1.
Loh, F., Geißler, S., Schaible, F., Hoßfeld, T.: Talk to Me: Investigating the Traffic Characteristics of Amazon Echo Dot and Google Home. International Conference on Communications and Electronics, ICCE (2021).
The application area of smart speakers is steadily increasing with the Amazon Echo family and Google Home being brand leaders. Use cases include, among others, updating the calendar, home automation, or simply assisting users in their every day life. With the increasing amount of devices, the traffic generation and requesting process becomes a relevant subject to be studied in order to create traffic models and predict the impact on networks by these types of devices. Furthermore, with a detailed understanding of the devices, service quality can be monitored and improved for the end user. For that reason, in this work the requesting and traffic generation process of the Amazon Echo Dot and Google Home are studied and the generated network load of both devices is compared. With the insights of this study and additional device usage statistics, detailed traffic and usage models can be created.
2.
Geissler, S., Lange, S., Tran-Gia, P., Hoßfeld, T.: Discrete-time Analysis of Multicomponent GI/GI/1 Queueing Networks. Conference on Networked Systems (NetSys) (2021).
In this work, we provide initial insights regarding the error introduced into multicomponent queueing systems by assuming the departure processes of arbitrary GI/GI/1-∞ queues to be renewal processes. To this end, we compute the sojourn time distribution as well as departure distributions of a linear chain of queueing components and compare the results to a simulation of the same system. By applying the renewal approximation, potential autocorrelations of the departure processes are lost. We investigate the magnitude of this error regarding both the sojourn time as well as interdeparture time distributions for a broad set of parameters. Although more indepth studies are needed, our results show that both distributions can be closely approximated, which allows the application of the model to asses the performance of real world NFV function chains.
3.
Geißler, S., Wamser, F., Bauer, W., Krolikowski, M., Gebert, S., Hoßfeld, T.: Signaling Traffic in Internet-of-Things Mobile Networks. IFIP/IEEE International Symposium on Integrated Network Management (IM 2021). , Bordeaux, France (Virtual Conference) (2021).
The Internet of Things (IoT) is expected to play an increasingly important role in next-generation mobile networks. Detailed knowledge of this type of traffic and the associated signaling in those networks is of interest to operators and standardization in order to run these networks successfully. The aim of this study is to characterize IoT signaling traffic for mobile communication networks (i) from a network operator’s point of view, (ii) at device level, and (iii) for the establishment of data connections. General statistics on signaling and device behavior, such as the observed IoT traffic volume and message composition, are given before device classes with statistically different signaling behavior are identified. Finally, by characterizing the aggre- gated signaling traffic, we show that the Markov assumption, widespread in standardization and literature, regarding the aggregated arrival process for data connections does not apply in reality.
4.
Geissler, S., Herrnleben, S., Bauer, R., Grigorjew, A., Jarschel, M., Zinner, T.: The Power of Composition: Abstracting a Multi-Device SDN Data Path Through a Single API. IEEE Transactions on Network and Service Management. (2019).
5.
Wamser, F., Tran-Gia, P., Geißler, S., Hoßfeld, T.: Modeling of Traffic Flows in Internet of Things Using Renewal Approximation. International Conference on Optimization and Decision Science (ODS2019) (2019).
This paper proposes a versatile approach to model aggregated traffic flows in the Internet of Things (IoT) using renewal approximation. The modeled traffic originates from a large number of sources or devices consisting of a set of sensors mixed with classical elastic random traffic modeled as Poisson arrival process. The work shows the exact derivation in the simple case for periodic sensors. It shows further results in the mixed case with periodic sensors and a background process. The renewal approximation allows to derive the required number of sensors such that the aggregated traffic can be approximated as Poisson process.
