Robust resource allocation in heterogeneous parallel and distributed computing systems
| dc.contributor.author | Smith, James T., II, author | |
| dc.contributor.author | Siegel, H. J., advisor | |
| dc.contributor.author | Maciejewski, A. A., advisor | |
| dc.date.accessioned | 2024-03-13T20:27:58Z | |
| dc.date.available | 2024-03-13T20:27:58Z | |
| dc.date.issued | 2008 | |
| dc.description.abstract | 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. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | ETDF_Smith_2008_3332752.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/237960 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.rights.license | Per the terms of a contractual agreement, all use of this item is limited to the non-commercial use of Colorado State University and its authorized users. | |
| dc.subject | distributed computing | |
| dc.subject | heterogeneous computing | |
| dc.subject | resource allocation | |
| dc.subject | electrical engineering | |
| dc.title | Robust resource allocation in heterogeneous parallel and distributed computing systems | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Electrical and Computer Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |
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