euro-par, 2006 1 a resource allocation approach for supporting time-critical applications in grid...
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Euro-Par, 2006 1
A Resource Allocation Approach for Supporting Time-Critical
Applications in Grid Environments
Qian Zhu and Gagan Agrawal
Department of Computer Science and Engineering
The Ohio State University
IPDPS 2009
IPDPS 2009 ConferenceMay 28th, 2009 Rome, Italy
Euro-Par, 2006
Context
• Ongoing research on supporting time-critical adaptive applications
• Fixed time, flexible computations– Maximize a QoS/Benefit function
• Previous work– Middleware design– Self-adaptation algorithm (ICAC 2008)
2
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Motivating Application: Real-timeVolume Rendering (VR)
• Flexibility: image quality, image size…• Time constraints
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Motivating Application: Great Lake Nowcasting and Forecasting (GLFS)
Model
WeatherData
WaterQuality
20 km2
0 k
m
1 km
1 k
m
• Flexibility – Grid resolution
– Internal time step
– External time step
• Time Constraints
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Summary of Application Needs
• Time-Critical Event Handling– Intense computation and communication– Time and resource constraints– Application-specific flexibility– benefit function
• VR application
• GLFS application
• Grid Resources
angles_view_number
)error_image_penalty)blocks_data_all_oncontributi(max(
output_number
)elmod_per_tcos)output_per_reward(max(
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Overview of Our Research
• To Optimize the Benefit Function within the Time Constraint
• Parameter Adaptation– VR application: error tolerance, image size
– GLFS application: internal/external time step
• Resource Allocation– Heterogeneous and dynamic resources
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Outline
• Motivation and Introduction
• Resource Allocation Approach
– Approach Overview– Efficiency Value– Scheduling Algorithm
• Experimental Evaluation
• Related Work
• Conclusion
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Experimental Study: Real-time Volume Rendering
• The CPU/memory usage increases as ErrorTolerance value decreases or the ImageSize value increases.• The change in the value of ErrorTolerance has a more significant impact, compared to the ImageSize parameter.
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Experimental Study: Great Lake Nowcasting and Forecasting
• The CPU usage changes as the values of ExternalTimeStep and InternalTimeStep vary.• The memory usage remains roughly the same.
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Problem Description
• Heterogeneous and Dynamic Resources
• Different CPU, Memory, and/or Bandwidth Usage– Different service components– Different values of adjustable service parameters within
the same service component
• Schedule the Service Components to Maximize the Benefit Function Within the Time Constraint
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Outline
• Motivation and Introduction
• Resource Allocation Approach
– Approach Overview– Efficiency Value– Scheduling Algorithm
• Experimental Evaluation
• Related Work
• Conclusion
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Application Model
CAC 2008
S1
S3
S2
S4
S6
S5data
control
S1
S2
S3
S4
S5
S6
• Each service component is deployed on a single node• Multiple processing round
WSTP TreeConstruction
Service
TemporalTree
ConstructionService
CompressionService
Unit ImageRendering
Service
DecompressionService
ImageComposition
Service
Error tolerance
Image size
Wavelet coefficient
Data Packet
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Resource Allocation Approach Overview
• Allocate Heterogeneous Resources to Services to Maximize the Benefit Within the Time Constraint– Unique characteristics of resource usage
– Extra resource usage by varying the values of adaptive parameters
• Normal Execution Phase– Train rules for Efficiency Value estimation
– Assign service priority
• Event Handling Phase– Apply the learned rules to infer Efficiency Value
– Priority-based scheduling
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Efficiency Value
• To capture the suitability of executing the Service on the Processing Node
• Definition– Benefit contribution
, where – Adaptation overhead
, where– Node status
• Weighted sum of standard deviation of the workload and resource variance every 30 seconds
iB
j,iV
j
iSjN
%100)(min
min
i
iicurr
i B
BBB
)(
)( minmin
icurr
icurr
ii
xfB
xfB
%100)(,min
,min
,
,
ji
jijicurr
ji V
VVV
),(
),(
2,
min1,min
icurrjk
jicurr
ijk
ji
xNRuleV
xNRuleV
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Efficiency Value – Cont’d
