TY  - JOUR
IS  - 8
JF  - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Y1  - 2016///
SP  - 1358
A1  - Das, Anup
A1  - Merrett, Geoff V.
A1  - Tribastone, Mirco
A1  - Al-Hashimi, Bashir M.
PB  - IEEE
VL  - 35
UR  - http://doi.org/10.1109/TCAD.2015.2504875
TI  - Workload Change Point Detection for Runtime Thermal Management of Embedded Systems
AV  - none
KW  - Embedded systems
KW  -  Hardware
KW  -  Thermal management
KW  -  Temperature dependence
KW  -  Central Processing Unit
KW  -  Voltage control
KW  -  Thermal stresses
N2  - Applications executed on multicore embedded systems interact with system software [such as the operating system (OS)] and hardware, leading to widely varying thermal profiles which accelerate some aging mechanisms, reducing the lifetime reliability. Effectively managing the temperature therefore requires: 1) autonomous detection of changes in application workload and 2) appropriate selection of control levers to manage thermal profiles of these workloads. In this paper, we propose a technique for workload change detection using density ratio-based statistical divergence between overlapping sliding windows of CPU performance statistics. This is integrated in a runtime approach for thermal management, which uses reinforcement learning to select workload-specific thermal control levers by sampling on-board thermal sensors. Identified control levers override the OSs native thread allocation decision and scale hardware voltage-frequency to improve average temperature, peak temperature, and thermal cycling. The proposed approach is validated through its implementation as a hierarchical runtime manager for Linux, with heuristic-based thread affinity selected from the upper hierarchy to reduce thermal cycling and learningbased voltage-frequency selected from the lower hierarchy to reduce average and peak temperatures. Experiments conducted with mobile, embedded, and high performance applications on ARM-based embedded systems demonstrate that the proposed approach increases workload change detection accuracy by an average 3.4×, reducing the average temperature by 4 °C-25 °C, peak temperature by 6 °C-24 °C, and thermal cycling by 7%-35% over state-of-the-art approaches.
SN  - 0278-0070
EP  - 1371
N1  - SCOPUS ID: 2-s2.0-84979539453; WOS Accession Number: WOS:000380061700011
ID  - eprints3573
ER  -