@inproceedings{
 title = {Big Data Benchmarks of High-Performance Storage Systems on Commercial Bare Metal Clouds},
 type = {inproceedings},
 year = {2019},
 websites = {https://conferences.computer.org/serviceswp/2019/pdfs/CLOUD2019-5XaiJ82Ya5AyIoh2T1E5Bm/6ex2QOZUKAReRoHl2O8jiP/5Fkht00rYCKTzblzYTjaNc.pdf},
 month = {5},
 day = {27},
 id = {8d081792-2291-34a8-8183-c85bc3d92727},
 created = {2019-10-01T17:20:54.710Z},
 file_attached = {false},
 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 last_modified = {2020-05-11T14:43:33.061Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Lee2019},
 private_publication = {false},
 abstract = {Bare metal servers are widely available on public clouds to provide direct access to hardware and the system configuration with high performance storage and network devices are well suited for big data applications. Highly-optimized server with additional CPU core count and dense storage may lead to better performance in certain workloads and to ensure responsiveness of deployed services. Recent work on Hadoop ecosystems has addressed the performance improvement of scale-up machines configured with SSD storage and increased network bandwidth. The paper evaluates big data processing on dedicated clusters and provides the performance analysis of NVMe devices and SSD block storage options available on Amazon, Google, Microsoft, and Oracle Clouds. We show the benchmark results along with the system performance tests as we want to demonstrate the compute resource requirements for large-scale applications. The system capacity and limits for the underlying servers are described along with the cost analysis of scaling workloads on these platforms.},
 bibtype = {inproceedings},
 author = {Lee, Hyungro and Fox, Geoffrey},
 doi = {10.1109/CLOUD.2019.00014},
 booktitle = {IEEE International Conference on Cloud Computing (IEEE CLOUD 2019)}
}

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