Abstract
Scalable storage systems are expected to scale to large numbers of physical storage devices and to service diverse applications without incuring high management costs. New storage virtualization architectures and techniques that are currently being proposed, aim at addressing these needs by providing the ability to configure storage systems to meet resource constraints and application requirements. However, this flexibility leads to a large number of options when configuring storage systems either statically or dynamically.
In this work we examine how this process can be automated. We present Conductor, a rule-based production system that is able to evaluate alternatives and minimize system cost, based on certain criteria. Conductor starts from a set of system resources and a set of application requirements and proposes specific system configurations that meet application requirements while minimizing resource costs. It captures human expertise in the form of rules to generate and evaluate configuration alternatives. In this work we focus on static configuration issues and examine various approaches for reducing complexity within a large configuration space. Our techniques manage to satisfy practical time and resource constraints.
Work performed while at FORTH-ICS, P.O. Box 1385, Heraklion, GR 71110, Greece.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Gartner Group. Total Cost of Storage Ownership — A User-oriented Approach. September 2000.
E. Anderson et al. Ergastulum: Quickly Finding Near-Optimal Storage System Designs. HP Labs SSP technical report HPL-SSP-2001-05 (2002)
E. Anderson et al. Selecting RAID Levels for Disk Arrays. Proc. of the USENIX FAST Conference, January 28–30, 2002, Monterey, CA, USA.
C Language Integrated Production System (CLIPS) http://www.ghg.net/clips/CLIPS.html and also CLIPS Reference Manual. Vol. 1, Version 6.24, 15 June 2006
M.D. Flouris, A. Bilas. Violin: A Framework for extensible Block-level Storage. Proc. of the 22nd IEEE / 13th NASA Goddard Conference on Mass Storage Systems and Technologies (MSST 2005) April 2005, Monterey, CA, USA. IEEE Computer Society.
M. Flouris, R. Lachaize, and A. Bilas. Violin: a Framework for Extensible Block-Level Storage. Book on Knowledge and Data Management in Grids, CoreGRID series, Springer Verlag, 3, 2006.
D. Klahr, P. Langley, R. Neches (eds.): Production System Models of Learning and Development. MIT Press, 1987.
W. Mettrey. A Comparative Evaluation of Expert System Tools. Computer 24,2 (Feb. 1991), pp. 19–31.
E. Riedel. Storage Systems. Not Just a Bunch of Disks Anymore. Queue, June 2003, ACM, pp. 32–41.
E. Thereska et al. Informed data distribution selection in a self-predicting storage system. Proc. of the International Conference on Autonomic Computing, ICAC-06, Dublin, Ireland, June 2006.
S. Uttamchandani et al. Polus: Growing Storage QoS Management Beyond a “Four-year Old Kid”. Proc. of the USENIX FAST’ 04 Conference, March 2004, San Francisco, CA, USA.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer Science+Business Media, LLC
About this paper
Cite this paper
Németh, Z., Flouris, M.D., Lachaize, R., Bilas, A. (2007). Conductor: Support for Autonomous Configuration of Storage Systems. In: Talia, D., Bilas, A., Dikaiakos, M.D. (eds) Knowledge and Data Management in GRIDs. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-37831-2_5
Download citation
DOI: https://doi.org/10.1007/978-0-387-37831-2_5
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-37830-5
Online ISBN: 978-0-387-37831-2
eBook Packages: Computer ScienceComputer Science (R0)