Priority Round-Robin Scheduling for Very Large Virtual Environments Chris Faisstnauer, Dieter Schmalstieg, Werner Purgathofer Vienna University of Technology

Priority Round-Robin SchedulingPriority Round-Robin Schedulingfor for

Very Large Virtual EnvironmentsVery Large Virtual Environments

Chris Faisstnauer, Chris Faisstnauer,

Dieter Schmalstieg, Werner PurgathoferDieter Schmalstieg, Werner Purgathofer

Vienna University of TechnologyVienna University of Technology

2 / 17Chris Faisstnauer

IntroductionIntroduction

Virtual Environments with large amounts of elementsVirtual Environments with large amounts of elements

Competition for limited resources (bottleneck)Competition for limited resources (bottleneck) Graphics pipeline (rendering)Graphics pipeline (rendering) Processing power (simulation)Processing power (simulation) Network bandwidth (updates)Network bandwidth (updates)

Selection of element subsetSelection of element subset Reduce absolute numberReduce absolute number Traditional scheduling for objectsTraditional scheduling for objects

Degradation of system performanceDegradation of system performance

Approximation must be madeApproximation must be made


GoalGoal

Development of a generic scheduling algorithmDevelopment of a generic scheduling algorithm Employ it as stand-alone schedulingEmploy it as stand-alone scheduling Combine with “element-reduction” methodsCombine with “element-reduction” methods

Graceful degradation (find best approximation)Graceful degradation (find best approximation) Output sensitiveOutput sensitive Immune to starvationImmune to starvation Enforce use of prioritiesEnforce use of priorities Priorities based on freely definable error metricPriorities based on freely definable error metric

E.g.: fast entities E.g.: fast entities frequent updates frequent updates slow entities slow entities seldom updates seldom updates


Related WorkRelated Work

Short-term schedulingShort-term scheduling independent processesindependent processes allocate processor timeallocate processor time optimize system behavioroptimize system behavior

First Come-First Served (FCFS) / Round-Robin (RR)First Come-First Served (FCFS) / Round-Robin (RR) execute in order of submission (no priorities)execute in order of submission (no priorities) output sensitive, immune to starvationoutput sensitive, immune to starvation

Multi-Level Feedback QueueMulti-Level Feedback Queue levels with decreasing prioritieslevels with decreasing priorities risk of starvation vs. constant monitoringrisk of starvation vs. constant monitoring


Basic Priority Round-Robin 1/3Basic Priority Round-Robin 1/3

Elements compete for resources Elements compete for resources accumulate error accumulate error

Error modeled as error metricError modeled as error metric Assign each element: Error Per Unit (EPU)Assign each element: Error Per Unit (EPU)

GoalGoal: minimize cumulative error: minimize cumulative error

No traditional sortingNo traditional sorting

Approximate sorting in multiple levels (FIFO)Approximate sorting in multiple levels (FIFO) Elements assigned to level according EPUElements assigned to level according EPU Level priority reflects scheduling frequencyLevel priority reflects scheduling frequency Combines advantage of Round-Robin & full sortingCombines advantage of Round-Robin & full sorting



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

Repetition counti = NrElementsi * NrLevels

Predicted error = ErrorPerUnit * Repetition Count

i=0

i=1

i=2



Assignment of elements to levelsAssignment of elements to levels Minimum overall errorMinimum overall error Average Error Per Unit Average Error Per Unit variable size levels variable size levels

Dynamic VE Dynamic VE dynamic error distribution dynamic error distribution

Varying traversal rate (level Varying traversal rate (level ii))

level

1ii

i

ii tr*

tr

nrc

ni: number of elements in level itri: traversal rate of level ilevel: number of levels


Optimum Traversal Rate 1/2 Optimum Traversal Rate 1/2

level

1ii

i

ii tr*

tr

nrc

level

1kk

i

i2i

iiii tr*tr

av*nrc*av*nlerr

level

1ii

level

1i i

i2i

level

1ii tr*

trav*n

lerrerr

level: number of levelsni: nr of elements in level i avi: average EPU of level irci: repetition count of level i tri: traversal rate of level ilerri: level error err: cumulative error


Optimum Traversal Rate 2/2 Optimum Traversal Rate 2/2

level

1i ii

2ilevel1 tr

1*) av* (n)tr,...,err(tr

1trconslevel

1ii

grad err = * grad cons

level

1kk

2k

i2

ii

av* n

av* n tr

level: number of levels ni: nr of elements in level iavi: average EPU of level irci: repetition count of level itri: traversal rate of level ilerri: level errorerr: cumulative error


EvaluationEvaluation

Client-server systemClient-server system Server hosts simulator (translates elements in 2D)Server hosts simulator (translates elements in 2D) Client visualizes scene (needs position updates)Client visualizes scene (needs position updates)

