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Stefan Schneider

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 Stefan  Schneider

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+49 5251 60-1753
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+49 5251 60-5377
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O3.170
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Pohlweg 51
33098 Paderborn
Profile

Please refer to my website for the latest information about my research, teaching, and service activities.

 

My current research focuses on network softwarization (NFV, SDN), edge & cloud computing, 5G & beyond. I am also very interested in machine learning and reinforcement learning and use it actively in my research.

I currently work in an industry research project on distributed and robust learning in 5G and beyond. In context of the Software Campus, I lead my own 2-year project called RealVNF on improved coordination of network services in NFV in collaboration with Huawei Munich.

Feel free to contact me anytime if you're interested in my research and/or want to collaborate.

 

Also see my:

Projects

I am currently project lead and coordinator of

I am also currently involved in

  • Distributed and Robust Learning in 5G and Beyond (2020-2021, Industry Research Project)

Previously, I worked on


Liste im Research Information System öffnen

2021

Distributed Online Service Coordination Using Deep Reinforcement Learning

S.B. Schneider, H. Qarawlus, H. Karl, in: IEEE International Conference on Distributed Computing Systems (ICDCS), IEEE, 2021

Services often consist of multiple chained components such as microservices in a service mesh, or machine learning functions in a pipeline. Providing these services requires online coordination including scaling the service, placing instance of all components in the network, scheduling traffic to these instances, and routing traffic through the network. Optimized service coordination is still a hard problem due to many influencing factors such as rapidly arriving user demands and limited node and link capacity. Existing approaches to solve the problem are often built on rigid models and assumptions, tailored to specific scenarios. If the scenario changes and the assumptions no longer hold, they easily break and require manual adjustments by experts. Novel self-learning approaches using deep reinforcement learning (DRL) are promising but still have limitations as they only address simplified versions of the problem and are typically centralized and thus do not scale to practical large-scale networks. To address these issues, we propose a distributed self-learning service coordination approach using DRL. After centralized training, we deploy a distributed DRL agent at each node in the network, making fast coordination decisions locally in parallel with the other nodes. Each agent only observes its direct neighbors and does not need global knowledge. Hence, our approach scales independently from the size of the network. In our extensive evaluation using real-world network topologies and traffic traces, we show that our proposed approach outperforms a state-of-the-art conventional heuristic as well as a centralized DRL approach (60% higher throughput on average) while requiring less time per online decision (1 ms).


Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning

S.B. Schneider, R. Khalili, A. Manzoor, H. Qarawlus, R. Schellenberg, H. Karl, A. Hecker, Transactions on Network and Service Management (2021)

Modern services consist of interconnected components,e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements 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 DeepCoord, 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 on 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, DeepCoord significantly improves flow throughput (up to 76%) and overall network utility (more than 2x) on realworld network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available.


Divide and Conquer: Hierarchical Network and Service Coordination

S.B. Schneider, M. Jürgens, H. Karl, in: IFIP/IEEE International Symposium on Integrated Network Management (IM), IFIP/IEEE, 2021

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.


2020

Machine Learning for Dynamic Resource Allocation in Network Function Virtualization

S.B. Schneider, N.P. Satheeschandran, M. Peuster, H. Karl, in: IEEE Conference on Network Softwarization (NetSoft), IEEE, 2020

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.


Benchmarking and Profiling 5G Verticals' Applications: An Industrial IoT Use Case

A. Zafeiropoulos, E. Fotopoulou, M. Peuster, S.B. Schneider, P. Gouvas, D. Behnke, M. Müller, P. Bök, P. Trakadas, P. Karkazis, H. Karl, in: IEEE Conference on Network Softwarization (NetSoft), 2020


Cloud-Native Threat Detection and Containment for Smart Manufacturing

M. Müller, D. Behnke, P. Bök, S.B. Schneider, M. Peuster, H. Karl, in: IEEE Conference on Network Softwarization (NetSoft) Demo Track, IEEE, 2020

