Abstract
Fueled by increasing demand of big data processing, distributed storage systems have been more and more widely used by enterprises. However, in these systems, few storage nodes holding enormous amount of hotspot data could become bottlenecks. This stems from the fact that most typical distributed storage systems mainly provide data amount balancing mechanisms without considering the difference of access load between different storage nodes. To eliminate bottlenecks and tune the performance, there is a demand for such systems to employ a work-load aware balancing and resource management framework to optimize the performance and computation resource utilization.
In this paper, we propose WABRM, a load balancing and resource management framework for Work-load Aware Balancing and Resource Management in Swift, a typical distributed storage system. By designing such an optimization framework, it is possible to eliminate bottlenecks caused by hotspot data. Our experimental results show that the framework can achieve its goals.
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References
Openstack Swift, http://docs.openstack.org/developer/swift/
Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T.L., Ho, A., Neugebauer, R., Pratt, I., Warfield, A.: Xen and the art of virtualization. In: Proceedings of ACM Symposium on Operating Systems Principles. ACM Press, New York (2003)
XenServer, http://www.citrix.com/products/xenserver/resources-and-support.html
Yamamoto, H., Maruta, D., Oie, Y.: Replication methods for load balancing on distributed storages in P2P networks. In: International Symposium on Applications and the Internet, pp. 264–271. IEEE Press, New York (2005)
Madathil, D.K., Thota, R.B., Paul, P., Xie, T.: A Static Data Placement Strategy towards Perfect Load-Balancing for Distributed Storage Clusters. In: International Symposium on Parallel and Distributed Processing, pp. 1–8. IEEE Press, New York (2008)
Deng, Y., Lau, R.: Heat Diffusion Based Dynamic Load Balancing for Distributed Virtual Environments. In: 17th ACM Symposium on Virtual Reality Software and Technology, pp. 203–210. ACM Press, New York (2010)
Liu, Y., Wan, Y., Jin, Y.: Research on The Improvement of MongoDB Auto-Sharding in Cloud Environment. In: 7th International Conference on Computer Science & Education, Melbourne, VIC, Australia, pp. 851–854 (2012)
MongoDB, http://www.mongodb.org/
Pearce, O., Gambliny, T., Supinskiy, B., et al.: Quantifying the Effectiveness of Load Balance Algorithms. In: 26th ACM International Conference on Supercomputing, pp. 185–194. ACM Press, New York (2012)
Zhu, Y., Yu, Y., Wang, W., et al.: A Balanced Allocation Strategy for File Assignment in Parallel I/O Systems. In: 5th IEEE International Conference on Networking, Architecture and Storage, pp. 257–266. IEEE Press, New York (2010)
Bui, T.N., Deng, X., Zrncic, C.M.: An Improved Ant-Based Algorithm for the DegreeConstrained Minimum SpanningTree Problem. J. IEEE Transactions on Evolutionary Computation 16, 266–278 (2012)
Qin, X., Zhang, W., Wang, W., et al.: Towards a Cost-Aware Data Migration Approach for Key-Value Stores. In: 2012 IEEE International Conference on Cluster Computing, pp. 551–556. IEEE Press, New York (2012)
Liu, Z., Lin, M., Wierman, A., et al.: Greening Geographical Load Balancing. In: Liu, Z., Lin, M., Wierman, A., et al. (eds.) 2011 ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems, pp. 233–244. ACM Press, New York (2011)
Lin, M., Wierman, A., Andrew, L.L.H., et al.: Dynamic Right-sizing for Powerproportional Data Centers. In: 2011 IEEE INFOCOM, pp. 1098–1106. IEEE Press, New York (2011)
Pylot, http://www.pylot.org/
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Wang, Z., Chen, H., Ban, Y. (2013). WABRM: A Work-Load Aware Balancing and Resource Management Framework for Swift on Cloud. In: Kołodziej, J., Di Martino, B., Talia, D., Xiong, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2013. Lecture Notes in Computer Science, vol 8285. Springer, Cham. https://doi.org/10.1007/978-3-319-03859-9_39
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DOI: https://doi.org/10.1007/978-3-319-03859-9_39
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03858-2
Online ISBN: 978-3-319-03859-9
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