An Adaptive Computation Offloading Decision for Energy-Efficient Execution of Mobile Applications in Clouds
Summary :
In recent years, computation offloading, through which applications on a mobile device can offload their computations onto more resource-rich clouds, has emerged as a promising technique to reduce battery consumption as well as augment the devices' limited computation and memory capabilities. In order for computation offloading to be energy-efficient, an accurate estimate of battery consumption is required to decide between local processing and computation offloading. In this paper, we propose a novel technique for estimating battery consumption without requiring detailed information about the mobile application's internal structure or its execution behavior. In our approach, the relationship is derived between variables that affect battery consumption (i.e., the input to the application, the transmitted data, and resource status) and the actual consumed energy from the application's past run history. We evaluated the performance of the proposed technique using two different types of mobile applications over different wireless network environments such as 3G, Wi-Fi, and LTE. The experimental results show that our technique can provide tolerable estimation accuracy and thus make correct decisions between local processing and computation offloading.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.7 pp.1804-1811
- Publication Date
- 2014/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E97.D.1804
- Type of Manuscript
- PAPER
- Category
- Information Network
Authors
Byoung-Dai LEE
Kyonggi Univ., Suwon
Kwang-Ho LIM
Kyonggi Univ., Suwon
Yoon-Ho CHOI
Kyonggi Univ., Suwon
Namgi KIM
Kyonggi Univ., Suwon
Keyword
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Byoung-Dai LEE, Kwang-Ho LIM, Yoon-Ho CHOI, Namgi KIM, "An Adaptive Computation Offloading Decision for Energy-Efficient Execution of Mobile Applications in Clouds" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 7, pp. 1804-1811, July 2014, doi: 10.1587/transinf.E97.D.1804.
Abstract: In recent years, computation offloading, through which applications on a mobile device can offload their computations onto more resource-rich clouds, has emerged as a promising technique to reduce battery consumption as well as augment the devices' limited computation and memory capabilities. In order for computation offloading to be energy-efficient, an accurate estimate of battery consumption is required to decide between local processing and computation offloading. In this paper, we propose a novel technique for estimating battery consumption without requiring detailed information about the mobile application's internal structure or its execution behavior. In our approach, the relationship is derived between variables that affect battery consumption (i.e., the input to the application, the transmitted data, and resource status) and the actual consumed energy from the application's past run history. We evaluated the performance of the proposed technique using two different types of mobile applications over different wireless network environments such as 3G, Wi-Fi, and LTE. The experimental results show that our technique can provide tolerable estimation accuracy and thus make correct decisions between local processing and computation offloading.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E97.D.1804/_p
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@ARTICLE{e97-d_7_1804,
author={Byoung-Dai LEE, Kwang-Ho LIM, Yoon-Ho CHOI, Namgi KIM, },
journal={IEICE TRANSACTIONS on Information},
title={An Adaptive Computation Offloading Decision for Energy-Efficient Execution of Mobile Applications in Clouds},
year={2014},
volume={E97-D},
number={7},
pages={1804-1811},
abstract={In recent years, computation offloading, through which applications on a mobile device can offload their computations onto more resource-rich clouds, has emerged as a promising technique to reduce battery consumption as well as augment the devices' limited computation and memory capabilities. In order for computation offloading to be energy-efficient, an accurate estimate of battery consumption is required to decide between local processing and computation offloading. In this paper, we propose a novel technique for estimating battery consumption without requiring detailed information about the mobile application's internal structure or its execution behavior. In our approach, the relationship is derived between variables that affect battery consumption (i.e., the input to the application, the transmitted data, and resource status) and the actual consumed energy from the application's past run history. We evaluated the performance of the proposed technique using two different types of mobile applications over different wireless network environments such as 3G, Wi-Fi, and LTE. The experimental results show that our technique can provide tolerable estimation accuracy and thus make correct decisions between local processing and computation offloading.},
keywords={},
doi={10.1587/transinf.E97.D.1804},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - An Adaptive Computation Offloading Decision for Energy-Efficient Execution of Mobile Applications in Clouds
T2 - IEICE TRANSACTIONS on Information
SP - 1804
EP - 1811
AU - Byoung-Dai LEE
AU - Kwang-Ho LIM
AU - Yoon-Ho CHOI
AU - Namgi KIM
PY - 2014
DO - 10.1587/transinf.E97.D.1804
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2014
AB - In recent years, computation offloading, through which applications on a mobile device can offload their computations onto more resource-rich clouds, has emerged as a promising technique to reduce battery consumption as well as augment the devices' limited computation and memory capabilities. In order for computation offloading to be energy-efficient, an accurate estimate of battery consumption is required to decide between local processing and computation offloading. In this paper, we propose a novel technique for estimating battery consumption without requiring detailed information about the mobile application's internal structure or its execution behavior. In our approach, the relationship is derived between variables that affect battery consumption (i.e., the input to the application, the transmitted data, and resource status) and the actual consumed energy from the application's past run history. We evaluated the performance of the proposed technique using two different types of mobile applications over different wireless network environments such as 3G, Wi-Fi, and LTE. The experimental results show that our technique can provide tolerable estimation accuracy and thus make correct decisions between local processing and computation offloading.
ER -
English
