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A survey on decision making for task migration in mobile cloud environments.

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Title: A survey on decision making for task migration in mobile cloud environments.
Authors: Zhang, Weishan1 zhangws@upc.edu.cn, Tan, Shouchao1 tansc.upc@foxmail.com, Xia, Feng f.xia@ieee.org, Chen, Xiufeng2 chenxiufeng@hisense.com, Li, Zhongwei1 lizhongwei@upc.edu.cn, Lu, Qinghua1 dr.qinghua.lu@gmail.com, Yang, Su3 suyang@fudan.edu.cn
Source: Personal & Ubiquitous Computing. Jun2016, Vol. 20 Issue 3, p295-309. 15p.
Subjects: Decision making, Systems migration, Mobile computing, Algorithms, Artificial intelligence
Abstract: The key idea of MCC is using powerful back-end computing nodes to enhance capabilities of small mobile devices and provide better user experiences. An effective and widely used approach to realize this is task migrations. Decision making is an important aspect of migrations which affects the feasibility and effectiveness of task migrations. There have been a number of research efforts to MCC which help make decisions for task migrations. In this paper, we present a comprehensive survey on decision making for task migrations in MCC, including decision factors and algorithms. We observe that there are still some challenges such as comprehensive context awareness, unified migration standards, large-scale experiments, more involvement of latest achievements from artificial intelligence, and flexible decision-making mechanisms. The paper highlights these issues and challenges to attract more efforts to work on MCC. [ABSTRACT FROM AUTHOR]
Copyright of Personal & Ubiquitous Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Weishan%22">Zhang, Weishan</searchLink><relatesTo>1</relatesTo><i> zhangws@upc.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Tan%2C+Shouchao%22">Tan, Shouchao</searchLink><relatesTo>1</relatesTo><i> tansc.upc@foxmail.com</i><br /><searchLink fieldCode="AR" term="%22Xia%2C+Feng%22">Xia, Feng</searchLink><i> f.xia@ieee.org</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Xiufeng%22">Chen, Xiufeng</searchLink><relatesTo>2</relatesTo><i> chenxiufeng@hisense.com</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Zhongwei%22">Li, Zhongwei</searchLink><relatesTo>1</relatesTo><i> lizhongwei@upc.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Lu%2C+Qinghua%22">Lu, Qinghua</searchLink><relatesTo>1</relatesTo><i> dr.qinghua.lu@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Su%22">Yang, Su</searchLink><relatesTo>3</relatesTo><i> suyang@fudan.edu.cn</i>
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  Data: The key idea of MCC is using powerful back-end computing nodes to enhance capabilities of small mobile devices and provide better user experiences. An effective and widely used approach to realize this is task migrations. Decision making is an important aspect of migrations which affects the feasibility and effectiveness of task migrations. There have been a number of research efforts to MCC which help make decisions for task migrations. In this paper, we present a comprehensive survey on decision making for task migrations in MCC, including decision factors and algorithms. We observe that there are still some challenges such as comprehensive context awareness, unified migration standards, large-scale experiments, more involvement of latest achievements from artificial intelligence, and flexible decision-making mechanisms. The paper highlights these issues and challenges to attract more efforts to work on MCC. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Personal & Ubiquitous Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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