Home > Published Issues > 2020 > Volume 15, No. 11, November 2020 >

Energy Savings by Using Traffic Estimation for Dynamic Capacity Adaptation in Communication Network Operations

Andreas Ahrens 1, Christoph Lange 2, and Jelena Zaščerinska 1
1. Hochschule Wismar: University of Applied Sciences: Technology, Business and Design, 23966 Wismar, Germany
2. Hochschule für Technik und Wirtschaft (HTW) Berlin, University of Applied Sciences, 10313 Berlin, Germany

Abstract—Energy efficiency of telecommunication networks plays an essential role in the context of sustainability and climate change – as those networks are large power-consuming distributed infrastructures. Furthermore, also an economical network operation calls for low energy demand. A challenging and crucial task for energy-ef ficient and sustainable network operation is the load-adaptive operation of network elements such as routers, switches and access multiplexers. Since the traffic is temporally fluctuating, load-adaptive control of the network requires a robust traffic demand estimation. This is also of overwhelming importance, as a stable network operation is a central task of network operators – since it is expected by their customers as the service they pay for. Here, Wiener filtering has been identified as a robust solution for reliable traffic demand forecasting on relevant time scales. The results presented in this paper show that the capacity dimensioning based on the proposed Wiener filtering traffic forecasting leads to reliable outcomes in terms of predicted traffic enabling sustainable and efficient network operation.
Index Terms—Network energy efficiency, load-adaptive network operation, traffic estimation, Wiener filtering

Cite: Andreas Ahrens, Christoph Lange, and Jelena Zaščerinska, "Energy Savings by Using Traffic Estimation for Dynamic Capacity Adaptation in Communication Network Operations," Journal of Communications vol. 15, no. 11, pp. 790-795, November 2020. Doi: 10.12720/jcm.15.11.790-795

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.