Task offloading paradigm in mobile edge computing-current issues, adopted approaches, and future directions
Many enterprise companies migrate their services and applications to the cloud to benefit from cloud computing advantages. Meanwhile, the rapidly increasing number of connected devices with the massive amount of generated data that use cloud services leads to high workload, congestion, and delay b...
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| Main Authors: | , |
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| Format: | Article |
| Published: |
Elsevier
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/109067/ https://linkinghub.elsevier.com/retrieve/pii/S1084804522002090 |
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| Summary: | Many enterprise companies migrate their services and applications to the cloud to benefit from cloud computing
advantages. Meanwhile, the rapidly increasing number of connected devices with the massive amount of
generated data that use cloud services leads to high workload, congestion, and delay bottleneck in the centralized
cloud architecture. Consequently, Mobile Edge Computing (MEC) is introduced as a new paradigm to expand
cloud capabilities near the end devices. In addition, new technologies such as the Internet of Things (IoT),
Autonomous Vehicles (AV), 5G, and Augmented Reality (AR) bring new demands and opportunities that MEC
can make possible. Offloading delay-sensitive and computationally intensive tasks to nearby MEC nodes is an
effective way that still is the most common open problem in MEC. The offloading problem in MEC has been
widely studied in areas such as Vehicular Edge Computing (VEC), IoT, Radio Access Networks (RAN), and 5G but
independently. Due to the high diversity of research areas, targeted issues, and adopted algorithms and techniques, finding the right research path in task offloading is highly demanding. To fill this gap, a comprehensive
survey is conducted using the mixed-method systematic literature review involving qualitative and quantitative
data from the studied papers. For each journal paper, the detailed information of the work area, targeted issue,
formulation technique, optimization approach, adopted algorithms, evaluation techniques, performance
matrices, dataset, utilized tools, and framework are extracted and analyzed using manual and automatic coding.
Major offloading-related issues in MEC are investigated, and the taxonomy of journal papers based on adopted
approaches is presented. For further future exploration, we suggest the potential areas of research, the contribution of the algorithms and technique, and the research direction in MEC. This review will give a quick and
overall view of MEC’s latest issues and solutions. |
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