Using Architectural Simulation Models to Aid the Design of Data Intensive Application

Performance is an open issue in data intensive applications. Finding the best implementation and influential performance factors of hardware and software platforms for the data intensive applications requires trial and error. However, it is very difficult and costly to perform these trials in a real large-scale environment. In this paper we use a generic simulation framework SIMCAN to simulate hardware and software platforms of data intensive applications for investigating the influential performance factors, and thereby making decisions on the design of data intensive application architectures. We have employed a typical use case of a data mining application, in which the architecture has been proposed using a pipeline model. We have simulated various scenarios to investigate factors that affect the system performance to assist the architecture design and the simulation results provide useful information for this decision-making.