Research on Cost-Sensitive Communication Models over Distributed Data Streams Processing

Aiping Li, Li Tian, Yan Jia, Shuqiang Yang
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引用次数: 0

Abstract

Large-scaled distributed monitoring systems are in face of the challenge of massive data and resource restriction. Prediction models can be used to reduce communication cost over the net. A framework is proposed which provides a mechanism to maintain adaptive prediction models that significantly reduce communication cost over the distributed environment while still guaranteeing sufficient precision of query results. Prediction models are also proposed to process prediction queries over future data streams in this paper. Three particular models, static model, linear model and acceleration model, and the corresponding tuning schemas are given. Experimentations are performed based on the simulated data and ocean air temperature data measured by TAO (tropical atmosphere ocean). Analytical and experimental evidence show that the proposed approach significantly reduces overall communication cost and performs well over prediction queries.
分布式数据流处理中成本敏感通信模型的研究
大规模分布式监控系统面临着海量数据和资源限制的挑战。预测模型可以用来降低网络上的通信成本。提出了一个框架,该框架提供了一种维护自适应预测模型的机制,在保证查询结果足够精度的同时,显著降低了分布式环境下的通信成本。本文还提出了对未来数据流进行预测查询的预测模型。给出了静态模型、线性模型和加速度模型,以及相应的调优方案。利用TAO(热带大气海洋)的模拟数据和海洋大气温度数据进行了实验。分析和实验证据表明,该方法显著降低了整体通信成本,并且优于预测查询。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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