Elastic Storage (SpringFS)
Distributed storage can and should be elastic, just like other aspects of cloud computing. When storage is provided via single-purpose storage devices or servers, separated from compute activities, elasticity is useful for reducing energy usage, allowing temporarily unneeded storage components to be powered down. However, for storage provided via multi-purpose servers (e.g. when a server operates as both a storage node in a distributed filesystem and a compute node), such elasticity is even more valuable— providing cloud infrastructures with the freedom to use such servers for other purposes, as tenant demands and priorities dictate. This freedom may be particularly important for increasingly prevalent dataintensive computing activities (e.g., data analytics).
This project develops novel designs for elastic storage capable of adapting rapidly to I/O workload intensity, including our groundbreaking SpringFS and Rabbit systems.
Michael A. Kozuch, Intel
- Agility and Performance in Elastic Distributed Storage. Lianghong Xu, James Cipar, Elie Krevat, Alexey Tumanov, And Nitin Gupta, Michael A. Kozuch, Gregory R. Ganger. ACM Transactions on Storage, Vol. 10, No. 4, Article 16, October 2014.
Abstact / PDF [1.34M]
- SpringFS: Bridging Agility and Performance in Elastic Distributed Storage.
Lianghong Xu, James Cipar, Elie Krevat, Alexey Tumanov,
Nitin Gupta, Michael A. Kozuch, Gregory R. Ganger. 12th USENIX Conference on File and Storage Technologies (FAST '14), Santa Clara, CA, February 17–20, 2014.
Abstract / PDF [319K]
- JackRabbit: Improved Agility In Elastic Distributed Storage. James Cipar, Lianghong Xu, Elie Krevat, Alexey Tumanov Nitin Gupta, Michael A. Kozuch, Gregory R. Ganger. Carnegie Mellon University Parallel Data Lab Technical Report CMU-PDL-12-112, October 2012.
Abstract / PDF [395K]
- Robust and Flexible Power-proportional Storage. Hrishikesh Amur, James Cipar, Varun Gupta, Gregory R. Ganger, Michael A. Kozuch, Karsten Schwan. ACM Symposium on Cloud Computing (SOCC). June 10-11, 2010, Indianapolis, IN. Supersedes Carnegie Mellon University Parallel Data Lab Technical Report CMU-PDL-10-106, February 2010.
Abstract / PDF [944K]
This research was funded (in part) by the Intel Science and Technology Center for Cloud Computing.
We thank the members and companies of the PDL Consortium: Amazon, Google, Hewlett Packard Enterprise, Hitachi Ltd., Intel Corporation, IBM, Meta, Microsoft Research, NetApp, Inc., Oracle Corporation, Pure Storage, Salesforce, Samsung Semiconductor Inc., Seagate Technology, Two Sigma, and Western Digital for their interest, insights, feedback, and support.