--- _id: '50066' author: - first_name: Feng full_name: Dou, Feng last_name: Dou - first_name: Lin full_name: Wang, Lin id: '102868' last_name: Wang - first_name: Shutong full_name: Chen, Shutong last_name: Chen - first_name: Fangming full_name: Liu, Fangming last_name: Liu citation: ama: 'Dou F, Wang L, Chen S, Liu F. X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics. In: Proceedings of the IEEE International Conference on Computer Communications (INFOCOM). IEEE.' apa: 'Dou, F., Wang, L., Chen, S., & Liu, F. (n.d.). X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics. Proceedings of the IEEE International Conference on Computer Communications (INFOCOM). IEEE International Conference on Computer Communications (INFOCOM), Vancouver, Canada.' bibtex: '@inproceedings{Dou_Wang_Chen_Liu, title={X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics}, booktitle={Proceedings of the IEEE International Conference on Computer Communications (INFOCOM)}, publisher={IEEE}, author={Dou, Feng and Wang, Lin and Chen, Shutong and Liu, Fangming} }' chicago: 'Dou, Feng, Lin Wang, Shutong Chen, and Fangming Liu. “X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics.” In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM). IEEE, n.d.' ieee: 'F. Dou, L. Wang, S. Chen, and F. Liu, “X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics,” presented at the IEEE International Conference on Computer Communications (INFOCOM), Vancouver, Canada.' mla: 'Dou, Feng, et al. “X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics.” Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), IEEE.' short: 'F. Dou, L. Wang, S. Chen, F. Liu, in: Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), IEEE, n.d.' conference: end_date: 2024-05-23 location: Vancouver, Canada name: IEEE International Conference on Computer Communications (INFOCOM) start_date: 2024-05-20 date_created: 2023-12-22T20:24:27Z date_updated: 2024-01-23T20:35:02Z department: - _id: '34' - _id: '7' - _id: '75' language: - iso: eng publication: Proceedings of the IEEE International Conference on Computer Communications (INFOCOM) publication_status: accepted publisher: IEEE status: public title: 'X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics' type: conference user_id: '102868' year: '2024' ... --- _id: '50065' author: - first_name: Marcel full_name: Blöcher, Marcel last_name: Blöcher - first_name: Nils full_name: Nedderhut, Nils last_name: Nedderhut - first_name: Pavel full_name: Chuprikov, Pavel last_name: Chuprikov - first_name: Ramin full_name: Khalili, Ramin last_name: Khalili - first_name: Patrick full_name: Eugster, Patrick last_name: Eugster - first_name: Lin full_name: Wang, Lin id: '102868' last_name: Wang citation: ama: 'Blöcher M, Nedderhut N, Chuprikov P, Khalili R, Eugster P, Wang L. Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES. In: Proceedings of the IEEE International Conference on Computer Communications (INFOCOM). IEEE.' apa: 'Blöcher, M., Nedderhut, N., Chuprikov, P., Khalili, R., Eugster, P., & Wang, L. (n.d.). Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES. Proceedings of the IEEE International Conference on Computer Communications (INFOCOM). IEEE International Conference on Computer Communications (INFOCOM), Vancouver, Canada.' bibtex: '@inproceedings{Blöcher_Nedderhut_Chuprikov_Khalili_Eugster_Wang, title={Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES}, booktitle={Proceedings of the IEEE International Conference on Computer Communications (INFOCOM)}, publisher={IEEE}, author={Blöcher, Marcel and Nedderhut, Nils and Chuprikov, Pavel and Khalili, Ramin and Eugster, Patrick and Wang, Lin} }' chicago: 'Blöcher, Marcel, Nils Nedderhut, Pavel Chuprikov, Ramin Khalili, Patrick Eugster, and Lin Wang. “Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES.” In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM). IEEE, n.d.' ieee: 'M. Blöcher, N. Nedderhut, P. Chuprikov, R. Khalili, P. Eugster, and L. Wang, “Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES,” presented at the IEEE International Conference on Computer Communications (INFOCOM), Vancouver, Canada.' mla: 'Blöcher, Marcel, et al. “Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES.” Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), IEEE.' short: 'M. Blöcher, N. Nedderhut, P. Chuprikov, R. Khalili, P. Eugster, L. Wang, in: Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), IEEE, n.d.' conference: end_date: 2024-05-23 location: Vancouver, Canada name: IEEE International Conference on Computer Communications (INFOCOM) start_date: 2024-05-20 date_created: 2023-12-22T20:06:42Z date_updated: 2024-01-23T20:35:09Z department: - _id: '75' language: - iso: eng publication: Proceedings of the IEEE International Conference on Computer Communications (INFOCOM) publication_status: accepted publisher: IEEE status: public title: 'Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES' type: conference user_id: '102868' year: '2024' ... --- _id: '50807' author: - first_name: Haichuan full_name: Hu, Haichuan last_name: Hu - first_name: Fangming full_name: Liu, Fangming last_name: Liu - first_name: Qiangyu full_name: Pei, Qiangyu last_name: Pei - first_name: Yongjie full_name: Yuan, Yongjie last_name: Yuan - first_name: Zichen full_name: Xu, Zichen last_name: Xu - first_name: Lin full_name: Wang, Lin id: '102868' last_name: Wang citation: ama: "Hu H, Liu F, Pei Q, Yuan Y, Xu Z, Wang L. \U0001D706Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing. In: Proceedings of the ACM Web Conference (WWW). ACM; 2024." apa: "Hu, H., Liu, F., Pei, Q., Yuan, Y., Xu, Z., & Wang, L. (2024). \U0001D706Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing. Proceedings of the ACM Web Conference (WWW). ACM Web Conference (WWW), Singapore." bibtex: "@inproceedings{Hu_Liu_Pei_Yuan_Xu_Wang_2024, title={\U0001D706Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing}, booktitle={Proceedings of the ACM Web Conference (WWW)}, publisher={ACM}, author={Hu, Haichuan and Liu, Fangming and Pei, Qiangyu and Yuan, Yongjie and Xu, Zichen and Wang, Lin}, year={2024} }" chicago: "Hu, Haichuan, Fangming Liu, Qiangyu Pei, Yongjie Yuan, Zichen Xu, and Lin Wang. “\U0001D706Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing.” In Proceedings of the ACM Web Conference (WWW). ACM, 2024." ieee: "H. Hu, F. Liu, Q. Pei, Y. Yuan, Z. Xu, and L. Wang, “\U0001D706Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing,” presented at the ACM Web Conference (WWW), Singapore, 2024." mla: "Hu, Haichuan, et al. “\U0001D706Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing.” Proceedings of the ACM Web Conference (WWW), ACM, 2024." short: 'H. Hu, F. Liu, Q. Pei, Y. Yuan, Z. Xu, L. Wang, in: Proceedings of the ACM Web Conference (WWW), ACM, 2024.' conference: end_date: 2024-05-17 location: Singapore name: ACM Web Conference (WWW) start_date: 2024-05-13 date_created: 2024-01-23T20:34:27Z date_updated: 2024-01-23T20:35:20Z department: - _id: '34' - _id: '7' - _id: '75' language: - iso: eng publication: Proceedings of the ACM Web Conference (WWW) publisher: ACM status: public title: "\U0001D706Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing" type: conference user_id: '102868' year: '2024' ... --- _id: '53095' author: - first_name: Kamran full_name: Razavi, Kamran last_name: Razavi - first_name: Saeid full_name: Ghafouri, Saeid last_name: Ghafouri - first_name: Max full_name: Mühlhäuser, Max last_name: Mühlhäuser - first_name: Pooyan full_name: Jamshidi, Pooyan last_name: Jamshidi - first_name: Lin full_name: Wang, Lin id: '102868' last_name: Wang citation: ama: 'Razavi K, Ghafouri S, Mühlhäuser M, Jamshidi P, Wang L. Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling. In: Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), Colocated with EuroSys 2024. ACM; 2024.' apa: 'Razavi, K., Ghafouri, S., Mühlhäuser, M., Jamshidi, P., & Wang, L. (2024). Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling. Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), Colocated with EuroSys 2024. The 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024, Athens, Greece.' bibtex: '@inproceedings{Razavi_Ghafouri_Mühlhäuser_Jamshidi_Wang_2024, title={Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling}, booktitle={Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024}, publisher={ACM}, author={Razavi, Kamran and Ghafouri, Saeid and Mühlhäuser, Max and Jamshidi, Pooyan and Wang, Lin}, year={2024} }' chicago: 'Razavi, Kamran, Saeid Ghafouri, Max Mühlhäuser, Pooyan Jamshidi, and Lin Wang. “Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling.” In Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), Colocated with EuroSys 2024. ACM, 2024.' ieee: 'K. Razavi, S. Ghafouri, M. Mühlhäuser, P. Jamshidi, and L. Wang, “Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling,” presented at the The 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024, Athens, Greece, 2024.' mla: 'Razavi, Kamran, et al. “Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling.” Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), Colocated with EuroSys 2024, ACM, 2024.' short: 'K. Razavi, S. Ghafouri, M. Mühlhäuser, P. Jamshidi, L. Wang, in: Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), Colocated with EuroSys 2024, ACM, 2024.' conference: end_date: 2024-04-22 location: Athens, Greece name: The 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024 start_date: 2024-04-22 date_created: 2024-03-28T12:00:49Z date_updated: 2024-03-28T12:02:23Z department: - _id: '34' - _id: '7' - _id: '75' language: - iso: eng publication: Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024 publisher: ACM status: public title: 'Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling' type: conference user_id: '102868' year: '2024' ... --- _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: '32811' abstract: - lang: eng text: 'The decentralized nature of multi-agent systems requires continuous data exchange to achieve global objectives. In such scenarios, Age of Information (AoI) has become an important metric of the freshness of exchanged data due to the error-proneness and delays of communication systems. Communication systems usually possess dependencies: the process describing the success or failure of communication is highly correlated when these attempts are ``close'''' in some domain (e.g. in time, frequency, space or code as in wireless communication) and is, in general, non-stationary. To study AoI in such scenarios, we consider an abstract event-based AoI process $\Delta(n)$, expressing time since the last update: If, at time $n$, a monitoring node receives a status update from a source node (event $A(n-1)$ occurs), then $\Delta(n)$ is reset to one; otherwise, $\Delta(n)$ grows linearly in time. This AoI process can thus be viewed as a special random walk with resets. The event process $A(n)$ may be nonstationary and we merely assume that its temporal dependencies decay sufficiently, described by $\alpha$-mixing. We calculate moment bounds for the resulting AoI process as a function of the mixing rate of $A(n)$. Furthermore, we prove that the AoI process $\Delta(n)$ is itself $\alpha$-mixing from which we conclude a strong law of large numbers for $\Delta(n)$. These results are new, since AoI processes have not been studied so far in this general strongly mixing setting. This opens up future work on renewal processes with non-independent interarrival times.' author: - first_name: Adrian full_name: Redder, Adrian id: '52265' last_name: Redder orcid: https://orcid.org/0000-0001-7391-4688 - first_name: Arunselvan full_name: Ramaswamy, Arunselvan id: '66937' last_name: Ramaswamy orcid: https://orcid.org/ 0000-0001-7547-8111 - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Redder A, Ramaswamy A, Karl H. Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law. In: Proceedings of the 58th Allerton Conference on Communication, Control, and Computing. ; 2022.' apa: Redder, A., Ramaswamy, A., & Karl, H. (2022). Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law. Proceedings of the 58th Allerton Conference on Communication, Control, and Computing. 