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Architecture of a distributed storage that combines file system, memory and computation in a single layer


Storage and memory systems for modern data analytics are heavily layered, managing shared persistent data, cached data, and non-shared execution data in separate systems such as a distributed file system like HDFS, an in-memory file system like Alluxio, and a computation framework like Spark. Such layering introduces significant performance and management costs. In this paper, we propose a single system called Pangea that can manage all data—both intermediate and long-lived data, and their buffer/caching, page replacement, data placement optimization, and failure recovery—all in one monolithic distributed storage system, without any layering. We present a detailed performance evaluation of Pangea and show that its performance compares favorably with several widely used layered systems such as Spark.

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  1. For example, there are only six active locality sets in a single k-means iteration as illustrated in Fig. 1.

  2. The inverse of the page’s reference distance can be seen as yet another reasonable estimate for \(\lambda \), as this replaces \(t_\mathrm{now} - t_\mathrm{ref}\) with the page’s last observed between-reference time as an estimate for the expected time interval between page references. We choose the time since last reference, however, as it requires only a single reference to be valid.



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Funding was provide by Defense Advanced Research Projects Agency (Grant No. FA8750-14-2-0270), Directorate for Computer and Information Science and Engineering (Grnat No. 1409543).

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Zou, J., Iyengar, A. & Jermaine, C. Architecture of a distributed storage that combines file system, memory and computation in a single layer. The VLDB Journal 29, 1049–1073 (2020).

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  • Distributed system
  • Monolithic storage
  • Big Data analytics
  • Heterogeneous replication