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

Arun Kejariwal
Lead Engineer, Independent

@arun_kejariwal

Arun Kejariwal is an independent lead engineer. Previously, he was he was a statistical learning principal at Machine Zone (MZ), where he led a team of top-tier researchers and worked 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, and his team built novel methods for bot detection, intrusion detection, and real-time anomaly detection; and he developed and open-sourced techniques for anomaly detection and breakout detection at Twitter. 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.

Sessions

17:05–17:45 Thursday, 29/10/2015
Location: Emerald Room
Arun Kejariwal (Independent)
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.