Ph.D., Colorado State University, Aug. 2008
Co-Major Professors: H. J. Siegel and Anthony A. Maciejewski
In a heterogeneous distributed computing environment, it is often advantageous to allocate system resources in a manner that optimizes a given system performance measure. However, this optimization is often dependent on system parameters whose values are subject to uncertainty. Thus, an important research problem arises when system resources must be allocated given uncertainty in system parameters. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. In this research, we define mathematical models of robustness in both static and dynamic stochastic environments. In addition, we model dynamic environments where estimates of system parameter values are provided as point estimates where these estimates are known to deviate substantially from their actual values.
The main contributions of this research are (1) mathematical models of robustness suitable for dynamic environments based on single estimates of system parameters (2) a mathematical model of robustness applicable to environments where the uncertainty in system parameters can be modeled stochastically, (3) a demonstration of the use of this metric to design resource allocation heuristics in a static environment, (4) a mathematical model of robustness in a stochastic dynamic environment, (5) we demonstrate the utility of this dynamic robustness metric through the design of resource allocation heuristics, (6) the derivation of a robustness metric for evaluating resource allocation decisions in an overlay network along with a near optimal resource allocation technique suitable to this environment.