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

TLDR

Multi‑tier client‑server applications are industry standard and performance‑sensitive, requiring dynamic resource provisioning that adapts to changing workloads with interdependent requests and shifting transaction mixes. The study applies a regression‑based approximation of CPU demand for client transactions to enable dynamic resource provisioning. An analytic queue‑network model incorporating the regression approximation is used to predict performance across diverse workloads, evaluated with the TPC‑W benchmark and varying transaction mixes. Experiments demonstrate that the regression‑based model offers a simple and powerful solution for efficient capacity planning and resource provisioning of multi‑tier applications under dynamic workloads.

Abstract

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 TPC- W 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.