2026-09-19T06:27:37Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1006862026-06-06T00:56:06Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Adam M. Ross and Donna H. Rhodes.
Prindle, Aaron L
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-01-04T20:53:27Z
2016-01-04T20:53:27Z
2015
2015
http://hdl.handle.net/1721.1/100686
933242626
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 109-112).
IVTea Suite is a tradespace exploration and analysis tool designed to allow users to gain insights into potential designs for large scale systems, and enables the analysis of tradeoffs, both static and dynamic, inherent in the selection of particular designs from amongst many possibilities. IVTea Suite's current architecture limits its ability to operate on large datasets, as well as prevents it from calculating important computationally complex lifecycle metrics needed to select value sustaining designs. This thesis analyses the current state of cloud technologies and provides solutions on how IVTea Suite can overcome its current architectural limitations. As a demonstration of potential new capabilities, the multi-era affordability with change paths problem, previously not solvable, is addressed using Markov decision processes and cloud technology. Additionally, this work describes a cloud framework that can be used in the future, which provides the potential ability to solve the multi-arc change paths problem for datasets previously too large to evaluate.
by Aaron L. Prindle.
M.Eng.
116 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Tradespace exploration in the Cloud : incorporating cloud technologies into IVTea Suite
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="e1810b39-2622-46bc-9143-805858b9a811">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Tradespace exploration in the Cloud : incorporating cloud technologies into IVTea Suite</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2015</PublicationDate>
<Authors>
<Author>
<DisplayName>Prindle, Aaron L</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>IVTea Suite is a tradespace exploration and analysis tool designed to allow users to gain insights into potential designs for large scale systems, and enables the analysis of tradeoffs, both static and dynamic, inherent in the selection of particular designs from amongst many possibilities. IVTea Suite's current architecture limits its ability to operate on large datasets, as well as prevents it from calculating important computationally complex lifecycle metrics needed to select value sustaining designs. This thesis analyses the current state of cloud technologies and provides solutions on how IVTea Suite can overcome its current architectural limitations. As a demonstration of potential new capabilities, the multi-era affordability with change paths problem, previously not solvable, is addressed using Markov decision processes and cloud technology. Additionally, this work describes a cloud framework that can be used in the future, which provides the potential ability to solve the multi-arc change paths problem for datasets previously too large to evaluate.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>