A locality-aware scientific workflow engine for fast-evolving spatiotemporal sensor data
| dc.contributor.author | Kachikaran Arulswamy, Johnson Charles, author | |
| dc.contributor.author | Pallickara, Sangmi Lee, advisor | |
| dc.contributor.author | Pallickara, Shrideep, committee member | |
| dc.contributor.author | von Fischer, Joseph, committee member | |
| dc.date.accessioned | 2017-06-09T15:43:01Z | |
| dc.date.available | 2017-06-09T15:43:01Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | Discerning knowledge from voluminous data involves a series of data manipulation steps. Scientists typically compose and execute workflows for these steps using scientific workflow management systems (SWfMSs). SWfMSs have been developed for several research communities including but not limited to bioinformatics, biology, astronomy, computational science, and physics. Parallel execution of workflows has been widely employed in SWfMSs by exploiting the storage and computing resources of grid and cloud services. However, none of these systems have been tailored for the needs of spatiotemporal analytics on real-time sensor data with high arrival rates. This thesis demonstrates the development and evaluation of a target-oriented workflow model that enables a user to specify dependencies among the workflow components, including data availability. The underlying spatiotemporal data dispersion and indexing scheme provides fast data search and retrieval to plan and execute computations comprising the workflow. This work includes a scheduling algorithm that targets minimizing data movement across machines while ensuring fair and efficient resource allocation among multiple users. The study includes empirical evaluations performed on the Google cloud. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | KachikaranArulswamy_colostate_0053N_14205.pdf | |
| dc.identifier.uri | http://hdl.handle.net/10217/181467 | |
| 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.subject | multidimensional data | |
| dc.subject | spatiotemporal analytics | |
| dc.subject | workflow scheduling | |
| dc.subject | scientific workflow | |
| dc.subject | distributed | |
| dc.subject | storage system | |
| dc.title | A locality-aware scientific workflow engine for fast-evolving spatiotemporal sensor data | |
| 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 | Computer Science | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
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