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Since only a small number of traces generated from distributed tracing helpsin troubleshooting, its storage requirement can be significantly reduced bybiasing the selection towards anomalous traces. To aid in this scenario, wepropose SampleHST, a novel approach to sample on-the-fly from a stream oftraces in an unsupervised manner. SampleHST adjusts the storage quota of normaland anomalous traces depending on the size of its budget. Initially, itutilizes a forest of Half Space Trees (HSTs) for trace scoring. This is basedon the distribution of the mass scores across the trees, which characterizesthe probability of observing different traces. The mass distribution from HSTsis subsequently used to cluster the traces online leveraging a variant of themean-shift algorithm. This trace-cluster association eventually drives thesampling decision. We have compared the performance of SampleHST with arecently suggested method using data from a cloud data center and demonstratedthat SampleHST improves sampling performance up to by 9.5x.

SampleHST: Efficient On-the-Fly Selection of Distributed Traces