Scheduling in Cyber-Physical Systems

Cyber-physical systems (CPS) refer to a promising class of systems featuring intimate coupling between the ‘cyber’ intelligence and the ‘physical’ world. Enabled by the ubiquitous availability of computation and communication capabilities, such systems are widely envisioned to redefine the way that people interact with the physical world, similar to the revolutionary role of internet in transforming how people interact with each other. As the whole society becomes increasingly dependent on such systems, it is crucial to develop a theory to understand and optimize the CPS in a systematic manner. This thesis contributes to the foundations of CPS by identifying and addressing a general class of scheduling-type applications for a vital class of CPS, the physical networks (PhyNets). Different from the abstract CPS, a PhyNet has a graph-type physical part, which represents the local interactions among users in the system, as specified by certain well-known physical laws. Thus, it is very promising to develop efficient distributed algorithms in PhyNets with proper communication infrastructure and protocols, due to the physical graph structure. The ‘scheduling’ refers to the applications where joint actions of all users are coordinated, in order to allocate system resources to satisfy certain long term and uncertain demands. Important applications of the scheduling in PhyNets include packet scheduling in wireless networks, coordinated charging of electric vehicles (EV) in electric power grids, and workload scheduling in data centers. In this thesis, we assume very mild assumptions on the stochastic processes, and provide probabilistic scheduling performance guarantees using the technique of fluid limits. In this thesis, we will investigate a broad range of scheduling algorithms and discuss their performance and distributed implementation. We first investigate the class of optimal scheduling algorithms in the dynamic regime, where the system modes change randomly with time. We focus on augmented max-weight scheduling schemes, which choose a max-weight schedule, where the