--- _id: '29672' author: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 citation: ama: 'Schneider SB. Network and Service Coordination: Conventional and Machine Learning Approaches".; 2022. doi:10.17619/UNIPB/1-1276 ' apa: 'Schneider, S. B. (2022). Network and Service Coordination: Conventional and Machine Learning Approaches". https://doi.org/10.17619/UNIPB/1-1276 ' bibtex: '@book{Schneider_2022, title={Network and Service Coordination: Conventional and Machine Learning Approaches"}, DOI={10.17619/UNIPB/1-1276 }, author={Schneider, Stefan Balthasar}, year={2022} }' chicago: 'Schneider, Stefan Balthasar. Network and Service Coordination: Conventional and Machine Learning Approaches", 2022. https://doi.org/10.17619/UNIPB/1-1276 .' ieee: 'S. B. Schneider, Network and Service Coordination: Conventional and Machine Learning Approaches". 2022.' mla: 'Schneider, Stefan Balthasar. Network and Service Coordination: Conventional and Machine Learning Approaches". 2022, doi:10.17619/UNIPB/1-1276 .' short: 'S.B. Schneider, Network and Service Coordination: Conventional and Machine Learning Approaches", 2022.' date_created: 2022-01-31T07:08:47Z date_updated: 2022-02-18T08:17:36Z department: - _id: '75' doi: '10.17619/UNIPB/1-1276 ' language: - iso: eng project: - _id: '1' name: 'SFB 901: SFB 901' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' - _id: '16' name: 'SFB 901 - C4: SFB 901 - Subproject C4' status: public supervisor: - first_name: Karl full_name: Holger, Karl last_name: Holger title: 'Network and Service Coordination: Conventional and Machine Learning Approaches"' type: dissertation user_id: '15504' year: '2022' ... --- _id: '30236' abstract: - lang: eng text: "Recent reinforcement learning approaches for continuous control in wireless mobile networks have shown impressive\r\nresults. But due to the lack of open and compatible simulators, authors typically create their own simulation environments for training and evaluation. This is cumbersome and time-consuming for authors and limits reproducibility and comparability, ultimately impeding progress in the field.\r\n\r\nTo this end, we propose mobile-env, a simple and open platform for training, evaluating, and comparing reinforcement learning and conventional approaches for continuous control in mobile wireless networks. mobile-env is lightweight and implements the common OpenAI Gym interface and additional wrappers, which allows connecting virtually any single-agent or multi-agent reinforcement learning framework to the environment. While mobile-env provides sensible default values and can be used out of the box, it also has many configuration options and is easy to extend. We therefore believe mobile-env to be a valuable platform for driving meaningful progress in autonomous coordination of\r\nwireless mobile networks." author: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Stefan full_name: Werner, Stefan last_name: Werner - first_name: Ramin full_name: Khalili, Ramin last_name: Khalili - first_name: Artur full_name: Hecker, Artur last_name: Hecker - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Schneider SB, Werner S, Khalili R, Hecker A, Karl H. mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks. In: IEEE/IFIP Network Operations and Management Symposium (NOMS). IEEE; 2022.' apa: 'Schneider, S. B., Werner, S., Khalili, R., Hecker, A., & Karl, H. (2022). mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks. IEEE/IFIP Network Operations and Management Symposium (NOMS). IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest.' bibtex: '@inproceedings{Schneider_Werner_Khalili_Hecker_Karl_2022, title={mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks}, booktitle={IEEE/IFIP Network Operations and Management Symposium (NOMS)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Werner, Stefan and Khalili, Ramin and Hecker, Artur and Karl, Holger}, year={2022} }' chicago: 'Schneider, Stefan Balthasar, Stefan Werner, Ramin Khalili, Artur Hecker, and Holger Karl. “Mobile-Env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks.” In IEEE/IFIP Network Operations and Management Symposium (NOMS). IEEE, 2022.' ieee: 'S. B. Schneider, S. Werner, R. Khalili, A. Hecker, and H. Karl, “mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks,” presented at the IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest, 2022.' mla: 'Schneider, Stefan Balthasar, et al. “Mobile-Env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks.” IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2022.' short: 'S.B. Schneider, S. Werner, R. Khalili, A. Hecker, H. Karl, in: IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2022.' conference: end_date: 2022-04-29 location: Budapest name: IEEE/IFIP Network Operations and Management Symposium (NOMS) start_date: 2022-04-25 date_created: 2022-03-10T18:28:14Z date_updated: 2022-03-10T18:28:19Z ddc: - '004' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2022-03-10T18:25:41Z date_updated: 2022-03-10T18:25:41Z file_id: '30237' file_name: author_version.pdf file_size: 223412 relation: main_file file_date_updated: 2022-03-10T18:25:41Z has_accepted_license: '1' keyword: - wireless mobile networks - network management - continuous control - cognitive networks - autonomous coordination - reinforcement learning - gym environment - simulation - open source language: - iso: eng oa: '1' project: - _id: '1' name: 'SFB 901: SFB 901' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' - _id: '16' name: 'SFB 901 - C4: SFB 901 - Subproject C4' publication: IEEE/IFIP Network Operations and Management Symposium (NOMS) publisher: IEEE quality_controlled: '1' status: public title: 'mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks' type: conference user_id: '35343' year: '2022' ... --- _id: '29220' abstract: - lang: eng text: "Modern services often comprise several components, such as chained virtual network functions, microservices, or\r\nmachine learning functions. Providing such services requires to decide how often to instantiate each component, where to place these instances in the network, how to chain them and route traffic through them. \r\nTo overcome limitations of conventional, hardwired heuristics, deep reinforcement learning (DRL) approaches for self-learning network and service management have emerged recently. These model-free DRL approaches are more flexible but typically learn tabula rasa, i.e., disregard existing understanding of networks, services, and their coordination. \r\n\r\nInstead, we propose FutureCoord, a novel model-based AI approach that leverages existing understanding of networks and services for more efficient and effective coordination without time-intensive training. FutureCoord combines Monte Carlo Tree Search with a stochastic traffic model. This allows FutureCoord to estimate the impact of future incoming traffic and effectively optimize long-term effects, taking fluctuating demand and Quality of Service (QoS) requirements into account. Our extensive evaluation based on real-world network topologies, services, and traffic traces indicates that FutureCoord clearly outperforms state-of-the-art model-free and model-based approaches with up to 51% higher flow success ratios." author: - first_name: Stefan full_name: Werner, Stefan last_name: Werner - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Werner S, Schneider SB, Karl H. Use What You Know: Network and Service Coordination Beyond Certainty. In: IEEE/IFIP Network Operations and Management Symposium (NOMS). IEEE; 2022.' apa: 'Werner, S., Schneider, S. B., & Karl, H. (2022). Use What You Know: Network and Service Coordination Beyond Certainty. IEEE/IFIP Network Operations and Management Symposium (NOMS). IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest.' bibtex: '@inproceedings{Werner_Schneider_Karl_2022, title={Use What You Know: Network and Service Coordination Beyond Certainty}, booktitle={IEEE/IFIP Network Operations and Management Symposium (NOMS)}, publisher={IEEE}, author={Werner, Stefan and Schneider, Stefan Balthasar and Karl, Holger}, year={2022} }' chicago: 'Werner, Stefan, Stefan Balthasar Schneider, and Holger Karl. “Use What You Know: Network and Service Coordination Beyond Certainty.” In IEEE/IFIP Network Operations and Management Symposium (NOMS). IEEE, 2022.' ieee: 'S. Werner, S. B. Schneider, and H. Karl, “Use What You Know: Network and Service Coordination Beyond Certainty,” presented at the IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest, 2022.' mla: 'Werner, Stefan, et al. “Use What You Know: Network and Service Coordination Beyond Certainty.” IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2022.' short: 'S. Werner, S.B. Schneider, H. Karl, in: IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2022.' conference: end_date: 2022-04-29 location: Budapest name: IEEE/IFIP Network Operations and Management Symposium (NOMS) start_date: 2022-04-25 date_created: 2022-01-11T08:43:26Z date_updated: 2022-01-11T08:44:04Z ddc: - '004' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2022-01-11T08:39:57Z date_updated: 2022-01-11T08:39:57Z file_id: '29222' file_name: author_version.pdf file_size: 528653 relation: main_file file_date_updated: 2022-01-11T08:39:57Z has_accepted_license: '1' keyword: - network management - service management - AI - Monte Carlo Tree Search - model-based - QoS language: - iso: eng oa: '1' project: - _id: '1' name: 'SFB 901: SFB 901' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' - _id: '16' name: 'SFB 901 - C4: SFB 901 - Subproject C4' publication: IEEE/IFIP Network Operations and Management Symposium (NOMS) publisher: IEEE quality_controlled: '1' status: public title: 'Use What You Know: Network and Service Coordination Beyond Certainty' type: conference user_id: '35343' year: '2022' ... --- _id: '21543' abstract: - lang: eng text: "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.