A Case for Packing and Indexing in Cloud File Systems


Saurabh Kadekodi, Carnegie Mellon University; Bin Fan and Adit Madan, Alluxio, Inc.; Garth A. Gibson, Carnegie Mellon University, Vector Institute; Gregory R. Ganger, Carnegie Mellon University


Small (kilobyte-sized) objects are the bane of highly scalable cloud object stores. Larger (at least megabyte-sized) objects not only improve performance, but also result in orders of magnitude lower cost, due to the current operation-based pricing model of commodity cloud object stores. For example, in Amazon S3's current pricing scheme, uploading 1GiB data by issuing 4KiB PUT requests (at 0.0005 cents each) is approximately $57\times$ more expensive than storing that same 1GiB for a month. To address this problem, we propose client-side packing of small immutable files into gigabyte-sized \textit{blobs} with embedded indices to identify each file's location. Experiments with a packing implementation in Alluxio (an open-source distributed file system) illustrate the potential benefits, such as simultaneously increasing file creation throughput by up to 60$\times$ and decreasing cost to $1/25000$ of the original.

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@inproceedings {216823,
author = {Saurabh Kadekodi and Bin Fan and Adit Madan and Garth A. Gibson and Gregory R. Ganger},
title = {A Case for Packing and Indexing in Cloud File Systems},
booktitle = {10th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 18)},
year = {2018},
address = {Boston, MA},
url = {https://www.usenix.org/conference/hotcloud18/presentation/kadekodi},
publisher = {USENIX Association},
month = jul,