Computation Migration Oriented Resource Allocation in Mobile Social Clouds

The rapid growth of mobile device (e.g., smart phone and bracelet) has spawned a lot of new applications, during which the requirements of applications are increasing, while the capacities of some mobile devices are still limited. Such contradiction drives the emergency of computation migration amon...

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Bibliographic Details
Published inIEEE transactions on cloud computing Vol. 11; no. 1; pp. 44 - 57
Main Authors Yi, Bo, Wang, Xingwei, Huang, Min, He, Qiang, Li, Fuliang
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Bandwidth
Cloud computing
computation migration
Delays
Electronic devices
Evolutionary algorithms
Mathematical models
Mobile computing
mobile edge computing
Mobile handsets
Mobile social cloud
multi-objective optimization
Resource allocation
Resource management
Smartphones
Social networking (online)
Task analysis
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Summary:The rapid growth of mobile device (e.g., smart phone and bracelet) has spawned a lot of new applications, during which the requirements of applications are increasing, while the capacities of some mobile devices are still limited. Such contradiction drives the emergency of computation migration among mobile edge devices, which is a lack of research currently. In this article, we focus on addressing the computation migration oriented resource allocation problem among mobile edge devices. Specifically, we first construct a framework for Mobile Social Cloud(MSC), in which the mobile devices with rich resources are abstracted as resource suppliers and those resource-lacking devices are abstracted as resource demanders. Then, a mathematical model is formulated and an evolutionary algorithm is proposed to effectively solve this model based on decomposition, dominance, and genetic operations. Moreover, the parallel computing is introduced to further improve the efficiency of the proposed algorithm. The experimental results indicate that the proposed algorithm outperforms the other state-of-the-art methods and it improves the calculation efficiency by about 178 percent (2 cores) and 262 percent (3 cores) by introducing parallel computing.
ISSN:2168-7161
2372-0018
DOI:10.1109/TCC.2021.3074159