Data-Intensive Workflow Optimization based on Application Task Graph Partitioning in Heterogeneous Computing Systems

Abstract—Stream based data processing model is proven to be an established method to optimize data-intensive applica-tions. 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 more challenging due to the difference in the computing capacity of execution nodes and variations in the data transfer capability of communication links between these nodes. This paper presents a dual objec-tive Partitioning based Data-intensive Workflow optimization Algorithm (PDWA) for heterogeneous computing systems. The proposed PDWA provides significantly reduced latency with increase in the throughput. In the proposed algorithm, the application task graph is partitioned such that the inter-partition data movement is minimal. Such optimized partition-ing enhances the throughput. Each partition is mapped to the execution node that gives minimum execution time for that par-ticular partition. PDWA also exploits partial task duplication to reduce the latency. We evaluated the proposed algorithm with synthesized benchmarks and workflows from the real-world workloads, and the proposed algorithm shows 60 % reduced latency with 47 % improvement in the throughput as compared to the approach when workflows are not partitioned. Keywords-Workflow optimization; Partitioning task graph; Heterogeneous computing; Stream-data processing. I