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Could Blobs Fuel Storage-Based Convergence Between HPC and Big Data?

Abstract : The increasingly growing data sets processed on HPC platforms raise major challenges for the underlying storage layer. A promising alternative to POSIX-IO-compliant file systems are simpler blobs (binary large objects), or object storage systems. They offer lower overhead and better performance at the cost of largely unused features such as file hierarchies or permissions. Similarly, blobs are increasingly considered for replacing distributed file systems for big data analytics or as a base for storage abstractions like key-value stores or time-series databases. This growing interest in such object storage on HPC and big data platforms raises the question: Are blobs the right level of abstraction to enable storage-based convergence between HPC and Big Data? In this paper we take a first step towards answering the question by analyzing the applicability of blobs for both platforms.
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Submitted on : Monday, October 16, 2017 - 7:54:04 PM
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Pierre Matri, Yevhen Alforov, Alvaro Brandon, Michael Kuhn, Philip Carns, et al.. Could Blobs Fuel Storage-Based Convergence Between HPC and Big Data?. CLUSTER 2017 - IEEE International Conference on Cluster Computing, Sep 2017, Honolulu, United States. pp.81 - 86, ⟨10.1109/CLUSTER.2017.63⟩. ⟨hal-01617655⟩



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