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检索条件"主题词=Distributed Vertex-Centric Graph Processing"

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Incremental Partitioning of Massive Dynamic graphs and Its Applications to vertex-centric graph processing Systems

Incremental Partitioning of Massive Dynamic Graphs and Its A...

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作者： Amirreza Abdolrashidi The University of Georgia

学位级别：博士

distributed vertex-centric graph processing systems have acquired significant pop- ularity in recent years for processing massive graphs. The manner in which graph data is partitioned and placed on the computational nodes has considerable impact on the performance of distributed vertex-centric clusters. In this dissertation, we propose a novel model for analyzing the performance of such clusters when different partitioning strategies are applied to the computation of various graph algorithms and also propose various metrics for measuring performance of such clusters. Moreover, in this research we consider partitioning of many real world graph data sets, which are dynamic and can essentially be modeled as Time-Evolving graphs (TEGs). We pro- pose a unique, continuous and multi-cost sensitive approach for partitioning dynamic graphs. Our approach incorporates novel cost functions that take into account major factors that impact the performance of big graph processing clusters. We also present incremental algorithms to efficaciously handle various types of graph dynamics.

关键词： distributed vertex-centric graph processing Performance Modeling Performance Evaluation graph Partitioning Time-Evolving graphs

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Continual and Cost-Effective Partitioning of Dynamic graphs for Optimizing Big graph processing Systems 5

Continual and Cost-Effective Partitioning of Dynamic Graphs ...

引用

IEEE International Congress on Big Data (BigData Congress)

作者： Abdolrashidi, Amirreza Ramaswamy, Lakshmish Univ Georgia Dept Comp Sci Athens GA 30602 USA

ISBN: (纸本)9781509026227

Recently, several cluster computing frameworks have been proposed for scalable and efficient processing of big graphs. The manner in which graph data is partitioned and placed on the compute nodes has a significant impact on cluster performance. While most existing graph partitioning and placement strategies have been designed for static graphs, the graphs in many modern applications are dynamic (time-evolving). In this paper, we propose a unique, continuous and multi-cost sensitive approach for partitioning dynamic graphs. Our approach incorporates novel cost functions that take into account major factors that impact the performance of big graph processing clusters. We also present incremental algorithms to efficaciously handle various types of graph dynamics. Our algorithms are unique in that they work by locally adjusting the partitions thus avoiding massive repartitioning. This paper reports a series of experiments to demonstrate the effectiveness of the proposed algorithms in maximizing the performance of big graph processing systems on dynamic graphs.

关键词： graph Partitioning Performance Evaluation Time-Evolving graphs distributed vertex-centric graph processing

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Performance Modeling of Computation and Communication Tradeoffs in vertex-centric graph processing Clusters 10

Performance Modeling of Computation and Communication Tradeo...

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10th IEEE International Conference on Collaborative Computing Networking Applications and Worksharing

作者： Abdolrashidi, Amirreza Ramaswamy, Lakshmish Narron, David Seamus Univ Georgia Dept Comp Sci Athens GA 30602 USA

ISBN: (纸本)9781631900433

distributed vertex-centric graph processing systems have been recently proposed to perform different types of analytics on large graphs. These systems utilize the parallelism of shared nothing clusters. In this work we propose a novel model for the performance cost of such clusters. We also define novel metrics related to the workload balance and network communication cost of clusters processing massive real graph datasets. We empirically investigate the effects of different graph partitioning mechanisms and their tradeoff for two different categories of graph processing algorithms.

关键词： distributed vertex-centric graph processing Performance Modeling Parallel processing graph Partitioning

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