SRConfig: An Empirical Method of Interdependent Soft Configurations for Improving Performance in n-Tier Application

Efficient resources utilization and better system performance are always two important objectives that service providers pursue to enjoy a maximum profit. In this paper, through analyzing experimental measurements, we study the performance impact of interdependent soft resources on an n-tier application benchmark - the RUBiS system. Soft resources are vital factors that influence hardware resources usage and overall application performance. Improper soft configurations can result in correlated bottlenecks and make performance degradation, so tuning the configuration of soft resources is imperative. Based on the experimental measurements, SRConfig method is applied to predict the soft configurations through adopting the back propagation neural network in n-tier application. Experimental results validate the accuracy and efficacy of our method.

[1]  V. Krishna Reddy,et al.  Performance analysis of load balancing techniques in cloud computing environment , 2015, 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT).

[2]  Lui Sha,et al.  Adaptive Control of Multi-Tiered Web Applications Using Queueing Predictor , 2006, 2006 IEEE/IFIP Network Operations and Management Symposium NOMS 2006.

[3]  George C. Necula,et al.  Capriccio: scalable threads for internet services , 2003, SOSP '03.

[4]  Jordi Torres,et al.  Understanding tuning complexity in multithreaded and hybrid web servers , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[5]  Calton Pu,et al.  The Impact of Software Resource Allocation on Consolidated n-Tier Applications , 2014, 2014 IEEE 7th International Conference on Cloud Computing.

[6]  Asser N. Tantawi,et al.  An analytical model for multi-tier internet services and its applications , 2005, SIGMETRICS '05.

[7]  Feng Li,et al.  Application Study of BP Neural Network on Stock Market Prediction , 2009, 2009 Ninth International Conference on Hybrid Intelligent Systems.

[8]  Calton Pu,et al.  Economical and Robust Provisioning of N-Tier Cloud Workloads: A Multi-level Control Approach , 2011, 2011 31st International Conference on Distributed Computing Systems.

[9]  Kang G. Shin,et al.  The impact of concurrency gains on the analysis and control of multi-threaded Internet services , 2004, IEEE INFOCOM 2004.

[10]  Ke Xiao,et al.  BP neural network-based web service selection algorithm in the smart distribution grid , 2014, The 16th Asia-Pacific Network Operations and Management Symposium.

[11]  Calton Pu,et al.  The Impact of Soft Resource Allocation on n-Tier Application Scalability , 2011, 2011 IEEE International Parallel & Distributed Processing Symposium.

[12]  Sasko Ristov,et al.  Porting an N-tier application on cloud using P-TOSCA: A case study , 2015, 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).