Metadata Workloads for Testing Big Storage Systems
Cristina L. Abad, Huong Vu Thanh Luu, Yi Lu, Roy H. Campbell · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · 2012
Efficient namespace metadata management is becoming more important as next-generation file systems are designed for the peta and exascale era. A number of new metadata management schemes have been proposed. However, evaluation of these designs has been insufficient, mainly due to a lack of appropriate namespace metadata traces. Specifically, no Big Data storage system metadata trace is publicly available, and existing traces 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. 1