DCR: Double Component Ranking for Building Reliable Cloud Applications

Since cloud applications are usually large-scale, it is too expensive to enhance the reliability of all components for building highly reliable cloud applications. Therefore, we need to identify significant components which have great impact on the system reliability. FTCloud, an existing approach, ranks the components only considering the impact of component internal failures and ignoring error propagation. However, error propagation is also an important factor on the system reliability. To attack the problem, we propose an improved component ranking framework, named DCR, to identify significant components in cloud applications. DCR employs two individual algorithms to rank the components twice and determines a set of the most significant components based on the two ranking results. In addition, DCR does not require information of component invocation frequencies. Extensive experiments are provided to evaluate DCR and compare it with FTCloud. The experimental results show that DCR outperforms FTCloud in almost all cases.

[1]  Brian Randell,et al.  The Evolution of the Recovery Block Concept , 1994 .

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

[3]  Xavier Défago,et al.  Reliability Prediction for Component-Based Systems: Incorporating Error Propagation Analysis and Different Execution Models , 2012, 2012 12th International Conference on Quality Software.

[4]  Michael R. Lyu,et al.  Handbook of software reliability engineering , 1996 .

[5]  Vincenzo Grassi,et al.  A Modeling Approach to Analyze the Impact of Error Propagation on Reliability of Component-Based Systems , 2007, CBSE.

[6]  Hector Garcia-Molina,et al.  Combating Web Spam with TrustRank , 2004, VLDB.

[7]  Christopher R. Myers,et al.  Software systems as complex networks: structure, function, and evolvability of software collaboration graphs , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  E. Michael Maximilien,et al.  Conceptual model of web service reputation , 2002, SGMD.

[9]  Zibin Zheng,et al.  FTCloud: A Component Ranking Framework for Fault-Tolerant Cloud Applications , 2010, 2010 IEEE 21st International Symposium on Software Reliability Engineering.

[10]  Sergey Brin,et al.  Reprint of: The anatomy of a large-scale hypertextual web search engine , 2012, Comput. Networks.

[11]  Shinji Kusumoto,et al.  Ranking significance of software components based on use relations , 2003, IEEE Transactions on Software Engineering.

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

[13]  Simon M. Kaplan,et al.  Scale-Free Nature of Java Software Package, Class and Method Collaboration Graphs , 2006 .

[14]  Michalis Faloutsos,et al.  On power-law relationships of the Internet topology , 1999, SIGCOMM '99.

[15]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[16]  Bethany S. Dohleman Exploratory social network analysis with Pajek , 2006 .

[17]  Gao Shu,et al.  OWLS-CSM: A Service Profile Based Similarity Framework for Web Service Discovery , 2014 .

[18]  Roger C. Cheung,et al.  A User-Oriented Software Reliability Model , 1978, IEEE Transactions on Software Engineering.

[19]  V. Bapuji,et al.  Cloud Computing : Research Issues and Implications , 2013, CloudCom 2013.

[20]  Guiming Lu,et al.  A Reliable Web Services Selection Method for Concurrent Requests , 2014 .

[21]  Anne H. H. Ngu,et al.  QoS-aware middleware for Web services composition , 2004, IEEE Transactions on Software Engineering.

[22]  Algirdas A. Avi The Methodology of N-Version Programming , 1995 .

[23]  Carl E. Landwehr,et al.  Basic concepts and taxonomy of dependable and secure computing , 2004, IEEE Transactions on Dependable and Secure Computing.