(Georgia Institute of Technology, 2013-07-02)
Huang, Ya-Lin
The continual growth of computing power in small devices has motivated the development of novel approaches to optimizing operational systems efficiently and effectively. These optimization problems are often so complex that solving them analytically may be difficult, if not prohibited. One method for solving such problems is to use online simulation. However, challenges in using online simulation include the issues of responsiveness (e.g., because of communication delays), scalability, and failure resistance. To tackle these issues, this study proposes embedding online simulations into a network of sensors that monitors the system under investigation.
This thesis explores an approach termed “ad hoc distributed simulation,” which is based on embedding online simulations into a sensor network and adding communication and synchronization among simulators to model operational systems. This approach offers several potential advantages over existing approaches: (1) it can provide rapid response to system dynamics as well as efficiency since data exchange is local to the sensor network, (2) it can achieve better scalability to incorporate more sensors, and (3) it can provide better robustness to failures because portions of the system are still under local control. This research addresses several statistical issues in this ad hoc approach: (1) rapid and effective estimation of the input processes at model boundaries, (2) estimation of system-wide performance measures from individual simulator outputs, and (3) correction mechanisms responding to unexpected events or inaccuracies within the model.
This thesis examines ad hoc distributed simulation analytically and experimentally, mainly focusing on the accuracy of predicting the performance of open queueing networks. First, the analytical part formalizes the ad hoc approach and evaluates its accuracy at modeling certain class of open queueing networks with regard to the steady-state system performance measures. This work concerning steady-state metrics is extended to a broader class of networks by an empirical study, which presents evidence to show that the ad hoc approach can generate predictions comparable to those from sequential simulations. Furthermore, a “buffered-area” mechanism is proposed to substantially reduce prediction bias with a moderate increase in execution time.
In addition to those steady-state studies, another empirical study targets the prediction accuracy of the ad hoc approach at open queueing networks with short-term system-state transients. This study demonstrates that, with slight modification to the prior design of the ad hoc queueing simulation method for those steady-state studies, system dynamics can be well modeled. The results, again, support the conclusion that the ad hoc approach is competitive to the sequential simulation method in terms of prediction accuracy.