ABSTRACT

    Carnegie Mellon University, Department of Electrical and Computer Engineering Ph.D. Dissertation,
    CMU-PDL-08-102. May 2008.

    Using Utility Functions to Control a Distributed Storage System

    John D. Strunk

    Electrical and Computer Engineering
    Carnegie Mellon University
    Pittsburgh, PA 15213

    http://www.pdl.cmu.edu

    Provisioning, and later optimizing, a storage system involves an extensive set of trade-offs between
    system metrics, including purchase cost, performance, reliability, availability, and power. Previous
    work has tried to simplify provisioning and tuning tasks by allowing a system administrator to
    specify goals for various storage metrics. While this helps by raising the level of specification from
    low-level mechanisms to high-level storage system metrics, it does not permit trade-offs between
    those metrics.

    This dissertation goes beyond goal-based requirements by allowing the system administrator to
    use a utility function to specify his objectives. Using utility, both the costs and benefits of configuration
    and tuning decisions can be examined within a single framework. This permits a provisioning
    system to make automated trade-offs across system metrics, such as performance, data protection
    and power consumption. It also allows an automated optimization system to properly balance the
    cost of data migration with its expected benefits.

    This work develops a prototype storage provisioning tool that uses an administrator-specified
    utility function to generate cost-effective storage configurations. The tool is then used to provide
    examples of how utility can be used to balance competing objectives (e.g., performance and data
    protection) and to provide guidance in the presence of external constraints. A framework for using
    utility to evaluate data migration is also developed. This framework balances data migration costs
    (decreases to current system metrics) against the potential benefits by discounting future expected
    utility. Experiments show that, by looking at utility over time, it is possible to choose the migration
    speed as well as weigh alternate optimization choices to provide the proper balance of current and
    future levels of service.

    FULLTHESIS: pdf


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