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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...
Tensor decomposition is a powerful computational tool for multiway data analysis. Many popular tensor decomposition approaches—such as the Tucker decomposition and CANDECOMP/PARAFAC (CP)—amount to multi-linear factorization. They are insufficient to model (i) complex interactions between data entities, (ii) various data types (e.g., missing data and binary data), and (iii) noisy observations and outliers...
Firstly choosing five forecasting model that respectively giving a virtual load forecasting for the same case, then using the decision theory to sift models through odds matrix method, abandon the unsatisfactory models. Finally determine weightings according to each period of time minimum quadratic sum of residual error as objective function. Using the selected models for combination short-term load...
A method of fault location is presented which is based on the traveling-wave scheme for rural MV power networks. There are two steps in this method, the first step is to determine the fault section, and the second is to find the fault point. When a single-phase-to-earth fault happened, injecting a narrow pulse signal with high voltage and detecting the wave signal returned back from the line, we analyze...
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