Abstract
Distributed key value storage systems are among the most important types of distributed storage systems currently deployed in data centers. Nowadays, enterprise data centers are facing growing pressure in reducing their power consumption. In this paper, we propose GreenCHT, a reliable power management scheme for consistent hashing based distributed key value storage systems. It consists of a multi-tier replication scheme, a reliable distributed log store, and a predictive power mode scheduler (PMS). Instead of randomly placing replicas of each object on a number of nodes in the consistent hash ring, we arrange the replicas of objects on nonoverlapping tiers of nodes in the ring. This allows the system to fall in various power modes by powering down subsets of servers while not violating data availability. The predictive PMS predicts workloads and adapts to load fluctuation. It cooperates with the multi-tier replication strategy to provide power proportionality for the system. To ensure that the reliability of the system is maintained when replicas are powered down, we distribute the writes to standby replicas to active servers, which ensures failure tolerance of the system. GreenCHT is implemented based on Sheepdog, a distributed key value storage system that uses consistent hashing as an underlying distributed hash table. By replaying 12 typical real workload traces collected from Microsoft, the evaluation results show that GreenCHT can provide significant power savings while maintaining a desired performance. We observe that GreenCHT can reduce power consumption by up to 35%–61%.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Amur, H., Cipar, J., Gupta, V., et al., 2010. Robust and flexible power-proportional storage. Proc. 1st ACM Symp. on Cloud Computing, p.217–228. http://dx.doi.org/10.1145/1807128.1807164
Bhagwan, R., Savage, S., Voelker, G.M., 2003. Replication strategies for highly available peer-to-peer storage. In: Future Directions in Distributed Computing. Springer-Verlag, p.153–158.
Box, G.E.P., Jenkins, G., 1990. Time series analysis forecasting and control. In: Wiley Series in Probability and Statistics. Holden-Day, Inc.
Brockwell, P.J., Davis, R.A., 1991. Time series: theory and methods. In: Springer Series in Statistics. Springer-Verlag, New York, NY,USA. http://dx.doi.org/10.1007/978-1-4419-0320-4
Cisco Systems, 2012. FNV-1. Available from http://www. isthe.com/chongo/tech/comp/fnv/index.html.
Colarelli, D., Grunwald, D., 2002. Massive arrays of idle disks for storage archives. Proc. ACM/IEEE Conf. on Supercomputing, p.1–11.
DeCandia, G., Hastorun, D., Jampani, M., et al., 2007. Dynamo: Amazon’s highly available key-value store. Proc. ACM SIGOPS Symp. on Operating Systems Principles, p.205–220. http://dx.doi.org/10.1145/1294261.1294281
Goiri, I., Le, K., Haque, M.E., et al., 2011. Greenslot: scheduling energy consumption in green datacenters. Proc. Int. Conf. for High Performance Computing, Networking, Storage and Analysis, p.1–11. http://dx.doi.org/10.1145/2063384.2063411
Goiri, I., Le, K., Nguyen, T.D., et al., 2012. GreenHadoop: leveraging green energy in data-processing frameworks. Proc. 7th ACM European Conf. on Computer Systems, p.57–70. http://dx.doi.org/10.1145/2168836.2168843
Gorini, S., Quirini, M., Menciassi, A., et al., 2007. PARAID: a Gear-Shifting Power-Aware Raid.
Harnik, D., Naor, D., Segall, I., 2009. Low power mode in cloud storage systems. Proc. Int. Symp. on Parallel and Distributed Processing Systems, p.1–8.
Karger, D., Lehman, E., Leighton, T., et al., 1997. Consistent hashing and random trees: distributed caching protocols for relieving hot spots on the World Wide Web. Proc. 29th Annual ACM Symp. on Theory of Computing, p.654–663. http://dx.doi.org/10.1145/258533.258660
Kaushik, R.T., Bhandarkar, M., 2010. GreenHDFS: towards an energy-conserving, storage-efficient, hybrid Hadoop compute cluster. Proc. Int. Conf. on Power Aware Computing and Systems, p.1–9.
