 2 papers
  • Points to focus on during the discussion
  • Discuss separately the concepts in the papers
  • Results and Evaluations

4
(No Transcript)
5
Peer to Peer vs. Client Server Architectures
  • P2P Robustness
  • Players fail independently
  • Server is not a point of failure
  • P2P Scalability
  • Server Capacity is not a bottleneck
  • Server Costs with scaling does not come into
    picture
  • P2P Amount of data is large since each client
    has to talk to possibly every other client
    However multicast takes care of a small part of
    the problem
  • P2P Theoretically reduced network delay since
    there is no intervening server between clients
    communication
  • CS Have multiple servers and dynamic server
    provisioning
  • CS Costs are involved when addressing
    scalability
  • CS Amount of data in the network is reduced
    since Server notifies the client of the state and
    each client has to talk only to the server
  • CS Due to localized presence of servers, this
    network delay maybe reduced to some extent

6
Peer to Peer vs. Client Server Architectures
  • P2P Synchronization is tougher to achieve in
    MiMaze they address this issue by one algorithm
    (of the several existing)
  • P2P Accounting is tough Not Addressed in
    MiMaze
  • P2P Cheating is easier. MiMaze doesnt address
    this.
  • P2P The game is closely linked with
    infrastructure
  • P2P Does not handle hotspots well (consistency
    is harder)
  • CS Architecture introduces natural global /
    local consistency at servers for synchronization
    issues
  • CS Cost Model and Accounting is simpler
  • CS Cheating is not easy
  • CS Game can be separated from infrastructure.
  • CS (Matrix) can address this

7
MiMaze Communication Architecture
8
MiMaze Concerns and Solutions
  • Network Delay, Dynamic Tree (Players can join /
    leave anytime), Distributed System Architecture
    For Scalability and Robustness
  • Continuity Real time behaviour of game objects
    (Avatars) in face of packet losses and delays
  • USP Distributed Synchronization
  • Multicast Distributed Tree (based on MBone)
  • Dead Reckoning
  • Bucket Synchronization

9
Bucket Synchronization
10
Dead Reckoning
  • Absence of ADU for a particular avatar in current
    bucket (due to loss / delay).
  • Go back to previous buckets and collect previous
    state of Avatar.
  • Act on it (Extrapolation).

11
Experiments
  • Tested with MiMaze on Mbone with upto 25 players
  • 1600 traces of 15-20 mins each collected
  • Average network delays are always less than 100ms
  • Metric Drift Distance - absolute value of the
    distance between the position of an avatar as
    displayed by its local entity, and the position
    of the same avatar displayed by a remote entity.
    The game is consistent if the drift distance is
    zero. But tolerance of shifts in the drift
    distance is assumed corresponding to the avatar
    radius.
  • Avatar radius is 32 units, Speed is 32 units per
    40 ms. Errors upto 50 units on a moving Avatar
    are not significant

12
Informal Analysis
  • Since number of players (max25) nor the network
    delay (average was always less than 100ms) did
    not affect the quality of the game giving
    preference to interactivity at expense of
    consistency is a good choice
  • Participation disconnection and NTP
    synchronization failures affected only the victim
    of the problem and not the participants thanks
    to distributed architecture ()

13
Results and Conjectures
  • Delay distribution between 2 nodes (longest path
    in tree) on a region of MBone
  • More than 15 of the ADUs experience network
    delays higher than 100ms
  • Standard Deviation is 50.44 ms. Mean delay is
    55.47 ms very close to the average delay
    measured during experiments
  • Reducing the standard deviation will reduce the
    number of late ADUs (?)

14
Delay Distribution and Clock
(a) Percentage and (b) distribution of late/lost
ADUs on the MBone
15
Bucket Synchronization Efficiency Consistency
in MiMaze (Drift)
  • Drift is 97 of the time less than 50 units
  • It is less than 20 units in 85 of the buckets
  • In 65 of the cases remote entities display exact
    position of the avatar

16
Bucket Synchronization Efficiency Impact of
Synchronization Mechanism
  • Synchronization reduces the drift for long delays
    (because it introduces another delay (?))
  • Synchronization does not have a great advantage
    in high loss conditions
  • Synchronization reduces consistency in no loss
    low delay environment (Maybe because of clock
    synchronization and the playout delays)

