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Graceful performance adaption through hardware-software interaction for autonomous battery management of multicore smartphones

Graceful performance adaption through hardware-software interaction for autonomous battery management of multicore smartphones
Graceful performance adaption through hardware-software interaction for autonomous battery management of multicore smartphones
Despite advances in multicore smartphone technologies, battery consumption still remains one of customer’s least satisfying features. This is because existing energy saving techniques do not consider the electrochemical characteristics of batteries, which causes battery consumption to vary unpredictably, both within and across applications. Additionally, these techniques provide application specific fixed performance degradation in order to reduce energy consumption. Having a performance penalty, even when a battery is fully charged, adds to customer dissatisfaction. We propose a control-based approach for runtime power management of multicore smartphones, which scales the frequency of processing cores in response to the battery consumption, taking into account the electrochemical characteristics of a battery. The objective is to enable graceful performance modulation, which adapts with application and battery availability in a predictable manner, improving quality-of-userexperience. Our control approach is practically demonstrated on embedded Linux running on Cortex A15-based smartphone development platform from nvidia. A thorough validation with mobile and Java workloads demonstrate 2.9x improvement in
battery availability compared to state-of-the-art approaches.
multiprocessor systems, power management, Performance Adaption
Das, Anup
fbeefceb-8221-45c2-807f-a4881a5d5839
Balsamo, Domenico
fa2dc20a-e3da-4d74-9070-9c61c6a471ba
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d
Catthoor, Francky
df1d4a2a-3f20-4882-980c-d8a5c5f097de
Das, Anup
fbeefceb-8221-45c2-807f-a4881a5d5839
Balsamo, Domenico
fa2dc20a-e3da-4d74-9070-9c61c6a471ba
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d
Catthoor, Francky
df1d4a2a-3f20-4882-980c-d8a5c5f097de

Das, Anup, Balsamo, Domenico, Merrett, Geoff, Al-Hashimi, Bashir and Catthoor, Francky (2018) Graceful performance adaption through hardware-software interaction for autonomous battery management of multicore smartphones. At THE 9th International Green and Sustainable Computing Conference (22/10/18) THE 9th International Green and Sustainable Computing Conference. 6 pp. (Submitted)

Record type: Conference or Workshop Item (Paper)

Abstract

Despite advances in multicore smartphone technologies, battery consumption still remains one of customer’s least satisfying features. This is because existing energy saving techniques do not consider the electrochemical characteristics of batteries, which causes battery consumption to vary unpredictably, both within and across applications. Additionally, these techniques provide application specific fixed performance degradation in order to reduce energy consumption. Having a performance penalty, even when a battery is fully charged, adds to customer dissatisfaction. We propose a control-based approach for runtime power management of multicore smartphones, which scales the frequency of processing cores in response to the battery consumption, taking into account the electrochemical characteristics of a battery. The objective is to enable graceful performance modulation, which adapts with application and battery availability in a predictable manner, improving quality-of-userexperience. Our control approach is practically demonstrated on embedded Linux running on Cortex A15-based smartphone development platform from nvidia. A thorough validation with mobile and Java workloads demonstrate 2.9x improvement in
battery availability compared to state-of-the-art approaches.

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More information

Submitted date: 2018
Venue - Dates: THE 9th International Green and Sustainable Computing Conference, 2018-10-22
Keywords: multiprocessor systems, power management, Performance Adaption

Identifiers

Local EPrints ID: 428031
URI: https://eprints.soton.ac.uk/id/eprint/428031
PURE UUID: 4fe47ba5-c8d4-4c49-be9b-c79383f40ac7
ORCID for Geoff Merrett: ORCID iD orcid.org/0000-0003-4980-3894

Catalogue record

Date deposited: 07 Feb 2019 17:30
Last modified: 08 Feb 2019 01:35

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