PRACTISE -- Demonstrating a Neural Network Based Framework for Robust Prediction of Data Center Workload

Abstract

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 the benefits of PRACTISE in two scenarios: i) using recorded resource utilization traces from private cloud data centers, and ii) using real-time data collected from live data center systems.