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

2. Outlines Introduction System Architecture QoS Prediction Approach
Experiments
Conclusion

3. 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

4. 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

5. 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.

6. System Architecture

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

8. Latent Features Learning
u1 u2 u3 u4
c c c c c c6
Latent-user matrix V
Latent-component matrix H

9. Similarity Computation
Pearson Correlation Coefficient (PCC)
Similarity between users:
Similarity between components:
u1 u2 u3 u4
Latent-user matrix V
c c c c c c6
Latent-component matrix H

10. 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:

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

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

13. Experimental Results

14. Experimental Results

15. Experimental Results

16. Experimental Results

17. 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

18. 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.

19. Thank you!