Reducing Energy Consumption with Batched Task Executions

Kenichi Yasukata; Tetsuro Horikawa; Michio Honda; Hideyuki Tokuda

doi:10.1145/2426656.2426699

Reducing Energy Consumption with Batched Task Executions

Kenichi Yasukata, Tetsuro Horikawa, Michio Honda, Hideyuki Tokuda

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In sensing systems, data compression is a promised way to save energy because it reduces the rate of data transmission, but less attention has been paid to the underlying task scheduling algorithms. We present a Double Rate Bundle Scheduling algorithm (DRBS) that maximizes the sleep state period of the CPU to reduce energy consumption. Our prototype implementation in a Mote device improves energy efﬁciency up to 8% compared to existing algorithms.

Original language	English
Title of host publication	Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
Place of Publication	New York, NY, USA
Publisher	ACM Association for Computing Machinery
Pages	343–344
Number of pages	2
ISBN (Print)	9781450311694
DOIs	https://doi.org/10.1145/2426656.2426699
Publication status	Published - 6 Nov 2012
Event	10th ACM Conference on Embedded Network Sensor Systems - Toronto, Canada Duration: 6 Nov 2012 → 9 Nov 2012 Conference number: 10

Conference

Conference	10th ACM Conference on Embedded Network Sensor Systems
Abbreviated title	SenSys '12
Country/Territory	Canada
City	Toronto
Period	6/11/12 → 9/11/12

Keywords

real-time and embedded systems
operating system

Access to Document

10.1145/2426656.2426699Licence: All Rights Reserved

https://dl.acm.org/doi/abs/10.1145/2426656.2426699Licence: All Rights Reserved

Cite this

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title = "Reducing Energy Consumption with Batched Task Executions",

abstract = "In sensing systems, data compression is a promised way to save energy because it reduces the rate of data transmission, but less attention has been paid to the underlying task scheduling algorithms. We present a Double Rate Bundle Scheduling algorithm (DRBS) that maximizes the sleep state period of the CPU to reduce energy consumption. Our prototype implementation in a Mote device improves energy efﬁciency up to 8% compared to existing algorithms.",

keywords = "real-time and embedded systems, operating system",

author = "Kenichi Yasukata and Tetsuro Horikawa and Michio Honda and Hideyuki Tokuda",

year = "2012",

month = nov,

day = "6",

doi = "10.1145/2426656.2426699",

language = "English",

isbn = "9781450311694",

pages = "343–344",

booktitle = "Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems",

publisher = "ACM Association for Computing Machinery",

note = "10th ACM Conference on Embedded Network Sensor Systems, SenSys '12 ; Conference date: 06-11-2012 Through 09-11-2012",

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Yasukata, K, Horikawa, T, Honda, M & Tokuda, H 2012, Reducing Energy Consumption with Batched Task Executions. in Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems. ACM Association for Computing Machinery, New York, NY, USA, pp. 343–344, 10th ACM Conference on Embedded Network Sensor Systems, Toronto, Ontario, Canada, 6/11/12. https://doi.org/10.1145/2426656.2426699

Reducing Energy Consumption with Batched Task Executions. / Yasukata, Kenichi; Horikawa, Tetsuro; Honda, Michio; Tokuda, Hideyuki.

Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems. New York, NY, USA : ACM Association for Computing Machinery, 2012. p. 343–344.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Reducing Energy Consumption with Batched Task Executions

AU - Yasukata, Kenichi

AU - Horikawa, Tetsuro

AU - Honda, Michio

AU - Tokuda, Hideyuki

N1 - Conference code: 10

PY - 2012/11/6

Y1 - 2012/11/6

N2 - In sensing systems, data compression is a promised way to save energy because it reduces the rate of data transmission, but less attention has been paid to the underlying task scheduling algorithms. We present a Double Rate Bundle Scheduling algorithm (DRBS) that maximizes the sleep state period of the CPU to reduce energy consumption. Our prototype implementation in a Mote device improves energy efﬁciency up to 8% compared to existing algorithms.

AB - In sensing systems, data compression is a promised way to save energy because it reduces the rate of data transmission, but less attention has been paid to the underlying task scheduling algorithms. We present a Double Rate Bundle Scheduling algorithm (DRBS) that maximizes the sleep state period of the CPU to reduce energy consumption. Our prototype implementation in a Mote device improves energy efﬁciency up to 8% compared to existing algorithms.

KW - real-time and embedded systems

KW - operating system

U2 - 10.1145/2426656.2426699

DO - 10.1145/2426656.2426699

M3 - Conference contribution

SN - 9781450311694

SP - 343

EP - 344

BT - Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems

PB - ACM Association for Computing Machinery

CY - New York, NY, USA

T2 - 10th ACM Conference on Embedded Network Sensor Systems

Y2 - 6 November 2012 through 9 November 2012

ER -

Reducing Energy Consumption with Batched Task Executions

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Keywords

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