Before revision:  AutoCon is the first learning based cache contention detectorthat does not require any positive example of contention anomalies. We evaluated AutoCon with 13 real world cache contentionanomalies as well as 7 open source programs.
After revision:  AutoCon is the first zero-positive learning performance anomaly detector, a system that trains entirely in the negative (non-anomalous) space to learn positive (anomalous) behaviors. We demonstrate AutoPerf's generality against three different types of performance anomalies: (i) true sharing cache contention, (ii) false sharing cache contention, and (iii) NUMA latencies across 15 real world performance anomalies and 7 open source programs.