Softwarization facilitates the introduction of smart manufacturing applications in the industry. Manifold devices such as machine computers, Industrial IoT devices, tablets, smartphones and smart glasses are integrated into factory networks to enable shop floor digitalization and big data analysis. To handle the increasing number of devices and the resulting traffic, a flexible and scalable factory network is necessary which can be realized using softwarization technologies like Network Function Virtualization (NFV). However, the security risks increase with the increasing number of new devices, so that cyber security must also be considered in NFV-based networks. Therefore, extending our previous work, we showcase threat detection using a cloud-native NFV-driven intrusion detection system (IDS) that is integrated in our industrial-specific network services. As a result of the threat detection, the affected network service is put into quarantine via automatic network reconfiguration. We use the 5GTANGO service platform to deploy our developed network services on Kubernetes and to initiate the network reconfiguration.


Every Node for Itself: Fully Distributed Service Coordination

S.B. Schneider, L.D. Klenner, H. Karl, in: IEEE International Conference on Network and Service Management (CNSM), IEEE, 2020

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).


Self-Driving Network and Service Coordination Using Deep Reinforcement Learning

S.B. Schneider, A. Manzoor, H. Qarawlus, R. Schellenberg, H. Karl, R. Khalili, A. Hecker, in: IEEE International Conference on Network and Service Management (CNSM), IEEE, 2020

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.


2019

Specifying and Analyzing Virtual Network Services Using Queuing Petri Nets

S.B. Schneider, A. Sharma, H. Karl, H. Wehrheim, in: 2019 IFIP/IEEE International Symposium on Integrated Network Management (IM), IFIP, 2019, pp. 116--124

For optimal placement and orchestration of network services, it is crucial that their structure and semantics are specified clearly and comprehensively and are available to an orchestrator. Existing specification approaches are either ambiguous or miss important aspects regarding the behavior of virtual network functions (VNFs) forming a service. We propose to formally and unambiguously specify the behavior of these functions and services using Queuing Petri Nets (QPNs). QPNs are an established method that allows to express queuing, synchronization, stochastically distributed processing delays, and changing traffic volume and characteristics at each VNF. With QPNs, multiple VNFs can be connected to complete network services in any structure, even specifying bidirectional network services containing loops. We discuss how management and orchestration systems can benefit from our clear and comprehensive specification approach, leading to better placement of VNFs and improved Quality of Service. Another benefit of formally specifying network services with QPNs are diverse analysis options, which allow valuable insights such as the distribution of end-to-end delay. We propose a tool-based workflow that supports the specification of network services and the automatic generation of corresponding simulation code to enable an in-depth analysis of their behavior and performance.


Introducing Automated Verification and Validation for Virtualized Network Functions and Services

M. Peuster, S.B. Schneider, M. Zhao, G. Xilouris, P. Trakadas, F. Vicens, W. Tavernier, T. Soenen, R. Vilalta, G. Andreou, D. Kyriazis, H. Karl, IEEE Communications Magazine (2019), pp. 96-102


"Producing Cloud-Native": Smart Manufacturing Use Cases on Kubernetes

S.B. Schneider, M. Peuster, K. Hannemann, D. Behnke, M. Müller, P. Bök, H. Karl, in: IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) Demo Track, IEEE, 2019

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.


Putting NFV into Reality: Physical Smart Manufacturing Testbed

M. Müller, D. Behnke, P. Bök, S.B. Schneider, M. Peuster, H. Karl, in: IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), IEEE, 2019


5G as Key Technology for Networked Factories: Application of Vertical-specific Network Services for Enabling Flexible Smart Manufacturing

M. Müller, D. Behnke, P. Bök, M. Peuster, S.B. Schneider, H. Karl, in: IEEE 17th International Conference on Industrial Informatics (IEEE-INDIN), IEEE, 2019


The Softwarised Network Data Zoo

M. Peuster, S.B. Schneider, H. Karl, in: IEEE/IFIP 15th International Conference on Network and Service Management (CNSM), IEEE/IFIP, 2019

