{"id":162390,"date":"2012-02-11T00:00:00","date_gmt":"2012-02-11T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/towards-self-optimizing-collaborative-systems\/"},"modified":"2021-09-14T11:33:35","modified_gmt":"2021-09-14T18:33:35","slug":"towards-self-optimizing-collaborative-systems","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/towards-self-optimizing-collaborative-systems\/","title":{"rendered":"Towards Self-Optimizing Collaborative Systems"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Two important performance metrics in collaborative systems are local and remote response times. Previous analytical and simulation work has shown that these response times depend on three important factors: processing architecture, communication architecture, and scheduling of tasks dictated by these two architectures. We show that it is possible to create a system that improves response times by dynamically adjusting these three system parameters in response to changes to collaboration parameters such as new users joining and network delays changing. We present practical approaches for collecting collaboration parameters, computing multicast overlays, applying analytical models of previous work, preserving coupling semantics during optimizations, and keeping overheads low. Simulations and experiments show that the system improves performance in practical scenarios.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Two important performance metrics in collaborative systems are local and remote response times. Previous analytical and simulation work has shown that these response times depend on three important factors: processing architecture, communication architecture, and scheduling of tasks dictated by these two architectures. We show that it is possible to create a system that improves response 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