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Browsing by Author "Maciejewski, A. A., advisor"

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    Robust resource allocation in heterogeneous parallel and distributed computing systems
    (Colorado State University. Libraries, 2008) Smith, James T., II, author; Siegel, H. J., advisor; Maciejewski, A. A., advisor
    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.
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    Robust resource-allocation methods for QOS-constrained parallel and distributed computing systems
    (Colorado State University. Libraries, 2008) Shestak, Valdimir, author; Maciejewski, A. A., advisor; Siegel, Howard Jay, advisor
    This research investigates the problem of robust resource allocation for distributed computing systems operating under imposed Quality of Service (QoS) constraints. Often, such systems are expected to function in a physical environment replete with uncertainty, which causes the amount of processing required over time to fluctuate substantially. In the first two studies, we show how an effective resource allocation can be achieved in the heterogeneous shipboard distributed computing system and IBM cluster based imaging system. The general form for a stochastic robustness metric is then presented based on a mathematical model where the relationship between uncertainty in system parameters and its impact on system performance are described stochastically. The utility of the established metric is exploited in the design of optimization techniques based on greedy and iterative approaches that address the problem of resource allocation in a large class of distributed systems operating on periodically updated data sets. One of the major reasons for possible QoS violations in distributed systems is a loss of resources, frequently caused by abnormal operating conditions. One aspect that makes a resource allocation problem extremely challenging in such systems is a random nature of resource failures and recoveries. The last study presented in this work describes a solution method that was developed for this case based on the concepts of the Derman-Lieberman-Ross theorem. The experimental results indicate a significant potential of this approach to generate robust resource allocations in unstable distributed systems.