{"id":354110,"date":"2020-02-28T18:56:08","date_gmt":"2017-01-17T20:48:12","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=354110"},"modified":"2024-03-18T09:49:39","modified_gmt":"2024-03-18T16:49:39","slug":"recurring-job-optimization-scope","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/recurring-job-optimization-scope\/","title":{"rendered":"Recurring Job Optimization in Scope"},"content":{"rendered":"<p>An increasing number of applications require distributed data storage and processing infrastructure over large clusters of commodity hardware for critical business decisions. The MapReduce programming model [2] helps programmers write distributed applications on large clusters, but requires dealing with complex implementation details (e.g., reasoning with data distribution and overall system configuration).<\/p>\n<span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span>","protected":false},"excerpt":{"rendered":"<p>An increasing number of applications require distributed data storage and processing infrastructure over large clusters of commodity hardware for critical business decisions. The MapReduce programming model [2] helps programmers write distributed applications on large clusters, but requires dealing with complex implementation details (e.g., reasoning with data distribution and overall system configuration). 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