More and more management and orchestration approaches for (software) networks are based on machine learning paradigms and solutions. These approaches depend not only on their program code to operate properly, but also require enough input data to train their internal models. However, such training data is barely available for the software networking domain and most presented solutions rely on their own, sometimes not even published, data sets. This makes it hard, or even infeasible, to reproduce and compare many of the existing solutions. As a result, it ultimately slows down the adoption of machine learning approaches in softwarised networks. To this end, we introduce the "softwarised network data zoo" (SNDZoo), an open collection of software networking data sets aiming to streamline and ease machine learning research in the software networking domain. We present a general methodology to collect, archive, and publish those data sets for use by other researches and, as an example, eight initial data sets, focusing on the performance of virtualised network functions.


NFV-driven intrusion detection for smart manufacturing

D. Behnke, M. Müller, P. Bök, S.B. Schneider, M. Peuster, H. Karl, in: IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), IEEE, 2019


Putting 5G into Production: Realizing a Smart Manufacturing Vertical Scenario

S.B. Schneider, M. Peuster, D. Behnke, M. Marcel, P. Bök, H. Karl, in: European Conference on Networks and Communications (EuCNC), IEEE, 2019

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.


Reproducible Functional Tests for Multi-scale Network Services

A. Nuriddinov, W. Tavernier, D. Colle, M. Pickavet, M. Peuster, S.B. Schneider, in: IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), IEEE, 2019


Prototyping and Demonstrating 5G Verticals: The Smart Manufacturing Case

M. Peuster, S.B. Schneider, D. Behnke, M. Müller, P. Bök, H. Karl, in: 5th IEEE International Conference on Network Softwarization (NetSoft 2019), 2019

5G together with software defined networking (SDN) and network function virtualisation (NFV) will enable a wide variety of vertical use cases. One of them is the smart man- ufacturing case which utilises 5G networks to interconnect production machines, machine parks, and factory sites to enable new possibilities in terms of flexibility, automation, and novel applications (industry 4.0). However, the availability of realistic and practical proof-of-concepts for those smart manufacturing scenarios is still limited. This demo fills this gap by not only showing a real-world smart manufacturing application entirely implemented using NFV concepts, but also a lightweight prototyping framework that simplifies the realisation of vertical NFV proof-of-concepts. Dur- ing the demo, we show how an NFV-based smart manufacturing scenario can be specified, on-boarded, and instantiated before we demonstrate how the presented NFV services simplify machine data collection, aggregation, and analysis.


2018

Scaling and Placing Bidirectional Services with Stateful Virtual and Physical Network Functions

S. Dräxler, S.B. Schneider, H. Karl, in: 4th IEEE International Conference on Network Softwarization (NetSoft 2018), IEEE, 2018, pp. 123--131

Network function virtualization requires scaling and placement, deciding the number and the location of function instances. Current approaches are limited in flexibility and practical applicability. Specifically, we study dynamic, single-step, joint scaling and placement of network services with bidirectional flows traversing Physical or Virtual Network Functions (VNFs) and returning to their sources. We develop models to support stateful components and legacy network functions with fixed locations in these network services as well as the possibility of reusing VNFs across network services. We formalize the problem of jointly scaling and placing such network services as a mixed- integer linear program (MILP). We show that this problem is NP-complete and also present a heuristic algorithm to find good solutions in short time. In an extensive evaluation with realistic scenarios, we investigate the capabilities of the two approaches.


A Prototyping Platform to Validate and Verify Network Service Header-based Service Chains

M. Peuster, S.B. Schneider, F. Christ, H. Karl, in: IEEE Conference on Network Function Virtualisation and Software Defined Networks (NFV-SDN) 5GNetApp, IEEE, 2018


5GTANGO: An Approach for Testing NFV Deployments

P. Twamley, M. Muller, P. Bok, G.K. Xilouris, C. Sakkas, M.A. Kourtis, M. Peuster, S.B. Schneider, P. Stavrianos, D. Kyriazis, in: 2018 European Conference on Networks and Communications (EuCNC), IEEE, 2018

Programmability, control and flexibility can be considered as some of the indirect enablers for the success of 5G technologies. A key driver towards this are mechanisms or methodologies to drive shorter time to market for suppliers and operators of virtual network functions (VNFs) and network services. 5GTANGO includes a DevOps approach that can be utilized for the validation and verification (V&V) of VNFs and network services. In this paper, we elaborate further on the approaches made in the areas of testing, catalogues and package management as a means to enable that full DevOps V&V workflow. Finally, we explore the deployment requirement of the V&V via one of our pilot use cases.