58th Allerton Conference on Communication, Control, and Computing. bibtex: '@inproceedings{Redder_Ramaswamy_Karl_2022, title={Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law}, booktitle={Proceedings of the 58th Allerton Conference on Communication, Control, and Computing}, author={Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}, year={2022} }' chicago: Redder, Adrian, Arunselvan Ramaswamy, and Holger Karl. “Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law.” In Proceedings of the 58th Allerton Conference on Communication, Control, and Computing, 2022. ieee: A. Redder, A. Ramaswamy, and H. Karl, “Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law,” presented at the 58th Allerton Conference on Communication, Control, and Computing, 2022. mla: Redder, Adrian, et al. “Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law.” Proceedings of the 58th Allerton Conference on Communication, Control, and Computing, 2022. short: 'A. Redder, A. Ramaswamy, H. Karl, in: Proceedings of the 58th Allerton Conference on Communication, Control, and Computing, 2022.' conference: name: 58th Allerton Conference on Communication, Control, and Computing date_created: 2022-08-15T09:59:17Z date_updated: 2022-11-18T09:31:19Z ddc: - '000' department: - _id: '75' has_accepted_license: '1' language: - iso: eng project: - _id: '16' name: 'SFB 901 - C4: SFB 901 - Subproject C4' - _id: '1' name: 'SFB 901: SFB 901' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' publication: Proceedings of the 58th Allerton Conference on Communication, Control, and Computing status: public title: Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law type: conference user_id: '477' year: '2022' ... --- _id: '30793' author: - first_name: Adrian full_name: Redder, Adrian id: '52265' last_name: Redder orcid: https://orcid.org/0000-0001-7391-4688 - first_name: Arunselvan full_name: Ramaswamy, Arunselvan id: '66937' last_name: Ramaswamy orcid: https://orcid.org/ 0000-0001-7547-8111 - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Redder A, Ramaswamy A, Karl H. Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication. In: Proceedings of the 14th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications; 2022. doi:10.5220/0010845400003116' apa: Redder, A., Ramaswamy, A., & Karl, H. (2022). Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication. Proceedings of the 14th International Conference on Agents and Artificial Intelligence. https://doi.org/10.5220/0010845400003116 bibtex: '@inproceedings{Redder_Ramaswamy_Karl_2022, title={Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication}, DOI={10.5220/0010845400003116}, booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence}, publisher={SCITEPRESS - Science and Technology Publications}, author={Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}, year={2022} }' chicago: Redder, Adrian, Arunselvan Ramaswamy, and Holger Karl. “Multi-Agent Policy Gradient Algorithms for Cyber-Physical Systems with Lossy Communication.” In Proceedings of the 14th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2022. https://doi.org/10.5220/0010845400003116. ieee: 'A. Redder, A. Ramaswamy, and H. Karl, “Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication,” 2022, doi: 10.5220/0010845400003116.' mla: Redder, Adrian, et al. “Multi-Agent Policy Gradient Algorithms for Cyber-Physical Systems with Lossy Communication.” Proceedings of the 14th International Conference on Agents and Artificial Intelligence, SCITEPRESS - Science and Technology Publications, 2022, doi:10.5220/0010845400003116. short: 'A. Redder, A. Ramaswamy, H. Karl, in: Proceedings of the 14th International Conference on Agents and Artificial Intelligence, SCITEPRESS - Science and Technology Publications, 2022.' date_created: 2022-04-06T07:18:36Z date_updated: 2022-11-18T09:32:14Z ddc: - '006' department: - _id: '75' doi: 10.5220/0010845400003116 file: - access_level: closed content_type: application/pdf creator: aredder date_created: 2022-08-31T07:10:13Z date_updated: 2022-08-31T07:10:13Z file_id: '33237' file_name: ICCART2022.pdf file_size: 298926 relation: main_file success: 1 file_date_updated: 2022-08-31T07:10:13Z has_accepted_license: '1' language: - iso: eng project: - _id: '16' name: 'SFB 901 - C4: SFB 901 - Subproject C4' - _id: '24' name: 'NICCI-CN: Netzgewahre Regelung & regelungsgewahre Netze' - _id: '1' name: 'SFB 901: SFB 901' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' publication: Proceedings of the 14th International Conference on Agents and Artificial Intelligence publication_status: published publisher: SCITEPRESS - Science and Technology Publications status: public title: Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication type: conference user_id: '477' year: '2022' ... --- _id: '30790' abstract: - lang: eng text: "Iterative distributed optimization algorithms involve multiple agents that\r\ncommunicate with each other, over time, in order to minimize/maximize a global\r\nobjective. In the presence of unreliable communication networks, the\r\nAge-of-Information (AoI), which measures the freshness of data received, may be\r\nlarge and hence hinder algorithmic convergence. In this paper, we study the\r\nconvergence of general distributed gradient-based optimization algorithms in\r\nthe presence of communication that neither happens periodically nor at\r\nstochastically independent points in time. We show that convergence is\r\nguaranteed provided the random variables associated with the AoI processes are\r\nstochastically dominated by a random variable with finite first moment. This\r\nimproves on previous requirements of boundedness of more than the first moment.\r\nWe then introduce stochastically strongly connected (SSC) networks, a new\r\nstochastic form of strong connectedness for time-varying networks. We show: If\r\nfor any $p \\ge0$ the processes that describe the success of communication\r\nbetween agents in a SSC network are $\\alpha$-mixing with $n^{p-1}\\alpha(n)$\r\nsummable, then the associated AoI processes are stochastically dominated by a\r\nrandom variable with finite $p$-th moment. In combination with our first\r\ncontribution, this implies that distributed stochastic gradient descend\r\nconverges in the presence of AoI, if $\\alpha(n)$ is summable." author: - first_name: Adrian full_name: Redder, Adrian id: '52265' last_name: Redder orcid: https://orcid.org/0000-0001-7391-4688 - first_name: Arunselvan full_name: Ramaswamy, Arunselvan id: '66937' last_name: Ramaswamy orcid: https://orcid.org/ 0000-0001-7547-8111 - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: Redder A, Ramaswamy A, Karl H. Distributed gradient-based optimization in the presence of dependent  aperiodic communication. arXiv:220111343. Published online 2022. apa: Redder, A., Ramaswamy, A., & Karl, H. (2022). Distributed gradient-based optimization in the presence of dependent  aperiodic communication. In arXiv:2201.11343. bibtex: '@article{Redder_Ramaswamy_Karl_2022, title={Distributed gradient-based optimization in the presence of dependent  aperiodic communication}, journal={arXiv:2201.11343}, author={Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}, year={2022} }' chicago: Redder, Adrian, Arunselvan Ramaswamy, and Holger Karl. “Distributed Gradient-Based Optimization in the Presence of Dependent  Aperiodic Communication.” ArXiv:2201.11343, 2022. ieee: A. Redder, A. Ramaswamy, and H. Karl, “Distributed gradient-based optimization in the presence of dependent  aperiodic communication,” arXiv:2201.11343. 2022. mla: Redder, Adrian, et al. “Distributed Gradient-Based Optimization in the Presence of Dependent  Aperiodic Communication.” ArXiv:2201.11343, 2022. short: A. Redder, A. Ramaswamy, H. Karl, ArXiv:2201.11343 (2022). date_created: 2022-04-06T06:53:38Z date_updated: 2022-11-18T09:33:01Z department: - _id: '75' external_id: arxiv: - '2201.11343' language: - iso: eng project: - _id: '16' name: 'SFB 901 - C4: SFB 901 - Subproject C4' - _id: '1' name: 'SFB 901: SFB 901' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' publication: arXiv:2201.11343 status: public title: Distributed gradient-based optimization in the presence of dependent aperiodic communication type: preprint user_id: '477' year: '2022' ... --- _id: '30791' abstract: - lang: eng text: "We present sufficient conditions that ensure convergence of the multi-agent\r\nDeep Deterministic Policy Gradient (DDPG) algorithm. It is an example of one of\r\nthe most popular paradigms of Deep Reinforcement Learning (DeepRL) for tackling\r\ncontinuous action spaces: the actor-critic paradigm. In the setting considered\r\nherein, each agent observes a part of the global state space in order to take\r\nlocal actions, for which it receives local rewards. For every agent, DDPG\r\ntrains a local actor (policy) and a local critic (Q-function). The analysis\r\nshows that multi-agent DDPG using neural networks to approximate the local\r\npolicies and critics converge to limits with the