Metadata Traces and Workload Models for Evaluating Big Storage Systems

Abstract

Efficient namespace metadata management is increasingly important as next-generation file systems are designed for peta and exascales. New schemes have been proposed, however, their evaluation has been insufficient due to a lack of appropriate namespace metadata traces. Specifically, no Big Data storage system metadata trace is publicly available and existing ones are a poor replacement. We studied publicly available traces and one Big Data trace from Yahoo! and note some of the differences and their implications to metadata management studies. We discuss the insufficiency of existing evaluation approaches and present a first step towards a statistical metadata workload model that can capture the relevant characteristics of a workload and is suitable for synthetic workload generation. We describe Mimesis, a synthetic workload generator, and evaluate its usefulness through a case study in a least recently used metadata cache for the Hadoop Distributed File System. Simulation results show that the traces generated by Mimesis mimic the original workload and can be used in place of the real trace providing accurate results.