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Data-Intensive Workflow Optimization Based on Application Task Graph Partitioning in Heterogeneous Computing Systems
2014
2014 IEEE Fourth International Conference on Big Data and Cloud Computing
Stream based data processing model is proven to be an established method to optimize data-intensive applications. Data-intensive applications involve movement of huge amount of data between execution nodes that incurs large costs. Data-streaming model improves the execution performance of such applications. In the stream-based data processing model, performance is usually measured by throughput and latency. Optimization of these performance metrics in heterogeneous computing environment becomes
doi:10.1109/bdcloud.2014.63
dblp:conf/bdcloud/AhmadLRMK14
fatcat:q4vegly4tbg5joh6po3miz3j2e