{"id":153485,"date":"2008-12-01T00:00:00","date_gmt":"2008-12-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/hunting-for-problems-with-artemis\/"},"modified":"2018-10-16T20:13:14","modified_gmt":"2018-10-17T03:13:14","slug":"hunting-for-problems-with-artemis","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/hunting-for-problems-with-artemis\/","title":{"rendered":"Hunting for problems with Artemis"},"content":{"rendered":"
Artemis is a modular application designed for analyzing and troubleshooting the performance of large clusters running datacenter services. Artemis is composed of four modules: (1) distributed log collection and data extraction, (2) a database storing the extracted data, (3) an interactive visualization tool for exploring the data, and (4) a plug-in interface (and a set of sample plug-ins) allowing users to implement data analysis tools including (a) the extraction and construction of new features from the basic measurements collected, and (b) the implementation and invocation of statistical and machine learning algorithms and tools. In this paper we describe each of these components and then we illustrate the power of the plug-in architecture by presenting a case-study using Artemis to analyze a Dryad application running on a 240-machine cluster.<\/p>\n<\/div>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
Artemis is a modular application designed for analyzing and troubleshooting the performance of large clusters running datacenter services. Artemis is composed of four modules: (1) distributed log collection and data extraction, (2) a database storing the extracted data, (3) an interactive visualization tool for exploring the data, and (4) a plug-in interface (and a set […]<\/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":[13556,13547],"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-153485","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"USENIX","msr_edition":"USENIX Workshop on the Analysis of System Logs (WASL)","msr_affiliation":"","msr_published_date":"2008-12-01","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":"","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":"222772","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"wasl08[1].pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2008\/12\/wasl081.pdf","id":222772,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":222772,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2008\/12\/wasl081.pdf"}],"msr-author-ordering":[{"type":"text","value":"Gabriela F. Cretu-Ciocarlie","user_id":0,"rest_url":false},{"type":"user_nicename","value":"mbudiu","user_id":32853,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=mbudiu"},{"type":"user_nicename","value":"moises","user_id":32991,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=moises"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[169537],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":169537,"post_title":"DryadLINQ","post_name":"dryadlinq","post_type":"msr-project","post_date":"2007-05-31 11:04:14","post_modified":"2017-06-08 12:01:42","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/dryadlinq\/","post_excerpt":"DryadLINQ is a simple, powerful, and elegant programming environment for writing large-scale data parallel applications running on large PC clusters. Overview The goal of DryadLINQ is to make distributed computing on large compute cluster simple enough for every programmer. DryadLINQ combines two important pieces of Microsoft technology: the Dryad distributed execution engine and the .NET Language Integrated Query (LINQ). Dryad provides reliable, distributed computing on thousands of servers for large-scale data parallel applications. 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