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Partitioning big graph with respect to arbitrary proportions in a streaming manner
تقسیم بندی نمودار بزرگ با توجه به نسبت های دلخواه به شیوه جریان-2018 Using a single commodity computational node to partition big graph is very difficult. This work studies
how to partition a big graph with respect to arbitrary proportions in a streaming manner. To meet diverse
requirements of big graph partitioning scenarios, we first devise 3 measurement schemes for measuring
the graph vertex count, graph workload, and graph processing time, respectively. These schemes are
the bases and prerequisites for big graph partitioning. Due to the difficulty in acquiring full big graph
information, we then design 8 streaming heuristics to partitioning a big graph during the process of
loading its data from external disks into memory. Each of these heuristics decides where to assign every
vertex in the stream based on the information calculated by one of the above 3 schemes. At last, we
demonstrate the performance and flexibility of our heuristics in partitioning real and synthetic graph
datasets on a medium-sized cluster. The characteristics of arbitrary proportions of our approach makes it
have a wide range of applications.
Keywords: Graph partitioning ، Arbitrary proportions ، Measurement scheme ، Streaming heuristic ، Metric |
مقاله انگلیسی |