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ChewAnalyzer: Workload-Aware Data Management Across Differentiated Storage Pools


October 04, 2018


Xiongzi Ge, NetApp, Inc.; Xuchao Xie, NUDT; David H.C. Du, University of Minnesota; Pradeep Ganesan, NetApp, Inc.; Dennis Hahn, NetApp, Inc.;

the 26th IEEE International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2018).

September 25 - 28, 2018 Milwaukee, Wisconsin, US.

In multi-tier storage systems, moving data from one tier to the next can be inefficient. And because each type of storage device has its own idiosyncrasies with respect to the workloads that it can best support, unnecessary data movement might result. In this paper, we explore a fully connected storage architecture in which data can move from any storage pool to another. We propose a Chunk-level storage-aware workload Analyzer framework, abbreviated as ChewAnalyzer, to facilitate efficient data placement. Access patterns are characterized in a flexible way by a collection of I/O accesses to a data chunk. ChewAnalyzer employs a Hierarchical Classifier [30] to analyze the chunk patterns step by step. In each classification step, the Chunk Placement Recommender suggests new data placement policies according to the device properties. Based on the analysis of access pattern changes, the Storage Manager can adequately distribute or migrate the data chunks across different storage pools. Our experimental results show that ChewAnalyzer improves the initial data placement and that it migrates data into the proper pools directly and efficiently.


A copy of the paper can be found at:

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