\r\n\r\nTo 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)." author: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Haydar full_name: Qarawlus, Haydar last_name: Qarawlus - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Schneider SB, Qarawlus H, Karl H. Distributed Online Service Coordination Using Deep Reinforcement Learning. In: IEEE International Conference on Distributed Computing Systems (ICDCS). IEEE; 2021.' apa: 'Schneider, S. B., Qarawlus, H., & Karl, H. (2021). Distributed Online Service Coordination Using Deep Reinforcement Learning. In IEEE International Conference on Distributed Computing Systems (ICDCS). Washington, DC, USA: IEEE.' bibtex: '@inproceedings{Schneider_Qarawlus_Karl_2021, title={Distributed Online Service Coordination Using Deep Reinforcement Learning}, booktitle={IEEE International Conference on Distributed Computing Systems (ICDCS)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Qarawlus, Haydar and Karl, Holger}, year={2021} }' chicago: Schneider, Stefan Balthasar, Haydar Qarawlus, and Holger Karl. “Distributed Online Service Coordination Using Deep Reinforcement Learning.” In IEEE International Conference on Distributed Computing Systems (ICDCS). IEEE, 2021. ieee: S. B. Schneider, H. Qarawlus, and H. Karl, “Distributed Online Service Coordination Using Deep Reinforcement Learning,” in IEEE International Conference on Distributed Computing Systems (ICDCS), Washington, DC, USA, 2021. mla: Schneider, Stefan Balthasar, et al. “Distributed Online Service Coordination Using Deep Reinforcement Learning.” IEEE International Conference on Distributed Computing Systems (ICDCS), IEEE, 2021. short: 'S.B. Schneider, H. Qarawlus, H. Karl, in: IEEE International Conference on Distributed Computing Systems (ICDCS), IEEE, 2021.' conference: location: Washington, DC, USA name: IEEE International Conference on Distributed Computing Systems (ICDCS) date_created: 2021-03-18T17:15:47Z date_updated: 2022-01-06T06:55:04Z ddc: - '000' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2021-03-18T17:12:56Z date_updated: 2021-03-18T17:12:56Z file_id: '21544' file_name: public_author_version.pdf file_size: 606321 relation: main_file title: Distributed Online Service Coordination Using Deep Reinforcement Learning file_date_updated: 2021-03-18T17:12:56Z has_accepted_license: '1' keyword: - network management - service management - coordination - reinforcement learning - distributed language: - iso: eng oa: '1' project: - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 publication: IEEE International Conference on Distributed Computing Systems (ICDCS) publisher: IEEE related_material: link: - relation: software url: https://github.com/ RealVNF/distributed-drl-coordination status: public title: Distributed Online Service Coordination Using Deep Reinforcement Learning type: conference user_id: '35343' year: '2021' ... --- _id: '20693' abstract: - lang: eng text: "In practical, large-scale networks, services are requested\r\nby users across the globe, e.g., for video streaming.\r\nServices consist of multiple interconnected components such as\r\nmicroservices in a service mesh. Coordinating these services\r\nrequires scaling them according to continuously changing user\r\ndemand, deploying instances at the edge close to their users,\r\nand routing traffic efficiently between users and connected instances.\r\nNetwork and service coordination is commonly addressed\r\nthrough centralized approaches, where a single coordinator\r\nknows everything and coordinates the entire network globally.\r\nWhile such centralized approaches can reach global optima, they\r\ndo not scale to large, realistic networks. In contrast, distributed\r\napproaches scale well, but sacrifice solution quality due to their\r\nlimited scope of knowledge and coordination decisions.\r\n\r\nTo this end, we propose a hierarchical coordination approach\r\nthat combines the good solution quality of centralized approaches\r\nwith the scalability of distributed approaches. In doing so, we divide\r\nthe network into multiple hierarchical domains and optimize\r\ncoordination in a top-down manner. We compare our hierarchical\r\nwith a centralized approach in an extensive evaluation on a real-world\r\nnetwork topology. Our results indicate that hierarchical\r\ncoordination can find close-to-optimal solutions in a fraction of\r\nthe runtime of centralized approaches." author: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Mirko full_name: Jürgens, Mirko last_name: Jürgens - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Schneider SB, Jürgens M, Karl H. Divide and Conquer: Hierarchical Network and Service Coordination. In: IFIP/IEEE International Symposium on Integrated Network Management (IM). IFIP/IEEE; 2021.' apa: 'Schneider, S. B., Jürgens, M., & Karl, H. (2021). Divide and Conquer: Hierarchical Network and Service Coordination. In IFIP/IEEE International Symposium on Integrated Network Management (IM). Bordeaux, France: IFIP/IEEE.' bibtex: '@inproceedings{Schneider_Jürgens_Karl_2021, title={Divide and Conquer: Hierarchical Network and Service Coordination}, booktitle={IFIP/IEEE International Symposium on Integrated Network Management (IM)}, publisher={IFIP/IEEE}, author={Schneider, Stefan Balthasar and Jürgens, Mirko and Karl, Holger}, year={2021} }' chicago: 'Schneider, Stefan Balthasar, Mirko Jürgens, and Holger Karl. “Divide and Conquer: Hierarchical Network and Service Coordination.” In IFIP/IEEE International Symposium on Integrated Network Management (IM). IFIP/IEEE, 2021.' ieee: 'S. B. Schneider, M. Jürgens, and H. Karl, “Divide and Conquer: Hierarchical Network and Service Coordination,” in IFIP/IEEE International Symposium on Integrated Network Management (IM), Bordeaux, France, 2021.' mla: 'Schneider, Stefan Balthasar, et al. “Divide and Conquer: Hierarchical Network and Service Coordination.” IFIP/IEEE International Symposium on Integrated Network Management (IM), IFIP/IEEE, 2021.' short: 'S.B. Schneider, M. Jürgens, H. Karl, in: IFIP/IEEE International Symposium on Integrated Network Management (IM), IFIP/IEEE, 2021.' conference: location: Bordeaux, France name: IFIP/IEEE International Symposium on Integrated Network Management (IM) date_created: 2020-12-11T08:39:47Z date_updated: 2022-01-06T06:54:32Z ddc: - '006' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2020-12-11T08:37:37Z date_updated: 2020-12-11T08:37:37Z file_id: '20694' file_name: preprint_with_header.pdf file_size: 7979772 relation: main_file title: 'Divide and Conquer: Hierarchical Network and Service Coordination' file_date_updated: 2020-12-11T08:37:37Z has_accepted_license: '1' keyword: - network management - service management - coordination - hierarchical - scalability - nfv language: - iso: eng oa: '1' project: - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 publication: IFIP/IEEE International Symposium on Integrated Network Management (IM) publisher: IFIP/IEEE quality_controlled: '1' status: public title: 'Divide and Conquer: Hierarchical Network and Service Coordination' type: conference user_id: '35343' year: '2021' ... --- _id: '21808' abstract: - lang: eng text: "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).