IEICE TRANSACTIONS on Communications
Energy-Efficient Resource Management in Mobile Cloud Computing
Summary :
Mobile cloud computing (MCC) has been proposed as a new approach to enhance mobile device performance via computation offloading. The growth in cloud computing energy consumption is placing pressure on both the environment and cloud operators. In this paper, we focus on energy-efficient resource management in MCC and aim to reduce cloud operators' energy consumption through resource management. We establish a deterministic resource management model by solving a combinatorial optimization problem with constraints. To obtain the resource management strategy in deterministic scenarios, we propose a deterministic strategy algorithm based on the adaptive group genetic algorithm (AGGA). Wireless networks are used to connect to the cloud in MCC, which causes uncertainty in resource management in MCC. Based on the deterministic model, we establish a stochastic model that involves a stochastic optimization problem with chance constraints. To solve this problem, we propose a stochastic strategy algorithm based on Monte Carlo simulation and AGGA. Experiments show that our deterministic strategy algorithm obtains approximate optimal solutions with low algorithmic complexity with respect to the problem size, and our stochastic strategy algorithm saves more energy than other algorithms while satisfying the chance constraints.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E101-B No.4 pp.1010-1020
- Publication Date
- 2018/04/01
- Publicized
- 2017/10/16
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2017EBP3177
- Type of Manuscript
- PAPER
- Category
- Energy in Electronics Communications
Authors
Xiaomin JIN
Beijing University of Posts and Telecommunications
Yuanan LIU
Beijing University of Posts and Telecommunications
Wenhao FAN
Beijing University of Posts and Telecommunications
Fan WU
Beijing University of Posts and Telecommunications
Bihua TANG
Beijing University of Posts and Telecommunications
Keyword
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Xiaomin JIN, Yuanan LIU, Wenhao FAN, Fan WU, Bihua TANG, "Energy-Efficient Resource Management in Mobile Cloud Computing" in IEICE TRANSACTIONS on Communications,
vol. E101-B, no. 4, pp. 1010-1020, April 2018, doi: 10.1587/transcom.2017EBP3177.
Abstract: Mobile cloud computing (MCC) has been proposed as a new approach to enhance mobile device performance via computation offloading. The growth in cloud computing energy consumption is placing pressure on both the environment and cloud operators. In this paper, we focus on energy-efficient resource management in MCC and aim to reduce cloud operators' energy consumption through resource management. We establish a deterministic resource management model by solving a combinatorial optimization problem with constraints. To obtain the resource management strategy in deterministic scenarios, we propose a deterministic strategy algorithm based on the adaptive group genetic algorithm (AGGA). Wireless networks are used to connect to the cloud in MCC, which causes uncertainty in resource management in MCC. Based on the deterministic model, we establish a stochastic model that involves a stochastic optimization problem with chance constraints. To solve this problem, we propose a stochastic strategy algorithm based on Monte Carlo simulation and AGGA. Experiments show that our deterministic strategy algorithm obtains approximate optimal solutions with low algorithmic complexity with respect to the problem size, and our stochastic strategy algorithm saves more energy than other algorithms while satisfying the chance constraints.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2017EBP3177/_p
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@ARTICLE{e101-b_4_1010,
author={Xiaomin JIN, Yuanan LIU, Wenhao FAN, Fan WU, Bihua TANG, },
journal={IEICE TRANSACTIONS on Communications},
title={Energy-Efficient Resource Management in Mobile Cloud Computing},
year={2018},
volume={E101-B},
number={4},
pages={1010-1020},
abstract={Mobile cloud computing (MCC) has been proposed as a new approach to enhance mobile device performance via computation offloading. The growth in cloud computing energy consumption is placing pressure on both the environment and cloud operators. In this paper, we focus on energy-efficient resource management in MCC and aim to reduce cloud operators' energy consumption through resource management. We establish a deterministic resource management model by solving a combinatorial optimization problem with constraints. To obtain the resource management strategy in deterministic scenarios, we propose a deterministic strategy algorithm based on the adaptive group genetic algorithm (AGGA). Wireless networks are used to connect to the cloud in MCC, which causes uncertainty in resource management in MCC. Based on the deterministic model, we establish a stochastic model that involves a stochastic optimization problem with chance constraints. To solve this problem, we propose a stochastic strategy algorithm based on Monte Carlo simulation and AGGA. Experiments show that our deterministic strategy algorithm obtains approximate optimal solutions with low algorithmic complexity with respect to the problem size, and our stochastic strategy algorithm saves more energy than other algorithms while satisfying the chance constraints.},
keywords={},
doi={10.1587/transcom.2017EBP3177},
ISSN={1745-1345},
month={April},}
Copy
TY - JOUR
TI - Energy-Efficient Resource Management in Mobile Cloud Computing
T2 - IEICE TRANSACTIONS on Communications
SP - 1010
EP - 1020
AU - Xiaomin JIN
AU - Yuanan LIU
AU - Wenhao FAN
AU - Fan WU
AU - Bihua TANG
PY - 2018
DO - 10.1587/transcom.2017EBP3177
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E101-B
IS - 4
JA - IEICE TRANSACTIONS on Communications
Y1 - April 2018
AB - Mobile cloud computing (MCC) has been proposed as a new approach to enhance mobile device performance via computation offloading. The growth in cloud computing energy consumption is placing pressure on both the environment and cloud operators. In this paper, we focus on energy-efficient resource management in MCC and aim to reduce cloud operators' energy consumption through resource management. We establish a deterministic resource management model by solving a combinatorial optimization problem with constraints. To obtain the resource management strategy in deterministic scenarios, we propose a deterministic strategy algorithm based on the adaptive group genetic algorithm (AGGA). Wireless networks are used to connect to the cloud in MCC, which causes uncertainty in resource management in MCC. Based on the deterministic model, we establish a stochastic model that involves a stochastic optimization problem with chance constraints. To solve this problem, we propose a stochastic strategy algorithm based on Monte Carlo simulation and AGGA. Experiments show that our deterministic strategy algorithm obtains approximate optimal solutions with low algorithmic complexity with respect to the problem size, and our stochastic strategy algorithm saves more energy than other algorithms while satisfying the chance constraints.
ER -
English
