TARTS: A Temperature-Aware Real-Time Deadline-Partitioned Fair Scheduler

Abstract

Modern multicore systems, which perform complex functionalities on densely packed multi-million gate platforms at high frequencies, are often susceptible to inappropriate surges in core temperatures, if not effectively controlled. Increasing temperature above specified limits not only leads to high cost of cooling but also results in higher dissipation of leakage power together with a reduction in performance and lower system life expectancy. In this work, we propose a two-level low-overhead proportional fair resource allocation strategy called Temperature-Aware Real-Time Deadline-Partitioned Fair Scheduler (TARTS), for periodic real-time tasks to be executed on a thermally bounded multicore device. The proposed algorithm’s first level divides time into distinct slices based on deadlines of tasks, so that exact proportional fairness is maintained at all slice boundaries. The second level accomplishes intra-slice scheduling with the aim of maximising the use of resources while not breaching a specified thermal threshold. Experimental results show that our scheme can deliver a significantly higher resource usage efficiency compared to a baseline greedy approach, TA-MTS. For example, TARTS is able to achieve up to 42% higher task acceptance ratios compared to TA-MTS on a fully loaded quad-core system with 80 tasks and temperature threshold of 90 °C.