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We present a web based tool to demonstrate PRACTISE, a neural network based framework for efficient and accurate prediction of server workload time series in data centers. For the evaluation, we focus on resource utilization traces of CPU, memory, disk, and network. Compared with ARIMA and baseline neural network models, PRACTISE achieves significantly smaller average prediction errors. We demonstrate...
We analyze workload traces from production data centers and focus on their VM usage patterns of CPU, memory, disk, and network bandwidth. Burstiness is a clear characteristic of many of these time series: there exist peak loads within clear periodic patterns but also within patterns that do not have clear periodicity. We present PRACTISE, a neural network based framework that can efficiently and accurately...
On the basis of analysis on the characteristics of single phase grounding fault occurred in small current neutral grounding system, a fault location method using Learn Vector Quantization Neural Network is put forward. Combined LVQ Neural Network with C-type of traveling wave location method, the purpose of precise location can be achieved. The simulation results of ATP-EMTP and MATLAB show that the...
On the basis of analysis on the characteristics of single phase grounding fault occurred in small current neutral grounding system, a fault location method using Learn Vector Quantization Neural Network is put forward. Combined LVQ Neural Network with C-type of traveling wave location method, the purpose of precise location can be achieved. A classical BP (Back-Propagation) Neural Network has been...
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