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Exploring Latent Features for Memory- Based QoS Prediction in Cloud Computing Yilei Zhang, Zibin Zheng, and Michael R. Lyu

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Presentation on theme: "Exploring Latent Features for Memory- Based QoS Prediction in Cloud Computing Yilei Zhang, Zibin Zheng, and Michael R. Lyu"— Presentation transcript:

1 Exploring Latent Features for Memory- Based QoS Prediction in Cloud Computing Yilei Zhang, Zibin Zheng, and Michael R. Lyu Department of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong, China School of Computer Science National University of Defence Technology Changsha, China SRDS 2011, Madrid, Spain, Oct , 2011

2 Outlines Introduction System Architecture QoS Prediction Approach Experiments 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 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. 5

6 System Architecture 6

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

8 Latent Features Learning 8 Latent-component matrix HLatent-user matrix V u1 u2 u3 u4 c1 c2 c3 c4 c5 c6

9 Similarity Computation Pearson Correlation Coefficient (PCC) Similarity between users: Similarity between components: 9 Latent-component matrix H Latent-user matrix V u1 u2 u3 u4 c1 c2 c3 c4 c5 c6

10 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: 10

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

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

13 Experimental Results 13

14 Experimental Results 14

15 Experimental Results 15

16 Experimental Results 16

17 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 17

18 Thank you! 18


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