TY - JOUR
AB - Model predictive control is a prominent approach to construct a feedback
control loop for dynamical systems. Due to real-time constraints, the major
challenge in MPC is to solve model-based optimal control problems in a very
short amount of time. For linear-quadratic problems, Bemporad et al. have
proposed an explicit formulation where the underlying optimization problems are
solved a priori in an offline phase. In this article, we present an extension
of this concept in two significant ways. We consider nonlinear problems and -
more importantly - problems with multiple conflicting objective functions. In
the offline phase, we build a library of Pareto optimal solutions from which we
then obtain a valid compromise solution in the online phase according to a
decision maker's preference. Since the standard multi-parametric programming
approach is no longer valid in this situation, we instead use interpolation
between different entries of the library. To reduce the number of problems that
have to be solved in the offline phase, we exploit symmetries in the dynamical
system and the corresponding multiobjective optimal control problem. The
results are verified using two different examples from autonomous driving.
AU - Ober-Blöbaum, Sina
AU - Peitz, Sebastian
ID - 16294
IS - 2
JF - International Journal of Robust and Nonlinear Control
TI - Explicit multiobjective model predictive control for nonlinear systems with symmetries
VL - 31
ER -
TY - CONF
AB - Datacenter applications have different resource requirements from network and developing flow scheduling heuristics for every workload is practically infeasible. In this paper, we show that deep reinforcement learning (RL) can be used to efficiently learn flow scheduling policies for different workloads without manual feature engineering. Specifically, we present LFS, which learns to optimize a high-level performance objective, e.g., maximize the number of flow admissions while meeting the deadlines. The LFS scheduler is trained through deep RL to learn a scheduling policy on continuous online flow arrivals. The evaluation results show that the trained LFS scheduler admits 1.05x more flows than the greedy flow scheduling heuristics under varying network load.
AU - Hasnain, Asif
AU - Karl, Holger
ID - 20125
KW - Flow scheduling
KW - Deadlines
KW - Reinforcement learning
T2 - 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)
TI - Learning Flow Scheduling
ER -
TY - THES
AU - Löken, Nils
ID - 15482
TI - Cryptography for the Crowd — A Study of Cryptographic Schemes with Applications to Crowd Work
ER -
TY - JOUR
AB - We derive a data-driven method for the approximation of the Koopman generator called gEDMD, which can be regarded as a straightforward extension of EDMD (extended dynamic mode decomposition). This approach is applicable to deterministic and stochastic dynamical systems. It can be used for computing eigenvalues, eigenfunctions, and modes of the generator and for system identification. In addition to learning the governing equations of deterministic systems, which then reduces to SINDy (sparse identification of nonlinear dynamics), it is possible to identify the drift and diffusion terms of stochastic differential equations from data. Moreover, we apply gEDMD to derive coarse-grained models of high-dimensional systems, and also to determine efficient model predictive control strategies. We highlight relationships with other methods and demonstrate the efficacy of the proposed methods using several guiding examples and prototypical molecular dynamics problems.
AU - Klus, Stefan
AU - Nüske, Feliks
AU - Peitz, Sebastian
AU - Niemann, Jan-Hendrik
AU - Clementi, Cecilia
AU - Schütte, Christof
ID - 16288
JF - Physica D: Nonlinear Phenomena
SN - 0167-2789
TI - Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
VL - 406
ER -
TY - CONF
AU - Pauck, Felix
AU - Bodden, Eric
AU - Wehrheim, Heike
ED - Felderer, Michael
ED - Hasselbring, Wilhelm
ED - Rabiser, Rick
ED - Jung, Reiner
ID - 16214
T2 - Software Engineering 2020, Fachtagung des GI-Fachbereichs Softwaretechnik, 24.-28. Februar 2020, Innsbruck, Austria
TI - Reproducing Taint-Analysis Results with ReproDroid
VL - {P-300}
ER -
TY - CONF
AB - Network function virtualization (NFV) proposes
to replace physical middleboxes with more flexible virtual
network functions (VNFs). To dynamically adjust to everchanging
traffic demands, VNFs have to be instantiated and
their allocated resources have to be adjusted on demand.
Deciding the amount of allocated resources is non-trivial.
Existing optimization approaches often assume fixed resource
requirements for each VNF instance. However, this can easily
lead to either waste of resources or bad service quality if too
many or too few resources are allocated.
To solve this problem, we train machine learning models
on real VNF data, containing measurements of performance
and resource requirements. For each VNF, the trained models
can then accurately predict the required resources to handle
a certain traffic load. We integrate these machine learning
models into an algorithm for joint VNF scaling and placement
and evaluate their impact on resulting VNF placements. Our
evaluation based on real-world data shows that using suitable
machine learning models effectively avoids over- and underallocation
of resources, leading to up to 12 times lower resource
consumption and better service quality with up to 4.5 times
lower total delay than using standard fixed resource allocation.
AU - Schneider, Stefan Balthasar
AU - Satheeschandran, Narayanan Puthenpurayil
AU - Peuster, Manuel
AU - Karl, Holger
ID - 16219
T2 - IEEE Conference on Network Softwarization (NetSoft)
TI - Machine Learning for Dynamic Resource Allocation in Network Function Virtualization
ER -
TY - JOUR
AU - Bellman, K.
AU - Dutt, N.
AU - Esterle, L.
AU - Herkersdorf, A.
AU - Jantsch, A.
AU - Landauer, C.
AU - R. Lewis, P.
AU - Platzner, Marco
AU - TaheriNejad, N.
AU - Tammemäe, K.
ID - 15836
JF - ACM Transactions on Cyber-Physical Systems
TI - Self-aware Cyber-Physical Systems
VL - Accepted for Publication
ER -
TY - CONF
AU - Krauter, Stefan
AU - Zhang, L.
ID - 16858
T2 - Proceedings of the 14 th International Renewable Energy Storage Conference, Düsseldorf (Deutschland), 10.–12. März 2020 (verschoben auf 16.–18. März 2021)
TI - Probability of Correct Decision–Making at Triggering of Load-Shifting Intended for low CO 2 -intensity and low EEX trading price via simple Grid Frequency Monitoring
ER -
TY - CONF
AU - Krumme, Matthias
AU - Webersen, Manuel
AU - Claes, Leander
AU - Webersen, Yvonne
ID - 13943
T2 - Fortschritte der Akustik - DAGA 2020
TI - Analoge Klangsynthese zur Vermittlung von Grundkenntnissen der Signalverarbeitung an Studierende nicht-technischer Fachrichtungen
ER -
TY - JOUR
AU - Liebendörfer, Michael
AU - Göller, Robin
AU - Biehler, Rolf
AU - Hochmuth, Reinhard
AU - Kortemeyer, Jörg
AU - Ostsieker, Laura
AU - Rode, Jana
AU - Schaper, Niclas
ID - 16961
JF - Journal für Mathematik-Didaktik
SN - 0173-5322
TI - LimSt – Ein Fragebogen zur Erhebung von Lernstrategien im mathematikhaltigen Studium
ER -