'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
HTTP video-streaming is leading delivery of video
content over the Internet. This phenomenon is explained by the
ubiquity of web browsers, the permeability of HTTP traffic
and the recent video technologies around HTML5. However,
the inclusion of multimedia requests imposes new requirements
on web servers due to responses with lifespans that can reach
dozens of minutes and timing requirements for data fragments
transmitted during the response period. Consequently, web-
servers require real-time performance control to avoid playback
outages caused by overloading and performance anomalies. We
present
SHStream
, a self-healing framework for web servers
delivering video-streaming content that provides (1) load admit-
tance to avoid server overloading; (2) prediction of performance
anomalies using online data stream learning algorithms; (3)
continuous evaluation and selection of the best algorithm for
prediction; and (4) proactive recovery by migrating the server
to other hosts using container-based virtualization techniques.
Evaluation of our framework using several variants of
Hoeffding
trees
and
ensemble algorithms
showed that with a small number of
learning instances, it is possible to achieve approximately 98% of
recall
and 99% of
precision
for failure predictions. Additionally,
proactive failover can be performed in less than 1 secon
