Journal article
Workload Change Point Detection for Runtime Thermal Management of Embedded Systems
IEEE transactions on computer-aided design of integrated circuits and systems, v 35(8), pp 1358-1371
Aug 2016
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
Metrics
Details
- Title
- Workload Change Point Detection for Runtime Thermal Management of Embedded Systems
- Creators
- Anup Das - University of SouthamptonGeoff V Merrett - University of SouthamptonMirco Tribastone - University of SouthamptonBashir M Al-Hashimi - University of Southampton
- Publication Details
- IEEE transactions on computer-aided design of integrated circuits and systems, v 35(8), pp 1358-1371
- Publisher
- IEEE
- Grant note
- EP/K034448/1 / PRiME Programme EP/L000563/1 / Engineering and Physical Sciences Research Council; EPSRC (10.13039/501100000266)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000380061700011
- Scopus ID
- 2-s2.0-84979539453
- Other Identifier
- 991019295305304721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
Source: SDGs in the Output
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Hardware & Architecture
- Computer Science, Interdisciplinary Applications
- Engineering, Electrical & Electronic