Nov 17, 2017
Lopata Hall, Room 101
"Holistic Cyber-Physical Management for Dependable Wireless Control Systems"
Adviser: Chenyang Lu
Wireless sensor-actuator networks (WSANs) are gaining momentum in industrial process automation as a communication infrastructure for lowering deployment and maintenance costs. In traditional wireless control systems, the plant controller and the network manager operate in isolation, which ignore the significant influence of network reliability on plant control performance. To enhance the dependability of industrial wireless control, we propose a holistic cyber-physical management framework that employs run-time coordination between the plant control and network management. Our design includes a holistic controller that generates actuation signals to physical plants and reconfigures the WSAN to maintain desired control performance while saving wireless resources. As a concrete example of holistic control, we design a holistic manager that dynamically reconfigures the number of transmissions in the WSAN based on online observations of physical and cyber variables. We have implemented the holistic management framework in the Wireless Cyber-Physical Simulator (WCPS). Systematic simulation results show that the holistic management design has significantly enhanced the dependability of the system against both wireless interferences and physical disturbances, while effectively prolonged the system lifetime. We also develop a real-time network-in-the-loop simulator that integrates Simulink and a three-floor wireless testbed at Washington University.
"Efficient Parallel Race Detection for Two-Dimensional Dags"
Adviser: Angelina Lee
A program is said to have a determinacy race if logically parallel parts of a program access the same memory location and one of the accesses is a write. These races are generally bugs in the program since they lead to non-deterministic program behavior --- different schedules of the program can lead to different results. Most prior work on detecting these races focuses on a subclass of programs with fork-join parallelism.
We present a race-detection algorithm, 2D-Order, for detecting races in a more general class of programs, namely programs whose dependence structure can be represented as planer dags embedded in 2D grids. Such dependence structures arise from programs that use pipelined parallelism or dynamic programming recurrences. Given a computation with T1 work and T∞ span, 2D-Order executes it while also detecting races in O(T1 /P+T∞ ) time on P processors, which is asymptotically optimal.
We also implemented PRacer, a race-detection algorithm based on 2D-Order for Cilk-P, which is a language for expressing pipeline parallelism. Empirical results demonstrates that PRacer incurs reasonable overhead and exhibits scalability similar to the baseline (executions without race detection) when running on multiple cores.