@misc{699,
  author       = {{Sundermeier, Jannik}},
  publisher    = {{Universität Paderborn}},
  title        = {{{Routing in Hybrid Communication Networks with Holes - Considering Bounding Boxes as Hole Abstractions}}},
  year         = {{2017}},
}

@inproceedings{70,
  author       = {{Feldkord, Björn and Markarian, Christine and Meyer auf der Heide, Friedhelm}},
  booktitle    = {{Proceedings of the 11th Annual International Conference on Combinatorial Optimization and Applications (COCOA)}},
  pages        = {{17 -- 31}},
  title        = {{{Price Fluctuations in Online Leasing}}},
  doi          = {{10.1007/978-3-319-71147-8_2}},
  year         = {{2017}},
}

@misc{700,
  author       = {{Knollmann, Till}},
  publisher    = {{Universität Paderborn}},
  title        = {{{A Self-Stabilizing Protocol for Graphs of Diameter Two}}},
  year         = {{2017}},
}

@misc{701,
  author       = {{Götte, Thorsten}},
  publisher    = {{Universität Paderborn}},
  title        = {{{Self-Stabilizing Spanners for Tree Metrics}}},
  year         = {{2017}},
}

@article{7011,
  author       = {{Hottung, André and Tanaka, Shunji and Tierney, Kevin}},
  journal      = {{CoRR abs/1709.09972}},
  title        = {{{Deep Learning Assisted Heuristic Tree Search for the Container Pre-marshalling Problem}}},
  year         = {{2017}},
}

@article{7012,
  author       = {{Fazal-Baqaie, Masud and Güldali, Baris and Oberthür, Simon}},
  journal      = {{CSE@SE 2017}},
  pages        = {{18--21}},
  title        = {{{Towards DevOps in Multi-provider Projects}}},
  year         = {{2017}},
}

@phdthesis{703,
  author       = {{Podlipyan, Pavel}},
  publisher    = {{Universität Paderborn}},
  title        = {{{Local Algorithms for the Continuous Gathering Problem}}},
  doi          = {{10.17619/UNIPB/1-230}},
  year         = {{2017}},
}

@phdthesis{704,
  author       = {{Riechers, Sören}},
  publisher    = {{Universität Paderborn}},
  title        = {{{Scheduling with Scarce Resources}}},
  doi          = {{10.17619/UNIPB/1-231}},
  year         = {{2017}},
}

@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{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}},
}

@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}},
}

