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Deep learning-based energy-efficient computational offloading strategy in heterogeneous fog computing networks.
- Source :
- Journal of Supercomputing; Sep2022, Vol. 78 Issue 13, p15089-15106, 18p
- Publication Year :
- 2022
-
Abstract
- In the era of the Internet of Things (IoT), the volume of data is increasing immensely causing rapid growth in network data communication and data congestion. Computational offloading thus becomes a crucial and imperative action in terms of delay-sensitive task completion and data processing for the resource constraint end-users. Nowadays fog computing, as a complement of cloud computing, has emerged a well-known concept in terms of enhancing data processing capability as well as energy conservation in low-powered networks. In this paper, we consider a heterogeneous fog-cloud network architecture where the data processing is performed on the local or remote computing device by adopting a binary offloading policy. Based on the proposed system model, we calculate the total delay and energy consumption of data processing throughout the network and formulate a mixed-integer optimization problem to jointly optimize the offloading decision and bandwidth allocation. In order to solve such an NP-hard problem, we have proposed a deep-learning-based binary offloading strategy that employs multiple parallel deep neural networks (DNNs) to make offloading decisions. Such offloading decisions are subsequently placed in a relay memory system to train and test all DNNs. Simulation results show a near-optimal performance of the proposed offloading strategy while remarkably maintaining the quality of service by decreasing overall delay and energy consumption. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09208542
- Volume :
- 78
- Issue :
- 13
- Database :
- Complementary Index
- Journal :
- Journal of Supercomputing
- Publication Type :
- Academic Journal
- Accession number :
- 158432410
- Full Text :
- https://doi.org/10.1007/s11227-022-04461-z