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Generative Machine Learning for Resource-Aware 5G and IoT Systems

: Piatkowski, Nico; Mueller-Roemer, Johannes; Hasse, Peter; Bachorek, Adam; Werner, Tim; Birnstill, Pascal; Morgenstern, Andreas; Stobbe, Lutz

Postprint urn:nbn:de:0011-n-6380157 (4.2 MByte PDF)
MD5 Fingerprint: cb0594ead51939b6ab25ed62064f8e67
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Created on: 3.9.2021

Institute of Electrical and Electronics Engineers -IEEE-:
IEEE International Conference on Communications Workshops (ICC Workshops) 2021. Proceedings : Virtual Conference, 14 - 23 June 2021
Piscataway, NJ: IEEE, 2021
ISBN: 978-1-7281-9441-7
ISBN: 978-1-7281-9442-4
6 pp.
International Conference on Communications (ICC) <2021, Online>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
01IS18038B; TRAICT
Conference Paper, Electronic Publication
Fraunhofer IGD ()
Fraunhofer IOSB ()
Fraunhofer IESE ()
Fraunhofer IAIS ()
Fraunhofer FOKUS ()
Fraunhofer IZM ()
Lead Topic: Digitized Work; Research Line: Machine Learning (ML); Machine learning; Energy efficiency; Communication services and networks; generative model; probabilistic graphical model; Internet of Things; 5G core; energy model

Extrapolations predict that the sheer number of Internet-of-Things (IoT) devices will exceed 40 billion in the next five years. Hand-crafting specialized energy models and monitoring sub-systems for each type of device is error prone, costly, and sometimes infeasible. In order to detect abnormal or faulty behavior as well as inefficient resource usage autonomously, it is of tremendous importance to endow upcoming IoT and 5G devices with sufficient intelligence to deduce an energy model from their own resource usage data. Such models can in-turn be applied to predict upcoming resource consumption and to detect system behavior that deviates from normal states. To this end, we investigate a special class of undirected probabilistic graphical model, the so-called integer Markov random fields (IntMRF). On the one hand, this model learns a full generative probability distribution over all possible states of the system—allowing us to predict system states and to measure the probability of observed states. On the other hand, IntMRFs are themselves designed to consume as less resources as possible—e.g., faithful modelling of systems with an exponentially large number of states, by using only 8-bit unsigned integer arithmetic and less than 16KB memory. We explain how IntMRFs can be applied to model the resource consumption and the system behavior of an IoT device and a 5G core network component, both under various workloads. Our results suggest, that the machine learning model can represent important characteristics of our two test systems and deliver reasonable predictions of the power consumption.