\r\n\r\nWe 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." article_type: original author: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Ramin full_name: Khalili, Ramin last_name: Khalili - first_name: Adnan full_name: Manzoor, Adnan last_name: Manzoor - first_name: Haydar full_name: Qarawlus, Haydar last_name: Qarawlus - first_name: Rafael full_name: Schellenberg, Rafael last_name: Schellenberg - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl - first_name: Artur full_name: Hecker, Artur last_name: Hecker citation: ama: Schneider SB, Khalili R, Manzoor A, et al. Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning. Transactions on Network and Service Management. 2021. doi:10.1109/TNSM.2021.3076503 apa: Schneider, S. B., Khalili, R., Manzoor, A., Qarawlus, H., Schellenberg, R., Karl, H., & Hecker, A. (2021). Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning. Transactions on Network and Service Management. https://doi.org/10.1109/TNSM.2021.3076503 bibtex: '@article{Schneider_Khalili_Manzoor_Qarawlus_Schellenberg_Karl_Hecker_2021, title={Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning}, DOI={10.1109/TNSM.2021.3076503}, journal={Transactions on Network and Service Management}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Khalili, Ramin and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Hecker, Artur}, year={2021} }' chicago: Schneider, Stefan Balthasar, Ramin Khalili, Adnan Manzoor, Haydar Qarawlus, Rafael Schellenberg, Holger Karl, and Artur Hecker. “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning.” Transactions on Network and Service Management, 2021. https://doi.org/10.1109/TNSM.2021.3076503. ieee: S. B. Schneider et al., “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning,” Transactions on Network and Service Management, 2021. mla: Schneider, Stefan Balthasar, et al. “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning.” Transactions on Network and Service Management, IEEE, 2021, doi:10.1109/TNSM.2021.3076503. short: S.B. Schneider, R. Khalili, A. Manzoor, H. Qarawlus, R. Schellenberg, H. Karl, A. Hecker, Transactions on Network and Service Management (2021). date_created: 2021-04-27T08:04:16Z date_updated: 2022-01-06T06:55:15Z ddc: - '000' department: - _id: '75' doi: 10.1109/TNSM.2021.3076503 file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2021-04-27T08:01:26Z date_updated: 2021-04-27T08:01:26Z description: Author version of the accepted paper file_id: '21809' file_name: ris-accepted-version.pdf file_size: 4172270 relation: main_file file_date_updated: 2021-04-27T08:01:26Z has_accepted_license: '1' keyword: - network management - service management - coordination - reinforcement learning - self-learning - self-adaptation - multi-objective language: - iso: eng oa: '1' project: - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 publication: Transactions on Network and Service Management publisher: IEEE status: public title: Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning type: journal_article user_id: '35343' year: '2021' ... --- _id: '33854' abstract: - lang: eng text: "Macrodiversity is a key technique to increase the capacity of mobile networks. It can be realized using coordinated multipoint (CoMP), simultaneously connecting users to multiple overlapping cells. Selecting which users to serve by how many and which cells is NP-hard but needs to happen continuously in real time as users move and channel state changes. Existing approaches often require strict assumptions about or perfect knowledge of the underlying radio system, its resource allocation scheme, or user movements, none of which is readily available in practice.\r\n\r\nInstead, we propose three novel self-learning and self-adapting approaches using model-free deep reinforcement learning (DRL): DeepCoMP, DD-CoMP, and D3-CoMP. DeepCoMP leverages central observations and control of all users to select cells almost optimally. DD-CoMP and D3-CoMP use multi-agent DRL, which allows distributed, robust, and highly scalable coordination. All three approaches learn from experience and self-adapt to varying scenarios, reaching 2x higher Quality of Experience than other approaches. They have very few built-in assumptions and do not need prior system knowledge, making them more robust to change and better applicable in practice than existing approaches." author: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl - first_name: Ramin full_name: Khalili, Ramin last_name: Khalili - first_name: Artur full_name: Hecker, Artur last_name: Hecker citation: ama: 'Schneider SB, Karl H, Khalili R, Hecker A. DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning.; 2021.' apa: 'Schneider, S. B., Karl, H., Khalili, R., & Hecker, A. (2021). DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning.' bibtex: '@book{Schneider_Karl_Khalili_Hecker_2021, title={DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning}, author={Schneider, Stefan Balthasar and Karl, Holger and Khalili, Ramin and Hecker, Artur}, year={2021} }' chicago: 'Schneider, Stefan Balthasar, Holger Karl, Ramin Khalili, and Artur Hecker. DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning, 2021.' ieee: 'S. B. Schneider, H. Karl, R. Khalili, and A. Hecker, DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning. 2021.' mla: 'Schneider, Stefan Balthasar, et al. DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning. 2021.' short: 'S.B. Schneider, H. Karl, R. Khalili, A. Hecker, DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning, 2021.' date_created: 2022-10-20T16:44:19Z date_updated: 2022-11-18T09:59:27Z ddc: - '004' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2022-10-20T16:41:10Z date_updated: 2022-10-20T16:41:10Z file_id: '33855' file_name: preprint.pdf file_size: 2521656 relation: main_file file_date_updated: 2022-10-20T16:41:10Z has_accepted_license: '1' keyword: - mobility management - coordinated multipoint - CoMP - cell selection - resource management - reinforcement learning - multi agent - MARL - self-learning - self-adaptation - QoE language: - iso: eng oa: '1' project: - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' - _id: '16' name: 'SFB 901 - C4: SFB 901 - Subproject C4' - _id: '1' name: 'SFB 901: SFB 901' status: public title: 'DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning' type: working_paper user_id: '477' year: '2021' ... --- _id: '35889' abstract: - lang: eng text: 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: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 citation: ama: Schneider SB. Conventional and Machine Learning Approaches for Network and Service Coordination.; 2021. apa: Schneider, S. B. (2021). Conventional and Machine Learning Approaches for Network and Service Coordination. bibtex: '@book{Schneider_2021, title={Conventional and Machine Learning Approaches for Network and Service Coordination}, author={Schneider, Stefan Balthasar}, year={2021} }' chicago: Schneider, Stefan Balthasar. Conventional and Machine Learning Approaches for Network and Service Coordination, 2021. ieee: S. B. Schneider, Conventional and Machine Learning Approaches for Network and Service Coordination. 2021. mla: Schneider, Stefan Balthasar. Conventional and Machine Learning Approaches for Network and Service Coordination. 2021. short: S.B. Schneider, Conventional and Machine Learning Approaches for Network and Service Coordination, 2021. date_created: 2023-01-10T15:08:50Z date_updated: 2023-01-10T15:09:05Z ddc: - '004' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2023-01-10T15:07:03Z date_updated: 2023-01-10T15:07:03Z file_id: '35890' file_name: main.pdf file_size: 133340 relation: main_file file_date_updated: 2023-01-10T15:07:03Z has_accepted_license: '1' keyword: - nfv - coordination - machine learning - reinforcement learning - phd - digest language: - iso: eng oa: '1' project: - _id: '1' name: 'SFB 901: SFB 901' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' - _id: '16' name: 'SFB 901 - C4: SFB 901 - Subproject C4' status: public title: Conventional and Machine Learning Approaches for Network and Service Coordination type: working_paper user_id: '35343' year: '2021' ... --- _id: '19607' abstract: - lang: eng text: "Modern services consist of modular, interconnected\r\ncomponents, e.g., microservices forming a service mesh. To\r\ndynamically adjust to ever-changing service demands, service\r\ncomponents have to be instantiated on nodes across the network.\r\nIncoming flows requesting a service then need to be routed\r\nthrough the deployed instances while considering node and link\r\ncapacities. Ultimately, the goal is to maximize the successfully\r\nserved flows and Quality of Service (QoS) through online service\r\ncoordination. Current approaches for service coordination are\r\nusually centralized, assuming up-to-date global knowledge and\r\nmaking global decisions for all nodes in the network. Such global\r\nknowledge and centralized decisions are not realistic in practical\r\nlarge-scale networks.