The Case For Prediction-based Best-effort Real-time Peter A. Dinda Bruce Lowekamp Loukas F....
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![Page 1: The Case For Prediction-based Best-effort Real-time Peter A. Dinda Bruce Lowekamp Loukas F. Kallivokas David R. O’Hallaron Carnegie Mellon University.](https://reader030.fdocuments.net/reader030/viewer/2022032800/56649d255503460f949fc38b/html5/thumbnails/1.jpg)
The Case For Prediction-based
Best-effort Real-time
Peter A. Dinda
Bruce Lowekamp
Loukas F. Kallivokas
David R. O’Hallaron
Carnegie Mellon University
![Page 2: The Case For Prediction-based Best-effort Real-time Peter A. Dinda Bruce Lowekamp Loukas F. Kallivokas David R. O’Hallaron Carnegie Mellon University.](https://reader030.fdocuments.net/reader030/viewer/2022032800/56649d255503460f949fc38b/html5/thumbnails/2.jpg)
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Overview
• Distributed interactive applications• Could benefit from best-effort real-time• Example: QuakeViz (Earthquake Visualization) and
the DV (Distributed Visualization) framework
• Evidence for feasibility of prediction-based best-effort RT service for these applications
• Mapping algorithms• Execution time model• Host load prediction
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Application Characteristics• Interactivity
• Users initiate tasks with deadlines• Timely, consistent, and predictable feedback
• Resilience• Missed deadlines are acceptable
• Distributability• Tasks can be initiated on any host
• Adaptability• Task computation and communication can be adjusted
Shared, unreserved computing environments
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Teora, Italy1980
Motivation for QuakeViz
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Northridge Earthquake Simulation
40 seconds of an aftershock of Jan 17, 1994 Northridge quake in San Fernando Valley of Southern California
50 x 50 x 10 km region13,422,563 nodes76,778,630 tetrahedrons1 Hz frequency resolution20 meter spatial resolution
16,666 40M x 40M SMVPs15 GBytes of RAM6.5 hours on 256 T3D PEs 80 trillion (1012) FLOPs3.5 sustained GFLOP/s1.4 peak GB/s
16,666 time steps13,422,563 3-tuples
per step
6 Terabytes6 Terabytes
Real Event
Huge Model
High Perf. Simulation
HUGE OUTPUT
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Must Visualize Massive Remote Datasets
ProblemOne Month Turnaround Time
Datasets must be kept at remote supercomputing site due to their sheer size
Visualization is inherently distributed
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QuakeViz: Distributed Interactive Visualizationof Massive Remote Earthquake Datasets
GoalInteractive manipulation of massive remote datasets from arbitrary clients
Sample 2 host visualization of Northridge Earthquake
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DV: A Framework For Building Distributed Interactive Visualizations of Massive Remote Datasets
Dataset
interpolationinterpolation isosurfaceextraction
isosurfaceextraction
scenesynthesis
scenesynthesis
interpolationinterpolation morphologyreconstruction
morphologyreconstruction
localdisplay
anduser
renderingrenderingreadingreading
ROI resolution contours
•Logical View: Distributed pipelines of vtk* modules
*Visualization Toolkit, open source C++ library
User feedback and quality settings
Display update latency
•Example:
deadline
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DV: A Framework For Building Distributed Interactive Visualizations of Massive Remote Datasets
Dataset
interpolationinterpolation isosurfaceextraction
isosurfaceextraction
scenesynthesis
scenesynthesis
interpolationinterpolation morphologyreconstruction
morphologyreconstruction
localdisplay
anduser
renderingrenderingreadingreading
ROI resolution contours
•Logical View: Distributed pipelines of vtk* modules
*Visualization Toolkit, open source C++ library
User feedback and quality settings
Display update latency
•Example:
deadline
![Page 10: The Case For Prediction-based Best-effort Real-time Peter A. Dinda Bruce Lowekamp Loukas F. Kallivokas David R. O’Hallaron Carnegie Mellon University.](https://reader030.fdocuments.net/reader030/viewer/2022032800/56649d255503460f949fc38b/html5/thumbnails/10.jpg)
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Active Frames
Active Frame
n+2?
interpolationinterpolation isosurfaceextraction
isosurfaceextraction
scenesynthesis
scenesynthesis
Physical View of Example Pipeline:
deadlineActive Frame
n+1?
deadlineActive Frame
n?
deadline
•Encapsulates data, computation, and path through pipeline•Launched from server by user interaction•Dynamically chose on which host each pipeline stage will execute and what quality settings to use
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Active Frames
Active Frame
n+2?
interpolationinterpolation isosurfaceextraction
isosurfaceextraction
scenesynthesis
scenesynthesis
Physical View of Example Pipeline:
deadlineActive Frame
n+1?
deadlineActive Frame
n?
