Controlling physical machinery and processes is at the core of production automation. However, challenged by inflexibility, automation and control is evaluating to outsource this control to resourceful cloud environments. While this enables to derive better control through a plethora of measurements, it challenges the control quality through delay introduced through networks. In this paper, we show how to unify control and communication by offloading delay sensitive control tasks from the cloud to local network elements-a previously unexplored area for in-network processing-enabling both, ultra high quality-of-control while enabling scalable orchestration through cloud environments. Our implementation demonstrates how we combine state of the art control with communication. We achieve this by expressing the control and the datapath in P4 which we synthesize to BPF programs that we execute in XDP environments on Netronome SmartNICs. Further, we highlight the demands of control towards communication to build more involved and complex in-network controllers.

In this paper we address the problem of information-constrained optimal control for an interconnected system subject to one-step communication delays and power constraints. The goal is to minimize a finite-horizon quadratic cost by optimally choosing the control inputs for the subsystems, accounting for power constraints in the overall system and different information available at the decision makers. To this purpose, due to the quadratic nature of the power constraints, the LQG problem is reformulated as a linear problem in the covariance of state-input aggregated vector. The zeroduality gap allows us to equivalently consider the dual problem, and decompose it into several sub-problems according to the information structure present in the system. Finally, the optimal control inputs are found in a form that allows for offline computation of the control gains.

Abstract-We address a cooling energy management problem in a multi-building setting where buildings need to maintain comfort conditions for the occupants by keeping their zones temperature within a certain range. To this purpose, each one of them has its own chiller and is connected to a shared cooling network. The goal is to minimize the overall district electricity cost over some finite time horizon by optimally setting the temperature set-points in the buildings and the energy exchange with the cooling network, compatibly with comfort and actuation constraints, while accounting for uncertainty, mainly due to outside temperature, people occupancy, and solar radiation. To this purpose, a distributed version of the scenario approach to stochastic constrained optimization is adopted, which allows to guarantee by design a predefined robustness level of the obtained solution against uncertainty.

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