Exploring Latent Features for Memory-Based QoS Prediction in Cloud Computing

With the increasing popularity of cloud computing as a solution for building high-quality applications on distributed components, efficiently evaluating user-side quality of cloud components becomes an urgent and crucial research problem. However, invoking all the available cloud components from user-side for evaluation purpose is expensive and impractical. To address this critical challenge, we propose a neighborhood-based approach, called CloudPred, for collaborative and personalized quality prediction of cloud components. CloudPred is enhanced by feature modeling on both users and components. Our approach CloudPred requires no additional invocation of cloud components on behalf of the cloud application designers. The extensive experimental results show that CloudPred achieves higher QoS prediction accuracy than other competing methods. We also publicly release our large-scale QoS dataset for future related research in cloud computing.

[1]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[2]  Mark Linderman,et al.  Towards Mobile Data Streaming in Service Oriented Architecture , 2010, 2010 29th IEEE Symposium on Reliable Distributed Systems.

[3]  Wolf-Tilo Balke,et al.  Towards Personalized Selection of Web Services , 2003, WWW.

[4]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[5]  George Karypis,et al.  Item-based top-N recommendation algorithms , 2004, TOIS.

[6]  Zibin Zheng,et al.  WSExpress: A QoS-aware Search Engine for Web Services , 2010, 2010 IEEE International Conference on Web Services.

[7]  Qiang Yang,et al.  Scalable collaborative filtering using cluster-based smoothing , 2005, SIGIR '05.

[8]  Tao Yu,et al.  Efficient algorithms for Web services selection with end-to-end QoS constraints , 2007, TWEB.

[9]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[10]  Zibin Zheng,et al.  Distributed QoS Evaluation for Real-World Web Services , 2010, 2010 IEEE International Conference on Web Services.

[11]  Zibin Zheng,et al.  CloudRank: A QoS-Driven Component Ranking Framework for Cloud Computing , 2010, 2010 29th IEEE Symposium on Reliable Distributed Systems.

[12]  Matthew Richardson,et al.  Yes, there is a correlation: - from social networks to personal behavior on the web , 2008, WWW.

[13]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[14]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[15]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[16]  Zibin Zheng,et al.  BFTCloud: A Byzantine Fault Tolerance Framework for Voluntary-Resource Cloud Computing , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[17]  Piero A. Bonatti,et al.  On optimal service selection , 2005, WWW '05.

[18]  Ricardo Jiménez-Peris,et al.  An Autonomic Approach for Replication of Internet-based Services , 2008, 2008 Symposium on Reliable Distributed Systems.

[19]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[20]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[21]  Prashant Pandey,et al.  Cloud computing , 2010, ICWET.

[22]  Michael R. Lyu,et al.  Effective missing data prediction for collaborative filtering , 2007, SIGIR.

[23]  Junfeng Zhao,et al.  Personalized QoS Prediction forWeb Services via Collaborative Filtering , 2007, IEEE International Conference on Web Services (ICWS 2007).

[24]  Kishor S. Trivedi,et al.  Quantifying Resiliency of IaaS Cloud , 2010, SRDS.