Energy-Efficient Thermal-Aware Scheduling for RT Tasks Using TCPN

This work leverages TCPNs to design an energy-efficient, thermal-aware real-time scheduler for a multiprocessor system that normally runs in a low state energy at maximum system utilization but its capable of increasing the clock frequency to serve aperiodic tasks, optimizing energy, and honoring temporal and thermal constraints. An off-line stage computes the minimum frequency required to run the periodic tasks at maximum CPU utilization, the proportion of each task''s job to be run on each CPU, the maximum clock frequency that keeps temperature under a limit, and the available cycles (slack) with respect to the system with minimum frequency. Then, a Zero-Laxity online scheduler dispatches the periodic tasks according to the offline calculation. Upon the arrival of aperiodic tasks, it increases clock frequency in such a way that all periodic and aperiodic tasks are properly executed. Thermal and temporal requirements are always guaranteed, and energy consumption is minimized

A flexible framework for real-time thermal-aware schedulers using timed continuous petri nets

This work presents TCPN-ThermalSim, a software tool for testing Real-Time Thermal-Aware Schedulers1. This framework consists of four main modules. The first one helps the user to define the problem: Task set with periods, deadlines and worst case execution times in CPU cycles, along with the CPU characteristics, temperature and energy consumption. The second module is the Kernel simulation, which builds up a global simulation model according to the configuration module. In the third module, the user selects the scheduler algorithm. Finally the last module allows the execution of the simulation and present the results. The framework encompasses two modes: Manual and automatic. In manual mode the simulator uses the task set data provided in the first section. In automatic mode the task set is generated by parameterizing the integrated UUniFast algorithm

Thermal-aware real-time scheduling using timed continuous Petri Nets

We present a thermal-aware, hard real-time (HRT) global scheduler for a multiprocessor system designed upon three novel techinques. First, we present a modeling methodology based on Timed Continuous Petri nets (TCPN) that yields a complete state variable model, including job arrivals, CPU usage, power, and thermal behavior. The model is accurate and avoids the calibration stage of RC thermal models. Second, based on this model, a linear programming problem (LPP) determines the existence of a feasible HRT thermal-aware schedule. Last, a sliding-mode controller and an online discretization algorithm implement the global HRT scheduler, which is capable of managing thermal constraints, context switching, migrations, and disturbances