This thesis explores hardware command capture as a viable means of analyzing real world hard drive usage. Hardware command capture provides insight into the IO stack where current tools fail to reach. A software platform is presented which provides trace conversion and analysis capabilities. This platform is written in Python and designed to handle traces of arbitrary size while being easily extensible for future projects to build upon. A novel Sequential Stream Detection algorithm built upon the software platform is then presented. This algorithm detects application level sequential streams and provides interesting insight into the sequential nature of the applications analyzed. The software platform and Sequential Stream Detector were validated and run against a range of workloads including video playback, large project compilations, and synthetic benchmarks. Where applicable, each workload was run on multiple file systems (ext2, ext3, ext4, Btrfs) to compare the effects of stream allocation across file systems. It is shown that stream allocation is consistent across file systems suggesting stream detection may be a valuable workload identification tool.
","abstract_html":"<p>This thesis explores hardware command capture as a viable means of analyzing real world hard drive usage. Hardware command capture provides insight into the IO stack where current tools fail to reach. A software platform is presented which provides trace conversion and analysis capabilities. This platform is written in Python and designed to handle traces of arbitrary size while being easily extensible for future projects to build upon. A novel Sequential Stream Detection algorithm built upon the software platform is then presented. This algorithm detects application level sequential streams and provides interesting insight into the sequential nature of the applications analyzed. The software platform and Sequential Stream Detector were validated and run against a range of workloads including video playback, large project compilations, and synthetic benchmarks. Where applicable, each workload was run on multiple file systems (ext2, ext3, ext4, Btrfs) to compare the effects of stream allocation across file systems. It is shown that stream allocation is consistent across file systems suggesting stream detection may be a valuable workload identification tool.</p>","abstract_has_math":false,"creators":["Miller, Adam David"],"institution":null,"degree_name":"MS in Computer Science","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Chris Lupo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-06-01T07:00:00Z","date_published":"2012-06-01T07:00:00Z","updated_at":"2026-07-24T01:31:17Z","subjects":["hard drive","serial bus analyzer","sequential stream detection"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2012.85"],"render_values":[{"text":"10.15368/theses.2012.85","href":"https://doi.org/10.15368/theses.2012.85","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/768","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chris Lupo"]},{"key":"dc:creator","label":"Author","values":["Miller, Adam David"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2012-06-11T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["hard drive","serial bus analyzer","sequential stream detection"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/768","10.15368/theses.2012.85"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis explores hardware command capture as a viable means of analyzing real world hard drive usage. Hardware command capture provides insight into the IO stack where current tools fail to reach. A software platform is presented which provides trace conversion and analysis capabilities. This platform is written in Python and designed to handle traces of arbitrary size while being easily extensible for future projects to build upon. A novel Sequential Stream Detection algorithm built upon the software platform is then presented. This algorithm detects application level sequential streams and provides interesting insight into the sequential nature of the applications analyzed. The software platform and Sequential Stream Detector were validated and run against a range of workloads including video playback, large project compilations, and synthetic benchmarks. Where applicable, each workload was run on multiple file systems (ext2, ext3, ext4, Btrfs) to compare the effects of stream allocation across file systems. It is shown that stream allocation is consistent across file systems suggesting stream detection may be a valuable workload identification tool.
"]},{"key":"dc:title","label":"Title","values":["Hard Drive Command Capture and Sequential Stream Detection"]}]}],"canonical_facts":{"dc:contributor":["Chris Lupo"],"dc:creator":["Miller, Adam David"],"dc:date.available":["2012-06-11T07:00:00Z"],"dc:description.abstract":["This thesis explores hardware command capture as a viable means of analyzing real world hard drive usage. Hardware command capture provides insight into the IO stack where current tools fail to reach. A software platform is presented which provides trace conversion and analysis capabilities. This platform is written in Python and designed to handle traces of arbitrary size while being easily extensible for future projects to build upon. A novel Sequential Stream Detection algorithm built upon the software platform is then presented. This algorithm detects application level sequential streams and provides interesting insight into the sequential nature of the applications analyzed. The software platform and Sequential Stream Detector were validated and run against a range of workloads including video playback, large project compilations, and synthetic benchmarks. Where applicable, each workload was run on multiple file systems (ext2, ext3, ext4, Btrfs) to compare the effects of stream allocation across file systems. It is shown that stream allocation is consistent across file systems suggesting stream detection may be a valuable workload identification tool.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/768","10.15368/theses.2012.85"],"dc:subject":["hard drive","serial bus analyzer","sequential stream detection"],"dc:title":["Hard Drive Command Capture and Sequential Stream Detection"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["MS in Computer Science"]},"updated_at":"2026-07-24T01:31:17Z"}