A Generic Emulation Framework for Reusing and Evaluating VNF Placement Algorithms

S.B. Schneider, M. Peuster, H. Karl, in: IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN 2018), IEEE, 2018

In recent years, a variety of different approaches have been proposed to tackle the problem of scaling and placing network services, consisting of interconnected virtual network functions (VNFs). This paper presents a placement abstraction layer (PAL) that provides a clear and simple northbound interface for using such algorithms while hiding their internal functionality and implementation. Through its southbound interface, PAL can connect to different back ends that evaluate the calculated placements, e.g., using simulations, emulations, or testbed approaches. As an example for such evaluation back ends, we introduce a novel placement emulation framework (PEF) that allows executing calculated placements using real, containerbased VNFs on real-world network topologies. In a case study, we show how PAL and PEF facilitate reusing and evaluating placement algorithms as well as validating their underlying models and performance claims.


A Fully Integrated Multi-Platform NFV SDK

S.B. Schneider, M. Peuster, W. Tavernier, H. Karl, in: IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN 2018), IEEE, 2018

A key challenge of network function virtualization (NFV) is the complexity of developing and deploying new network services. Currently, development requires many manual steps that are time-consuming and error-prone (e.g., for creating service descriptors). Furthermore, existing management and orchestration (MANO) platforms only offer limited support of standardized descriptor models or package formats, limiting the re-usability of network services. To this end, we introduce a fully integrated, open-source NFV service development kit (SDK) with multi-MANO platform support. Our SDK simplifies many NFV service development steps by offering initial generation of descriptors, advanced project management, as well as fully automated packaging and submission for on-boarding. To achieve multi-platform support, we present a package format that extends ETSI’s VNF package format. In this demonstration, we present the end-to-end workflow to develop an NFV service that is then packaged for multiple platforms, i.e., 5GTANGO and OSM.


Trade-offs in Dynamic Resource Allocation in Network Function Virtualization

S.B. Schneider, S. Dräxler, H. Karl, in: IEEE Global Communications Conference (GLOBECOM 2018), IEEE, 2018

Dynamic allocation of resources is a key feature in network function virtualization (NFV), enabling flexible adjustment of slices and contained network services to ever-changing service demands. Considering resource allocation across the entire network, many authors have proposed approaches to optimize the placement and chaining of virtual network function (VNF) instances and the allocation of resources to these VNF instances. In doing so, various optimization objectives are conceivable, e.g., minimizing certain required resources or the end-to-end delay of the placed services. In this paper, we investigate the relationship between four typical optimization objectives when coordinating the placement and resource allocation of chained VNF instances. We observe an interesting trade-off between minimizing the overhead of starting/stopping VNF instances and all other objectives when adapting to changed service demands.


2017

Verification and validation framework for 5G network services and apps

M. Zhao, F. Le Gall, P. Cousin, R. Vilalta, R. Munoz, S. Castro, M. Peuster, S.B. Schneider, M. Siapera, E. Kapassa, D. Kyriazis, P. Hasselmeyer, G. Xilouris, C. Tranoris, S. Denazis, J. Martrat, in: 2017 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), IEEE, 2017

DOI


Demo: OpenC2X — An open source experimental and prototyping platform supporting ETSI ITS-G5

S. Laux, G.S. Pannu, S.B. Schneider, J. Tiemann, F. Klingler, C. Sommer, F. Dressler, in: 2016 IEEE Vehicular Networking Conference (VNC), IEEE, 2017

DOI


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