Reliability Analysis on Shifted and Random Declustering Block Layouts in Scale-Out Storage Architectures

Reliability is a critical metric in the design and development of scale-out data storage clusters. A general multiway replication-based declustering scheme has been widely used in enterprise large-scale storage systems to improve the I/O parallelism. Unfortunately, given an increasing number of node failures, how often a cluster starts losing data when being scaled-out is not well investigated. In this paper, we studied the reliability of multi-way declustering layouts by developing an extended model, more specifically abstracting the Continuous Time Markov chain to an ordinary differentiate equation group, and analyzing their potential parallel recovery possibilities. Our comprehensive simulation results on Mat lab and SHARPE show that the shifted declustering layout outperforms the random declustering layout in a multi-way replication scale-out architecture, in terms of data loss probability and system reliability by up to 63% and 85% respectively. Our study on both 5-year and 10-year system reliability equipped with various recovery bandwidth settings shows that, the shifted declustering layout surpasses the random declustering layout in both cases by consuming up to 5.2% and 11% less recovery bandwidth.

[1]  Jun Wang,et al.  Shifted declustering: a placement-ideal layout scheme for multi-way replication storage architecture , 2008, ICS '08.

[2]  Ethan L. Miller,et al.  Replication under scalable hashing: a family of algorithms for scalable decentralized data distribution , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..

[3]  Bianca Schroeder,et al.  Understanding disk failure rates: What does an MTTF of 1,000,000 hours mean to you? , 2007, TOS.

[4]  Sachin Katti,et al.  Copysets: Reducing the Frequency of Data Loss in Cloud Storage , 2013, USENIX Annual Technical Conference.

[5]  Randal E. Bryant,et al.  Data-Intensive Supercomputing: The case for DISC , 2007 .

[6]  Bruce G. Lindsay,et al.  Random sampling techniques for space efficient online computation of order statistics of large datasets , 1999, SIGMOD '99.

[7]  Jacob R. Lorch,et al.  Farsite: federated, available, and reliable storage for an incompletely trusted environment , 2002, OSDI '02.

[8]  Garth A. Gibson,et al.  Parity declustering for continuous operation in redundant disk arrays , 1992, ASPLOS V.

[9]  Donald F. Towsley,et al.  A Performance Evaluation of RAID Architectures , 1996, IEEE Trans. Computers.

[10]  Philip S. Yu,et al.  Using rotational mirrored declustering for replica placement in a disk-array-based video server , 1997, Multimedia Systems.

[11]  Randolph Nelson,et al.  Probability, Stochastic Processes, and Queueing Theory , 1995 .

[12]  David J. DeWitt,et al.  Chained declustering: a new availability strategy for multiprocessor database machines , 1990, [1990] Proceedings. Sixth International Conference on Data Engineering.

[13]  Shankar Pasupathy,et al.  An analysis of latent sector errors in disk drives , 2007, SIGMETRICS '07.

[14]  Werner Vogels,et al.  Dynamo: amazon's highly available key-value store , 2007, SOSP.

[15]  Mario Blaum,et al.  Mirrored Disk Organization Reliability Analysis , 2006, IEEE Transactions on Computers.

[16]  David A. Patterson,et al.  Computer Architecture: A Quantitative Approach , 1969 .

[17]  Kishor S. Trivedi Probability and Statistics with Reliability, Queuing, and Computer Science Applications , 1984 .

[18]  Scott A. Brandt,et al.  Reliability mechanisms for very large storage systems , 2003, 20th IEEE/11th NASA Goddard Conference on Mass Storage Systems and Technologies, 2003. (MSST 2003). Proceedings..

[19]  Ali Saman Tosun Analysis and Comparison of Replicated Declustering Schemes , 2007, IEEE Transactions on Parallel and Distributed Systems.

[20]  Tom W. Keller,et al.  A comparison of high-availability media recovery techniques , 1989, SIGMOD '89.

[21]  Philip S. Yu,et al.  Using rotational mirrored declustering for replica placement in a disk-array-based video server , 1997, MULTIMEDIA '95.

[22]  Bin Zhou,et al.  Scalable Performance of the Panasas Parallel File System , 2008, FAST.

[23]  Ethan L. Miller,et al.  Understanding and coping with failures in large-scale storage systems , 2005 .

[24]  Ernst W. Biersack,et al.  Modeling and Performance Comparison of Reliability Strategies for Distributed Video Servers , 2000, IEEE Trans. Parallel Distributed Syst..

[25]  GhemawatSanjay,et al.  The Google file system , 2003 .

[26]  Peter J. Varman,et al.  pClock: an arrival curve based approach for QoS guarantees in shared storage systems , 2007, SIGMETRICS '07.