@article{706, author = {{Mäcker, Alexander and Malatyali, Manuel and Meyer auf der Heide, Friedhelm and Riechers, Sören}}, journal = {{Journal of Combinatorial Optimization}}, number = {{4}}, pages = {{1168--1194}}, publisher = {{Springer}}, title = {{{Cost-efficient Scheduling on Machines from the Cloud}}}, doi = {{10.1007/s10878-017-0198-x}}, volume = {{36}}, year = {{2017}}, } @phdthesis{707, author = {{Walther, Sven}}, publisher = {{Universität Paderborn}}, title = {{{Knowledge-based Verification of Service Compositions}}}, doi = {{10.17619/UNIPB/1-307}}, year = {{2017}}, } @inproceedings{708, author = {{Schwabe, Arne and Rojas, Elisa and Karl, Holger}}, booktitle = {{2017 {IEEE} Conference on Network Softwarization, NetSoft 2017, Bologna, Italy, July 3-7, 2017}}, location = {{Bologna}}, pages = {{1----5}}, title = {{{Minimizing downtimes: Using dynamic reconfiguration and state management in SDN}}}, doi = {{10.1109/NETSOFT.2017.8004209}}, year = {{2017}}, } @inproceedings{71, abstract = {{Today, software verification tools have reached the maturity to be used for large scale programs. Different tools perform differently well on varying code. A software developer is hence faced with the problem of choosing a tool appropriate for her program at hand. A ranking of tools on programs could facilitate the choice. Such rankings can, however, so far only be obtained by running all considered tools on the program.In this paper, we present a machine learning approach to predicting rankings of tools on programs. The method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for programs. Our kernels employ a graph representation for software source code that mixes elements of control flow and program dependence graphs with abstract syntax trees. Using data sets from the software verification competition SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy (rank correlation > 0.6).}}, author = {{Czech, Mike and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike}}, booktitle = {{Proceedings of the 3rd International Workshop on Software Analytics}}, pages = {{23--26}}, title = {{{Predicting Rankings of Software Verification Tools}}}, doi = {{10.1145/3121257.3121262}}, year = {{2017}}, } @inproceedings{717, abstract = {{In conventional large-scale networks, creation and management of network services are costly and complex tasks that often consume a lot of resources, including time and manpower. Network softwarization and network function virtualization have been introduced to tackle these problems, aiming at decreasing costs and complexity of implementing new services, maintaining the implemented services, and managing available resources in service provisioning platforms and underlying infrastructures. To experience the full potential of these approaches, innovative development support tools and service provisioning environments are needed. To answer these needs, we introduce the architecture of the open-source SONATA system, a service programming, orchestration, and management framework. We present a development toolchain for virtualized network services, fully integrated with a service platform and orchestration system. We introduce the modular and flexible architecture of our system and discuss its main components and features, such as function- and service-specific managers that allow fine-grained service management, slicing support to facilitate multi-tenancy, recursiveness for improved scalability, and full-featured DevOps support.}}, author = {{Dräxler, Sevil and Karl, Holger and Peuster, Manuel and Razzaghi Kouchaksaraei, Hadi and Bredel, Michael and Lessmann, Johannes and Soenen, Thomas and Tavernier, Wouter and Mendel-Brin, Sharon and Xilouris, George}}, booktitle = {{2017 IEEE International Conference on Communications Workshops (ICC Workshops)}}, isbn = {{9781509015252}}, location = {{Paris, France}}, publisher = {{IEEE}}, title = {{{SONATA: Service programming and orchestration for virtualized software networks}}}, doi = {{10.1109/iccw.2017.7962785}}, year = {{2017}}, } @techreport{72, abstract = {{Software verification competitions, such as the annual SV-COMP, evaluate software verification tools with respect to their effectivity and efficiency. Typically, the outcome of a competition is a (possibly category-specific) ranking of the tools. For many applications, such as building portfolio solvers, it would be desirable to have an idea of the (relative) performance of verification tools on a given verification task beforehand, i.e., prior to actually running all tools on the task.In this paper, we present a machine learning approach to predicting rankings of tools on verification tasks. The method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for verification tasks. Our kernels employ a graph representation for software source code that mixes elements of control flow and program dependence graphs with abstract syntax trees. Using data sets from SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy. In particular, our method outperforms a recently proposed feature-based approach of Demyanova et al. (when applied to rank predictions). }}, author = {{Czech, Mike and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike}}, title = {{{Predicting Rankings of Software Verification