following properties: The\r\ncritic limits minimize the average squared Bellman loss; the actor limits\r\nparameterize a policy that maximizes the local critic's approximation of\r\n$Q_i^*$, where $i$ is the agent index. The averaging is with respect to a\r\nprobability distribution over the global state-action space. It captures the\r\nasymptotics of all local training processes. Finally, we extend the analysis to\r\na fully decentralized setting where agents communicate over a wireless network\r\nprone to delays and losses; a typical scenario in, e.g., robotic applications." author: - first_name: Adrian full_name: Redder, Adrian id: '52265' last_name: Redder orcid: https://orcid.org/0000-0001-7391-4688 - first_name: Arunselvan full_name: Ramaswamy, Arunselvan id: '66937' last_name: Ramaswamy orcid: https://orcid.org/ 0000-0001-7547-8111 - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: Redder A, Ramaswamy A, Karl H. Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms. arXiv:220100570. Published online 2022. apa: Redder, A., Ramaswamy, A., & Karl, H. (2022). Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms. In arXiv:2201.00570. bibtex: '@article{Redder_Ramaswamy_Karl_2022, title={Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms}, journal={arXiv:2201.00570}, author={Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}, year={2022} }' chicago: Redder, Adrian, Arunselvan Ramaswamy, and Holger Karl. “Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms.” ArXiv:2201.00570, 2022. ieee: A. Redder, A. Ramaswamy, and H. Karl, “Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms,” arXiv:2201.00570. 2022. mla: Redder, Adrian, et al. “Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms.” ArXiv:2201.00570, 2022. short: A. Redder, A. Ramaswamy, H. Karl, ArXiv:2201.00570 (2022). date_created: 2022-04-06T06:53:52Z date_updated: 2022-11-18T09:33:42Z department: - _id: '75' external_id: arxiv: - '2201.00570' language: - iso: eng project: - _id: '16' name: 'SFB 901 - C4: SFB 901 - Subproject C4' - _id: '1' name: 'SFB 901: SFB 901' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' publication: arXiv:2201.00570 status: public title: Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms type: preprint user_id: '477' year: '2022' ... --- _id: '32854' author: - first_name: Adrian full_name: Redder, Adrian id: '52265' last_name: Redder orcid: https://orcid.org/0000-0001-7391-4688 - first_name: Arunselvan full_name: Ramaswamy, Arunselvan id: '66937' last_name: Ramaswamy orcid: https://orcid.org/ 0000-0001-7547-8111 - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: Redder A, Ramaswamy A, Karl H. Practical Network Conditions for the Convergence of Distributed Optimization. IFAC-PapersOnLine. 2022;55(13):133–138. apa: Redder, A., Ramaswamy, A., & Karl, H. (2022). Practical Network Conditions for the Convergence of Distributed Optimization. IFAC-PapersOnLine, 55(13), 133–138. bibtex: '@article{Redder_Ramaswamy_Karl_2022, title={Practical Network Conditions for the Convergence of Distributed Optimization}, volume={55}, number={13}, journal={IFAC-PapersOnLine}, publisher={Elsevier}, author={Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}, year={2022}, pages={133–138} }' chicago: 'Redder, Adrian, Arunselvan Ramaswamy, and Holger Karl. “Practical Network Conditions for the Convergence of Distributed Optimization.” IFAC-PapersOnLine 55, no. 13 (2022): 133–138.' ieee: A. Redder, A. Ramaswamy, and H. Karl, “Practical Network Conditions for the Convergence of Distributed Optimization,” IFAC-PapersOnLine, vol. 55, no. 13, pp. 133–138, 2022. mla: Redder, Adrian, et al. “Practical Network Conditions for the Convergence of Distributed Optimization.” IFAC-PapersOnLine, vol. 55, no. 13, Elsevier, 2022, pp. 133–138. short: A. Redder, A. Ramaswamy, H. Karl, IFAC-PapersOnLine 55 (2022) 133–138. conference: name: IFAC Conference on Networked Systems date_created: 2022-08-16T09:12:55Z date_updated: 2022-11-18T10:05:14Z ddc: - '006' department: - _id: '75' file: - access_level: closed content_type: application/pdf creator: aredder date_created: 2022-08-31T07:06:30Z date_updated: 2022-08-31T07:06:30Z file_id: '33236' file_name: NecSys2022____Practical_Conditions_for_Conv.pdf