|Institution:||Technische Universitt Darmstadt|
|Full text PDF:||http://tuprints.ulb.tu-darmstadt.de/6295/|
The demand on communication networks has increased over the past years and is predicted to continue for the foreseeable future [Cis16]. Cellular network access with a compound annual growth rate (CAGR) of 53 % is the main area of growth [Cis16]. This affects the network quality, bringing current network technologies to their limits [Qua13]. Future network standards like 5G promise to satisfy this demand, providing a 1000-fold increase in data rates and latencies as low as 1 ms [Qua13].With information and communications technology (ICT) causing 10 % of the global energy consumption [Mil13], the increasing demand is also reflected in a growing energy consumption of communication networks [BBD+11]. The major contributor to the network power consumption are home gateways (HGWs) in the fixed access network, and mobile base stations in the cellular network [VHD+11]. This trend is predicted to continue [BBD+11].To assess and optimize the power consumption of communication networks, power models of the involved devices are required. Using these, the efficiency of proposed optimization approaches can be assessed before deployment. A number of power models of conventional network equipment for different device classes can be derived from literature. Still, models of new device classes such as single-board computers (SBCs) and OpenFlow switches are not available. For each class, representative power models of several device types are presented. Further, the power consumption caused by new communication protocols such as MultiPath TCP (MPTCP) is not fully analyzed yet. This work is, to the best of the authors knowledge, the first to publish SBC and OpenFlow power models and contributes to the understanding of MPTCP power consumption during constant bit rate (CBR) streaming.For the analysis of the power consumption, also the knowledge of network performance is required, as it defines relative costs and the maximum number of supported users. This is well known and comparatively simple in fixed networks, but more challenging in a wireless context. A number of approaches are described in literature and implemented as commercial software (e.g. [SSM13; OpS]), but the data required for analysis and optimization is not available. Hence, extensive measurements of the cellular network are conducted in this work. The location-based availability and performance of cellular and WiFi networks are assessed in a crowd-sensing study. Based on measurements on regional trains, the predictability of the cellular service quality based only on available network technology and latency is shown to be feasible. Anomalies observed within the crowd-sensing data are analyzed using dedicated, stationary measurements. The main observation is that network management decisions have significant effects on end-to-end performance. By allocating users to random points of presence (PoPs)/exit gateways of the mobile network operator (MNO), the latency compared to the best observed allocation is increased by more than 58 % in over 80 % of the time.Advisors/Committee Members: Hausheer, David (advisor), Widmer, Joerg (advisor).