Redder A, Ramaswamy A, Karl H. Distributed gradient-based optimization in the presence of dependent  aperiodic communication. arXiv:220111343. Published online 2022.
LibreCat: /record/30790
arXiv: 2201.11343

Redder A, Ramaswamy A, Karl H. Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms. arXiv:220100570. Published online 2022.
LibreCat: /record/30791
arXiv: 2201.00570

Redder A, Ramaswamy A, Karl H. Practical Network Conditions for the Convergence of Distributed Optimization. IFAC-PapersOnLine. 2022;55(13):133–138.
LibreCat: /record/32854

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.
LibreCat: /record/29220

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
LibreCat: /record/20125
DOI: https://doi.org/10.1109/CCNC49032.2021.9369514

Hasnain A. Automating Network Resource Allocation for Coflows with Deadlines.; 2021. doi:10.17619/UNIPB/1-1241 
LibreCat: /record/27503
DOI: 10.17619/UNIPB/1-1241 

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
LibreCat: /record/21005
DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484599

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.
LibreCat: /record/21543

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.
LibreCat: /record/20693

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
LibreCat: /record/21808
DOI: 10.1109/TNSM.2021.3076503

Schneider SB, Karl H, Khalili R, Hecker A. DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning.; 2021.
LibreCat: /record/33854

Schneider SB. Conventional and Machine Learning Approaches for Network and Service Coordination.; 2021.
LibreCat: /record/35889

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.
LibreCat: /record/19607

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.
LibreCat: /record/19609

Hasnain A, Karl H. Coflow Scheduling with Performance Guarantees for Data Center Applications. In: 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID). IEEE Computer Society; 2020. doi:https://doi.org/10.1109/CCGrid49817.2020.00010
LibreCat: /record/17082
DOI: https://doi.org/10.1109/CCGrid49817.2020.00010

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.
LibreCat: /record/16219

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.
LibreCat: /record/16222

Nomikos N, Michailidis ET, Trakadas P, et al. A UAV-based moving 5G RAN for massive connectivity of mobile users and IoT devices. Vehicular Communications. 2020. doi:10.1016/j.vehcom.2020.100250
LibreCat: /record/16278
DOI: 10.1016/j.vehcom.2020.100250

Li F, Yu D, Yang H, Yu J, Karl H, Cheng X. Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey. IEEE Wireless Communications. 2020:24-30. doi:10.1109/mwc.001.1900280
LibreCat: /record/16280
DOI: 10.1109/mwc.001.1900280

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.
LibreCat: /record/16400