{"id":166540,"date":"2014-08-01T00:00:00","date_gmt":"2014-08-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/decentralized-task-aware-scheduling-for-data-center-networks-2\/"},"modified":"2018-10-16T20:21:05","modified_gmt":"2018-10-17T03:21:05","slug":"decentralized-task-aware-scheduling-for-data-center-networks-2","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/decentralized-task-aware-scheduling-for-data-center-networks-2\/","title":{"rendered":"Decentralized Task-aware Scheduling for Data Center Networks"},"content":{"rendered":"
\n

Many data center applications perform rich and complex tasks (e.g., executing a search query or generating a user’s news-feed). From a network perspective, these tasks typically comprise multiple flows, which traverse different parts of the network at potentially different times. Most network resource allocation schemes, however, treat all these flows in isolation –rather than as part of a task — and therefore only optimize flow-level metrics.<\/p>\n

In this paper, we show that task-aware network scheduling, which groups flows of a task and schedules them together, can reduce both the average as well as tail completion time for typical data center applications. To achieve these benefits in practice, we design and implement Baraat, a decentralized task-aware scheduling system. Baraat schedules tasks in a FIFO order but avoids head-of-line blocking by dynamically changing the level of multiplexing in the network. Through experiments with Memcached on a small testbed and large-scale simulations, we show that Baraat outperforms state-of-the-art decentralized schemes (e.g., pFabric) as well as centralized schedulers (e.g., Orchestra) for a wide range of workloads (e.g., search, analytics, etc).<\/p>\n

<\/div>\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Many data center applications perform rich and complex tasks (e.g., executing a search query or generating a user’s news-feed). From a network perspective, these tasks typically comprise multiple flows, which traverse different parts of the network at potentially different times. Most network resource allocation schemes, however, treat all these flows in isolation –rather than as […]<\/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-166540","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"SIGCOMM","msr_affiliation":"","msr_published_date":"2014-08-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"SIGCOMM","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"217498","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"baraat-sigcomm14.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2014\/08\/baraat-sigcomm14.pdf","id":217498,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":217498,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2014\/08\/baraat-sigcomm14.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"fdogar","user_id":31788,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=fdogar"},{"type":"user_nicename","value":"thomkar","user_id":34031,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=thomkar"},{"type":"user_nicename","value":"hiballan","user_id":32008,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=hiballan"},{"type":"user_nicename","value":"antr","user_id":31061,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=antr"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[170657],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":170657,"post_title":"Predictable Data Centers (PDC)","post_name":"predictable-data-centers-pdc","post_type":"msr-project","post_date":"2011-02-11 10:10:03","post_modified":"2018-08-20 10:30:03","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/predictable-data-centers-pdc\/","post_excerpt":"Performance predictability is a key requirement for high-performant applications in today's multi-tenant datacenters. Online services running in infrastructure datacenters need such predictability to satisfy applications SLAs. Cloud datacenters require guaranteed performance to bound customer costs and spur adoption. However, the network and storage stack used in today\u2019s datacenters is unaware of such application requirements. This projects examines how to enable preditable datacenters. 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