\r\n\r\nTo solve this problem, we propose two algorithms for fully\r\ndistributed service coordination. The proposed algorithms can be\r\nexecuted individually at each node in parallel and require only\r\nvery limited global knowledge. We compare and evaluate both\r\nalgorithms with a state-of-the-art centralized approach in extensive\r\nsimulations on a large-scale, real-world network topology.\r\nOur results indicate that the two algorithms can compete with\r\ncentralized approaches in terms of solution quality but require\r\nless global knowledge and are magnitudes faster (more than\r\n100x)." author: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Lars Dietrich full_name: Klenner, Lars Dietrich last_name: Klenner - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Schneider SB, Klenner LD, Karl H. Every Node for Itself: Fully Distributed Service Coordination. In: IEEE International Conference on Network and Service Management (CNSM). IEEE; 2020.' apa: 'Schneider, S. B., Klenner, L. D., & Karl, H. (2020). Every Node for Itself: Fully Distributed Service Coordination. In IEEE International Conference on Network and Service Management (CNSM). IEEE.' bibtex: '@inproceedings{Schneider_Klenner_Karl_2020, title={Every Node for Itself: Fully Distributed Service Coordination}, booktitle={IEEE International Conference on Network and Service Management (CNSM)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Klenner, Lars Dietrich and Karl, Holger}, year={2020} }' chicago: 'Schneider, Stefan Balthasar, Lars Dietrich Klenner, and Holger Karl. “Every Node for Itself: Fully Distributed Service Coordination.” In IEEE International Conference on Network and Service Management (CNSM). IEEE, 2020.' ieee: 'S. B. Schneider, L. D. Klenner, and H. Karl, “Every Node for Itself: Fully Distributed Service Coordination,” in IEEE International Conference on Network and Service Management (CNSM), 2020.' mla: 'Schneider, Stefan Balthasar, et al. “Every Node for Itself: Fully Distributed Service Coordination.” IEEE International Conference on Network and Service Management (CNSM), IEEE, 2020.' short: 'S.B. Schneider, L.D. Klenner, H. Karl, in: IEEE International Conference on Network and Service Management (CNSM), IEEE, 2020.' date_created: 2020-09-22T06:23:40Z date_updated: 2022-01-06T06:54:08Z ddc: - '006' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2020-09-22T06:25:57Z date_updated: 2020-09-22T06:36:25Z file_id: '19608' file_name: ris_with_copyright.pdf file_size: 500948 relation: main_file file_date_updated: 2020-09-22T06:36:25Z has_accepted_license: '1' keyword: - distributed management - service coordination - network coordination - nfv - softwarization - orchestration language: - iso: eng oa: '1' project: - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 publication: IEEE International Conference on Network and Service Management (CNSM) publisher: IEEE status: public title: 'Every Node for Itself: Fully Distributed Service Coordination' type: conference user_id: '35343' year: '2020' ... --- _id: '19609' abstract: - lang: eng text: "Modern services comprise interconnected components,\r\ne.g., microservices in a service mesh, that can scale and\r\nrun on multiple nodes across the network on demand. To process\r\nincoming traffic, service components have to be instantiated and\r\ntraffic assigned to these instances, taking capacities and changing\r\ndemands into account. This challenge is usually solved with\r\ncustom approaches designed by experts. While this typically\r\nworks well for the considered scenario, the models often rely\r\non unrealistic assumptions or on knowledge that is not available\r\nin practice (e.g., a priori knowledge).\r\n\r\nWe propose a novel deep reinforcement learning approach that\r\nlearns how to best coordinate services and is geared towards\r\nrealistic assumptions. It interacts with the network and relies on\r\navailable, possibly delayed monitoring information. Rather than\r\ndefining a complex model or an algorithm how to achieve an\r\nobjective, our model-free approach adapts to various objectives\r\nand traffic patterns. An agent is trained offline without expert\r\nknowledge and then applied online with minimal overhead. Compared\r\nto a state-of-the-art heuristic, it significantly improves flow\r\nthroughput and overall network utility on real-world network\r\ntopologies and traffic traces. It also learns to optimize different\r\nobjectives, generalizes to scenarios with unseen, stochastic traffic\r\npatterns, and scales to large real-world networks." author: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Adnan full_name: Manzoor, Adnan last_name: Manzoor - first_name: Haydar full_name: Qarawlus, Haydar last_name: Qarawlus - first_name: Rafael full_name: Schellenberg, Rafael last_name: Schellenberg - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl - first_name: Ramin full_name: Khalili, Ramin last_name: Khalili - first_name: Artur full_name: Hecker, Artur last_name: Hecker citation: ama: 'Schneider SB, Manzoor A, Qarawlus H, et al. Self-Driving Network and Service Coordination Using Deep Reinforcement Learning. In: IEEE International Conference on Network and Service Management (CNSM). IEEE; 2020.' apa: Schneider, S. B., Manzoor, A., Qarawlus, H., Schellenberg, R., Karl, H., Khalili, R., & Hecker, A. (2020). Self-Driving Network and Service Coordination Using Deep Reinforcement Learning. In IEEE International Conference on Network and Service Management (CNSM). IEEE. bibtex: '@inproceedings{Schneider_Manzoor_Qarawlus_Schellenberg_Karl_Khalili_Hecker_2020, title={Self-Driving Network and Service Coordination Using Deep Reinforcement Learning}, booktitle={IEEE International Conference on Network and Service Management (CNSM)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin and Hecker, Artur}, year={2020} }' chicago: Schneider, Stefan Balthasar, Adnan Manzoor, Haydar Qarawlus, Rafael Schellenberg, Holger Karl, Ramin Khalili, and Artur Hecker. “Self-Driving Network and Service Coordination Using Deep Reinforcement Learning.” In IEEE International Conference on Network and Service Management (CNSM). IEEE, 2020. ieee: S. B. Schneider et al., “Self-Driving Network and Service Coordination Using Deep Reinforcement Learning,” in IEEE International Conference on Network and Service Management (CNSM), 2020. mla: Schneider, Stefan Balthasar, et al. “Self-Driving Network and Service Coordination Using Deep Reinforcement Learning.” IEEE International Conference on Network and Service Management (CNSM), IEEE, 2020. short: '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.' date_created: 2020-09-22T06:28:22Z date_updated: 2022-01-06T06:54:08Z ddc: - '006' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2020-09-22T06:29:16Z date_updated: 2020-09-22T06:36:00Z file_id: '19610' file_name: ris_with_copyright.pdf file_size: 642999 relation: main_file file_date_updated: 2020-09-22T06:36:00Z has_accepted_license: '1' keyword: - self-driving networks - self-learning - network coordination - service coordination - reinforcement learning - deep learning - nfv language: - iso: eng oa: '1' project: - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 publication: IEEE International Conference on Network and Service Management (CNSM) publisher: IEEE status: public title: Self-Driving Network and Service Coordination Using Deep Reinforcement Learning type: conference user_id: '35343' year: '2020' ... --- _id: '16219' abstract: - lang: eng text: "Network function virtualization (NFV) proposes\r\nto replace physical middleboxes with more flexible virtual\r\nnetwork functions (VNFs). To dynamically adjust to everchanging\r\ntraffic demands, VNFs have to be instantiated and\r\ntheir allocated resources have to be adjusted on demand.\r\nDeciding the amount of allocated resources is non-trivial.\r\nExisting optimization approaches often assume fixed resource\r\nrequirements for each VNF instance. However, this can easily\r\nlead to either waste of resources or bad service quality if too\r\nmany or too few resources are allocated.