Title :
Power and performance analysis of network traffic prediction techniques
Author :
Iqbal, Muhammad Faisal ; John, Lizy K.
Author_Institution :
Univ. of Texas at Austin, Austin, TX, USA
Abstract :
We study power and performance characteristics of different traffic predictors for online one-step-ahead predictions. The goal is to identify a predictor with reasonable accuracy and low power consumption. Our experiments on a large number of real network traces indicate that Double Exponential Smoothing and Auto-Regressive Moving Average are low cost predictors with reasonable accuracy.
Keywords :
autoregressive moving average processes; microprocessor chips; multiprocessing systems; network analysis; prediction theory; smoothing methods; autoregressive moving average; double exponential smoothing; low power consumption; network traffic prediction techniques; online one-step-ahead prediction; performance analysis; power analysis; traffic predictor; Accuracy; Approximation methods; Artificial neural networks; Predictive models; Program processors; Smoothing methods; Time series analysis;
Conference_Titel :
Performance Analysis of Systems and Software (ISPASS), 2012 IEEE International Symposium on
Conference_Location :
New Brunswick, NJ
Print_ISBN :
978-1-4673-1143-4
Electronic_ISBN :
978-1-4673-1145-8
DOI :
10.1109/ISPASS.2012.6189212
