| dc.contributor.advisor | Katrina LaCurts and Ronald D. Chaney. | en_US |
| dc.contributor.author | Friis, Erick Y. | en_US |
| dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
| dc.date.accessioned | 2019-12-05T18:05:38Z | |
| dc.date.available | 2019-12-05T18:05:38Z | |
| dc.date.copyright | 2019 | en_US |
| dc.date.issued | 2019 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1721.1/123139 | |
| dc.description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. | en_US |
| dc.description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 | en_US |
| dc.description | Cataloged from student-submitted PDF version of thesis. | en_US |
| dc.description | Includes bibliographical references (pages 69-70). | en_US |
| dc.description.abstract | I present the Global Learning Anomalous Stream Service (GLASS): a monitoring system for Internet overlay networks that helps identify and investigate unusual behavior. I designed, implemented, and tested GLASS at Akamai Technologies to monitor their internationally distributed content delivery network (CDN) for early signs of special network events. In this thesis, I document my design process, GLASS' architecture and algorithms, and an evaluation of the system based on one year of historic aggregate signals. | en_US |
| dc.description.statementofresponsibility | by Erick Y. Friis. | en_US |
| dc.format.extent | 70 pages | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| dc.rights | MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. | en_US |
| dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
| dc.subject | Electrical Engineering and Computer Science. | en_US |
| dc.title | GLASS : Global Learning Anomalous Stream Service | en_US |
| dc.title.alternative | Global Learning Anomalous Stream Service | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | M. Eng. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
| dc.identifier.oclc | 1128819877 | en_US |
| dc.description.collection | M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science | en_US |
| dspace.imported | 2019-12-05T18:05:37Z | en_US |
| mit.thesis.degree | Master | en_US |
| mit.thesis.department | EECS | en_US |