\r\n\r\nTo solve this problem, we train machine learning models\r\non real VNF data, containing measurements of performance\r\nand resource requirements. For each VNF, the trained models\r\ncan then accurately predict the required resources to handle\r\na certain traffic load. We integrate these machine learning\r\nmodels into an algorithm for joint VNF scaling and placement\r\nand evaluate their impact on resulting VNF placements. Our\r\nevaluation based on real-world data shows that using suitable\r\nmachine learning models effectively avoids over- and underallocation\r\nof resources, leading to up to 12 times lower resource\r\nconsumption and better service quality with up to 4.5 times\r\nlower total delay than using standard fixed resource allocation." author: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Narayanan Puthenpurayil full_name: Satheeschandran, Narayanan Puthenpurayil last_name: Satheeschandran - first_name: Manuel full_name: Peuster, Manuel id: '13271' last_name: Peuster - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Schneider SB, Satheeschandran NP, Peuster M, Karl H. Machine Learning for Dynamic Resource Allocation in Network Function Virtualization. In: IEEE Conference on Network Softwarization (NetSoft). IEEE; 2020.' apa: 'Schneider, S. B., Satheeschandran, N. P., Peuster, M., & Karl, H. (2020). Machine Learning for Dynamic Resource Allocation in Network Function Virtualization. In IEEE Conference on Network Softwarization (NetSoft). Ghent, Belgium: IEEE.' bibtex: '@inproceedings{Schneider_Satheeschandran_Peuster_Karl_2020, title={Machine Learning for Dynamic Resource Allocation in Network Function Virtualization}, booktitle={IEEE Conference on Network Softwarization (NetSoft)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Satheeschandran, Narayanan Puthenpurayil and Peuster, Manuel and Karl, Holger}, year={2020} }' chicago: Schneider, Stefan Balthasar, Narayanan Puthenpurayil Satheeschandran, Manuel Peuster, and Holger Karl. “Machine Learning for Dynamic Resource Allocation in Network Function Virtualization.” In IEEE Conference on Network Softwarization (NetSoft). IEEE, 2020. ieee: S. B. Schneider, N. P. Satheeschandran, M. Peuster, and H. Karl, “Machine Learning for Dynamic Resource Allocation in Network Function Virtualization,” in IEEE Conference on Network Softwarization (NetSoft), Ghent, Belgium, 2020. mla: Schneider, Stefan Balthasar, et al. “Machine Learning for Dynamic Resource Allocation in Network Function Virtualization.” IEEE Conference on Network Softwarization (NetSoft), IEEE, 2020. short: 'S.B. Schneider, N.P. Satheeschandran, M. Peuster, H. Karl, in: IEEE Conference on Network Softwarization (NetSoft), IEEE, 2020.' conference: location: Ghent, Belgium name: IEEE Conference on Network Softwarization (NetSoft) date_created: 2020-03-03T11:42:22Z date_updated: 2022-01-06T06:52:46Z ddc: - '000' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2020-03-03T11:42:16Z date_updated: 2020-03-03T11:42:16Z file_id: '16220' file_name: ris_preprint.pdf file_size: 476590 relation: main_file file_date_updated: 2020-03-03T11:42:16Z has_accepted_license: '1' language: - iso: eng oa: '1' project: - _id: '28' grant_number: '761493' name: 5G Development and validation platform for global industry-specific network services and Apps - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 publication: IEEE Conference on Network Softwarization (NetSoft) publisher: IEEE status: public title: Machine Learning for Dynamic Resource Allocation in Network Function Virtualization type: conference user_id: '35343' year: '2020' ... --- _id: '16222' author: - first_name: A. full_name: Zafeiropoulos, A. last_name: Zafeiropoulos - first_name: E. full_name: Fotopoulou, E. last_name: Fotopoulou - first_name: Manuel full_name: Peuster, Manuel id: '13271' last_name: Peuster - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: P. full_name: Gouvas, P. last_name: Gouvas - first_name: D. full_name: Behnke, D. last_name: Behnke - first_name: M. full_name: Müller, M. last_name: Müller - first_name: P. full_name: Bök, P. last_name: Bök - first_name: P. full_name: Trakadas, P. last_name: Trakadas - first_name: P. full_name: Karkazis, P. last_name: Karkazis - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Zafeiropoulos A, Fotopoulou E, Peuster M, et al. Benchmarking and Profiling 5G Verticals’ Applications: An Industrial IoT Use Case. In: IEEE Conference on Network Softwarization (NetSoft). ; 2020.' apa: 'Zafeiropoulos, A., Fotopoulou, E., Peuster, M., Schneider, S. B., Gouvas, P., Behnke, D., … Karl, H. (2020). Benchmarking and Profiling 5G Verticals’ Applications: An Industrial IoT Use Case. In IEEE Conference on Network Softwarization (NetSoft).' bibtex: '@inproceedings{Zafeiropoulos_Fotopoulou_Peuster_Schneider_Gouvas_Behnke_Müller_Bök_Trakadas_Karkazis_et al._2020, title={Benchmarking and Profiling 5G Verticals’ Applications: An Industrial IoT Use Case}, booktitle={IEEE Conference on Network Softwarization (NetSoft)}, author={Zafeiropoulos, A. and Fotopoulou, E. and Peuster, Manuel and Schneider, Stefan Balthasar and Gouvas, P. and Behnke, D. and Müller, M. and Bök, P. and Trakadas, P. and Karkazis, P. and et al.}, year={2020} }' chicago: 'Zafeiropoulos, A., E. Fotopoulou, Manuel Peuster, Stefan Balthasar Schneider, P. Gouvas, D. Behnke, M. Müller, et al. “Benchmarking and Profiling 5G Verticals’ Applications: An Industrial IoT Use Case.” In IEEE Conference on Network Softwarization (NetSoft), 2020.' ieee: 'A. Zafeiropoulos et al., “Benchmarking and Profiling 5G Verticals’ Applications: An Industrial IoT Use Case,” in IEEE Conference on Network Softwarization (NetSoft), 2020.' mla: 'Zafeiropoulos, A., et al. “Benchmarking and Profiling 5G Verticals’ Applications: An Industrial IoT Use Case.” IEEE Conference on Network Softwarization (NetSoft), 2020.' short: '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.' date_created: 2020-03-03T11:51:22Z date_updated: 2022-01-06T06:52:46Z department: - _id: '75' language: - iso: eng project: - _id: '28' grant_number: '761493' name: 5G Development and validation platform for global industry-specific network services and Apps publication: IEEE Conference on Network Softwarization (NetSoft) status: public title: 'Benchmarking and Profiling 5G Verticals'' Applications: An Industrial IoT Use Case' type: conference user_id: '35343' year: '2020' ... --- _id: '16400' abstract: - lang: eng text: "Softwarization facilitates the introduction of smart\r\nmanufacturing applications in the industry. Manifold devices\r\nsuch as machine computers, Industrial IoT devices, tablets,\r\nsmartphones and smart glasses are integrated into factory networks\r\nto enable shop floor digitalization and big data analysis. To\r\nhandle the increasing number of devices and the resulting traffic,\r\na flexible and scalable factory network is necessary which can be\r\nrealized using softwarization technologies like Network Function\r\nVirtualization (NFV). However, the security risks increase with\r\nthe increasing number of new devices, so that cyber security must\r\nalso be considered in NFV-based networks.\r\n\r\nTherefore, extending our previous work, we showcase threat\r\ndetection using a cloud-native NFV-driven intrusion detection\r\nsystem (IDS) that is integrated in our industrial-specific network\r\nservices. As a result of the threat detection, the affected network\r\nservice is put into quarantine via automatic network reconfiguration.\r\nWe use the 5GTANGO service platform to deploy our\r\ndeveloped network services on Kubernetes and to initiate the\r\nnetwork reconfiguration." author: - first_name: Marcel full_name: Müller, Marcel last_name: Müller - first_name: Daniel full_name: Behnke, Daniel last_name: Behnke - first_name: Patrick-Benjamin full_name: Bök, Patrick-Benjamin last_name: Bök - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Manuel full_name: Peuster, Manuel id: '13271' last_name: Peuster - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Müller M, Behnke D, Bök P-B, Schneider SB, Peuster M, Karl H. Cloud-Native Threat Detection and Containment for Smart Manufacturing. In: IEEE Conference on Network Softwarization (NetSoft) Demo Track. Ghent, Belgium: IEEE; 2020.' apa: 'Müller, M., Behnke, D., Bök, P.-B., Schneider, S. B., Peuster, M., & Karl, H. (2020). Cloud-Native Threat Detection and Containment for Smart Manufacturing. In IEEE Conference on Network Softwarization (NetSoft) Demo Track. Ghent, Belgium: IEEE.' bibtex: '@inproceedings{Müller_Behnke_Bök_Schneider_Peuster_Karl_2020, place={Ghent, Belgium}, title={Cloud-Native Threat Detection and Containment for Smart Manufacturing}, booktitle={IEEE Conference on Network Softwarization (NetSoft) Demo Track}, publisher={IEEE}, author={Müller, Marcel and Behnke, Daniel and Bök, Patrick-Benjamin and Schneider, Stefan Balthasar and Peuster, Manuel and Karl, Holger}, year={2020} }' chicago: 'Müller, Marcel, Daniel Behnke, Patrick-Benjamin Bök, Stefan Balthasar Schneider, Manuel Peuster, and Holger Karl. “Cloud-Native Threat Detection and Containment for Smart Manufacturing.” In IEEE Conference on Network Softwarization (NetSoft) Demo Track. Ghent, Belgium: IEEE, 2020.' ieee: M. Müller, D. Behnke, P.