NDLI: Improving Reliability with Dynamic Syndrome Allocation in Intelligent Software Defined Data Centers

Content Provider	IEEE Xplore Digital Library
Author	Bayram, U. Rozier, E.W.D. Divine, D. Pin Zhou
Copyright Year	2015
Description	Author affiliation: Datos IO, Inc., San Jose, CA, USA (Pin Zhou) \|\| Illinois Natural History Survey, Univ. of Illinois, Urbana, IL, USA (Divine, D.) \|\| Dept. of EECS, Univ. of Cincinnati, Cincinnati, OH, USA (Bayram, U.; Rozier, E.W.D.)
Abstract	We propose new algorithms for implementing a software-defined data center (SDDC) to improve the dependability of storage systems without the addition of new hardware. We define the construction of a system that can predict its future resource requirements and act on these predictions to allocate overprovisioned resources to improve reliability. We introduce algorithms for implementing a smart SDDC (SSDDC) that characterizes user I/O transactions (writes and deletes), and use these models to predict the level of overprovisioning within a system, overbooking excess resources to improve reliability, while mitigating the impact on quality of service. We compare several implementations of our methods experimentally, and discuss methods for improving the fault-tolerance of our S2DDC, present experimental results showcasing our ability to improve system reliability showing the decrease in expected annual block loss due to disk failures and latent sector errors, and highlight the benefit of dependence based usage models in estimating overprovisioning.
Starting Page	219
Ending Page	230
File Size	697768
Page Count	12
File Format	PDF
e-ISBN	9781479986293
DOI	10.1109/DSN.2015.46
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-22
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Reliability Clustering algorithms Predictive models Markov processes Quality of service Prediction algorithms Data models markov modeling reliability dependability storage data center big data predictive modeling
Content Type	Text
Resource Type	Article

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2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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