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FAWN: a fast array of wimpy nodes

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ACM Symposium on Operating Systems Principles archive
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles table of contents

Big Sky, Montana, USA

SESSION: Scalability table of contents

Pages 1-14

Year of Publication: 2009

ISBN:978-1-60558-752-3

Authors

David G. Andersen	Carnegie Mellon University, Pittsburgh, PA, USA
Jason Franklin	Carnegie Mellon University, Pittsburgh, PA, USA
Michael Kaminsky	Intel Labs, Pittsburgh, PA, USA
Amar Phanishayee	Carnegie Mellon University, Pittsburgh, PA, USA
Lawrence Tan	Carnegie Mellon University, Pittsburgh, PA, USA
Vijay Vasudevan	Carnegie Mellon University, Pittsburgh, PA, USA

Sponsors

ACM: Association for Computing Machinery
SIGOPS: ACM Special Interest Group on Operating Systems

Publisher

ACM New York, NY, USA

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ABSTRACT

This paper presents a new cluster architecture for low-power data-intensive computing. FAWN couples low-power embedded CPUs to small amounts of local flash storage, and balances computation and I/O capabilities to enable efficient, massively parallel access to data.

The key contributions of this paper are the principles of the FAWN architecture and the design and implementation of FAWN-KV--a consistent, replicated, highly available, and high-performance key-value storage system built on a FAWN prototype. Our design centers around purely log-structured datastores that provide the basis for high performance on flash storage, as well as for replication and consistency obtained using chain replication on a consistent hashing ring. Our evaluation demonstrates that FAWN clusters can handle roughly 350 key-value queries per Joule of energy--two orders of magnitude more than a disk-based system.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Flexible I/O Tester. http://freshmeat.net/projects/fio/.
	2	M. Al-Fares, A. Loukissas, and A. Vahdat. A scalable, commodity, data center network architecture. In Proc. ACM SIGCOMM, Aug. 2008.
	3	L.A. Barroso and U. Hölzle. The case for energy-proportional computing. phComputer, 40 (12): 33--37, 2007.
	4	BerkeleyDB Reference Guide. Memory-only or Flash configurations. http://www.oracle.com/technology/documentation/berkeley-db/db/ref/program/ram.html.
	5	W. Bowman, N. Cardwell, C. Kozyrakis, C. Romer, and H. Wang. Evaluation of existing architectures in IRAM systems. In Workshop on Mixing Logic and DRAM, 24th International Symposium on Computer Architecture, June 1997.
	6	A.M. Caulfield, L.M. Grupp, and S. Swanson. Gordon: Using flash memory to build fast, power-efficient clusters for data-intensive applications. In 14th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS'09), Mar. 2009.
	7	J.S. Chase, D. Anderson, P. Thakar, A. Vahdat, and R. Doyle. Managing energy and server resources in hosting centers. In Proc. 18th ACM Symposium on Operating Systems Principles (SOSP), Oct. 2001.
	8	H. Dai, M. Neufeld, and R. Han. ELF: an efficient log-structured flash file system for micro sensor nodes. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys), Nov. 2004.
	9	J. Dean and S. Ghemawat. MapReduce: Simplified data processing on large clusters. In Proc. 6th USENIX OSDI, Dec. 2004.
	10	G. DeCandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman, A. Pilchin, S. Sivasubramanian, P. Vosshall, and W. Vogels. Dynamo: Amazon's highly available key-value store. In Proc. 21st ACM Symposium on Operating Systems Principles (SOSP), Oct. 2007.
