{"id":159095,"date":"2010-06-10T00:00:00","date_gmt":"2010-06-10T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/hermes-clustering-users-in-large-scale-e-mail-services\/"},"modified":"2018-10-16T21:27:12","modified_gmt":"2018-10-17T04:27:12","slug":"hermes-clustering-users-in-large-scale-e-mail-services","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/hermes-clustering-users-in-large-scale-e-mail-services\/","title":{"rendered":"Hermes: Clustering Users in Large-Scale E-mail Services"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Hermes is an optimization engine for large-scale enterprise e-mail services. Such services could be hosted by a virtualized e-mail service provider, or by dedicated enterprise data centers. In both cases we observe that the pattern of e-mails between employees of an enterprise forms an implicit social graph. Hermes tracks this implicit social graph, periodically identifies clusters of strongly connected users within the graph, and co-locates such users on the same server. Co-locating the users reduces storage requirements: senders and recipients who reside on the same server can share a single copy of an e-mail. Co-location also reduces inter-server bandwidth usage.<\/p>\n<p>We evaluate Hermes using a trace of all e-mails within a major corporation over a five month period. The e-mail service supports over 120,000 users on 68 servers. Our evaluation shows that using Hermes results in storage savings of 37% and bandwidth savings of 50% compared to current approaches. The overheads are low: a single commodity server can run the optimization for the entire system.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Hermes is an optimization engine for large-scale enterprise e-mail services. Such services could be hosted by a virtualized e-mail service provider, or by dedicated enterprise data centers. In both cases we observe that the pattern of e-mails between employees of an enterprise forms an implicit social graph. Hermes tracks this implicit social graph, periodically identifies [&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-159095","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"Association for Computing Machinery, Inc.","msr_edition":"ACM Symposium on Cloud Computing 2010 (ACM SOCC 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