GLASS : Global Learning Anomalous Stream Service
Author(s)Friis, Erick Y.
Global Learning Anomalous Stream Service
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Katrina LaCurts and Ronald D. Chaney.
MetadataShow full item record
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.
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, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 69-70).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.