University: University of Toronto
Professor: Ben Liang
Department: Department of Electrical and Computer Engineering

OPNET Simulation of Data Prefetching in Heterogeneous Wireless Networks

We study the performance of multiuser document prefetching in a two-tier
heterogeneous wireless system. Mobility-aware prefetching was previously introduced
to enhance the experience of a mobile user roaming between heterogeneous
wireless access networks. However, an undesirable effect of multiple prefetching
users is the potential for system instability due to the racing behavior between
the document access delay and the user prefetching quantity. This phenomenon
is particularly acute in the heterogeneous environment. We investigate into alleviating
the system traffic load through prefetch thresholding, accounting for server
queuing prioritization. We propose a novel analysis framework to evaluate the
performance of the thresholding approach. Numerical and simulation results show
that the proposed analysis is accurate for a wide variety of access, service, and mobility
patterns. We further demonstrate that stability can be maintained even under
heavy usage, providing both the same scalability as a non-prefetching system and
the performance gain associated with prefetching.

OPNET Simulation for Routing Optimization in Ad Hoc Networks

On-demand routing reduces the control overhead in mobile ad hoc networks, but it has the major drawback of introducing latency between route-request arrival and the determination of a valid route. This work addresses the issue of minimizing the delay in on-demand routing protocols through optimizing the Time-to-Live (TTL) interval for route caching. An analytical framework is introduced to compute the expected routing delay when a source node or an intermediate node has a cached route with any given TTL value. Furthermore, numerical methods are proposed to determine the optimal TTL of a newly cached route. We present simulation results that support the validity of our analysis. Using the proposed analytical framework, we study how the routing delay is affected by route length, route-request frequency, and the frequency of topology variation. We show that the proposed optimal route-cache TTL strategy can significantly reduce the routing delay over systems that either does not use route-cache or keeps route-cache indefinitely long. We further show that the performance gain of optimizing the route-cache TTL increases with increasing traffic pattern localization.