{"id":165999,"date":"2014-02-16T00:00:00","date_gmt":"2014-02-16T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/on-the-energy-overhead-of-mobile-storage-systems\/"},"modified":"2018-10-16T20:04:50","modified_gmt":"2018-10-17T03:04:50","slug":"on-the-energy-overhead-of-mobile-storage-systems","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/on-the-energy-overhead-of-mobile-storage-systems\/","title":{"rendered":"On the Energy Overhead of Mobile Storage Systems"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Secure digital cards and embedded multimedia cards are pervasively used as secondary storage devices in portable electronics, such as smartphones and tablets. These devices cost under 70 cents per gigabyte. They deliver more than 4000 random IOPS and 70 MBps of sequential access bandwidth. Additionally, they operate at a peak power lower than 250 milliwatts. However, software storage stack above the device level on most existing mobile platforms is not optimized to exploit the low-energy characteristics of such devices. This paper examines the energy consumption of the storage stack on mobile platforms.<\/p>\n<p>We conduct several experiments on mobile platforms to analyze the energy requirements of their respective storage stacks. Software storage stack consumes up to 200 times more energy when compared to storage hardware, and the security and privacy requirements of mobile apps are a major cause. A storage energy model for mobile platforms is proposed to help developers optimize the energy requirements of storage intensive applications. Finally, a few optimizations are proposed to reduce the energy consumption of storage systems on these platforms.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Secure digital cards and embedded multimedia cards are pervasively used as secondary storage devices in portable electronics, such as smartphones and tablets. These devices cost under 70 cents per gigabyte. They deliver more than 4000 random IOPS and 70 MBps of sequential access bandwidth. Additionally, they operate at a peak power lower than 250 milliwatts. [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"USENIX - Advanced Computing Systems Association","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Conference on File and Storage Technologies (FAST'14)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Conference on File and Storage Technologies (FAST'14)","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Jing Li, Steven Swanson, Bruce Worthington, Qi Zhang","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2014-02-16","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":2014,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13547],"msr-publication-type":[193716],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-165999","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"USENIX - 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The project explores how to best leverage such new memory technologies inside systems of all sizes and shapes: from mobile to data center scale. Former Interns: Jian Huang, Georgia Tech (2013, 2014, &amp; 2015) Jing Li, UCSD (2013) Yanqi&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171456"}]}},{"ID":171446,"post_title":"Battery Research at Microsoft","post_name":"battery-research-at-microsoft","post_type":"msr-project","post_date":"2010-03-19 13:09:57","post_modified":"2025-02-05 12:05:14","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/battery-research-at-microsoft\/","post_excerpt":"Mobile &amp; Internet of Things (IoT) devices, along with other battery operated devices, are energy constrained. While hardware capabilities have increased tremendously over the last ten years, battery energy density has only doubled. 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