Competitions}}}, year = {{2017}}, } @inproceedings{723, abstract = {{Developing a virtualized network service does not only involve the implementation and configuration of the network functions it is composed of but also its integration and test with management solutions that will control the service in its production environment. These integration tasks require testbeds that offer the needed network function virtualization infrastructure~(NFVI), like OpenStack, introducing a lot of management and maintenance overheads. Such testbed setups become even more complicated when the multi point-of-presence~(PoP) case, with multiple infrastructure installations, is considered. In this demo, we showcase an emulation platform that executes containerized network services in user-defined multi-PoP topologies. The platform does not only allow network service developers to locally test their services but also to connect real-world management and orchestration solutions to the emulated PoPs. During our interactive demonstration we focus on the integration between the emulated infrastructure and state-of-the-art orchestration solutions like SONATA or OSM.}}, author = {{Peuster, Manuel and Dräxler, Sevil and Razzaghi Kouchaksaraei, Hadi and van Rossem, Steven and Tavernier, Wouter and Karl, Holger}}, booktitle = {{IEEE Conference on Network Softwarization, NetSoft 2017, Bologna, Italy, July 3-7, 2017}}, location = {{Bologna}}, pages = {{1----3}}, title = {{{A flexible multi-pop infrastructure emulator for carrier-grade MANO systems}}}, doi = {{10.1109/NETSOFT.2017.8004250}}, year = {{2017}}, } @misc{74, author = {{Knorr, Christoph}}, publisher = {{Universität Paderborn}}, title = {{{OpenCL-basierte Videoverarbeitung auf heterogenen Rechenknoten}}}, year = {{2017}}, } @inproceedings{8559, author = {{Liebendörfer, Michael and Hochmuth, Reinhard}}, booktitle = {{Didactics of Mathematics in Higher Education as a Scientific Discipline - Conference Proceedings}}, editor = {{Göller, Robin and Biehler, Rolf and Hochmuth, Reinhard and Rück, Hans-Georg}}, pages = {{286--293}}, publisher = {{Universität Kassel}}, title = {{{Perceived Competence and Incompetence in the First Year of Mathematics Studies: Forms and Situations}}}, year = {{2017}}, } @article{8564, author = {{Liebendörfer, Michael and Schukajlow, Stanislaw}}, issn = {{1863-9690, 1863-9704}}, journal = {{ZDM}}, number = {{3}}, pages = {{355--366}}, title = {{{Interest development during the first year at university: do mathematical beliefs predict interest in mathematics?}}}, doi = {{10.1007/s11858-016-0827-3}}, volume = {{49}}, year = {{2017}}, } @inproceedings{87, abstract = {{Management of complex network services requires flexible and efficient service provisioning as well as optimized handling of continuous changes in the workload of the service.To adapt to changes in the demand, service components need to be replicated (scaling) and allocated to physical resources (placement) dynamically. In this paper, we propose a fullyautomated approach to the joint optimization problem of scaling and placement, enabling quick reaction to changes. We formalize the problem, analyze its complexity, and develop two algorithms to solve it. Extensive empirical results show the applicability andeffectiveness of the proposed approach.}}, author = {{Dräxler, Sevil and Karl, Holger and Mann, Zoltan Adam}}, booktitle = {{Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2017)}}, title = {{{Joint Optimization of Scaling and Placement of Virtual Network Services}}}, doi = {{10.1109/CCGRID.2017.25}}, year = {{2017}}, } @inproceedings{8752, abstract = {{In this article we develop a gradient-based algorithm for the solution of multiobjective optimization problems with uncertainties. To this end, an additional condition is derived for the descent direction in order to account for inaccuracies in the gradients and then incorporated into a subdivision algorithm for the computation of global solutions to multiobjective optimization problems. Convergence to a superset of the Pareto set is proved and an upper bound for the maximal distance to the set of substationary points is given. Besides the applicability to problems with uncertainties, the algorithm is developed with the intention to use it in combination with model order reduction techniques in order to efficiently solve PDE-constrained multiobjective optimization problems.}}, author = {{Peitz, Sebastian and Dellnitz, Michael}}, booktitle = {{NEO 2016}}, isbn = {{9783319640624}}, issn = {{1860-949X}}, pages = {{159--182}}, title = {{{Gradient-Based Multiobjective Optimization with Uncertainties}}}, doi = {{10.1007/978-3-319-64063-1_7}}, year = {{2017}}, } @misc{88, author = {{Ganesh Athreya, Advait}}, publisher = {{Universität Paderborn}}, title = {{{Instantiating a Predicate Encryption Scheme via Pair Encodings}}}, year = {{2017}}, } @misc{8843, author = {{Jovanovikj, Ivan}}, title = {{{Presentation: Framework for Constructing Context-Specific Migration Methods for Test Cases}}}, year = {{2017}}, } @article{9607, author = {{Kakvi, Saqib and Kiltz, Eike}}, issn = {{0933-2790}}, journal = {{Journal of Cryptology}}, pages = {{276--306}}, title = {{{Optimal Security Proofs for Full Domain Hash, Revisited}}}, doi = {{10.1007/s00145-017-9257-9}}, year = {{2017}}, } @inproceedings{97, abstract = {{Bridging the gap between informal, imprecise, and vague user requirements descriptions and precise formalized specifications is the main task of requirements engineering. Techniques such as interviews or story telling are used when requirements engineers try to identify a user's needs. The requirements specification process is typically done in a dialogue between users, domain experts, and requirements engineers. In our research, we aim at automating the specification of requirements. The idea is to distinguish between untrained users and trained users, and to exploit domain knowledge learned from previous runs of our system. We let untrained users provide unstructured natural language descriptions, while we allow trained users to provide examples of behavioral descriptions. In both cases, our goal is to synthesize formal requirements models similar to statecharts. From requirements specification processes with trained users, behavioral ontologies are learned which are later used to support the requirements specification process for untrained users. Our research method is original in combining natural language processing and search-based techniques for the synthesis of requirements specifications. Our work is embedded in a larger project that aims at automating the whole software development and deployment process in envisioned future software service markets.}}, author = {{van Rooijen, Lorijn and Bäumer, Frederik Simon and Platenius, Marie Christin and Geierhos, Michaela and Hamann, Heiko and Engels, Gregor}}, booktitle = {{2017 IEEE 25th International Requirements Engineering Conference Workshops (REW)}}, isbn = {{978-1-5386-3489-9}}, keywords = {{Software, Unified modeling language, Requirements engineering, Ontologies, Search problems, Natural languages}}, location = {{Lisbon, Portugal}}, pages = {{379--385}}, publisher = {{IEEE}}, title = {{{From User Demand to Software Service: Using Machine Learning to Automate the Requirements Specification Process}}}, doi = {{10.1109/REW.2017.26}}, year = {{2017}}, } @inproceedings{981, abstract = {{Benchmarking and profiling virtual network functions (VNFs) generates input knowledge for resource management decisions taken by management and orchestration systems. Such VNFs are usually not executed in isolation but are often deployed as part of a service function chain (SFC) that connects single functions into complex structures. To manage such chains, isolated performance profiles of single functions have to be combined to get insights into the overall behavior of an SFC. This becomes particularly challenging in highly agile DevOps environments in which profiling processes need to be fully automated and detailed insights about a chain's internal structures are not always available. In this paper, we introduce a fully automatable, flexible, and platform-agnostic profiling system that allows to profile entire SFCs at once. This obviates manual modeling procedures to combine profiling results from single VNFs to reflect SFC performance. We use a case study with different SFC configurations to show that it is hard to model the resulting SFC performance based on single-VNF measurements and that performance interactions between real, non-trivial functions that are deployed in a chain exist. }}, author = {{Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE Conference on Network Function Virtualisation and Software Defined Networks (NFV-SDN)}}, location = {{Berlin}}, title = {{{Profile Your Chains, Not Functions. Automated Network Service Profiling in DevOps Environments}}}, doi = {{10.1109/NFV-SDN.2017.8169826}}, year = {{2017}}, } @inproceedings{983, author = {{Auroux, Sébastien and Scholz, S. and Karl, Holger}}, booktitle = {{Proc. European Wireless}}, title = {{{Assessing Genetic Algorithms for Placing Flow Processing-aware Control Applications}}}, year = {{2017}}, } @article{9919, abstract = {{This is a study of a combined load restoration and generator start-up procedure. The procedure is structured into three stages according to the power system status and the goal of load restoration. Moreover, for each load restoration stage, the proposed algorithm determines a load restoration sequence by considering renewable energy such as solar and wind park to achieve objective functions. The validity and performance of the proposed algorithm is demonstrated through simulations using IEEE-39 network.}}, author = {{Shen, Cong and Kaufmann, Paul and Braun, Martin}}, journal = {{Elsevier International Journal of Electrical Power and Energy Systems (IJEPES)}}, keywords = {{Load restorationRestoration stageRenewable energyVoltage/frequency fluctuations}}, pages = {{287--299}}, title = {{{Three-Stage Power System Restoration Methodology Considering Renewable Energies}}}, doi = {{10.1016/j.ijepes.2017.07.007}}, volume = {{94}}, year = {{2017}}, } @inproceedings{5204, author = {{Späth, Johannes and Ali, Karim and Bodden, Eric}}, booktitle = {{2017 International Conference on Object-Oriented Programming, Languages and Applications (OOPSLA/SPLASH)}}, keywords = {{ATTRACT, ITSECWEBSITE, CROSSING}}, publisher = {{ACM Press}}, title = {{{IDEal: Efficient and Precise Alias-aware Dataflow Analysis}}}, year = {{2017}}, }