Snapshot Judgements: Obtaining Data Insights without Tracing

Metadata snapshots are a favored method for gaining filesystem insights due to their small size and relative ease of acquisition compared to access traces. Since snapshots do not include an access history; typically they are used for relatively simple analyses such as file lifetime and size distributions, and researchers still gather and store full block or file access traces for any higher level analysis such as cache prediction or scheduling variable replication. We claim that one can gain rich insights into file system and user behavior by clustering metadata snapshots and comparing the entropy within clusters to the entropy within natural partitions such as directory hierarchies or single attributes. We have preliminary results indicating that agglomerative clustering methods produce groups of data with high information purity, which may be a sign of functional correlation.