Build resilient systems at scale
28–30 October 2015 • Amsterdam, The Netherlands
Arun Kejariwal

Arun Kejariwal
Statistical Learning Principal, MZ


Arun Kejariwal is a statistical learning principal at Machine Zone (MZ), where he leads a team of top-tier researchers and works on research and development of novel techniques for install and click fraud detection and assessing the efficacy of TV campaigns and optimization of marketing campaigns. In addition, his team is building novel methods for bot detection, intrusion detection, and real-time anomaly detection. Previously, Arun worked at Twitter, where he developed and open-sourced techniques for anomaly detection and breakout detection. His research includes the development of practical and statistically rigorous techniques and methodologies to deliver high-performance, availability, and scalability in large-scale distributed clusters. Some of the techniques he helped develop have been presented at international conferences and published in peer-reviewed journals.


17:05–17:45 Thursday, 29/10/2015
Location: Emerald Room
Average rating: ***..
(3.07, 15 ratings)
Finding slow nodes in large clusters is akin to finding a needle in a haystack; hence, manual identification of slow/bad nodes is not practical. The focus of this talk is to present a statistical approach to automatically detect slow/bad nodes, thereby mitigating user impact. Read more.