-B. Bök, S. B. Schneider, M. Peuster, and H. Karl, “Cloud-Native Threat Detection and Containment for Smart Manufacturing,” in IEEE Conference on Network Softwarization (NetSoft) Demo Track, Ghent, Belgium, 2020. mla: Müller, Marcel, et al. “Cloud-Native Threat Detection and Containment for Smart Manufacturing.” IEEE Conference on Network Softwarization (NetSoft) Demo Track, IEEE, 2020. short: 'M. Müller, D. Behnke, P.-B. Bök, S.B. Schneider, M. Peuster, H. Karl, in: IEEE Conference on Network Softwarization (NetSoft) Demo Track, IEEE, Ghent, Belgium, 2020.' conference: location: Ghent, Belgium name: IEEE Conference on Network Softwarization (NetSoft) Demo Track date_created: 2020-04-03T11:53:00Z date_updated: 2022-01-06T06:52:50Z department: - _id: '75' language: - iso: eng place: Ghent, Belgium project: - _id: '28' grant_number: '761493' name: 5G Development and validation platform for global industry-specific network services and Apps - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 publication: IEEE Conference on Network Softwarization (NetSoft) Demo Track publisher: IEEE status: public title: Cloud-Native Threat Detection and Containment for Smart Manufacturing type: conference user_id: '35343' year: '2020' ... --- _id: '3287' abstract: - lang: eng text: "For optimal placement and orchestration of network services, it is crucial\r\nthat their structure and semantics are specified clearly and comprehensively\r\nand are available to an orchestrator. Existing specification approaches are\r\neither ambiguous or miss important aspects regarding the behavior of virtual\r\nnetwork functions (VNFs) forming a service. We propose to formally and\r\nunambiguously specify the behavior of these functions and services using\r\nQueuing Petri Nets (QPNs). QPNs are an established method that allows to\r\nexpress queuing, synchronization, stochastically distributed processing delays,\r\nand changing traffic volume and characteristics at each VNF. With QPNs,\r\nmultiple VNFs can be connected to complete network services in any structure,\r\neven specifying bidirectional network services containing loops.\r\n We discuss how management and orchestration systems can benefit from our\r\nclear and comprehensive specification approach, leading to better placement of\r\nVNFs and improved Quality of Service. Another benefit of formally specifying\r\nnetwork services with QPNs are diverse analysis options, which allow valuable\r\ninsights such as the distribution of end-to-end delay. We propose a tool-based\r\nworkflow that supports the specification of network services and the automatic\r\ngeneration of corresponding simulation code to enable an in-depth analysis of\r\ntheir behavior and performance." author: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Arnab full_name: Sharma, Arnab id: '67200' last_name: Sharma - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl - first_name: Heike full_name: Wehrheim, Heike id: '573' last_name: Wehrheim citation: ama: 'Schneider SB, Sharma A, Karl H, Wehrheim H. Specifying and Analyzing Virtual Network Services Using Queuing Petri Nets. In: 2019 IFIP/IEEE International Symposium on Integrated Network Management (IM). Washington, DC, USA: IFIP; 2019:116--124.' apa: 'Schneider, S. B., Sharma, A., Karl, H., & Wehrheim, H. (2019). Specifying and Analyzing Virtual Network Services Using Queuing Petri Nets. In 2019 IFIP/IEEE International Symposium on Integrated Network Management (IM) (pp. 116--124). Washington, DC, USA: IFIP.' bibtex: '@inproceedings{Schneider_Sharma_Karl_Wehrheim_2019, place={Washington, DC, USA}, title={Specifying and Analyzing Virtual Network Services Using Queuing Petri Nets}, booktitle={2019 IFIP/IEEE International Symposium on Integrated Network Management (IM)}, publisher={IFIP}, author={Schneider, Stefan Balthasar and Sharma, Arnab and Karl, Holger and Wehrheim, Heike}, year={2019}, pages={116--124} }' chicago: 'Schneider, Stefan Balthasar, Arnab Sharma, Holger Karl, and Heike Wehrheim. “Specifying and Analyzing Virtual Network Services Using Queuing Petri Nets.” In 2019 IFIP/IEEE International Symposium on Integrated Network Management (IM), 116--124. Washington, DC, USA: IFIP, 2019.' ieee: S. B. Schneider, A. Sharma, H. Karl, and H. Wehrheim, “Specifying and Analyzing Virtual Network Services Using Queuing Petri Nets,” in 2019 IFIP/IEEE International Symposium on Integrated Network Management (IM), Washington, DC, USA, 2019, pp. 116--124. mla: Schneider, Stefan Balthasar, et al. “Specifying and Analyzing Virtual Network Services Using Queuing Petri Nets.” 2019 IFIP/IEEE International Symposium on Integrated Network Management (IM), IFIP, 2019, pp. 116--124. short: 'S.B. Schneider, A. Sharma, H. Karl, H. Wehrheim, in: 2019 IFIP/IEEE International Symposium on Integrated Network Management (IM), IFIP, Washington, DC, USA, 2019, pp. 116--124.' conference: end_date: 2019-04-12 location: Washington, DC, USA name: 2019 IFIP/IEEE International Symposium on Integrated Network Management (IM) start_date: 2019-04-08 date_created: 2018-06-18T15:23:18Z date_updated: 2022-01-06T06:59:09Z ddc: - '040' department: - _id: '77' - _id: '75' file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2019-01-07T12:38:35Z date_updated: 2019-01-07T12:38:35Z file_id: '6504' file_name: ris_preprint.pdf file_size: 497528 relation: main_file file_date_updated: 2019-01-07T12:38:35Z has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: http://dl.ifip.org/db/conf/im/im2019/188490.pdf oa: '1' page: 116--124 place: Washington, DC, USA project: - _id: '3' name: SFB 901 - Project Area B - _id: '11' name: SFB 901 - Subproject B3 - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 - _id: '28' grant_number: '761493' name: 5G Development and validation platform for global industry-specific network services and Apps - _id: '25' call_identifier: 5G PPP Phase 1 grant_number: '671517' name: 'SONATA NFV: Agile Service Development and Orchestration in 5G Virtualized Networks' publication: 2019 IFIP/IEEE International Symposium on Integrated Network Management (IM) publisher: IFIP status: public title: Specifying and Analyzing Virtual Network Services Using Queuing Petri Nets type: conference user_id: '35343' year: '2019' ... --- _id: '9270' abstract: - lang: eng text: "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. \r\n\r\nUsing 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: - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Manuel full_name: Peuster, Manuel id: '13271' last_name: Peuster - first_name: Daniel full_name: Behnke, Daniel last_name: Behnke - first_name: Müller full_name: Marcel, Müller last_name: Marcel - first_name: Patrick-Benjamin full_name: Bök, Patrick-Benjamin last_name: Bök - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Schneider SB, Peuster M, Behnke D, Marcel M, Bök P-B, Karl H. Putting 5G into Production: Realizing a Smart Manufacturing Vertical Scenario. In: European Conference on Networks and Communications (EuCNC). Valencia, Spain: IEEE; 2019. doi:10.1109/eucnc.2019.8802016' apa: 'Schneider, S. B., Peuster, M., Behnke, D., Marcel, M., Bök, P.-B., & Karl, H. (2019). Putting 5G into Production: Realizing a Smart Manufacturing Vertical Scenario. In European Conference on Networks and Communications (EuCNC). Valencia, Spain: IEEE. https://doi.org/10.1109/eucnc.2019.8802016' bibtex: '@inproceedings{Schneider_Peuster_Behnke_Marcel_Bök_Karl_2019, place={Valencia, Spain}, title={Putting 5G into Production: Realizing a Smart Manufacturing Vertical Scenario}, DOI={10.1109/eucnc.2019.8802016}, booktitle={European Conference on Networks and Communications (EuCNC)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Peuster, Manuel and Behnke, Daniel and Marcel, Müller and Bök, Patrick-Benjamin and Karl, Holger}, year={2019} }' chicago: 'Schneider, Stefan Balthasar, Manuel Peuster, Daniel Behnke, Müller Marcel, Patrick-Benjamin Bök, and Holger Karl. “Putting 5G into Production: Realizing a Smart Manufacturing Vertical Scenario.” In European Conference on Networks and Communications (EuCNC). Valencia, Spain: IEEE, 2019. https://doi.org/10.1109/eucnc.2019.8802016.' ieee: 'S. B. Schneider, M. Peuster, D. Behnke, M. Marcel, P.-B. Bök, and H. Karl, “Putting 5G into Production: Realizing a Smart Manufacturing Vertical Scenario,” in European Conference on Networks and Communications (EuCNC), 2019.' mla: 'Schneider, Stefan Balthasar, et al. “Putting 5G into Production: Realizing a Smart Manufacturing Vertical Scenario.” European Conference on Networks and Communications (EuCNC), IEEE, 2019, doi:10.1109/eucnc.2019.8802016.' short: 'S.B. Schneider, M. Peuster, D. Behnke, M. Marcel, P.