Exploring Latent Features for Memory- Based QoS Prediction in Cloud Computing Yilei Zhang, Zibin Zheng, and Michael R. Lyu {ylzhang,zbzheng,lyu}@cse.cuhk.edu.hk.

Exploring Latent Features for Memory-Based QoS Prediction in Cloud Computing

Yilei Zhang, Zibin Zheng, and Michael R. Lyu{ylzhang,zbzheng,lyu}@cse.cuhk.edu.hk

Department of Computer Science & EngineeringThe Chinese University of Hong Kong

Hong Kong, ChinaSchool of Computer Science

National University of Defence TechnologyChangsha, China

SRDS 2011, Madrid, Spain, Oct. 4 - 7, 2011

Outlines

• Introduction• System Architecture• QoS Prediction Approach• Experiments• Conclusion

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Cloud Computing Cloud computing provides a model for enabling convenient, on-

demand network access to a shared pool of computing resources : Networks Servers Databases Services

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Cloud Applications Building on a number of distributed cloud components

Large-scale Complicated Time sensitive High-quality

Case 1: New York Times Used EC2 and S3 to convert 15 million scanned news articles to

PDF (4TB data) 100 Linux computers 24 hours

Case 2: Nasdaq Uses S3 to deliver historic stock and fund information Millions of files showing price changes of entities over 10

minute segments

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Performance of Cloud Components

High-quality cloud applications rely on the high-quality of cloud components. remote network access Location independence

Personalized performance evaluation on cloud components is essential. Method 1: evaluating all the components to obtain their QoS

performance. Impractical: time-consuming, expensive, thousands of components.

Method 2: collaborative filtering approach Predicting component QoS by employing usage experiences from similar users.

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System Architecture

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Example

• User-component matrix: m × n, each entry is a QoS value.– Sparse– Prediction accuracy is greatly influenced by

similarity computation.

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Latent Features Learning

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Latent-component matrix HLatent-user matrix V

u1 u2 u3 u4 c1 c2 c3 c4 c5 c6

Similarity Computation

• Pearson Correlation Coefficient (PCC) • Similarity between users:

• Similarity between components:

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Latent-component matrix H

Latent-user matrix V

u1 u2 u3 u4

c1 c2 c3 c4 c5 c6

Neighbors Selection

• For every entry wi,j in the matrix, a set of similar users towards user ui can be found by:

• A set of similar items towards component cj can be found by:

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Missing Value Prediction

• Similar User-based:

• Similar Component-based:

• Hybrid:

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Experiments

QoS Dataset

Metrices

: the expected QoS value.

: the predicted QoS value N: the number of predicted values.

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Experimental Results

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Experimental Results

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Experimental Results

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Experimental Results

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Conclusions and Future Work

Conclusions: A collaborative approach for personalized cloud

component QoS value prediction A large-scale real-world experiment A publicly released real-world QoS dataset

Future Work: Investigation of more QoS properties Experiments on different kinds of cloud

components

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Thank you!

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