Integrating Theoretical Modeling and Experimental Measurement for Soft Resource Allocation in Multi-tier Web Systems

Soft resources, which are system software components that use hardware or synchronize the use of hardware, are playing a critical role in the performance of multi-tier web systems, and thus it is quite important to tune the soft resource allocation for using the limited hardware resources to obtain maximum effectiveness. In this paper, we integrate both theoretical and experimental studies to the soft resource allocation problem. Specifically, we apply the queueing network model for formulating multi-tier web systems, and conduct experimental measurements based on the RUBiS benchmark system to obtain precise model parameters. Quantitative analysis is carried out, based on which an optimization model as well as an algorithm are put forward for soft resource allocation. The efficacy of our approach is validated by both theoretical analyses and experimental results.

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

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

[3]  Xiaobo Zhou,et al.  Autonomic performance and power control for co-located Web applications on virtualized servers , 2013, 2013 IEEE/ACM 21st International Symposium on Quality of Service (IWQoS).

[4]  MengChu Zhou,et al.  Stochastic Modeling and Quality Evaluation of Infrastructure-as-a-Service Clouds , 2015, IEEE Transactions on Automation Science and Engineering.

[5]  Yong Jin,et al.  Web server performance enhancement by suppressing network traffic for high performance client , 2015, 2015 17th Asia-Pacific Network Operations and Management Symposium (APNOMS).

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

[7]  Prashant J. Shenoy,et al.  Dynamic Provisioning of Multi-tier Internet Applications , 2005, Second International Conference on Autonomic Computing (ICAC'05).

[8]  MengChu Zhou,et al.  Application-Aware Dynamic Fine-Grained Resource Provisioning in a Virtualized Cloud Data Center , 2017, IEEE Transactions on Automation Science and Engineering.

[9]  Jordi Torres,et al.  Improving Web Server Performance Through Main Memory Compression , 2008, 2008 14th IEEE International Conference on Parallel and Distributed Systems.

[10]  Yuting Zhang,et al.  Friendly virtual machines: leveraging a feedback-control model for application adaptation , 2005, VEE '05.

[11]  MengChu Zhou,et al.  Stochastic Modeling and Performance Analysis of Migration-Enabled and Error-Prone Clouds , 2015, IEEE Transactions on Industrial Informatics.

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

[13]  Qian Zhu,et al.  Resource Provisioning with Budget Constraints for Adaptive Applications in Cloud Environments , 2010, IEEE Transactions on Services Computing.

[14]  Edward Chlebus,et al.  Nonstationary Poisson modeling of web browsing session arrivals , 2007, Inf. Process. Lett..

[15]  Yixin Diao,et al.  Controlling Quality of Service in Multi-Tier Web Applications , 2006, 26th IEEE International Conference on Distributed Computing Systems (ICDCS'06).

[16]  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.

[17]  Calton Pu,et al.  Profit-Based Experimental Analysis of IaaS Cloud Performance: Impact of Software Resource Allocation , 2012, 2012 IEEE Ninth International Conference on Services Computing.

[18]  Jie Lu,et al.  Optimal Cloud Resource Auto-Scaling for Web Applications , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.