Build resilient systems at scale
October 12–14, 2015 • New York, NY
Arun Kejariwal

Arun Kejariwal
R&D Leader, Independent

@arun_kejariwal

Until recently, Arun Kejariwal 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. In addition, his team built 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.

Sessions

4:35pm–5:15pm Tuesday, 10/13/2015
Location: Beekman Parlor
Puneet Khanduri (Twitter), Arun Kejariwal (Independent)
Average rating: ***..
(3.80, 20 ratings)
The service oriented architecture (SOA) at Twitter comprises of hundreds of services. Each service pushes out code at velocity. In light of this, we developed a statistically robust approach to detect both functional and performance regressions. This is critical in order to minimize impact on the end-user experience. In this talk we shall walk through the challenges and how we addressed them. Read more.

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