deadline
•Encapsulates data, computation, and path through pipeline•Launched from server by user interaction•Dynamically chose on which host each pipeline stage will execute and what quality settings to use
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Active Frame Execution ModelActive Frame
Host LoadMeasurement
NetworkMeasurementRemos
Measurement Infrastructure
Mapping Algorithm
Prediction Prediction
CMU Remos API
ResourcePredictionsExec Time Model
•pipeline stage•quality params
deadline
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Active Frame Execution ModelActive Frame
Host LoadMeasurement
NetworkMeasurementRemos
Measurement Infrastructure
Mapping Algorithm
Prediction Prediction
CMU Remos API
ResourcePredictionsExec Time Model
•pipeline stage•quality params
deadline
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Active Frame Execution ModelActive Frame
Host LoadMeasurement
NetworkMeasurementRemos
Measurement Infrastructure
Mapping Algorithm
Prediction Prediction
CMU Remos API
ResourcePredictionsExec Time Model
•pipeline stage•quality params
deadline
![Page 15: The Case For Prediction-based Best-effort Real-time Peter A. Dinda Bruce Lowekamp Loukas F. Kallivokas David R. O’Hallaron Carnegie Mellon University.](https://reader030.fdocuments.net/reader030/viewer/2022032800/56649d255503460f949fc38b/html5/thumbnails/15.jpg)
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Feasibility of Best-effort Mapping Algorithms
0
10
20
30
40
50
60
70
80
90
100
0.01 0.1 1 10 100tnominal (seconds)
Random
Best Individual Host
RangeCounter(50)
Optimal (RC)
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Active Frame Execution ModelActive Frame
Host LoadMeasurement
NetworkMeasurementRemos
Measurement Infrastructure
Mapping Algorithm
Prediction Prediction
CMU Remos API
ResourcePredictionsExec Time Model
•pipeline stage•quality params
deadline
![Page 17: The Case For Prediction-based Best-effort Real-time Peter A. Dinda Bruce Lowekamp Loukas F. Kallivokas David R. O’Hallaron Carnegie Mellon University.](https://reader030.fdocuments.net/reader030/viewer/2022032800/56649d255503460f949fc38b/html5/thumbnails/17.jpg)
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Feasibility of Execution Time Models
1 3 5 7Measured Load
0
5
10
15
20
25E
xecu
tion
TIm
e (S
econ
ds)
42,000 pointsCoefficient of Correlation = 0.998
nominal
tt
t
tdttload
execnow
now
)(1
1
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Active Frame Execution ModelActive Frame
Host LoadMeasurement
NetworkMeasurementRemos
Measurement Infrastructure
Mapping Algorithm
Prediction Prediction
CMU Remos API
ResourcePredictionsExec Time Model
•pipeline stage•quality params
deadline
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Why Is Prediction Important?Bad Prediction
No obvious choiceGood PredictionTwo good choices
Pre
dict
ed E
xec
Tim
e
Good predictions result in smaller confidence intervals
Smaller confidence intervals simplify mapping decision
Pre
dict
ed E
xec
Tim
e
deadline
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Feasibility of Host Load Prediction
1 2 3 4 5 6 7 8 9 10 11 12 13 14 150
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Lead (seconds)
testcase 1681847547 from unix16 trace from all4.txt - resampled
By signal variance
By AR(9)
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Comparing Prediction Models
Good models achieve consistently low error
Mea
n S
quar
ed E
rror
Model A Model B Model C
Inconsistentlow error
Consistent low error
Consistent high error
Run 1000s of randomized testcases, measure prediction error for each, datamine results:
2.5%
25%
50%
Mean
75%
97.5%
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Comparing Linear Models for Host Load Prediction15 second predictions for one host
Title:axp0_lead15_8to8.epsCreator:MATLAB, The Mathworks, Inc.Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.
2.5%
25%
50%
Mean
75%
97.5%
Raw Cheap Expensive Very $
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Conclusions
• Identified and described class of applications that benefit from best-effort real-time
• Distributed interactive applications• Example: QuakeViz / DV
• Showed feasibility of prediction-based best-effort real-time systems
• Mapping algorithms, execution time model, host load prediction
![Page 24: The Case For Prediction-based Best-effort Real-time Peter A. Dinda Bruce Lowekamp Loukas F. Kallivokas David R. O’Hallaron Carnegie Mellon University.](https://reader030.fdocuments.net/reader030/viewer/2022032800/56649d255503460f949fc38b/html5/thumbnails/24.jpg)
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Status - http://www.cs.cmu.edu/~cmcl• QuakeViz / DV
• Overview: PDPTA'99, Aeschlimann, et al• http://www.cs.cmu.edu/~quake • Currently under construction
• Remos• Overview: HPDC’98, DeWitt, et al• Available from http://www.cs.cmu.edu/~cmcl/remulac/remos.html• Integrating prediction services
• Network measurement and analysis• HPDC’98, DeWitt, et al; HPDC’99, Lowekamp, et al• Currently studying network prediction
• Host load measurement and analysis• LCR’98, Dinda; SciProg’99, Dinda
• Host load prediction• HPDC’99, Dinda, et al
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Feasibility of Best-effort Mapping Algorithms
0
10
20
30
40
50
60
70
80
90
100
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3tmax/tnominal
Random
Best Individual Host
RangeCounter(50)
Optimal(RC)
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Feasibility of Host Load Prediction
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 250
0.5
1
1.5
2
2.5
Lead (seconds)
testcase -1619784968 from axpfea.psc trace
By signal variance
By AR(18)
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Comparing Linear Models for Host Load Prediction
Title:all_lead15_8to8.epsCreator:MATLAB, The Mathworks, Inc.Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.
15 second predictions aggregated over 38 hosts
2.5%
25%
50%
Mean
75%
97.5%
Raw Cheap Expensive Very $