Abstract
Cloud object stores today are deployed using a single set of configuration parameters for all different types of applications. This homogeneous setup results in all applications experiencing the same service level (e.g., data transfer throughput, etc.). However, the vast variety of applications expose extremely different latency and throughput requirements. To this end, we propose MOS, a Micro Object Storage architecture with independently configured microstores each tuned dynamically for a particular type of workload. We then expose these microstores to the tenant who can then choose to place their data in the appropriate microstore according the latency and throughput requirements of their workloads. Our evaluation shows that compared with default setup, MOS can improve the performance up to 200% for small objects and 28% for large objects while providing opportunity of tradeoff between two.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of PDSW 2015 |
| Subtitle of host publication | 10th Parallel Data Storage Workshop - Held in conjunction with SC 2015: The International Conference for High Performance Computing, Networking, Storage and Analysis |
| Publisher | Association for Computing Machinery |
| Pages | 7-12 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781450340083 |
| DOIs | |
| State | Published - Nov 15 2015 |
| Externally published | Yes |
| Event | 10th Parallel Data Storage Workshop, PDSW 2015 - Held as part of the 27th ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2015 - Austin, United States Duration: Nov 16 2014 → Nov 20 2014 |
Publication series
| Name | Proceedings of PDSW 2015: 10th Parallel Data Storage Workshop - Held in conjunction with SC 2015: The International Conference for High Performance Computing, Networking, Storage and Analysis |
|---|
Conference
| Conference | 10th Parallel Data Storage Workshop, PDSW 2015 - Held as part of the 27th ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2015 |
|---|---|
| Country/Territory | United States |
| City | Austin |
| Period | 11/16/14 → 11/20/14 |
Bibliographical note
Publisher Copyright:© 2015 ACM.
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