Subset of element’s position can be updatedSubset of element’s position can be updated Select subset using PRR-schedulingSelect subset using PRR-scheduling

Visual error: distance object position on server / clientVisual error: distance object position on server / client

Evaluation of PRR (Priority Round-Robin)Evaluation of PRR (Priority Round-Robin) Comparison PRR vs. plain RRComparison PRR vs. plain RR Comparison DR+PRR vs. plain DRComparison DR+PRR vs. plain DR


Testbed (Video)Testbed (Video)


Example 1Example 1Scheduling 1000 out of 10000 simulated cars (10%)Scheduling 1000 out of 10000 simulated cars (10%)

Velocities (in units):Velocities (in units): 500 cars - velocity 500 cars - velocity [9,10] [9,10]

1500 cars - velocity 1500 cars - velocity [3,4] [3,4]

8000 cars - velocity 8000 cars - velocity [0.1,0.5] [0.1,0.5]

Overall error of PRR is 73% lower than RR


Example 2Example 2

Scheduling 1000 out of 10000 simulated cars (10%)Scheduling 1000 out of 10000 simulated cars (10%)

Velocities (in units):Velocities (in units): 10000 cars - velocity 10000 cars - velocity [1,10] [1,10]

Overall error of PRR is 7.5% lower than RROverall error of PRR is 7.5% lower than RR


Example 3Example 3Scheduling 1000 out of 10000 simulated cars (10%)Scheduling 1000 out of 10000 simulated cars (10%)

Simulating cars using Dead Reckoning (Simulating cars using Dead Reckoning ( 25% cars above threshold) 25% cars above threshold)

500 cars 500 cars - velocity- velocity[9,10] [9,10] - angle offset - angle offset [19[19,20,20]] every 10 stepsevery 10 steps

1500 cars1500 cars - velocity- velocity[3,4] [3,4] - angle offset - angle offset [4[4,5,5] ] every 10 stepsevery 10 steps

8000 cars8000 cars - velocity- velocity[0.1,0.5][0.1,0.5] - angle offset - angle offset [1[1,2,2] ] every 10 stepsevery 10 steps

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


Example 4Example 4

Quake Deathmatch: scheduling 2 out of 9 playersQuake Deathmatch: scheduling 2 out of 9 players

Position / velocity of entities given by recorded demoPosition / velocity of entities given by recorded demo

Overall error of PRR is 48% lower than RROverall error of PRR is 48% lower than RR


ConclusionsConclusions

Enhance (plain) RR Enhance (plain) RR Priority Round-Robin (PRR) Priority Round-Robin (PRR) Enforcement of prioritiesEnforcement of priorities Output sensitiveOutput sensitive Immune to starvationImmune to starvation Freely definable error metricFreely definable error metric

PRR is a suitable substitute for RR in most casesPRR is a suitable substitute for RR in most cases Minimize overall visual error in VEMinimize overall visual error in VE

Combine Priority Round-Robin and Dead ReckoningCombine Priority Round-Robin and Dead Reckoning

Combine Priority Round-Robin and visibility techniquesCombine Priority Round-Robin and visibility techniques


Future WorkFuture Work

Measure for object “activity”Measure for object “activity”

Use of visibility informationUse of visibility information Temporal Bounding Volumes (TBV)Temporal Bounding Volumes (TBV) Temporally invariant Bounding Volumes (tiBV)Temporally invariant Bounding Volumes (tiBV)

Evaluate motion data from large Virtual EnvironmentsEvaluate motion data from large Virtual Environments E.g. “Everquest”, “Ultima Online”E.g. “Everquest”, “Ultima Online” Scheduling humanoid avatars Scheduling humanoid avatars Multiple Levels Of Detail (LOD)Multiple Levels Of Detail (LOD)


OS vs. VE - SchedulingOS vs. VE - Scheduling

Operating systemsOperating systems

Scheduled onceScheduled once Priorities: scheduling orderPriorities: scheduling order Small number of elementsSmall number of elements Constant monitoringConstant monitoring Variable amount resourcesVariable amount resources

Virtual EnvironmentsVirtual Environments

Scheduled repeatedlyScheduled repeatedly Priorities: scheduling frequency Priorities: scheduling frequency (Very) large number elements(Very) large number elements Output sensitiveOutput sensitive Constant amount resourcesConstant amount resources

Optimize system parametersOptimize system parameters Enforce prioritiesEnforce priorities Minimize risk starvationMinimize risk starvation

Documents

Priority Round-Robin Scheduling for Very Large Virtual Environments Chris Faisstnauer, Dieter Schmalstieg, Werner Purgathofer Vienna University of Technology