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Priority Round-Robin Scheduling for Very Large Virtual Environments. Chris Faisstnauer, Dieter Schmalstieg, Werner Purgathofer Vienna University of Technology. Introduction. Virtual Environments with large amounts of elements Competition for limited resources (bottleneck)

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priority round robin scheduling for very large virtual environments

Priority Round-Robin Schedulingfor Very Large Virtual Environments

Chris Faisstnauer,

Dieter Schmalstieg, Werner Purgathofer

Vienna University of Technology

introduction
Introduction
  • Virtual Environments with large amounts of elements
  • Competition for limited resources (bottleneck)
    • Graphics pipeline (rendering)
    • Processing power (simulation)
    • Network bandwidth (updates)
  • Selection of element subset
    • Reduce absolute number
    • Traditional scheduling for objects
  • Degradation of system performance
  • Approximation must be made
slide3
Goal
  • Development of a generic scheduling algorithm
    • Employ it as stand-alone scheduling
    • Combine with “element-reduction” methods
  • Graceful degradation (find best approximation)
  • Output sensitive
  • Immune to starvation
  • Enforce use of priorities
  • Priorities based on freely definable error metric
    • E.g.: fast entities  frequent updates
    • slow entities  seldom updates
related work
Related Work
  • Short-term scheduling
    • independent processes
    • allocate processor time
    • optimize system behavior
  • First Come-First Served (FCFS) / Round-Robin (RR)
    • execute in order of submission (no priorities)
    • output sensitive, immune to starvation
  • Multi-Level Feedback Queue
    • levels with decreasing priorities
    • risk of starvation vs. constant monitoring
basic priority round robin 1 3
Basic Priority Round-Robin 1/3
  • Elements compete for resources  accumulate error
  • Error modeled as error metric
    • Assign each element: Error Per Unit (EPU)
  • Goal: minimize cumulative error
  • No traditional sorting
  • Approximate sorting in multiple levels (FIFO)
    • Elements assigned to level according EPU
    • Level priority reflects scheduling frequency
    • Combines advantage of Round-Robin & full sorting
basic priority round robin 2 3
Basic Priority Round-Robin 2/3

i=0

i=1

i=2

Repetition counti = NrElementsi * NrLevels

Predicted error = ErrorPerUnit * Repetition Count

Selected elements: A,C,G - B,D,G - A,E,G - B,F,G

basic priority round robin 3 3
Basic Priority Round-Robin 3/3
  • Assignment of elements to levels
    • Minimum overall error
    • Average Error Per Unit  variable size levels
  • Dynamic VE  dynamic error distribution
  • Varying traversal rate (level i)

ni: number of elements in level i

tri: traversal rate of level i

level: number of levels

optimum traversal rate 1 2
Optimum Traversal Rate 1/2

level: number of levels

ni: nr of elements in level i avi: average EPU of level i

rci: repetition count of level i tri: traversal rate of level i

lerri: level error err: cumulative error

slide9

Optimum Traversal Rate 2/2

level: number of levels

ni: nr of elements in level i

avi: average EPU of level i

rci: repetition count of level i

tri: traversal rate of level i

lerri: level error

err: cumulative error

evaluation
Evaluation
  • Client-server system
    • Server hosts simulator (translates elements in 2D)
    • Client visualizes scene (needs position updates)
  • Subset of element’s position can be updated
    • Select subset using PRR-scheduling
  • Visual error: distance object position on server / client
  • Evaluation of PRR (Priority Round-Robin)
    • Comparison PRR vs. plain RR
    • Comparison DR+PRR vs. plain DR
example 1
Example 1
  • Scheduling 1000 out of 10000 simulated cars (10%)
  • Velocities (in units): 500 cars - velocity  [9,10]
  • 1500 cars - velocity  [3,4]
  • 8000 cars - velocity  [0.1,0.5]

Overall error of PRR is 73% lower than RR

example 2
Example 2
  • Scheduling 1000 out of 10000 simulated cars (10%)
  • Velocities (in units): 10000 cars - velocity  [1,10]
  • Overall error of PRR is 7.5% lower than RR
example 3
Example 3
  • Scheduling 1000 out of 10000 simulated cars (10%)
  • Simulating cars using Dead Reckoning ( 25% cars above threshold)
  • 500 cars - velocity[9,10] - angle offset [19,20] every 10 steps
  • 1500 cars - velocity[3,4] - angle offset [4,5] every 10 steps
  • 8000 cars - velocity[0.1,0.5] - angle offset [1,2] every 10 steps

Overall error of DR+PRR is 63% lower than DR

example 4
Example 4
  • Quake Deathmatch: scheduling 2 out of 9 players
  • Position / velocity of entities given by recorded demo
  • Overall error of PRR is 48% lower than RR
conclusions
Conclusions
  • Enhance (plain) RR  Priority Round-Robin (PRR)
    • Enforcement of priorities
    • Output sensitive
    • Immune to starvation
    • Freely definable error metric
  • PRR is a suitable substitute for RR in most cases
    • Minimize overall visual error in VE
  • Combine Priority Round-Robin and Dead Reckoning
  • Combine Priority Round-Robin and visibility techniques
future work
Future Work
  • Measure for object “activity”
  • Use of visibility information
    • Temporal Bounding Volumes (TBV)
    • Temporally invariant Bounding Volumes (tiBV)
  • Evaluate motion data from large Virtual Environments
    • E.g. “Everquest”, “Ultima Online”
    • Scheduling humanoid avatars
    • Multiple Levels Of Detail (LOD)
os vs ve scheduling
Operating systems

Scheduled once

Priorities: scheduling order

Small number of elements

Constant monitoring

Variable amount resources

Virtual Environments

Scheduled repeatedly

Priorities: scheduling frequency

(Very) large number elements

Output sensitive

Constant amount resources

OS vs. VE - Scheduling
  • Optimize system parameters
  • Enforce priorities
  • Minimize risk starvation