Kaushik, R., Cherkasova, L., Campbell, R., et al., 2010. Lightning: self-adaptive, energy-conserving, multizoned, commodity green cloud storage system. Proc. 19th ACM Int. Symp. on High Performance Distributed Computing, p.332–335. http://dx.doi.org/10.1145/1851476.1851523
Lakshman, A., Malik, P., 2010. Cassandra—a decentralized structured storage system. ACM SIGOPS Oper. Syst. Rev., 44(2): 35–40. http://dx.doi.org/10.1145/1773912.1773922
Li, C., Qouneh, A., Li, T., 2012. iSwitch: coordinating and optimizing renewable energy powered node clusters. Proc. 39th Annual Int. Symp. on Computer Architecture, p.512–523. http://dx.doi.org/10.1145/2366231.2337218
LinkedIn, 2009. Voldemort Project. Available from http://www.project-voldemort.com/voldemort/.
Microsoft Research Ltd., 2014. MRS Cambridge Traces. Available from http://project-voldemort.com/.
MySQL, 2004. SysBench. Available from http://sysbench. sourceforge.net/.
Narayanan, D., Donnelly, A., Rowstron, A., 2008. Write offloading: practical power management for enterprise storage. ACM Trans. Stor., 4(3): 1–10. http://dx.doi.org/10.1145/1416944.1416949
NTT Group, 2011. Sheepdog. Available from https:// github.com/sheepdog/sheepdog/wiki.
Open Source and Linux Organization, 2007. Blktrace User Guide. Hewlett-Packard Company. Available from http://www.cse.unsw.edu.au/aaronc/iosched/doc/blk trace.html.
Park, H., Park, K., 2001. Parallel algorithms for red-black trees. Theor. Comput. Sci., 262(1-2):415–435. http://dx.doi.org/10.1016/S0304-3975(00)00287-5
Pinheiro, E., Bianchini, R., 2004. Energy conservation techniques for disk array-based servers. Proc. 18th Annual Int. Conf. on Supercomputing, p.68–78. http://dx.doi.org/10.1145/1006209.1006220
Pinheiro, E., Bianchini, R., Dubnicki, C., 2006. Exploiting redundancy to conserve energy in storage systems. Proc. Joint Int. Conf. on Measurement and Modeling of Computer Systems, p.15–26. http://dx.doi.org/10.1145/1140277.1140281
Stoica, I., Morris, R., Karger, D., et al., 2001. Chord: a scalable peer-to-peer lookup service for Internet applications. Proc. Conf. on Applications, Technologies, Architectures, and Protocols for Computer Communications, p.149–160. http://dx.doi.org/10.1145/383059.383071
Thereska, E., Donnelly, A., Narayanan, D., 2011. Sierra: practical power-proportionality for data center storage. Proc. 6th Conf. on Computer Systems, p.169–182. http://dx.doi.org/10.1145/1966445.1966461
Zhu, Q., Chen, Z., Tan, L., et al., 2005. Hibernator: helping disk arrays sleep through the winter. 20th ACM Symp. on Operating Systems Principles, p.177–190. http://dx.doi.org/10.1145/1095810.1095828
Author information
Authors and Affiliations
Corresponding author
Additional information
A preliminary version of this paper was presented at the 31st International Conference on Massive Storage Systems and Technology, Santa Clara, CA, USA, June 1–5, 2015
ORCID: Nan-nan ZHAO, http://orcid.org/0000-0002-7219-7856
Rights and permissions
About this article
Cite this article
Zhao, Nn., Wan, Jg., Wang, J. et al. A reliable power management scheme for consistent hashing based distributed key value storage systems. Frontiers Inf Technol Electronic Eng 17, 994–1007 (2016). https://doi.org/10.1631/FITEE.1601162
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.1601162