Minimization of Resource Consumption through Workload Consolidation in Large-Scale Distributed Data Platforms
نویسنده
چکیده
Title of dissertation: MINIMIZATION OF RESOURCE CONSUMPTION THROUGH WORKLOAD CONSOLIDATION IN LARGE-SCALE DISTRIBUTED DATA PLATFORMS Ashwin Kumar Kayyoor, Doctor of Philosophy, 2014 Dissertation directed by: Associate Professor Amol Deshpande, Associate Professor Jimmy Lin Department of Computer Science The rapid increase in the data volumes encountered in many application domains has led to widespread adoption of parallel and distributed data management systems like parallel databases and MapReduce-based frameworks (e.g., Hadoop) in recent years. Use of such parallel and distributed frameworks is expected to accelerate in the coming years, putting further strain on already-scarce resources like compute power, network bandwidth, and energy. To reduce total execution times, there is a trend towards increasing execution parallelism by spreading out data across a large number of machines. However, this often increases the total resource consumption, and especially energy consumption, significantly because of process startup costs and other overheads (e.g., communication overheads). In this disserta-
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