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EFFECTIVE GROUPING FOR ENERGY AND PERFORMANCE: CONSTRUCTION OF ADAPTIVE, SUSTAINABLE, AND MAINTAINABLE DATA STORAGE

Essary, David (2011) EFFECTIVE GROUPING FOR ENERGY AND PERFORMANCE: CONSTRUCTION OF ADAPTIVE, SUSTAINABLE, AND MAINTAINABLE DATA STORAGE. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

The performance gap between processors and storage systems has been increasingly critical overthe years. Yet the performance disparity remains, and further, storage energy consumption israpidly becoming a new critical problem. While smarter caching and predictive techniques domuch to alleviate this disparity, the problem persists, and data storage remains a growing contributorto latency and energy consumption.Attempts have been made at data layout maintenance, or intelligent physical placement ofdata, yet in practice, basic heuristics remain predominant. Problems that early studies soughtto solve via layout strategies were proven to be NP-Hard, and data layout maintenance todayremains more art than science. With unknown potential and a domain inherently full of uncertainty,layout maintenance persists as an area largely untapped by modern systems. But uncertainty inworkloads does not imply randomness; access patterns have exhibited repeatable, stable behavior.Predictive information can be gathered, analyzed, and exploited to improve data layouts. Ourgoal is a dynamic, robust, sustainable predictive engine, aimed at improving existing layouts byreplicating data at the storage device level.We present a comprehensive discussion of the design and construction of such a predictive engine,including workload evaluation, where we present and evaluate classical workloads as well asour own highly detailed traces collected over an extended period. We demonstrate significant gainsthrough an initial static grouping mechanism, and compare against an optimal grouping method ofour own construction, and further show significant improvement over competing techniques. We also explore and illustrate the challenges faced when moving from static to dynamic (i.e. online)grouping, and provide motivation and solutions for addressing these challenges. These challengesinclude metadata storage, appropriate predictive collocation, online performance, and physicalplacement. We reduced the metadata needed by several orders of magnitude, reducing the requiredvolume from more than 14% of total storage down to less than 12%. We also demonstrate how ourcollocation strategies outperform competing techniques. Finally, we present our complete modeland evaluate a prototype implementation against real hardware. This model was demonstrated tobe capable of reducing device-level accesses by up to 65%.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Essary, Davidessary@cs.pitt.edu
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairMosse, Danielmosse@cs.pitt.eduMOSSE
Committee CoChairAmer, Ahmeda.amer@acm.org
Committee MemberPruhs, Kirkkirk@cs.pitt.eduKRP2
Committee MemberChrysanthis, Panospanos@cs.pitt.eduPANOS
Date: 8 June 2011
Date Type: Completion
Defense Date: 22 November 2010
Approval Date: 8 June 2011
Submission Date: 26 October 2010
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: grouping; performance; collocation; power; prediction; metadata
Other ID: http://etd.library.pitt.edu/ETD/available/etd-10262010-123016/, etd-10262010-123016
Date Deposited: 10 Nov 2011 20:03
Last Modified: 15 Nov 2016 13:50
URI: http://d-scholarship.pitt.edu/id/eprint/9518

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