Abstract: Data network components and frameworks for the control and optimization of network traffic using layered architectures, according to embodiments of the invention, are disclosed. In one embodiment, a data network comprises a switch, a data link, a tracking processor, and a planning processor. The planning processor is configured to generate a reference trajectory by receiving a local system state from the tracking processor, calculating a reference trajectory by solving a planning problem, and sending the reference trajectory to the tracking processor. The tracking processor is configured to track the reference trajectory by determining a set of states of the switch over a time interval, sending a local system state to the planning processor, receiving the reference trajectory from the planning processor, computing a control action by solving a tracking problem based on the reference trajectory, determining an instruction based on the control action, and sending the instruction to the switch.

Abstract: Data network components and frameworks for the control and optimization of network traffic using layered architectures, according to embodiments of the invention, are disclosed. In one embodiment, a data network comprises a switch, a data link, a tracking processor, and a planning processor. The planning processor is configured to generate a reference trajectory by receiving a local system state from the tracking processor, calculating a reference trajectory by solving a planning problem, and sending the reference trajectory to the tracking processor. The tracking processor is configured to track the reference trajectory by determining a set of states of the switch over a time interval, sending a local system state to the planning processor, receiving the reference trajectory from the planning processor, computing a control action by solving a tracking problem based on the reference trajectory, determining an instruction based on the control action, and sending the instruction to the switch.

Abstract: A method of generating a combination therapy model for treating HIV is described. The method includes receiving a group of selected antibodies for the treatment of HIV and fitness parameters for a number of escape mutants and utilizing an iterative algorithm to determine suboptimal or optimal combinations of antibodies and concentrations for each selected antibody. The algorithm explicitly accounts for evolutionary dynamics of a generic disease.