relation: http://eprints.imtlucca.it/3573/
title: Workload Change Point Detection for Runtime Thermal Management of Embedded Systems
creator: Das, Anup
creator: Merrett, Geoff V.
creator: Tribastone, Mirco
creator: Al-Hashimi, Bashir M.
subject: QA76 Computer software
description: 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.
publisher: IEEE
date: 2016
type: Article
type: PeerReviewed
identifier:   Das, Anup and Merrett, Geoff V. and Tribastone, Mirco and Al-Hashimi, Bashir M.  Workload Change Point Detection for Runtime Thermal Management of Embedded Systems.  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 35 (8).  pp. 1358-1371.  ISSN 0278-0070      (2016)  
relation: http://doi.org/10.1109/TCAD.2015.2504875
relation: doi:10.1109/TCAD.2015.2504875