Performance modeling of distributed multi-tier enterprise systems

Abstract

Management techniques for achieving self-adaptive behavior in enterprise computing systems has been recently investigated by both academia and industry. Primarily, these techniques observe application measurements and take corrective action, based on a given model, to achieve a specified Quality of Service (QoS). Examples of such approaches include task scheduling, CPU provisioning [1], and power management [2]. Effective management of enterprise systems require finegrained decisions that balance various, often conflicting, service level agreements while adjusting to changes in the operating environment, caused by factors such as time-varying user workload and incomplete knowledge of the system operating state. Such decisions, affect how an application behaves under different workload intensities [3]. Over time, they influence system reliability, energy efficiency and customer experience. These decisions are implemented through a control system, requiring a detailed model of the system that reflects how various management choices affect the system behavior under varying environment inputs and operating conditions. An example of such a model is the layered queuing model presented in [4]. In this paper, we present the results of recent experiments aiming to model a distributed multi-tier enterprise application. Using dynamic regression and queuing modeling techniques, we have been able to obtain an approximate model structure that captures the system behavior under various operation conditions with a high degree of accuracy. In future, these models will be used to implement a distributed control structure that optimizes the system parameters for a given set of objectives [5]. This paper is organized as follows. Section 2 discusses the experimental setup. Section 3 discusses the performance management objectives, and discusses our experiments and modeling efforts. Section 4 concludes this paper.