Partitioning large-scale artificial society on distributed cluster with statistical movement graph

التفاصيل البيبلوغرافية
العنوان: Partitioning large-scale artificial society on distributed cluster with statistical movement graph
المؤلفون: Dandan Ning, Zhen Li, Bin Chen, Gang Guo, Zhichao Song, Xiaogang Qiu
المصدر: Journal of Statistical Computation and Simulation. 87:3413-3439
بيانات النشر: Informa UK Limited, 2017.
سنة النشر: 2017
مصطلحات موضوعية: Statistics and Probability, 021103 operations research, Theoretical computer science, Cyclostationary process, Stochastic process, Applied Mathematics, Artificial society, 0211 other engineering and technologies, Graph partition, 02 engineering and technology, Graph model, Scheduling (computing), Modeling and Simulation, 0202 electrical engineering, electronic engineering, information engineering, Graph (abstract data type), 020201 artificial intelligence & image processing, Statistics, Probability and Uncertainty, Mathematics
الوصف: Distributed agent-based simulation is a popular method to realize computational experiment on large-scale artificial society. Meanwhile, the partitioning strategy of the artificial society models among hosts is playing an essential role for simulation engine to offer high execution efficiency as it has great impact on the communication overheads and computational load-balancing during simulation. Aiming at the problem, we firstly analyze the execution and scheduling process of agents during simulation and model it as wide-sense cyclostationary random process. Then, a static statistical partitioning model is proposed to obtain the optimal partitioning strategy with minimum average communication cost and load imbalance factor. To solve the static statistical partitioning model, this paper turns it into a graph-partitioning problem. A statistical movement graph-based partitioning algorithm is then devised which generates task graph model by mining the statistical movement information from initializat...
تدمد: 1563-5163
0094-9655
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::9ef99312fa9d6b7b3d7ddb3de403647bTest
https://doi.org/10.1080/00949655.2017.1369540Test
رقم الانضمام: edsair.doi...........9ef99312fa9d6b7b3d7ddb3de403647b
قاعدة البيانات: OpenAIRE
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