Minimizing the Maximum Processor Temperature by Temperature-Aware Scheduling of Real-Time Tasks

被引:4
|
作者
Ozceylan, Baver [1 ]
Haverkort, Boudewijn R. [2 ]
de Graaf, Maurits [3 ]
Gerards, Marco E. T. [1 ]
机构
[1] Univ Twente, Dept Comp Sci & Elect Engn, NL-7522 NB Enschede, Netherlands
[2] Tilburg Univ, Sch Humanities & Digital Sci, NL-5037 AB Tilburg, Netherlands
[3] Thales Netherlands BV, NL-1271 ZA Huizen, Netherlands
基金
荷兰研究理事会;
关键词
Temperature sensors; Temperature measurement; Thermal management; Real-time systems; Performance evaluation; Task analysis; Reliability; Leakage current; model predictive control; processor scheduling; reliability; resource allocation; thermal management; THERMAL MANAGEMENT; POWER;
D O I
10.1109/TVLSI.2022.3160601
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Thermal management is gaining importance since it is a promising method for increasing the reliability and lifespan of mobile devices. Although the temperature can be decreased by reducing processor speed, one must take care not to increase the processing times too much; violations of deadline constraints must be prevented. This article focuses on the tradeoff between performance and device temperature. We first analyze this tradeoff and show how to determine the optimal lower bound for the maximum temperature for a given set of jobs with known workloads and deadlines. To do so, we use a thermal model, which describes how future decisions impact temperature dynamics. Then, we introduce a processor scheduling algorithm that computes the resource allocation that achieves this lower bound. Consequently, our algorithm finds the optimal resource allocation for the purpose of minimizing the maximum processor temperature for a set of jobs with known workloads and deadlines. Our experimental validation shows that our thermal management algorithm can achieve a reduction of up to 15 degrees C (42%) of the maximum temperature when the workload is high, where a previously proposed method achieved a reduction of up to 10 degrees C (25%). Another advantage of our method is that it decreases the variance in the temperature profile by 16% compared to previously proposed methods.
引用
收藏
页码:1084 / 1097
页数:14
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