Providing timely results in the face of rapid growth in data volumes has become important for analytical frameworks. For this reason, frameworks increasingly operate on only a subset of the input data. A key property of such sampling is that combinatorially many subsets of the input are present. We present KMN, a system that leverages these choices to perform data-aware scheduling, i.e., minimize time taken by tasks to read their inputs, for a DAG of tasks. KMN not only uses choices to co-locate tasks with their data but also percolates such combinatorial choices to downstream tasks in the DAG by launching a few additional tasks at every upstream stage. Evaluations using workloads from Facebook and Conviva on a 100-machine EC2 cluster show that KMN reduces average job duration by 81% using just 5% additional resources.<\/p>\n","protected":false},"excerpt":{"rendered":"
Providing timely results in the face of rapid growth in data volumes has become important for analytical frameworks. For this reason, frameworks increasingly operate on only a subset of the input data. A key property of such sampling is that combinatorially many subsets of the input are present. 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