8,808 research outputs found
Fake View Analytics in Online Video Services
Online video-on-demand(VoD) services invariably maintain a view count for
each video they serve, and it has become an important currency for various
stakeholders, from viewers, to content owners, advertizers, and the online
service providers themselves. There is often significant financial incentive to
use a robot (or a botnet) to artificially create fake views. How can we detect
the fake views? Can we detect them (and stop them) using online algorithms as
they occur? What is the extent of fake views with current VoD service
providers? These are the questions we study in the paper. We develop some
algorithms and show that they are quite effective for this problem.Comment: 25 pages, 15 figure
Towards Identifying Performance Anomalies
AbstractLarge-scale-software systems (LSSs) are composed of hundreds of subsystems that interact with each other in an unforeseen and complex ways. The operators of these LSSs strictly monitor thousands of metrics (performance counters) to quickly identify performance anomalies before a catastrophe. The existing monitoring tools and methodologies have not kept in pace with the rapid growth and inherit complexity of these LSSs; hence are ineffective in assisting practitioners to effectively pinpoint performance anomalies. We propose a methodology that uses entropy analysis to assist practitioners/operators of LSSs in quickly detecting underlying anomalies in the system. Our performance tests conducted on an open source benchmark system reveal that the proposed methodology is robust in pinpointing anomalies, do not require any domain knowledge to operate, and avoid information overload on practitioners
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