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Online tuning of Dynamic Power Management for efficient execution of interactive workloads

Online tuning of Dynamic Power Management for efficient execution of interactive workloads
Online tuning of Dynamic Power Management for efficient execution of interactive workloads
Modern mobile devices contain powerful Multi-Processor System-on-Chips (MPSoCs) that are performance throttled by Dynamic Power Management (DPM) runtime systems to extend battery lifetime. Applications on mobile devices commonly generate highly interactive workloads, dependent on interaction between the processor cores, peripherals, external resources and the user, such as touch input during web-browsing. Inevitably, a subset of interactive workloads are affected by delays caused by data unavailability, e.g. loss or delay of data packets during voice-over-IP. At the same time, the system is required to respond quickly upon data retrieval to ensure that the user Quality of Experience (QoE) metrics (frame-rate, latency, etc.) are not degraded. Traditionally, operating systems have mitigated this problem with periodic sampling or event-driven approaches. Through experimentation using a mobile MPSoC platform, however, we demonstrate that improving the tuning of DPM parameters for certain interactive user inputs can provide energy savings of up to 21% or QoE improvements of up to 36%, when compared with the traditional approach. To capture these improvements, we propose a dynamic modeling of user input and data resource access times (e.g. mobile network bandwidth and latency) for interactive workloads, which is based on workload profiling and which we refer to herein as inelasticity analysis. The proposed approach is implemented through online tuning of a DPM runtime in the Android operating system and is validated through a Monte Carlo simulation of interactive workloads. In comparison to the default DPM tuning, the proposed approach achieves energy savings of 13% or QoE improvement of 27% or a selectable trade-off, e.g. 9% energy savings and 15% QoE improvement.
IEEE
Bantock, James, Robert Benjamin
96aee509-d437-4c00-ae46-7b5899947e49
Tenentes, Vasileios
1bff9ebc-9186-438b-850e-6c738994fa39
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d
Merrett, Geoffrey
89b3a696-41de-44c3-89aa-b0aa29f54020
Bantock, James, Robert Benjamin
96aee509-d437-4c00-ae46-7b5899947e49
Tenentes, Vasileios
1bff9ebc-9186-438b-850e-6c738994fa39
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d
Merrett, Geoffrey
89b3a696-41de-44c3-89aa-b0aa29f54020

Bantock, James, Robert Benjamin, Tenentes, Vasileios, Al-Hashimi, Bashir and Merrett, Geoffrey (2017) Online tuning of Dynamic Power Management for efficient execution of interactive workloads. In IEEE/ACM International Symposium on Low Power Electronics and Design. IEEE. 6 pp. (doi:10.1109/ISLPED.2017.8009195).

Record type: Conference or Workshop Item (Paper)

Abstract

Modern mobile devices contain powerful Multi-Processor System-on-Chips (MPSoCs) that are performance throttled by Dynamic Power Management (DPM) runtime systems to extend battery lifetime. Applications on mobile devices commonly generate highly interactive workloads, dependent on interaction between the processor cores, peripherals, external resources and the user, such as touch input during web-browsing. Inevitably, a subset of interactive workloads are affected by delays caused by data unavailability, e.g. loss or delay of data packets during voice-over-IP. At the same time, the system is required to respond quickly upon data retrieval to ensure that the user Quality of Experience (QoE) metrics (frame-rate, latency, etc.) are not degraded. Traditionally, operating systems have mitigated this problem with periodic sampling or event-driven approaches. Through experimentation using a mobile MPSoC platform, however, we demonstrate that improving the tuning of DPM parameters for certain interactive user inputs can provide energy savings of up to 21% or QoE improvements of up to 36%, when compared with the traditional approach. To capture these improvements, we propose a dynamic modeling of user input and data resource access times (e.g. mobile network bandwidth and latency) for interactive workloads, which is based on workload profiling and which we refer to herein as inelasticity analysis. The proposed approach is implemented through online tuning of a DPM runtime in the Android operating system and is validated through a Monte Carlo simulation of interactive workloads. In comparison to the default DPM tuning, the proposed approach achieves energy savings of 13% or QoE improvement of 27% or a selectable trade-off, e.g. 9% energy savings and 15% QoE improvement.

Text Online Tuning of Dynamic Power Management for Efficient Execution of Interactive Workloads - Accepted Manuscript
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More information

Accepted/In Press date: 3 May 2017
Published date: 24 July 2017
Organisations: Electronics & Computer Science, Faculty of Physical Sciences and Engineering, Electronic & Software Systems

Identifiers

Local EPrints ID: 410367
URI: https://eprints.soton.ac.uk/id/eprint/410367
PURE UUID: bfc43f0f-13e7-4fbb-8c1b-f8834ef3faf6
ORCID for Geoffrey Merrett: ORCID iD orcid.org/0000-0003-4980-3894

Catalogue record

Date deposited: 07 Jun 2017 16:31
Last modified: 06 Jun 2018 12:42

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