{"id":166678,"date":"2013-01-01T00:00:00","date_gmt":"2013-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/towards-resource-elastic-machine-learning\/"},"modified":"2018-10-16T20:01:44","modified_gmt":"2018-10-17T03:01:44","slug":"towards-resource-elastic-machine-learning","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/towards-resource-elastic-machine-learning\/","title":{"rendered":"Towards Resource-Elastic Machine Learning"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In this article, we argue that resource elasticity is a key requirement for distributed machine learning. Not only do computational resources disappear without warning (e.g. due to machine failure), modern resource managers also re-negotiate the available resources while a job is running: Additional machines may have become available or already reserved ones have been re-assigned to other jobs. We show how to formalize this problem and present an initial approach for linear learners.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this article, we argue that resource elasticity is a key requirement for distributed machine learning. Not only do computational resources disappear without warning (e.g. due to machine failure), modern resource managers also re-negotiate the available resources while a job is running: Additional machines may have become available or already reserved ones have been re-assigned 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