file_size: 298395 relation: main_file success: 1 file_date_updated: 2022-08-31T07:06:30Z has_accepted_license: '1' intvolume: ' 55' issue: '13' language: - iso: eng page: 133–138 project: - _id: '16' name: 'SFB 901 - C4: SFB 901 - Subproject C4' - _id: '1' name: 'SFB 901: SFB 901' - _id: '4' name: 'SFB 901 - C: SFB 901 - Project Area C' publication: IFAC-PapersOnLine publisher: Elsevier status: public title: Practical Network Conditions for the Convergence of Distributed Optimization type: journal_article user_id: '477' volume: 55 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: '20125' abstract: - lang: eng text: Datacenter applications have different resource requirements from network and developing flow scheduling heuristics for every workload is practically infeasible. In this paper, we show that deep reinforcement learning (RL) can be used to efficiently learn flow scheduling policies for different workloads without manual feature engineering. Specifically, we present LFS, which learns to optimize a high-level performance objective, e.g., maximize the number of flow admissions while meeting the deadlines. The LFS scheduler is trained through deep RL to learn a scheduling policy on continuous online flow arrivals. The evaluation results show that the trained LFS scheduler admits 1.05x more flows than the greedy flow scheduling heuristics under varying network load. author: - first_name: Asif full_name: Hasnain, Asif id: '63288' last_name: Hasnain - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Hasnain A, Karl H. Learning Flow Scheduling. In: 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC). IEEE Computer Society. doi:https://doi.org/10.1109/CCNC49032.2021.9369514' apa: 'Hasnain, A., & Karl, H. (n.d.). Learning Flow Scheduling. In 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC). Las Vegas, USA: IEEE Computer Society. https://doi.org/10.1109/CCNC49032.2021.9369514' bibtex: '@inproceedings{Hasnain_Karl, title={Learning Flow Scheduling}, DOI={https://doi.org/10.1109/CCNC49032.2021.9369514}, booktitle={2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)}, publisher={IEEE Computer Society}, author={Hasnain, Asif and Karl, Holger} }' chicago: Hasnain, Asif, and Holger Karl. “Learning Flow Scheduling.” In 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC). IEEE Computer Society, n.d. https://doi.org/10.1109/CCNC49032.2021.9369514. ieee: A. Hasnain and H. Karl, “Learning Flow Scheduling,” in 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, USA. mla: Hasnain, Asif, and Holger Karl. “Learning Flow Scheduling.” 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), IEEE Computer Society, doi:https://doi.org/10.1109/CCNC49032.2021.9369514. short: 'A. Hasnain, H. Karl, in: 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), IEEE Computer Society, n.d.' conference: end_date: 2021-01-12 location: Las Vegas, USA name: 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC) start_date: 2021-01-09 date_created: 2020-10-19T14:27:17Z date_updated: 2022-01-06T06:54:20Z ddc: - '000' department: - _id: '75' doi: https://doi.org/10.1109/CCNC49032.2021.9369514 keyword: - Flow scheduling - Deadlines - Reinforcement learning language: - iso: eng main_file_link: - url: https://ieeexplore.ieee.org/document/9369514 project: - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 - _id: '1' name: SFB 901 publication: 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC) publication_status: accepted publisher: IEEE Computer Society status: public title: Learning Flow Scheduling type: conference user_id: '63288' year: '2021' ... --- _id: '27503' author: - first_name: Asif full_name: Hasnain, Asif last_name: Hasnain citation: ama: Hasnain A. Automating Network Resource Allocation for Coflows with Deadlines.; 2021. doi:10.17619/UNIPB/1-1241 apa: Hasnain, A. (2021). Automating Network Resource Allocation for Coflows with Deadlines. https://doi.org/10.17619/UNIPB/1-1241 bibtex: '@book{Hasnain_2021, title={Automating Network Resource Allocation for Coflows with Deadlines}, DOI={10.17619/UNIPB/1-1241 }, author={Hasnain, Asif}, year={2021} }' chicago: Hasnain, Asif. Automating Network Resource Allocation for Coflows with Deadlines, 2021. https://doi.org/10.17619/UNIPB/1-1241 . ieee: A. Hasnain, Automating Network Resource Allocation for Coflows with Deadlines. 