	11	Dell XS11-VX8. Dell fortuna. "http://www1.euro.dell.com/content/topics/topic.aspx/emea/corporate/pre%ssoffice/2009/uk/en/2009_05_20_brk_000?c=uk&l=en", 2009.
	12	F. Douglis, F. Kaashoek, B. Marsh, R. Caceres, K. Li, and J. Tauber. Storage alternatives for mobile computers. In Proc. 1st USENIX OSDI, pages 25--37, Nov. 1994.
	13	L. Ganesh, H. Weatherspoon, M. Balakrishnan, and K. Birman. Optimizing power consumption in large scale storage systems. In Proc. HotOS XI, May 2007.
	14	A. Gara, M.A. Blumrich, D. Chen, G. L.-T. Chiu, et al. Overview of the Blue Gene/L system architecture. IBM J. Res and Dev., 49 (2/3), May 2005.
	15	S. Ghemawat, H. Gobioff, and S.-T. Leung. The Google file system. In Proc. 19th ACM Symposium on Operating Systems Principles (SOSP), Oct. 2003.
	16	A. Greenberg, N. Jain, S. Kandula, C. Kim, P. Lahiri, D. Maltz, P. Patel, and S. Sengupta. VL2: A scalable and flexible data center network. In Proc. ACM SIGCOMM, Aug. 2009.
	17	S.D. Gribble, E.A. Brewer, J.M. Hellerstein, and D. Culler. Scalable, distributed data structures for Internet service construction. In Proc. 4th USENIX OSDI, Nov. 2000.
	18	C. Guo, H. Wu, K. Tan, L. Shi, Y. Zhang, and S. Lu. DCell: A scalable and fault-tolerant network structure for data centers. In Proc. ACM SIGCOMM, Aug. 2008.
	19	C. Guo, G. Lu, D. Li, H. Wu, X. Zhang, Y. Shi, C. Tian, Y. Zhang, and S. Lu. BCube: A high performance, server-centric network architecture for modular data centers. In Proc. ACM SIGCOMM, Aug. 2009.
	20	J. Hamilton. Cooperative expendable micro-slice servers (CEMS): Low cost, low power servers for Internet scale services. http://mvdirona.com/jrh/TalksAndPapers/JamesHamilton_CEMS.pdf, 2009.
	21	Intel. Penryn Press Release. http://www.intel.com/pressroom/archive/releases/20070328fact.htm.
	22	Iozone. Filesystem Benchmark. http://www.iozone.org.
	23	JFFS2. The Journaling Flash File System. http://sources.redhat.com/jffs2/.
	24	B. Johnson. Facebook, personal communication, Nov. 2008.
	25	R.H. Katz. Tech titans building boom. IEEE Spectrum, Feb. 2009.
	26	A. Kawaguchi, S. Nishioka, and H. Motoda. A flash-memory based file system. In Proc. USENIX Annual Technical Conference, Jan. 1995.
	27	L. Lamport. The part-time parliament. ACM Trans. Comput. Syst., 16 (2): 133--169, 1998. ISSN 0734-2071.
	28	S.-W. Lee, B. Moon, C. Park, J.-M. Kim, and S.-W. Kim. A case for flash memory SSD in enterprise database applications. In Proc. ACM SIGMOD, June 2008.
	29	Y. Li, B. He, Q. Luo, and K. Yi. Tree indexing on flash disks. In Proceedings of 25th International Conference on Data Engineering, Mar. 2009.
	30	K. Lim, P. Ranganathan, J. Chang, C. Patel, T. Mudge, and S. Reinhardt. Understanding and designing new server architectures for emerging warehouse-computing environments. In International Symposium on Computer Architecture (ISCA), June 2008.
	31	J. MacCormick, N. Murphy, M. Najork, C.A. Thekkath, and L. Zhou. Boxwood: abstractions as the foundation for storage infrastructure. In Proc. 6th USENIX OSDI, Dec. 2004.
	32	G. Mathur, P. Desnoyers, D. Ganesan, and P. Shenoy. Capsule: an energy-optimized object storage system for memory-constrained sensor devices. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys), Oct. 2006.
	33	Memcached. A distributed memory object caching system. http://www.danga.com/memcached/.
	34	Microsoft Marlowe. Peering into future of cloud computing. http://research.microsoft.com/en-us/news/features/ccf-022409.aspx, 2009.
	35	D. Myers. On the use of NAND flash memory in high-performance relational databases. M.S. Thesis, MIT, Feb. 2008.
	36	S. Nath and P.B. Gibbons. Online maintenance of very large random samples on flash storage. In phProc. VLDB, Aug. 2008.
