@inproceedings{20693,
  abstract     = {{In practical, large-scale networks, services are requested
by users across the globe, e.g., for video streaming.
Services consist of multiple interconnected components such as
microservices in a service mesh. Coordinating these services
requires scaling them according to continuously changing user
demand, deploying instances at the edge close to their users,
and routing traffic efficiently between users and connected instances.
Network and service coordination is commonly addressed
through centralized approaches, where a single coordinator
knows everything and coordinates the entire network globally.
While such centralized approaches can reach global optima, they
do not scale to large, realistic networks. In contrast, distributed
approaches scale well, but sacrifice solution quality due to their
limited scope of knowledge and coordination decisions.

To this end, we propose a hierarchical coordination approach
that combines the good solution quality of centralized approaches
with the scalability of distributed approaches. In doing so, we divide
the network into multiple hierarchical domains and optimize
coordination in a top-down manner. We compare our hierarchical
with a centralized approach in an extensive evaluation on a real-world
network topology. Our results indicate that hierarchical
coordination can find close-to-optimal solutions in a fraction of
the runtime of centralized approaches.}},
  author       = {{Schneider, Stefan Balthasar and Jürgens, Mirko and Karl, Holger}},
  booktitle    = {{IFIP/IEEE International Symposium on Integrated Network Management (IM)}},
  keywords     = {{network management, service management, coordination, hierarchical, scalability, nfv}},
  location     = {{Bordeaux, France}},
  publisher    = {{IFIP/IEEE}},
  title        = {{{Divide and Conquer: Hierarchical Network and Service Coordination}}},
  year         = {{2021}},
}

@techreport{35889,
  abstract     = {{Network and service coordination is important to provide modern services consisting of multiple interconnected components, e.g., in 5G, network function virtualization (NFV), or cloud and edge computing. In this paper, I outline my dissertation research, which proposes six approaches to automate such network and service coordination. All approaches dynamically react to the current demand and optimize coordination for high service quality and low costs. The approaches range from centralized to distributed methods and from conventional heuristic algorithms and mixed-integer linear programs to machine learning approaches using supervised and reinforcement learning. I briefly discuss their main ideas and advantages over other state-of-the-art approaches and compare strengths and weaknesses.}},
  author       = {{Schneider, Stefan Balthasar}},
  keywords     = {{nfv, coordination, machine learning, reinforcement learning, phd, digest}},
  title        = {{{Conventional and Machine Learning Approaches for Network and Service Coordination}}},
  year         = {{2021}},
}

@inproceedings{19607,
  abstract     = {{Modern services consist of modular, interconnected
components, e.g., microservices forming a service mesh. To
dynamically adjust to ever-changing service demands, service
components have to be instantiated on nodes across the network.
Incoming flows requesting a service then need to be routed
through the deployed instances while considering node and link
capacities. Ultimately, the goal is to maximize the successfully
served flows and Quality of Service (QoS) through online service
coordination. Current approaches for service coordination are
usually centralized, assuming up-to-date global knowledge and
making global decisions for all nodes in the network. Such global
knowledge and centralized decisions are not realistic in practical
large-scale networks.

To solve this problem, we propose two algorithms for fully
distributed service coordination. The proposed algorithms can be
executed individually at each node in parallel and require only
very limited global knowledge. We compare and evaluate both
algorithms with a state-of-the-art centralized approach in extensive
simulations on a large-scale, real-world network topology.
Our results indicate that the two algorithms can compete with
centralized approaches in terms of solution quality but require
less global knowledge and are magnitudes faster (more than
100x).}},
  author       = {{Schneider, Stefan Balthasar and Klenner, Lars Dietrich and Karl, Holger}},
  booktitle    = {{IEEE International Conference on Network and Service Management (CNSM)}},
  keywords     = {{distributed management, service coordination, network coordination, nfv, softwarization, orchestration}},
  publisher    = {{IEEE}},
  title        = {{{Every Node for Itself: Fully Distributed Service Coordination}}},
  year         = {{2020}},
}

@inproceedings{19609,
  abstract     = {{Modern services comprise interconnected components,
e.g., microservices in a service mesh, that can scale and
run on multiple nodes across the network on demand. To process
incoming traffic, service components have to be instantiated and
traffic assigned to these instances, taking capacities and changing
demands into account. This challenge is usually solved with
custom approaches designed by experts. While this typically
works well for the considered scenario, the models often rely
on unrealistic assumptions or on knowledge that is not available
in practice (e.g., a priori knowledge).

