Temperature-Aware Scheduler Based On Thermal Behavior Grouping In Multicore Systems

Dynamic Thermal Management techniques have been widely accepted as a thermal solution for their low cost and simplicity. The techniques have been used to manage the heat dissipation and operating temperature to avoid thermal emergencies, but are not aware of application behavior in Chip Multiprocessors (CMPs). In this paper, we propose a temperature-aware scheduler based on applications' thermal behavior groups classified by a K-means clustering method in multicore systems. The application's thermal behavior group has similar thermal pattern as well as thermal parameters. With these thermal behavior groups, we provide thermal balances among cores with negligible performance overhead. We implement and evaluate our schemes in the 4-core (Intel Quad Core Q6600) and 8-core (two Quad Core Intel XEON E5310 processors) systems running several benchmarks. The experimental results show that. the temperature-aware scheduler based on thermal behavior grouping reduces the peak temperature by up to 8 degrees C and 5 degrees C in our 4-core system and 8-core system with only 12% and 7.52% performance overhead, respectively, compared to Linux standard scheduler.