Euro-Par, 2006 1 A Resource Allocation Approach for Supporting Time-Critical Applications in Grid...

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





• 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




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





• 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





• 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





• 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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Thank you!

IPDPS 2009

Euro-Par, 2006 1 A Resource Allocation Approach for Supporting Time-Critical Applications in Grid...

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