Redundant Data Placement Strategies for Cluster Storage Environments

  • André Brinkmann
  • Sascha Effert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5401)


The continued exponential increase in stored data as well as the high demand for I/O performance is imposing high pressure on the scalability properties of storage environments. The resulting number of disk drives in huge environments does not only lead to management, but also to reliability problems. The foundations of scalable and reliable storage systems are data distribution algorithms, which are able to scale performance and capacity based on the number of disk drives and which are able to efficiently support multi-error correcting codes. In this paper, we propose data distribution strategies, which are competitive concerning the number of data movements required to optimally adapt to a changing number of heterogeneous disk drives under these constraints.


Competitive Ratio Disk Drive Restricted Environment Data Layout Storage Environment 
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  1. 1.
    Brinkmann, A., Effert, S., Meyer auf der Heide, F., Scheideler, C.: Dynamic and Redundant Data Placement. In: Proceedings of the 27th IEEE International Conference on Distributed Computing Systems, ICDCS (2007)Google Scholar
  2. 2.
    Brinkmann, A., Salzwedel, K., Scheideler, C.: Compact, adaptive placement schemes for non-uniform distribution requirements. In: Proceedings of the 14th ACM Symposium on Parallel Algorithms and Architectures (SPAA) (2002)Google Scholar
  3. 3.
    Honicky, R.J., Miller, E.L.: Replication Under Scalable Hashing: A Family of Algorithms for Scalable Decentralized Data Distribution. In: Proceedings of the 18th IEEE International Parallel and Distributed Processing Symposium (IPDPS) (2004)Google Scholar
  4. 4.
    Karger, D., Lehman, E., Leighton, T., Levine, M., Lewin, D., Panigrahy, R.: Consistent hashing and random trees: Distributed caching protocols for relieving hot spots on the World Wide Web. In: Proceedings of the 29th ACM Symposium on Theory of Computing (STOC) (1997)Google Scholar
  5. 5.
    Mense, M., Scheideler, C.: Spread: An adaptive scheme for redundant and fair storage in dynamic heterogeneous storage systems. In: Proceedings of the 19th ACM-SIAM Symposium on Discrete Algorithms (SODA) (2008)Google Scholar
  6. 6.
    Patterson, D.A., Gibson, G., Katz, R.H.: A Case for Redundant Arrays of Inexpensive Disks (RAID). In: Proceedings of the 1988 ACM Conference on Management of Data (SIGMOD) (1988)Google Scholar
  7. 7.
    Weil, S.A., Brandt, S.A., Miller, E.L., Maltzahn, C.: CRUSH: Controlled, Scalable And Decentralized Placement Of Replicated Data. In: Proceedings of the ACM/IEEE Conference on Supercomputing (SC) (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • André Brinkmann
    • 1
  • Sascha Effert
    • 1
  1. 1.University of PaderbornGermany

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