In this study, a full-factorial experimental evaluation of a sensor fusion system based on a real vehicle was performed. The experimental results for different driving scenarios and parameters are discussed and the factors that make the most impact are identified. The performance of sensor fusion depends on many factors such as the sensors used, kinematic model used in the Extended Kalman Filter(EKF) motion of the vehicles, type of road, density of vehicles, and gating methods. The interactions between parameters and the extent to which individual parameters contribute to the overall accuracy of a sensor fusion system can be difficult to assess. This study identified that the distance between the vehicles has the largest impact on the estimation error because the vision sensor performs poorly with increased distance. In addition, it was identified that the kinematic models had no significant impact on the estimation. Last but not least, the ellipsoid gates performed better than rectangular gates.

While workload characterization and performance of web systems have been studied extensively, reliability has received much less attention. In this paper, we propose a framework for session reliability modeling which integrates the user view represented by the session layer and the system view represented by the service layer. A unique characteristic of the session layer is that, in addition to the user navigation patterns, it incorporates the session length in number of requests and allows us to account for heavy-tailed workloads shown to exist in real web systems. The service layer is focused on the request reliability as it is observed at the service provider side. It considers the multifier web server architecture and the way components interact in serving each request. Within this framework, we develop a session reliability model and solve it using simulation. Instead of the traditional one-factor-at-a-time sensitivity analysis, we use statistical design and analysis of experiments, which allow us to identify the factors and interactions that have statistically significant effect on session reliability. Our findings indicate that session reliability, which accounts for the distribution of failed requests within sessions, provides better representation of the user perceived quality than the request-based reliability.

Many systems, including Web and Software as a Service (SaaS) are best characterized with session-based workloads. Empirical studies have shown that Web session arrivals exhibit long range dependence and that the number of request in a session is well modeled with skewed or heavy-tailed distributions. However, models that account for session workloads characterized by empirically observed phenomena and studies of their impact on performance metrics are lacking. In this paper, we use partly-open queue to account for session-based workloads in a physically meaningful way and use simulation to analyze the behavior of the Web system under Long Range Dependent (LRD) session arrival process and skewed distribution for the number of requests in a session. Our results show that the percentage of dropped sessions, mean queue length, mean waiting time, and the useful server utilization are all affected by the LRD session arrivals and the statistics of the number of requests within a session. The impact is higher in the case of more prominent longrange dependence. Interestingly, both request arrival process and request departure process are long-range dependent, even in the case when session arrivals are Poisson.