We propose a novel deep reinforcement learning approach that
learns how to best coordinate services and is geared towards
realistic assumptions. It interacts with the network and relies on
available, possibly delayed monitoring information. Rather than
defining a complex model or an algorithm how to achieve an
objective, our model-free approach adapts to various objectives
and traffic patterns. An agent is trained offline without expert
knowledge and then applied online with minimal overhead. Compared
to a state-of-the-art heuristic, it significantly improves flow
throughput and overall network utility on real-world network
topologies and traffic traces. It also learns to optimize different
objectives, generalizes to scenarios with unseen, stochastic traffic
patterns, and scales to large real-world networks.}},
  author       = {{Schneider, Stefan Balthasar and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin and Hecker, Artur}},
  booktitle    = {{IEEE International Conference on Network and Service Management (CNSM)}},
  keywords     = {{self-driving networks, self-learning, network coordination, service coordination, reinforcement learning, deep learning, nfv}},
  publisher    = {{IEEE}},
  title        = {{{Self-Driving Network and Service Coordination Using Deep Reinforcement Learning}}},
  year         = {{2020}},
}

@inproceedings{9270,
  abstract     = {{As 5G and network function virtualization (NFV) are maturing, it becomes crucial to demonstrate their feasibility and benefits by means of vertical scenarios. While 5GPPP has identified smart manufacturing as one of the most important vertical industries, there is still a lack of specific, practical use cases. 

Using the experience from a large-scale manufacturing company, Weidm{\"u}ller Group, we present a detailed use case that reflects the needs of real-world manufacturers. We also propose an architecture with specific network services and virtual network functions (VNFs) that realize the use case in practice. As a proof of concept, we implement the required services and deploy them on an emulation-based prototyping platform. Our experimental results indicate that a fully virtualized smart manufacturing use case is not only feasible but also reduces machine interconnection and configuration time and thus improves productivity by orders of magnitude.}},
  author       = {{Schneider, Stefan Balthasar and Peuster, Manuel and Behnke, Daniel and Marcel, Müller and Bök, Patrick-Benjamin and Karl, Holger}},
  booktitle    = {{European Conference on Networks and Communications (EuCNC)}},
  keywords     = {{5g, vertical, smart manufacturing, nfv}},
  publisher    = {{IEEE}},
  title        = {{{Putting 5G into Production: Realizing a Smart Manufacturing Vertical Scenario}}},
  doi          = {{10.1109/eucnc.2019.8802016}},
  year         = {{2019}},
}

@inproceedings{15368,
  abstract     = {{Service Level Agreements are essential tools enabling clients and telco operators to specify required quality of service. The 5GTANGO NFV platform enables SLAs through policies and custom service lifecycle management components. This allows the operator to trigger certain lifecycle management events for a service, and the network service developer to define how to execute such events (e.g., how to scale). In this demo we will demonstrate this unique 5GTANGO concept using an elastic proxy service supported by a high availability SLA enforced through a range of traffic regimes.}},
  author       = {{Soenen, Thomas and Vicens, Felipe and Bonnet, José and Parada, Carlos and Kapassa, Evgenia and Touloupou, Marious and Fotopulou, Eleni and Zafeiropoulos, Anastasios and Pol, Ana and Kolometsos, Stavros and Xilouris, George and Alemany, Pol and Vilalta, Ricard and Trakadas, Panos and Karkazis, Panos and Peuster, Manuel and Tavernier, Wouter}},
  booktitle    = {{2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)}},
  issn         = {{1573-0077}},
  keywords     = {{5G mobile communication, contracts, quality of service, telecommunication traffic, virtualisation, custom service lifecycle management components, lifecycle management events, network service developer, elastic proxy service, SLA-controlled proxy service, customisable MANO, operator policies, Service Level Agreements, unique 5G TANGO concept, 5G TANGO NFV platform, quality of service, traffic regimes, high availability SLA, Monitoring, Probes, Portals, Quality of service, Tools, Servers, Graphical user interfaces}},
  location     = {{Arlington, VA, USA, USA}},
  pages        = {{707--708}},
  title        = {{{SLA-controlled Proxy Service Through Customisable MANO Supporting Operator Policies}}},
  year         = {{2019}},
}

@inproceedings{13292,
  abstract     = {{Building on 5G and network function virtualization (NFV), smart manufacturing has the potential to drastically increase productivity, reduce cost, and introduce novel, flexible manufacturing services. Current work mostly focuses on high-level scenarios or emulation-based prototype deployments. 

Extending our previous work, we showcase one of the first cloud-native 5G verticals focusing on the deployment of smart manufacturing use cases on production infrastructure. In particular, we use the 5GTANGO service platform to deploy our developed network services on Kubernetes. For this demo, we implemented a series of cloud-native virtualized network functions (VNFs) and created suitable service descriptors. Their light-weight, stateless deployment on Kubernetes enables quick instantiation, scalability, and robustness.}},
  author       = {{Schneider, Stefan Balthasar and Peuster, Manuel and Hannemann, Kai and Behnke, Daniel and Müller, Marcel and Bök, Patrick-Benjamin and Karl, Holger}},
  booktitle    = {{IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) Demo Track}},
  keywords     = {{5G, NFV, Smart Manufacturing, Cloud-Native, Kubernetes}},
  location     = {{Dallas, TX, USA}},
  publisher    = {{IEEE}},
  title        = {{{"Producing Cloud-Native": Smart Manufacturing Use Cases on Kubernetes}}},
  year         = {{2019}},
}