17
Discussion
  • Only" 65 of buckets deliver the exact position
    of a given avatar. At the same time, players were
    very satisfied during the entire game session.
    This indicates that this type of application is
    more tolerant to network impairments than
    numerical observations would tend to show.
  • Parameters which also could have been involved -
    characteristics of the avatar (slow avatar is
    more sensitive to error), game nature (no
    terrain limits implies difficulty in dead
    reckoning trajectories make it easier)
  • This deliberate lack of precision (due to the
    unreliability of the architecture) allows more
    scalability, provides real-time interaction
    between participants, and does not alter
    participants satisfaction

18
Take Away
  • Relaxed Reliability is Tolerated
  • No study of scalability (todays games have a
    much larger participation)
  • Would increase in computational time at a node
    due to more complex game semantics and dead
    reckoning algorithm affect consistency? the
    bucket synchronization parameters

19
Matrix - Pitch
  • Static partitioning schemes while provisioning
    servers for client base does not help
  • Tradeoff between client response latency and
    consistency (larger the user base and network
    topology, longer it takes to maintain
    consistency). But consistency is linked to user
    satisfaction also.
  • MMOGs today are nearly decomposable systems.
    (number of interactions among subsystems, in some
    geometric space, is of a lower order of magnitude
    than the number of interactions within an
    individual subsystem)
  • radius or zone of visibility for a game
    player MiMaze game object

20
Promises Middleware architecture
  • Scalable
  • Low latency (both local and occasional global
    communication)
  • Provides pockets of local consistency
  • Claim easy to use API which requires minimal
    changes to existing MMOGs
  • Can handle transient hot-spots and dynamic loads
    (load spikes) ()
  • Needs no change in security model (P2P would
    lower the ability to handle cheating and denial
    of service)
  • Uses preferred client server architecture
  • Supports multiple gaming platforms

21
Providing Local Consistency
  • Assignment of partitions of MMOGs spatial map
    among game servers
  • Consistency set any point in the spatial map
    handled by server 1 which lies in server 2s
    radius of visibility (peripheral points) must be
    applied consistently to both the servers (This
    happens via parent Matrix Servers)

22
Consistency..contd
  • If radius of visibility is small compared to the
    size of partition, updates are restricted to the
    game server. If it is infinite global updates
    are required.
  • Overlap Regions groups of points which have a
    non empty consistency set. This information is
    maintained at Matrix Servers

23
Architecture
  • Clients are mobile so they must be able to
    switch game servers transparently (handled by
    Matrix servers and Matrix co-ordinators).
  • The users must always be identified by globally
    unique ids
  • Game servers are on the same machine as parent
    matrix servers
  • All client packets are spatially tagged by Game
    Servers and sent to Matrix servers
  • If server is overloaded, Matrix server splits the
    game world between the overloaded game server and
    new game server and forward game specific state
    and clients to the new game server via Matrix.
    The old matrix server becomes parent of the new
    matrix server.
  • Each Matrix server maintains an Overlap table
    of the regions of overlap between the space it
    manages and that of its Matrix peers. This
    information is sent by the Matrix Coordinator
  • Space split Split to left convention
  • Game state corresponding to clients is minimal in
    current games and can be transferred
    efficiently. The large state regarding map
    textures is static and can be pre-cached on all
    new servers
  • No longer needed servers when client base
    reduces / moves are reclaimed.

24
Architecture …contd
  • Matrix Coordinator (MC) calculates overlap tables
    using geometric algorithms for computing bounding
    boxes between spatial regions
  • Avoids making MC a bottleneck each Matrix
    server usually does as O(1) lookup to determine
    consistency set of a point in consideration. In
    case of non proximal interaction only, MC is
    contacted regarding the consistency set.
    Otherwise MC is contacted only during splits and
    reclamations which are infrequent. Mainly packet
    forwarding is latency critical, and MC need not
    be contacted most of the time for this.

25
Results
26
Other results (not in this paper)
  • Matrix overheads were acceptable
  • Matrix scaling (via splitting and reclamation)
    was completely transparent and sustained
    performance levels
  • Matrix scaling was limited by maximum I/O
    capacity of individual servers
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