Enabling autoscaling for in-memory storage in cluster computing framework
| dc.contributor.author | Shrestha, Bibek Raj, author | |
| dc.contributor.author | Pallickara, Sangmi Lee, advisor | |
| dc.contributor.author | Pallickara, Shrideep, committee member | |
| dc.contributor.author | Hayne, Stephen C., committee member | |
| dc.date.accessioned | 2019-06-14T17:06:43Z | |
| dc.date.available | 2019-06-14T17:06:43Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | IoT enabled devices and observational instruments continuously generate voluminous data. A large portion of these datasets are delivered with the associated geospatial locations. The increased volumes of geospatial data, alongside the emerging geospatial services, pose computational challenges for large-scale geospatial analytics. We have designed and implemented STRETCH , an in-memory distributed geospatial storage that preserves spatial proximity and enables proactive autoscaling for frequently accessed data. STRETCH stores data with a delayed data dispersion scheme that incrementally adds data nodes to the storage system. We have devised an autoscaling feature that proactively repartitions data to alleviate computational hotspots before they occur. We compared the performance of S TRETCH with Apache Ignite and the results show that STRETCH provides up to 3 times the throughput when the system encounters hotspots. STRETCH is built on Apache Spark and Ignite and interacts with them at runtime. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Shrestha_colostate_0053N_15430.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/195383 | |
| 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 | geospatial analysis | |
| dc.subject | scalable analytics | |
| dc.subject | in-memory cluster computing | |
| dc.subject | dynamic partitioning | |
| dc.title | Enabling autoscaling for in-memory storage in cluster computing framework | |
| 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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