Abstract Storage: Moving file format-specific abstractions into petabyte-scale storage systems

Abstract

High-end computing is increasingly I/O bound as computations become more data-intensive, and data transport technologies struggle to keep pace with the demands of large-scale, distributed computations. One approach to avoiding unnecessary I/O is to move the processing to the data, as seen in Google’s successful, but relatively specialized, MapReduce system. This paper discusses our investigation towards a general solution for enabling in-situ computation in a peta-scale storage system. We believe our work with flexible, application-specific structured storage is the key to addressing the I/O overhead caused by data partitioning across storage nodes. In order to manage competing workloads on storage nodes, our research in system performance management is leveraged. Our ultimate goal is a general framework for in-situ data-intensive processing, indexing, and searching, which we expect to provide orders of magnitude performance increases for data-intensive workloads.

Publication
2nd International Workshop on Data-Aware Distributed Computing (in conjunction with HPDC-18)

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