{"id":152129,"date":"2007-06-01T00:00:00","date_gmt":"2007-06-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/scalable-local-area-service-discovery\/"},"modified":"2018-10-16T20:05:05","modified_gmt":"2018-10-17T03:05:05","slug":"scalable-local-area-service-discovery","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/scalable-local-area-service-discovery\/","title":{"rendered":"Scalable Local Area Service Discovery"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Existing methods for local area service discovery either don\u2019t scale or rely on a trustworthy directory server; in some environments these restrictions are unacceptable or impractical. This paper describes \u201cRepeat-BAND\u201d, a generic method for service discovery that scales automatically without depending on any central component. The automatic scaling makes it fast on small networks and automatically load controlled on large networks, irrespective of the number of simultaneous discoveries taking place. It is generic in the sense that the automatic scaling technique can be applied to improve any particular service discovery system, and it is applicable across a large variety of types of network because we show how all the tuning parameters are derived. We present results showing controlled load discovery with scalability up to very large networks. We also show that the algorithms are simple and easy to implement; an important practical requirement since the method is used in Windows Vista and licensed by many hardware vendors. In addition, we consider the industrial requirement as to the certification of independent implementations, an aspect normally ignored in the academic literature.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Existing methods for local area service discovery either don\u2019t scale or rely on a trustworthy directory server; in some environments these restrictions are unacceptable or impractical. This paper describes \u201cRepeat-BAND\u201d, a generic method for service discovery that scales automatically without depending on any central component. The automatic scaling makes it fast on small networks and [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"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-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-152129","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"IEEE Communications Society","msr_edition":"","msr_affiliation":"","msr_published_date":"2007-06-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","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":"June 2007, Glasgow Scotland, IEEE International Conference on Communications 2007.","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":"","msr_publicationurl":"http:\/\/ieeexplore.ieee.org\/document\/4289011\/","msr_doi":"","msr_publication_uploader":[{"type":"url","title":"http:\/\/ieeexplore.ieee.org\/document\/4289011\/","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":0,"url":"http:\/\/ieeexplore.ieee.org\/document\/4289011\/"}],"msr-author-ordering":[{"type":"user_nicename","value":"rjblack","user_id":33417,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=rjblack"},{"type":"text","value":"Laurent Massouli\u00e9","user_id":0,"rest_url":false},{"type":"text","value":"Heimir 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