1. Collaborative QoS Prediction in Cloud Computing Department of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong, China Rocky Yilei Zhang Nov. 15, 2011

2. Outlines Introduction System Architecture Memory-Based QoS Prediction Time-Aware QoS Prediction Conclusion 2

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 3

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 4

5. Non-Functional Performance of Cloud Components Non-functional performance of cloud components is essential for building cloud applications: – Cloud Component selection – Cloud Component composition – Cloud Component recommendation 5

6. 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

7. System Architecture 7

8. Memory-Based QoS Prediction 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. 8

9. Example User-component matrix: m × n, each entry is a QoS value. – Sparse – Prediction accuracy is greatly influenced by similarity computation. 9

10. Latent Features Learning 10 Latent-component matrix HLatent-user matrix V u1 u2 u3 u4 c1 c2 c3 c4 c5 c6

11. Similarity Computation Pearson Correlation Coefficient (PCC) Similarity between users: Similarity between components: 11 Latent-component matrix H Latent-user matrix V u1 u2 u3 u4 c1 c2 c3 c4 c5 c6

12. Neighbors Selection For every entry w i,j in the matrix, a set of similar users towards user u i can be found by: A set of similar items towards component c j can be found by: 12

13. Missing Value Prediction Similar User-based: Similar Component-based: Hybrid: 13

14. Experiments  QoS Dataset  Metrices  : the expected QoS value.  : the predicted QoS value  N : the number of predicted values. 14

15. Performance Comparisons 15

16. Impact of Matrix Density 16

17. Impact of Top-K 17

18. Impact of Dimensionality 18

19. 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 19

20. Time-Aware QoS Prediction Yilei Zhang, Zibin Zheng, and Michael R. Lyu, “WSPred: A Time-Aware Personalized QoS Prediction Framework for Web Services”, in Proceedings of the 22th IEEE Symposium on Software Reliability Engineering (ISSRE 2011), Hiroshima, Japan, Nov. 29-Dec. 2, 2011. 20

21. Quality-of-Service Quality-of-Service (QoS): Non-functional performance. – User/Time-independent QoS properties. price, popularity. No need for evaluation – User/Time-dependent QoS properties. failure probability, response time, throughput. Different users receive different performance at different time. Impact factors: – Remote network access – Location – Invocation time 21

22. Time-Aware QoS Performance Time-aware personalized QoS evaluation on cloud components is essential for: – Automatically selection – Dynamically composition 22

23. Challenge: How to Evaluate? – Evaluating all the cloud components to obtain their QoS performance before building cloud application s. Time-consuming Expensive Thousands of cloud components – QoS prediction Predicting QoS values by employing usage experiences in the past. 23

24. Related Work Predicting average performance – Memory-based – Model-based Need to considering the difference in terms of time 24

25. Case Study 25

26. Tensor Factorization User 26 Component Time

27. Objective Function 27

28. Missing Value Prediction 28

29. Dataset Time-Aware Web Service QoS Dataset 29

30. Metrics Mean Absolute Error (MAE) Root Mean Squared Error (RMSE) – : the expected QoS value (ground truth). – : the predicted QoS value – N : the number of predicted values. 30

31. Comparison with Other Methods MF1 – This method considers the user-service-time tensor as a set of user- service matrix slices in terms of time. Then employ MF. MF2 – compresses the user-service-time tensor into a user-service matrix. Then apply MF. TF – tensor factorization-based prediction method WSPred – tensor factorization-based recommendation with average QoS value constraints 31

32. Experimental Results 32

33. Impact of Tensor Density 33

34. Impact of Dimensionality 34

35. Conclusions  A time-aware approach for Cloud Component QoS value prediction  A large-scale experiment  A publicly released Time-Aware QoS dataset 35

36. Thank you! 36