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Accurate and Fast Recovery of Network Monitoring Data With GPU-Accelerated Tensor Completion

机译:使用GPU加速的张量完成准确快速地恢复网络监控数据

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Monitoring the performance of a large network would involve a high measurement cost. To reduce the overhead, sparse network monitoring techniques may be applied to select paths or time intervals to take the measurements, while the remaining monitoring data can be inferred leveraging the spatial-temporal correlations among data. The quality of missing data recovery, however, highly relies on the specific inference technique adopted. Tensor completion is a promising technique for more accurate missing data inference by exploiting the multi-dimensional data structure. However, data processing for higher dimensional tensors involves a large amount of computation, which prevents conventional tensor completion algorithms from practical application in the presence of large amount of data. This work takes the initiative to investigate the potential and methodologies of performing parallel processing for high-speed and high accuracy tensor completion over Graphics Processing Units (GPUs). We propose a GPU-accelerated parallel Tensor Completion scheme (GPU-TC) for accurate and fast recovery of missing data. To improve the data recovery accuracy and speed, we propose three novel techniques to well exploit the tensor factorization structure and the GPU features: grid-based tensor partition, independent task assignment based on Fisher-Yates shuffle, sphere facilitated and memory-correlated scheduling. We have conducted extensive experiments using network traffic trace data to compare the proposed GPU-TC with the state of art tensor completion algorithms and matrix-based algorithms. The experimental results demonstrate that GPU-TC can achieve significantly better performance in terms of two relative error ratio metrics and computation time.
机译:监控大型网络的性能将涉及高测量成本。为了减少开销,可以应用稀疏网络监视技术来选择路径或时间间隔以进行测量,而可以推断剩余的监视数据利用数据之间的空间时间相关性。然而,缺失数据恢复的质量非常依赖于所采用的特定推理技术。张量完成是一种希望通过利用多维数据结构更准确地缺少数据推断的有希望的技术。然而,对较高维度张量的数据处理涉及大量计算,这防止了在存在大量数据的情况下从实际应用中防止传统的张量完成算法。这项工作主动地研究了在图形处理单元(GPU)上进行高速和高精度张力完成执行并行处理的潜在和方法。我们提出了一种GPU加速的并行张量完成方案(GPU-TC),用于准确快速地恢复缺失数据。为了提高数据恢复精度和速度,我们提出了三种新颖的技术来利用张量分解结构和GPU特征:基于网格的张量分区,基于Fisher-yates Shuffle的独立任务分配,球体促进和内存相关调度。我们使用网络流量跟踪数据进行了广泛的实验,以将所提出的GPU-TC与最先进的GPU-TC与最新的抗度完成算法和基于矩阵的算法进行比较。实验结果表明,GPU-TC就两个相对误差比度量和计算时间而言,可以实现显着更好的性能。

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