01 21 , standard deviation of workload and resource variance
)T,T(e)V
B(sigmoidE j,i
compicomm2
)(
j,i
i1j,i
j
how efficient is for supporting parameter adaptation of for overall benefit optimization
jN iS
• Efficiency value estimation– Fuzzy logic rules
• Calculating Efficiency Value
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Efficiency Value -- Example
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CPU=2.0GHzMem=800MB =0.2
CPU=1.2GHzMem=1.0GB =0.4
CPU=2.8GHzMem=2.5GB =0.6
CPU=1.0GHzMem=3.0GB =0.1
CPU=3.0GHzMem=2.0GB =0.05
S1 S2
N1 N2 N3 N4 N5
E1,1 E1,2 E1,3 E1,4 E1,5
0.92 0.46 0.52 0.35 0.72E2,1 E2,2 E2,3 E2,4 E2,5
0.96 0.78 0.56 0.28 0.30
Parameter:x1Priority = 8
Parameter:x2Priority = 4
CPUintensive
Memoryintensive
N1 N2 N3 N4N5 N1
N2N3
N4 N50%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
No
rmal
ized
Ben
efit
S1S2
Figure: Example of Efficiency Value Calculation: (a) Computed Values (b) Normalized Benefit with Different Allocations
• Assigning to and to yields the maximum benefit• Our definition of efficiency value captures the suitability of different nodes for different services
1S 2S1N 2N
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Scheduling Algorithm
• Greedy Scheduling– Service priority based
• Benefit Optimization and Meeting the Time Deadline– Adjust and 1 2
5050 21 .,.
communication time of iS computation time of iS
)T,T(e)V
B(sigmoidE j,i
compicomm2
)(
j,i
i1j,i
j
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Outline
• Motivation and Introduction
• Resource Allocation Approach
– Approach Overview– Efficiency Value– Scheduling Algorithm
• Experimental Evaluation
• Related Work
• Conclusion
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Experiments Setup
• Algorithms Compared– GrADS (UCSD)
– Optimal
• Metrics– Normalized benefit
– Success-rate
• Simulated Grid Environments– HighReHetero, ModReHetero, and LowReHetero
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Experiment1: Effectiveness of Our Learning Approach
• MSE converges within 20mins, 35mins and 1hour for a 5-hour run
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Experiment2: Normalized Benefit Comparison (VolumeRendering)
* Our algorithm achieves an average of 87% normalized benefit comparing to the Optimal and it is 32% higher than GrADS.
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Figure 10: Normalized Benefit Comparison of Our Approach with GrADS and Optimal: Highly Heterogeneous Environment
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Experiment2: Success-Rate Comparison (VolumeRendering)
* Our algorithm achieves 90% to 100% success-rate comparing to the Optimal. While GrADS can achieve 80% to 90%.
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Figure 10: Success-Rate Comparison of Our Approach with GrADS and Optimal: Highly Heterogeneous Environment
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Experiment2: Overhead Comparison
* The overhead caused by our algorithm is within 10% and 7% of that of the GrADS for VR and GLFS applications.
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Figure 14: Resource Allocation Overhead Comparison of Our Approach with GrADS: (a) Volume Rendering Application (b) GLFS Application
(a) (b)
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Experiment 3: Scalability
• An average slowdown of 9%, 7%, and 3%, respectively, in the three grid environments• Scheduling 160 service components is 26.4 seconds
Figure 15: Scalability of Different Resource Allocation Approaches
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Outline
• Motivation and Introduction
• Resource Allocation Approach
– Approach Overview– Efficiency Value– Scheduling Algorithm
• Experimental Evaluation
• Related Work
• Conclusion
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Related Work
• Resource Allocation in Grid Computing– Iosup et al. (SC2007)– Xu et al. (ICAC2007)– Huang et al. (SC2007)– Tesauro et al. (ICAC2006)
• Real-Time Scheduling– Survey (Real Time Systems, 2004)– Q-RAM (RTSS1998)– Gopalan et al. (MMCN2002)
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Outline
• Motivation and Introduction
• Resource Allocation Approach
– Approach Overview– Efficiency Value– Scheduling Algorithm
• Experimental Evaluation
• Related Work
• Conclusion
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• Capture How Effectively of Processing a Service on a Node– Efficiency value estimation
– Greedy scheduling
• Evaluate Our Resource Allocation Approach using Two Adaptive Applications– 32% more benefit comparing to GrADS
– Within 10% overhead comparing to GrADS
– Our approach is scalable
Conclusion
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