A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications

[摘要] The multi-tier implementation has become the industry standard for developing scalable client-server enterprise applications. Since these applications are performance sensitive, effective models for dynamic resource provisioning and for delivering quality of service to these applications become critical. Workloads in such environments are characterized by client sessions of interdependent requests with changing transaction mix and load over time, making model adaptivity to the observed workload changes a critical requirement for model effectiveness. In this work, we apply a regression-based approximation of the CPU demand of client transactions on a given hardware. Then we use this approximation in an analytic model of a simple network of queues, each queue representing a tier, and show the approximation’s effectiveness for modeling diverse workloads with a changing transaction mix over time. Using the TPCW benchmark and its three different transaction mixes we investigate factors that impact the efficiency and accuracy of the proposed performance prediction models. Experimental results show that this regression-based approach provides a simple and powerful solution for efficient capacity planning and resource provisioning of multi-tier applications under changing workload conditions. Publication Info: Copyright 2007 IEEE. Published in the 4th International Conference on Autonomic Computing, 11-15 June 2007, Jacksonville, FL, USA 10 Pages

[发布机构] HP Development Company

[学科分类] 计算机科学（综合）

[关键词] three-tier-applications;TPCW;regression;transaction cost;capacity planning;performance modeling