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Adaptive and Decentralized Operator Placement for In-Network Query Processing

机译:用于网络内查询处理的自适应和分散的操作员位置

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In-network query processing is critical for reducing network traffic when accessing and manipulating sensor data. It requires placing a tree of query operators such as filters and aggregations but also correlations onto sensor nodes in order to minimize the amount of data transmitted in the network. In this paper, we show that this problem is a variant of the task assignment problem for which polynomial algorithms have been developed. These algorithms are however centralized and cannot be used in a sensor network. We describe an adaptive and decentralized algorithm that progressively refines the placement of operators by walking through neighbor nodes. Simulation results illustrate the potential benefits of our approach. They also show that our placement strategy can achieve near optimal placement onto various graph topologies despite the risks of local minima.
机译:在访问和操作传感器数据时,网络内查询处理对于减少网络流量至关重要。它需要将一个查询运算符(如过滤器和聚合)放置到传感器节点上的相关性,以便最小化网络中传输的数据量。在本文中,我们表明这个问题是开发了多项式算法的任务分配问题的变体。然而,这些算法是集中的并且不能在传感器网络中使用。我们描述了一种自适应和分散的算法,通过邻居节点来逐步地改进运营商的放置。仿真结果说明了我们方法的潜在好处。他们还表明,尽管存在局部最小值的风险,我们的位置策略可以在各种图形拓扑上实现接近最佳位置。

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