-B. Bök, H. Karl, in: European Conference on Networks and Communications (EuCNC), IEEE, Valencia, Spain, 2019.' date_created: 2019-04-23T09:27:06Z date_updated: 2022-01-06T07:04:12Z ddc: - '000' department: - _id: '75' doi: 10.1109/eucnc.2019.8802016 file: - access_level: open_access content_type: application/pdf creator: stschn date_created: 2019-04-23T09:29:49Z date_updated: 2019-12-12T09:15:57Z file_id: '9272' file_name: preprint_ris_with_header.pdf file_size: 374397 relation: main_file file_date_updated: 2019-12-12T09:15:57Z has_accepted_license: '1' keyword: - 5g - vertical - smart manufacturing - nfv language: - iso: eng main_file_link: - url: https://ieeexplore.ieee.org/document/8802016 oa: '1' place: Valencia, Spain project: - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 - _id: '28' grant_number: '761493' name: 5G Development and validation platform for global industry-specific network services and Apps publication: European Conference on Networks and Communications (EuCNC) publisher: IEEE status: public title: 'Putting 5G into Production: Realizing a Smart Manufacturing Vertical Scenario' type: conference user_id: '35343' year: '2019' ... --- _id: '8792' abstract: - lang: eng text: "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.\r\nThis 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." author: - first_name: Manuel full_name: Peuster, Manuel id: '13271' last_name: Peuster - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Daniel full_name: Behnke, Daniel last_name: Behnke - first_name: Marcel full_name: Müller, Marcel last_name: Müller - first_name: Patrick-Benjamin full_name: Bök, Patrick-Benjamin last_name: Bök - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Peuster M, Schneider SB, Behnke D, Müller M, Bök P-B, Karl H. Prototyping and Demonstrating 5G Verticals: The Smart Manufacturing Case. In: 5th IEEE International Conference on Network Softwarization (NetSoft 2019). Paris; 2019. doi:10.1109/NETSOFT.2019.8806685' apa: 'Peuster, M., Schneider, S. B., Behnke, D., Müller, M., Bök, P.-B., & Karl, H. (2019). Prototyping and Demonstrating 5G Verticals: The Smart Manufacturing Case. In 5th IEEE International Conference on Network Softwarization (NetSoft 2019). Paris. https://doi.org/10.1109/NETSOFT.2019.8806685' bibtex: '@inproceedings{Peuster_Schneider_Behnke_Müller_Bök_Karl_2019, place={Paris}, title={Prototyping and Demonstrating 5G Verticals: The Smart Manufacturing Case}, DOI={10.1109/NETSOFT.2019.8806685}, booktitle={5th IEEE International Conference on Network Softwarization (NetSoft 2019)}, author={Peuster, Manuel and Schneider, Stefan Balthasar and Behnke, Daniel and Müller, Marcel and Bök, Patrick-Benjamin and Karl, Holger}, year={2019} }' chicago: 'Peuster, Manuel, Stefan Balthasar Schneider, Daniel Behnke, Marcel Müller, Patrick-Benjamin Bök, and Holger Karl. “Prototyping and Demonstrating 5G Verticals: The Smart Manufacturing Case.” In 5th IEEE International Conference on Network Softwarization (NetSoft 2019). Paris, 2019. https://doi.org/10.1109/NETSOFT.2019.8806685.' ieee: 'M. Peuster, S. B. Schneider, D. Behnke, M. Müller, P.-B. Bök, and H. Karl, “Prototyping and Demonstrating 5G Verticals: The Smart Manufacturing Case,” in 5th IEEE International Conference on Network Softwarization (NetSoft 2019), Paris, 2019.' mla: 'Peuster, Manuel, et al. “Prototyping and Demonstrating 5G Verticals: The Smart Manufacturing Case.” 5th IEEE International Conference on Network Softwarization (NetSoft 2019), 2019, doi:10.1109/NETSOFT.2019.8806685.' short: 'M. Peuster, S.B. Schneider, D. Behnke, M. Müller, P.-B. Bök, H. Karl, in: 5th IEEE International Conference on Network Softwarization (NetSoft 2019), Paris, 2019.' conference: end_date: 2019-06-28 location: Paris name: 5th IEEE International Conference on Network Softwarization (NetSoft 2019) start_date: 2019-06-24 date_created: 2019-04-01T13:37:05Z date_updated: 2022-01-06T07:04:01Z ddc: - '000' department: - _id: '75' doi: 10.1109/NETSOFT.2019.8806685 file: - access_level: open_access content_type: application/pdf creator: peuster date_created: 2019-04-01T13:46:18Z date_updated: 2019-04-01T13:46:18Z file_id: '8794' file_name: main_for_ris.pdf file_size: 1693793 relation: main_file file_date_updated: 2019-04-01T13:46:18Z has_accepted_license: '1' language: - iso: eng main_file_link: - url: https://doi.org/10.1109/NETSOFT.2019.8806685 oa: '1' place: Paris project: - _id: '28' grant_number: '761493' name: 5G Development and validation platform for global industry-specific network services and Apps - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 publication: 5th IEEE International Conference on Network Softwarization (NetSoft 2019) status: public title: 'Prototyping and Demonstrating 5G Verticals: The Smart Manufacturing Case' type: conference user_id: '13271' year: '2019' ... --- _id: '9824' author: - first_name: Manuel full_name: Peuster, Manuel id: '13271' last_name: Peuster - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Mengxuan full_name: Zhao, Mengxuan last_name: Zhao - first_name: George full_name: Xilouris, George last_name: Xilouris - first_name: Panagiotis full_name: Trakadas, Panagiotis last_name: Trakadas - first_name: Felipe full_name: Vicens, Felipe last_name: Vicens - first_name: Wouter full_name: Tavernier, Wouter last_name: Tavernier - first_name: Thomas full_name: Soenen, Thomas last_name: Soenen - first_name: Ricard full_name: Vilalta, Ricard last_name: Vilalta - first_name: George full_name: Andreou, George last_name: Andreou - first_name: Dimosthenis full_name: Kyriazis, Dimosthenis last_name: Kyriazis - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: Peuster M, Schneider SB, Zhao M, et al. Introducing Automated Verification and Validation for Virtualized Network Functions and Services. IEEE Communications Magazine. 2019:96-102. doi:10.1109/mcom.2019.1800873 apa: Peuster, M., Schneider, S. B., Zhao, M., Xilouris, G., Trakadas, P., Vicens, F., … Karl, H. (2019). Introducing Automated Verification and Validation for Virtualized Network Functions and Services. IEEE Communications Magazine, 96–102. https://doi.org/10.1109/mcom.2019.1800873 bibtex: '@article{Peuster_Schneider_Zhao_Xilouris_Trakadas_Vicens_Tavernier_Soenen_Vilalta_Andreou_et al._2019, title={Introducing Automated Verification and Validation for Virtualized Network Functions and Services}, DOI={10.1109/mcom.2019.1800873}, journal={IEEE Communications Magazine}, author={Peuster, Manuel and Schneider, Stefan Balthasar and Zhao, Mengxuan and Xilouris, George and Trakadas, Panagiotis and Vicens, Felipe and Tavernier, Wouter and Soenen, Thomas and Vilalta, Ricard and Andreou, George and et al.}, year={2019}, pages={96–102} }' chicago: Peuster, Manuel, Stefan Balthasar Schneider, Mengxuan Zhao, George Xilouris, Panagiotis Trakadas, Felipe Vicens, Wouter Tavernier, et al. “Introducing Automated Verification and Validation for Virtualized Network Functions and Services.” IEEE Communications Magazine, 2019, 96–102. https://doi.org/10.1109/mcom.2019.1800873. ieee: M. Peuster et al., “Introducing Automated Verification and Validation for Virtualized Network Functions and Services,” IEEE Communications Magazine, pp. 96–102, 2019. mla: Peuster, Manuel, et al. “Introducing Automated Verification and Validation for Virtualized Network Functions and Services.” IEEE Communications Magazine, 2019, pp. 96–102, doi:10.1109/mcom.2019.1800873. short: 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) 96–102. date_created: 2019-05-16T09:09:16Z date_updated: 2022-01-06T07:04:23Z ddc: - '000' department: - _id: '75' doi: 10.1109/mcom.2019.1800873 file: - access_level: open_access content_type: application/pdf creator: peuster date_created: 2019-05-16T09:13:40Z date_updated: 2019-05-16T09:13:40Z description: |+ Preprint of original article: M. Peuster et al., "Introducing Automated Verification and Validation for Virtualized Network Functions and Services," in IEEE Communications Magazine, vol. 57, no. 5, pp. 96-102, May 2019. doi: 10.1109/MCOM.2019.1800873 file_id: '9825' file_name: main_for_ris.pdf file_size: 1735036 relation: main_file title: Introducing Automated Verification and Validation for Virtualized Network Functions and Services file_date_updated: 2019-05-16T09:13:40Z has_accepted_license: '1' language: - iso: eng main_file_link: - url: https://ieeexplore.ieee.org/document/8713807 oa: '1' page: 96-102 project: - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 publication: IEEE Communications Magazine publication_identifier: issn: - 0163-6804 - 1558-1896 publication_status: published status: public title: Introducing Automated Verification and Validation for Virtualized Network Functions and Services type: journal_article user_id: '13271' year: '2019' ... ... --- _id: '15369' author: - first_name: Marcel full_name: Müller, Marcel last_name: Müller - first_name: Daniel full_name: Behnke, Daniel last_name: Behnke - first_name: Patrick-Benjamin full_name: Bök, Patrick-Benjamin last_name: Bök - first_name: Manuel full_name: Peuster, Manuel id: '13271' last_name: Peuster - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Müller M, Behnke D, Bök P-B, Peuster M, Schneider SB, Karl H. 5G as Key Technology for Networked Factories: Application of Vertical-specific Network Services for Enabling Flexible Smart Manufacturing. In: IEEE 17th International Conference on Industrial Informatics (IEEE-INDIN). Helsinki: IEEE; 2019.' apa: 'Müller, M., Behnke, D., Bök, P.