Adaptive performance prediction for distributed data-intensive applications
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Host load prediction using linear models
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Realistic CPU Workloads through Host Load Trace Playback
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This paper evaluates linear models for predicting the Digital Unix five-second host load average from 1 to 30 seconds into the future. A detailed statistical study of a large number of long, fine grain load traces from a variety of real machines leads to consideration of the Box-Jenkins models (AR, MA, ARMA, ARIMA), and the ARFIMA models (due to self-similarity.)These models, as well as a simple windowed-mean scheme, are then rigorously evaluated by running a large number of randomized testcases on the load traces and data-miningtheir results. The main conclusions are that load is consistently predictable to a very useful degree, and that the simpler models such as AR are sufficient for doing this prediction.