	37	S. Nath and A. Kansal. FlashDB: Dynamic self-tuning database for NAND flash. In Proceedings of ACM/IEEE International Conference on Information Processing in Sensor Networks, Apr. 2007.
	38	Netezza. Business intelligence data warehouse appliance. http://www.netezza.com/, 2006.
	39	nilfs. Continuous snapshotting filesystem for Linux. http://www.nilfs.org.
	40	M. Polte, J. Simsa, and G. Gibson. Enabling enterprise solid state disks performance. In phProc. Workshop on Integrating Solid-state Memory into the Storage Hierarchy, Mar. 2009.
	41	Project Voldemort. A distributed key-value storage system. http://project-voldemort.com.
	42	S. Quinlan and S. Dorward. Venti: A new approach to archival storage. In Proc. USENIX Conference on File and Storage Technologies (FAST), pages 89--101, Jan. 2002.
	43	E. Riedel, C. Faloutsos, G.A. Gibson, and D. Nagle. Active disks for large-scale data processing. phIEEE Computer, 34 (6): 68--74, June 2001.
	44	S. Rivoire, M.A. Shah, P. Ranganathan, and C. Kozyrakis. JouleSort: A balanced energy-efficient benchmark. In Proc. ACM SIGMOD, June 2007.
	45	M. Rosenblum and J.K. Ousterhout. The design and implementation of a log-structured file system. ACM Transactions on Computer Systems, 10 (1): 26--52, 1992.
	46	S.W. Schlosser, J.L. Griffin, D.F. Nagle, and G.R. Ganger. Filling the memory access gap: A case for on-chip magnetic storage. Technical Report CMU-CS-99-174, Carnegie Mellon University, Nov. 1999.
	47	F.B. Schneider. Byzantine generals in action: implementing fail-stop processors. ACM Trans. Comput. Syst., 2 (2): 145--154, 1984. ISSN 0734-2071.
	48	I. Stoica, R. Morris, D. Karger, M.F. Kaashoek, and H. Balakrishnan. Chord: A scalable peer-to-peer lookup service for Internet applications. In Proc. ACM SIGCOMM, Aug. 2001.
	49	M.W. Storer, K.M. Greenan, E.L. Miller, and K. Voruganti. Pergamum: Replacing tape with energy efficient, reliable, disk-based archival storage. In Proc. USENIX Conference on File and Storage Technologies, Feb. 2008.
	50	A. Szalay, G. Bell, A. Terzis, A. White, and J. Vandenberg. Low power Amdahl blades for data intensive computing, 2009.
	51	J. Terrace and M.J. Freedman. Object storage on CRAQ: High-throughput chain replication for read-mostly workloads. In Proc. USENIX Annual Technical Conference, June 2009.
	52	N. Tolia, Z. Wang, M. Marwah, C. Bash, P. Ranganathan, and X. Zhu. Delivering energy proportionality with non energy-proportional systems -- optimizing the ensemble. In Proc. HotPower, Dec. 2008.
	53	D. Tsirogiannis, S. Harizopoulos, M.A. Shah, J.L. Wiener, and G. Graefe. Query processing techniques for solid state drives. In Proc. ACM SIGMOD, June 2009.
	54	R. van Renesse and F.B. Schneider. Chain replication for supporting high throughput and availability. In Proc. 6th USENIX OSDI, Dec. 2004.
	55	WattsUp. .NET Power Meter. http://wattsupmeters.com.
	56	M. Weiser, B. Welch, A. Demers, and S. Shenker. Scheduling for reduced CPU energy. In Proc. 1st USENIX OSDI, pages 13--23, Nov. 1994.
	57	M. Wu and W. Zwaenepoel. eNVy: A non-volatile, main memory storage system. In Proc. 6th International Conf. on Architectural Support for Programming Languages and Operating Systems (ASPLOS), Oct. 1994.
	58	D. Zeinalipour-Yazti, S. Lin, V. Kalogeraki, D. Gunopulos, and W.A. Najjar. Microhash: An efficient index structure for flash-based sensor devices. In Proc. 4th USENIX Conference on File and Storage Technologies, Dec. 2005.
	59	Q. Zhu, Z. Chen, L. Tan, Y. Zhou, K. Keeton, and J. Wilkes. Hibernator: Helping disk arrays sleep through the winter. In Proc. 20th ACM Symposium on Operating Systems Principles (SOSP), Oct. 2005.