-B., Peuster, M., Schneider, S. B., & Karl, H. (2019). 5G as Key Technology for Networked Factories: Application of Vertical-specific Network Services for Enabling Flexible Smart Manufacturing. In IEEE 17th International Conference on Industrial Informatics (IEEE-INDIN). Helsinki: IEEE.' bibtex: '@inproceedings{Müller_Behnke_Bök_Peuster_Schneider_Karl_2019, place={Helsinki}, title={5G as Key Technology for Networked Factories: Application of Vertical-specific Network Services for Enabling Flexible Smart Manufacturing}, booktitle={IEEE 17th International Conference on Industrial Informatics (IEEE-INDIN)}, publisher={IEEE}, author={Müller, Marcel and Behnke, Daniel and Bök, Patrick-Benjamin and Peuster, Manuel and Schneider, Stefan Balthasar and Karl, Holger}, year={2019} }' chicago: 'Müller, Marcel, Daniel Behnke, Patrick-Benjamin Bök, Manuel Peuster, Stefan Balthasar Schneider, and Holger Karl. “5G as Key Technology for Networked Factories: Application of Vertical-Specific Network Services for Enabling Flexible Smart Manufacturing.” In IEEE 17th International Conference on Industrial Informatics (IEEE-INDIN). Helsinki: IEEE, 2019.' ieee: 'M. Müller, D. Behnke, P.-B. Bök, M. Peuster, S. B. Schneider, and H. Karl, “5G as Key Technology for Networked Factories: Application of Vertical-specific Network Services for Enabling Flexible Smart Manufacturing,” in IEEE 17th International Conference on Industrial Informatics (IEEE-INDIN), 2019.' mla: 'Müller, Marcel, et al. “5G as Key Technology for Networked Factories: Application of Vertical-Specific Network Services for Enabling Flexible Smart Manufacturing.” IEEE 17th International Conference on Industrial Informatics (IEEE-INDIN), IEEE, 2019.' short: 'M. Müller, D. Behnke, P.-B. Bök, M. Peuster, S.B. Schneider, H. Karl, in: IEEE 17th International Conference on Industrial Informatics (IEEE-INDIN), IEEE, Helsinki, 2019.' date_created: 2019-12-18T07:27:24Z date_updated: 2022-01-06T06:52:21Z department: - _id: '75' language: - iso: eng place: Helsinki project: - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 - _id: '28' grant_number: '761493' name: 5G Development and validation platform for global industry-specific network services and Apps publication: IEEE 17th International Conference on Industrial Informatics (IEEE-INDIN) publisher: IEEE status: public title: '5G as Key Technology for Networked Factories: Application of Vertical-specific Network Services for Enabling Flexible Smart Manufacturing' type: conference user_id: '13271' year: '2019' ... --- _id: '15371' abstract: - lang: eng text: "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.\r\n" author: - first_name: Manuel full_name: Peuster, Manuel id: '13271' last_name: Peuster - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Peuster M, Schneider SB, Karl H. The Softwarised Network Data Zoo. In: IEEE/IFIP 15th International Conference on Network and Service Management (CNSM). Halifax: IEEE/IFIP; 2019.' apa: 'Peuster, M., Schneider, S. B., & Karl, H. (2019). The Softwarised Network Data Zoo. In IEEE/IFIP 15th International Conference on Network and Service Management (CNSM). Halifax: IEEE/IFIP.' bibtex: '@inproceedings{Peuster_Schneider_Karl_2019, place={Halifax}, title={The Softwarised Network Data Zoo}, booktitle={IEEE/IFIP 15th International Conference on Network and Service Management (CNSM)}, publisher={IEEE/IFIP}, author={Peuster, Manuel and Schneider, Stefan Balthasar and Karl, Holger}, year={2019} }' chicago: 'Peuster, Manuel, Stefan Balthasar Schneider, and Holger Karl. “The Softwarised Network Data Zoo.” In IEEE/IFIP 15th International Conference on Network and Service Management (CNSM). Halifax: IEEE/IFIP, 2019.' ieee: M. Peuster, S. B. Schneider, and H. Karl, “The Softwarised Network Data Zoo,” in IEEE/IFIP 15th International Conference on Network and Service Management (CNSM), 2019. mla: Peuster, Manuel, et al. “The Softwarised Network Data Zoo.” IEEE/IFIP 15th International Conference on Network and Service Management (CNSM), IEEE/IFIP, 2019. short: 'M. Peuster, S.B. Schneider, H. Karl, in: IEEE/IFIP 15th International Conference on Network and Service Management (CNSM), IEEE/IFIP, Halifax, 2019.' date_created: 2019-12-18T07:30:45Z date_updated: 2022-01-06T06:52:21Z ddc: - '000' department: - _id: '75' file: - access_level: open_access content_type: application/pdf creator: peuster date_created: 2019-12-18T08:10:23Z date_updated: 2019-12-18T08:10:23Z file_id: '15377' file_name: main_for_ris.pdf file_size: 515208 relation: main_file file_date_updated: 2019-12-18T08:10:23Z has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: http://dl.ifip.org/db/conf/cnsm/cnsm2019/1570555677.pdf oa: '1' place: Halifax project: - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 - _id: '28' grant_number: '761493' name: 5G Development and validation platform for global industry-specific network services and Apps publication: IEEE/IFIP 15th International Conference on Network and Service Management (CNSM) publisher: IEEE/IFIP status: public title: The Softwarised Network Data Zoo type: conference user_id: '13271' year: '2019' ... --- _id: '15372' author: - first_name: Askhat full_name: Nuriddinov, Askhat last_name: Nuriddinov - first_name: Wouter full_name: Tavernier, Wouter last_name: Tavernier - first_name: Didier full_name: Colle, Didier last_name: Colle - first_name: Mario full_name: Pickavet, Mario last_name: Pickavet - first_name: Manuel full_name: Peuster, Manuel id: '13271' last_name: Peuster - first_name: Stefan Balthasar full_name: Schneider, Stefan Balthasar id: '35343' last_name: Schneider orcid: 0000-0001-8210-4011 citation: ama: 'Nuriddinov A, Tavernier W, Colle D, Pickavet M, Peuster M, Schneider SB. Reproducible Functional Tests for Multi-scale Network Services. In: IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). Dallas: IEEE; 2019.' apa: 'Nuriddinov, A., Tavernier, W., Colle, D., Pickavet, M., Peuster, M., & Schneider, S. B. (2019). Reproducible Functional Tests for Multi-scale Network Services. In IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). Dallas: IEEE.' bibtex: '@inproceedings{Nuriddinov_Tavernier_Colle_Pickavet_Peuster_Schneider_2019, place={Dallas}, title={Reproducible Functional Tests for Multi-scale Network Services}, booktitle={ IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)}, publisher={IEEE}, author={Nuriddinov, Askhat and Tavernier, Wouter and Colle, Didier and Pickavet, Mario and Peuster, Manuel and Schneider, Stefan Balthasar}, year={2019} }' chicago: 'Nuriddinov, Askhat, Wouter Tavernier, Didier Colle, Mario Pickavet, Manuel Peuster, and Stefan Balthasar Schneider. “Reproducible Functional Tests for Multi-Scale Network Services.” In IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). Dallas: IEEE, 2019.' ieee: A. Nuriddinov, W. Tavernier, D. Colle, M. Pickavet, M. Peuster, and S. B. Schneider, “Reproducible Functional Tests for Multi-scale Network Services,” in IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), 2019. mla: Nuriddinov, Askhat, et al. “Reproducible Functional Tests for Multi-Scale Network Services.” IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), IEEE, 2019. short: '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, Dallas, 2019.' date_created: 2019-12-18T07:36:04Z date_updated: 2022-01-06T06:52:21Z department: - _id: '75' language: - iso: eng place: Dallas project: - _id: '28' grant_number: '761493' name: 5G Development and validation platform for global industry-specific network services and Apps publication: ' IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)' publisher: IEEE status: public title: Reproducible Functional Tests for Multi-scale Network Services type: conference user_id: '13271' year: '2019' ...