Exploring Latent Features for Memory-Based QoS Prediction in Cloud Computing Yilei Zhang 17/05/2011

Outlines Introduction System Architecture QoS Prediction Approach Experiments Conclusion

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

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

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.

System Architecture

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

Latent Features Learning u1 u2 u3 u4 c1 c2 c3 c4 c5 c6 Latent-user matrix V Latent-component matrix H

Similarity Computation Pearson Correlation Coefficient (PCC) Similarity between users: Similarity between components: u1 u2 u3 u4 Latent-user matrix V c1 c2 c3 c4 c5 c6 Latent-component matrix H

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:

Missing Value Prediction Similar User-based: Similar Component-based: Hybrid:

Experiments QoS Dataset Metrices : the expected QoS value. : the predicted QoS value N: the number of predicted values.

Experimental Results

Experimental Results

Experimental Results

Experimental Results

Conclusions and Future Work 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

Publications Yilei Zhang, Zibin Zheng, and Michael R. Lyu, “Exploring Latent Features for Memory-Based QoS Prediction in Cloud Computing”, in Proceedings of the 30th IEEE Symposium on Reliable Distributed Systems (SRDS 2011), Madrid, Spain, Oct. 4-7, 2011. Yilei Zhang, Zibin Zheng, and Michael R. Lyu, “BFTCloud: A Byzantine Fault Tolerance Framework for Voluntary-Resource Cloud Computing”, in Proceedings of the 4th IEEE International Conference on Cloud Computing (CLOUD 2011), Washington DC, USA, Jul. 4-9, 2011.

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