H. Karl, M. Maack, F. Meyer auf der Heide, S. Pukrop, and A. Redder, “On-The-Fly Compute Centers II: Execution of Composed Services in Configurable Compute Centers,” in On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 183–202.

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.

A. Redder, A. Ramaswamy, and H. Karl, “Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication,”10.5220/0010845400003116__2022, doi:10.5220/0010845400003116 .

A. Redder, A. Ramaswamy, and H. Karl, “Distributed gradient-based optimization in the presence of dependent  aperiodic communication,” arXiv:2201.11343. 2022.

A. Redder, A. Ramaswamy, and H. Karl, “Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms,” arXiv:2201.00570. 2022.

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.

A. Ramaswamy, A. Redder, and D. E. Quevedo, “Distributed optimization over time-varying networks with stochastic information delays,”10.1109/TAC.2021.3108492__IEEE Transactions on Automatic Control, pp. 1–1, 2021, doi:10.1109/TAC.2021.3108492 .

A. Redder, A. Ramaswamy, and D. Quevedo, “Deep reinforcement learning for scheduling in large-scale networked control systems,” in Proceedings of the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems, Chicago, USA, 2019.