{"id":163269,"date":"2012-10-01T00:00:00","date_gmt":"2012-10-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/flat-datacenter-storage\/"},"modified":"2018-10-16T21:51:34","modified_gmt":"2018-10-17T04:51:34","slug":"flat-datacenter-storage","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/flat-datacenter-storage\/","title":{"rendered":"Flat Datacenter Storage"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Flat Datacenter Storage (FDS) is a high-performance, fault-tolerant, large-scale, locality-oblivious blob store. Using a novel combination of full bisection bandwidth networks, data and metadata striping, and flow control, FDS multiplexes an application&#8217;s large-scale I\/O across the available throughput and latency budget of every disk in a cluster. FDS therefore makes many optimizations around data locality unnecessary. Disks also communicate with each other at their full bandwidth, making recovery from disk failures extremely fast. FDS is designed for datacenter scale, fully distributing metadata operations that might otherwise become a bottleneck.<\/p>\n<p>FDS applications achieve single-process read and write performance of more than 2 GB\/s. We measure recovery of 92 GB data lost to disk failure in 6.2 s and recovery from a total machine failure with 655 GB of data in 33.7 s. Application performance is also high: we describe our FDS-based sort application which set the 2012 world record for disk-to-disk sorting.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Flat Datacenter Storage (FDS) is a high-performance, fault-tolerant, large-scale, locality-oblivious blob store. Using a novel combination of full bisection bandwidth networks, data and metadata striping, and flow control, FDS multiplexes an application&#8217;s large-scale I\/O across the available throughput and latency budget of every disk in a cluster. FDS therefore makes many optimizations around data locality [&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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-163269","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"USENIX","msr_edition":"The 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI '12), 10th USENIX Symposium on Operating Systems Design and Implementation 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