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• W2955291460 abstract "Task scheduling is the key to the full utilization of heterogeneous cloud capabilities for parallel processing of big graphs. Most graph processing systems adopt single-granularity scheduling mechanisms without considering the heterogeneity of the cloud, leading to poor performance. To alleviate it by learning from the excellent directed acyclic graph (DAG)-based scheduling techniques accumulated in traditional parallel computing, we first present a streaming DAG-construction heuristic. It transforms a big graph along with graph traversal algorithms to be carried out into a DAG. We then propose a three-phase heterogeneous-aware cluster-scheduling algorithm to schedule the DAG into a heterogeneous cloud for parallel processing. In the first phase, we design a parallel linear clustering algorithm to cluster the DAG into a series of linear clusters with different granularities. In the second phase, we design a heterogeneous-aware load balancing algorithm to map these clusters to different computational nodes of the cloud. In the last phase, we design a task ordering algorithm to assigns these clusters as-early-as-possible start times. The experimental results show that our scheme can generate high-quality schedules and improve the efficiency and performance of parallel processing of big graphs in the heterogeneous cloud." @default.
• W2955291460 created "2019-07-12" @default.
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• W2955291460 date "2019-01-01" @default.
• W2955291460 modified "2023-10-14" @default.
• W2955291460 title "Cluster-Scheduling Big Graph Traversal Task for Parallel Processing in Heterogeneous Cloud Based on DAG Transformation" @default.
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• W2955291460 doi "https://doi.org/10.1109/access.2019.2921477" @default.
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