2021. mla: Hasnain, Asif. Automating Network Resource Allocation for Coflows with Deadlines. 2021, doi:10.17619/UNIPB/1-1241 . short: A. Hasnain, Automating Network Resource Allocation for Coflows with Deadlines, 2021. date_created: 2021-11-16T13:05:12Z date_updated: 2022-01-06T06:57:40Z department: - _id: '75' doi: '10.17619/UNIPB/1-1241 ' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '4' name: SFB 901 - Project Area C - _id: '16' name: SFB 901 - Subproject C4 status: public supervisor: - first_name: Holger full_name: Karl, Holger last_name: Karl title: Automating Network Resource Allocation for Coflows with Deadlines type: dissertation user_id: '15504' year: '2021' ... --- _id: '21005' abstract: - lang: eng text: Data-parallel applications are developed using different data programming models, e.g., MapReduce, partition/aggregate. These models represent diverse resource requirements of application in a datacenter network, which can be represented by the coflow abstraction. The conventional method of creating hand-crafted coflow heuristics for admission or scheduling for different workloads is practically infeasible. In this paper, we propose a deep reinforcement learning (DRL)-based coflow admission scheme -- LCS -- that can learn an admission policy for a higher-level performance objective, i.e., maximize successful coflow admissions, without manual feature engineering. LCS is trained on a production trace, which has online coflow arrivals. The evaluation results show that LCS is able to learn a reasonable admission policy that admits more coflows than state-of-the-art Varys heuristic while meeting their deadlines. author: - first_name: Asif full_name: Hasnain, Asif id: '63288' last_name: Hasnain - first_name: Holger full_name: Karl, Holger id: '126' last_name: Karl citation: ama: 'Hasnain A, Karl H. Learning Coflow Admissions. In: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE Communications Society. doi:10.1109/INFOCOMWKSHPS51825.2021.9484599' apa: 'Hasnain, A., & Karl, H. (n.d.). Learning Coflow Admissions. In IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). Vancouver BC Canada: IEEE Communications Society. https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599' bibtex: '@inproceedings{Hasnain_Karl, title={Learning Coflow Admissions}, DOI={10.1109/INFOCOMWKSHPS51825.2021.9484599}, booktitle={IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)}, publisher={IEEE Communications Society}, author={Hasnain, Asif and Karl, Holger} }' chicago: Hasnain, Asif, and Holger Karl. “Learning Coflow Admissions.” In IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE Communications Society, n.d. https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599. ieee: A. Hasnain and H. Karl, “Learning Coflow Admissions,” in IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Vancouver BC Canada. mla: Hasnain, Asif, and Holger Karl. “Learning Coflow Admissions.” IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE Communications Society, doi:10.1109/INFOCOMWKSHPS51825.2021.9484599. short: 'A. Hasnain, H. Karl, in: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE Communications Society, n.d.' conference: end_date: 2021-05-13 location: Vancouver BC Canada name: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications start_date: 2021-05-10 date_created: 2021-01-16T18:24:19Z date_updated: 2022-01-06T06:54:42Z ddc: - '000' department: - _id: '75' doi: 10.1109/INFOCOMWKSHPS51825.2021.9484599 keyword: - Coflow scheduling - Reinforcement learning - Deadlines language: - iso: eng main_file_link: - url: https://ieeexplore.ieee.org/document/9484599 project: - _id: '16' name: SFB 901 - Subproject C4 - _id: '4' name: SFB 901 - Project Area C - _id: '1' name: SFB 901 publication: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) publication_status: accepted publisher: IEEE Communications Society related_material: link: - relation: confirmation url: https://ieeexplore.ieee.org/document/9484599 status: public title: Learning Coflow Admissions type: conference user_id: '63288' year: '2021' ... --- _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' ...