In this article, we propose a scheduling based temperature and power-aware heuristic for multi-core systems. Instead of using a fixed value of temperature threshold in load balancing techniques, the proposed heuristic suggests adjusting the value of threshold temperature as a function of the workload. As the workload reduces, the value of the threshold is lowered accordingly and vice versa. Lowering the value of threshold temperature decreases the temperature peaks, temperature spatial, and temporal gradients. Furthermore, it also reduces the total power consumed by the processing unit. The effectiveness of the approach is evaluated in a simulation-based environment that includes a scheduling and a thermal modeling tool. For evaluation, the heuristic is integrated with a state of the art load balancing technique based on global scheduling and a comparison is performed with the popular thermal-aware techniques. The results analysis shows that the use of proposed heuristic lowers the temperature peak by up to 7 °C, temperature spatial gradients by up to 35%, temporal gradient by 65%, and power utilization by up to 5.5% when compared with the state-of-the-art thermal balancing techniques and predictive thermal-aware models.

在本文中，我们提出了针对多核系统的基于调度的温度和功耗感知启发式方法。代替在负载平衡技术中使用温度阈值的固定值，建议的启发式方法建议根据工作负载调整阈值温度的值。随着工作负载的减少，阈值的值将相应降低，反之亦然。降低阈值温度的值会降低温度峰值，温度空间和时间梯度。此外，它还减少了处理单元消耗的总功率。在包括调度和热建模工具的基于仿真的环境中评估该方法的有效性。为了评估，启发式算法与基于全局调度的最新负载均衡技术集成在一起，并与流行的热感知技术进行比较。结果分析表明，与状态相比，使用提议的启发式方法可将温度峰值降低多达7°C，温度空间梯度降低35％，时间梯度降低65％，功率利用率降低5.5％。最新的热平衡技术和可预测的热感知模型。