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.
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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
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DOI: https://doi.org/10.1007/978-0-387-37831-2_5
Publisher Name: Springer, Boston, MA
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