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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