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Fog Computing Dynamic Load Balancing Mechanism Based on Graph Repartitioning 被引量:8
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作者 SONG Ningning GONG Chao +1 位作者 AN Xingshuo ZHAN Qiang 《China Communications》 SCIE CSCD 2016年第3期156-164,共9页
Because of cloud computing's high degree of polymerization calculation mode, it can't give full play to the resources of the edge device such as computing, storage, etc. Fog computing can improve the resource ... Because of cloud computing's high degree of polymerization calculation mode, it can't give full play to the resources of the edge device such as computing, storage, etc. Fog computing can improve the resource utilization efficiency of the edge device, and solve the problem about service computing of the delay-sensitive applications. This paper researches on the framework of the fog computing, and adopts Cloud Atomization Technology to turn physical nodes in different levels into virtual machine nodes. On this basis, this paper uses the graph partitioning theory to build the fog computing's load balancing algorithm based on dynamic graph partitioning. The simulation results show that the framework of the fog computing after Cloud Atomization can build the system network flexibly, and dynamic load balancing mechanism can effectively configure system resources as well as reducing the consumption of node migration brought by system changes. 展开更多
关键词 fog computing graph partitioning load balancing
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A communication-reduced and computation-balanced framework for fast graph computation 被引量:1
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作者 Yongli CHENG Fang WANG +4 位作者 Hong JIANG Yu HUA Dan FENG Lingling ZHANG Jun ZHOU 《Frontiers of Computer Science》 SCIE EI CSCD 2018年第5期887-907,共21页
The bulk synchronous parallel (BSP) model is very user friendly for coding and debugging parallel graph algorithms. However, existing BSP-based distributed graphprocessing frameworks, such as Pregel, GPS and Giraph,... The bulk synchronous parallel (BSP) model is very user friendly for coding and debugging parallel graph algorithms. However, existing BSP-based distributed graphprocessing frameworks, such as Pregel, GPS and Giraph, routinely suffer from high communication costs. These high communication costs mainly stem from the fine-grained message-passing communication model. In order to address this problem, we propose a new computation model with low communication costs, called LCC-BSE We use this model to design and implement a high-performance distributed graphprocessing framework called LCC-Graph. This framework eliminates high communication costs in existing distributed graph-processing frameworks. Moreover, LCC-Graph also balances the computation workloads among all compute nodes by optimizing graph partitioning, significantly reducing the computation time for each superstep. Evaluation of LCC-Graph on a 32-node cluster, driven by real-world graph datasets, shows that it significantly outperforms existing distributed graph-processing frameworks in terms of runtime, particularly when the system is supported by a highbandwidth network. For example, LCC-Graph achieves an order of magnitude performance improvement over GPS and GraphLab. 展开更多
关键词 graph computation communication decrease computation balance
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