GLASS : Global Learning Anomalous Stream Service
Name
1128819877-MIT.pdf
Size
22.15 MB
Format
Adobe PDF
Checksum (MD5)
d070fa377df6910a8335bf3166085575
Author(s)
Friis, Erick Y.
Advisor(s)
Katrina LaCurts and Ronald D. Chaney.
Alternative Title
Global Learning Anomalous Stream Service
Date Issued
2019
Publisher
Massachusetts Institute of Technology
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.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 69-70).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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