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					Collaborative QoS Prediction in Cloud Computing



                   Rocky Yilei Zhang
                      Nov. 15, 2011

       Department of Computer Science & Engineering
            The Chinese University of Hong Kong
                     Hong Kong, China
                  Outlines
•   Introduction
•   System Architecture
•   Memory-Based QoS Prediction
•   Time-Aware QoS Prediction
•   Conclusion




                                  2
                 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
               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
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
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
    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
                    Example




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



u1   u2   u3   u4      c1   c2   c3   c4   c5   c6




Latent-user matrix V   Latent-component matrix H




                                                     10
        Similarity Computation
• Pearson Correlation Coefficient (PCC)
• Similarity between users:          u1         u2    u3    u4




                                         Latent-user matrix V
• Similarity between components:
                               c1   c2     c3    c4    c5        c6




                                Latent-component matrix H
                                                             11
           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:



                                                 12
       Missing Value Prediction
• Similar User-based:


• Similar Component-based:


• Hybrid:



                                  13
                Experiments
QoS Dataset


Metrices


       : the expected QoS value.
       : the predicted QoS value
  N: the number of predicted values.

                                        14
Performance Comparisons




                          15
Impact of Matrix Density




                           16
Impact of Top-K




                  17
Impact of Dimensionality




                           18
   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
      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
                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
   Time-Aware QoS Performance

• Time-aware personalized QoS evaluation on
  cloud components is essential for:
  – Automatically selection
  – Dynamically composition




                                              22
  Challenge: How to Evaluate?
– Evaluating all the cloud components to obtain their
  QoS performance before building cloud
  applications.
  •   Time-consuming
  •   Expensive
  •   Thousands of cloud components
– QoS prediction
  •   Predicting QoS values by employing usage experiences
      in the past.




                                                         23
               Related Work

• Predicting average performance
  – Memory-based
  – Model-based
• Need to considering the difference in terms of
  time



                                               24
Case Study




             25
              Tensor Factorization


Time

User

       Component




                                     26
Objective Function




                     27
Missing Value Prediction




                           28
                 Dataset
• Time-Aware Web Service QoS Dataset




                                       29
                 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
 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
Experimental Results




                       32
Impact of Tensor Density




                           33
Impact of Dimensionality




                           34
                 Conclusions

A time-aware approach for Cloud Component QoS
 value prediction

A large-scale experiment

A publicly released Time-Aware QoS dataset



                                                 35
Thank you!



             36

				
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posted:8/14/2012
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