A Neural Network Approach to Forecasting Computing-Resource Exhaustion with Workload
A Neural Network Approach to Forecasting Computing-Resource Exhaustion with Workload
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DOI：
10.1109/qsic.2009.48
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发表时间：
2009-08
期刊：
影响因子：
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通讯作者：
Ke-Xian Xue;Liang Su;Yun-Fei Jia;K. Cai
中科院分区：
文献类型：
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作者：
Ke-Xian Xue;Liang Su;Yun-Fei Jia;K. Cai
软件老化是指应用程序在长时间运行后会出现故障率增加或性能下降的现象。据报道，这种现象通常与计算资源耗尽有密切关系。本文分析了局域网上收集的计算资源使用数据，定量地研究了计算资源耗尽趋势与工作负载之间的关系。首先讨论了工作量的定义，然后训练了一个多层反向传播神经网络，建立了输入（工作量）和输出（计算资源利用率）之间的非线性关系。然后，我们使用训练好的神经网络来预测计算资源的使用，即，释放内存和已使用的交换，并以工作负载作为输入。最后，将结果与文献中报告的工作量影响无关的结果进行基准比较，如非参数统计技术或参数时间序列模型。
Software aging refers to the phenomenon that applications will show growing failure rate or performance degradation after longtime execution. It is reported that this phenomenon usually has close relationship with computing-resource exhaustion. This paper analyzes computing-resource usage data collected on a LAN, and quantitatively investigates the relationship between computing-resource exhaustion trend and workload. First, we discuss the definition of workload, and then a Multi-Layer Back propagation neural network is trained to construct the nonlinear relationship between input (workload) and output (computing-resource usage). Then we use the trained neural network to forecast the computing-resource usage, i.e., free memory and used swap, with workload as its input. Finally, the results were benchmarked against those obtained without regard to influence of workload reported in the literatures, such as non-parametric statistical techniques or parametric time series models.