Energy-Efficient Computation Offloading in Mobile Edge Computing Systems With Uncertainties

Computation offloading is indispensable for mobile edge computing (MEC). It uses edge resources to enable intensive computations and save energy for resource-constrained devices. Existing works generally impose strong assumptions on radio channels and network queue sizes. However, practical MEC syst...

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Bibliographic Details
Published in:IEEE transactions on wireless communications 2022-08, Vol.21 (8), p.5717-5729
Main Authors: Ji, Tianxi, Luo, Changqing, Yu, Lixing, Wang, Qianlong, Chen, Siheng, Thapa, Arun, Li, Pan
Format: Article
Language:English
Subjects:
Algorithms
Computation offloading
Computational efficiency
Computational modeling
Edge computing
Energy conservation
Energy consumption
Energy efficiency
Extreme value theory
Extreme values
Mobile applications
Mobile computing
Mobile edge computing
network uncertainties
Servers
Smartphones
Task analysis
Uncertainty
Wireless communication
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Summary:Computation offloading is indispensable for mobile edge computing (MEC). It uses edge resources to enable intensive computations and save energy for resource-constrained devices. Existing works generally impose strong assumptions on radio channels and network queue sizes. However, practical MEC systems are subject to various uncertainties rendering these assumptions impractical. In this paper, we investigate the energy-efficient computation offloading problem by relaxing those common assumptions and considering intrinsic uncertainties in the network. Specifically, we minimize the worst-case expected energy consumption of a local device when executing a time-critical application modeled as a directed acyclic graph. We employ the extreme value theory to bound the occurrence probability of uncertain events. To solve the formulated problem, we develop an \epsilon -bounded approximation algorithm based on column generation. The proposed algorithm can efficiently identify a feasible solution that is less than (1+\epsilon) of the optimal one. We implement the proposed scheme on an Android smartphone and conduct extensive experiments using a real-world application. Experiment results corroborate that it will lead to lower energy consumption for the client device by considering the intrinsic uncertainties during computation offloading. The proposed computation offloading scheme also significantly outperforms other schemes in terms of energy saving.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2022.3142685